CAMPER: mechanistic artificial intelligence for designing peptides that target MRSA persisters

Domain: chemistry
Published: 2026-03-09
Editorial outcome: minor_revision

Abstract
Developing short, stable, and potent antimicrobial peptides is a promising strategy to combat antibiotic resistance and persistence. We present CAMPER (Constraint-driven AMP Engineering with Ranking), a mechanistic artificial intelligence framework that integrates machine learning with biophysical ranking to prioritize membrane-targeting peptides effective against persister and biofilm forms of methicillin-resistant Staphylococcus aureus. We apply CAMPER to identify WP-CAMPER1 (12mer) that kills S. aureus MW2 at an minimal inhibitory concentration of 4 µg/mL. A 2% topical WP-CAMPER1 formulation reduces S. aureus MW2 burden by 2.5 log (p < 0.0002) in a murine prophylactic skin infection model, while its D-enantiomer, WP-CAMPER1-d, achieves 1.37 log (p < 0.0001) reduction in an established biofilm infection model. Single-cell analysis using a high-throughput microfluidic system shows that WP-CAMPER1-d reduces exponential-phase persisters of S. aureus USA300, and, in a deep-seated murine thigh infection model, decreases stationary-phase S. aureus MW2 persisters by 1.6 log (p < 0.0001).

Introduction
The Centers for Disease Control and Prevention (CDC) identifies antimicrobial resistance (AMR) as an urgent global public health threat.1,2 Annually, over 2.8 million antimicrobial-resistant infections occur in the United States, resulting in more than 35,000 deaths1, with Staphylococcus aureus being a leading cause of severe healthcare-associated infections, including sepsis, pneumonia, endocarditis, and osteomyelitis.3 The CDC estimates that methicillin-resistant S. aureus (MRSA) alone accounts for over 70,000 serious infections and nearly 9,000 deaths each year,4 while vancomycin-resistant S. aureus (VRSA), even though it remains rare, is another emerging threat.5 Further compounding the AMR challenge, S. aureus can transition into low-energy states, such as persisters,6 and form biofilms,7 adaptations that enable it to evade conventional antibiotic therapy and contribute to chronic and relapsing infections,8 underscoring the urgent need for alternative therapies.

Antimicrobial peptides (AMPs) represent a promising class of therapeutic agents capable of targeting drug-resistant bacteria and physiologically distinct forms such as persisters and biofilms, without promoting resistance.8,9 Unlike conventional antibiotics, which primarily target active biosynthetic pathways in metabolically active bacterial cells,10 AMPs primarily disrupt the bacterial membranes,11 an appealing strategy against both persisters and biofilms, as membrane disruption occurs independently of the bacterial growth state.12 Precisely designing AMPs for selective bacterial membrane disruption using traditional methods13,14 relies on rational modifications to properties such as charge, hydrophobicity, helicity, and amphipathicity.11 These approaches are often constrained by the time and resources required to explore complex sequence spaces. As an alternative, machine learning (ML) has emerged as a powerful tool for AMP discovery, enabling rapid candidate prediction by identifying patterns in sequence and physicochemical data.15-28

ML-based approaches provide important statistical associations but overlook key mechanistic features, such as membrane selectivity and structural feasibility, limiting their biological relevance.15-28 Several prior studies have incorporated physicochemical and structural features into ML classifiers, including hydrophobicity, net charge, and helix-forming propensity, as part of their feature sets.17,18,29 While these approaches capture statistical associations between biophysical properties and activity, they treat these properties as input variables for supervised learning rather than as explicit mechanistic constraints. As a result, they overlook the biophysical requirements that define membrane-active AMP behavior, such as selective membrane interaction, amphipathic folding, and the structural feasibility of helical conformations.

To address this gap, we developed a Mechanistic Artificial Intelligence (AI) framework that integrates an ML model with a biophysically informed scoring function. We refer to our hybrid model as CAMPER (Constraint-driven AMP Engineering with Ranking), reflecting its dual-stage approach: an ML classifier trained on experimental AMP data, and a biophysical scoring function that imposes mechanistic constraints. The ML component scores peptides based on key physicochemical properties required for membrane activity,11 effectively ranking candidates by both statistical likelihood and mechanistic plausibility. Our ML classifier, a random forest trained on chemical, structural, and sequence features, provides high-throughput probabilistic predictions of antimicrobial activity. The biophysical scoring function evaluates the potential of each peptide to function as a membrane-active AMP based on well-established biophysical properties: net charge, hydrophobicity, hydrophobic moment, and helicity.8,11,24

We developed CAMPER to prioritize membrane-active AMPs with the specific aim of targeting drug-tolerant bacterial phenotypes such as biofilms and persister cells. We applied CAMPER to screen a mastoparan-derived peptide library and identified WP-CAMPER1 as the top candidate. In a proof of concept study, we evaluated WP-CAMPER1 across a series of in vitro and in vivo models, confirming its potent activity against the staphylococcal membrane, a property that strongly predicts efficacy against persisters.8,30 Then, we confirmed its activity against drug-resistant S. aureus, including biofilms and persister populations. We developed the D-enantiomer of WP-CAMPER1, and tested it in the Mother Machine,31,32 a single-cell microfluidic assay used to evaluate activity against rare exponential-phase persisters, and confirmed its efficacy against rare exponential phase persisters. The Mother Machine enables real-time tracking of individual bacteria under controlled conditions that simulate in vivo environments, including nutrient limitation and dynamic drug exposure.31 Finally, we confirmed the anti-persister potency of WP-CAMPER1-d against stationary phase persisters in a well-established neutropenic murine deep-tissue thigh infection model, which is widely used to assess anti-persister therapeutics with translational relevance for systemic clinical applications.12,30,33

Results
To advance the discovery of target-specific AMPs, we focused on membrane-active peptides against S. aureus and developed CAMPERT as a dual-component in silico pipeline that combines a machine learning classifier trained on curated AMP sequences with a scoring function based on four biophysical constraints relevant to the membrane-disruptive activity of α-helical peptides: net charge, hydrophobicity, hydrophobic moment, and helicity (Figure 1). We focused on α-helical peptides because they are structurally simple, synthetically accessible, and tunable, making them ideal candidates for rational design and optimization using computational pipelines like CAMPER.

Dataset Assembly and Feature Engineering
We began by training a random forest (RF) classifier to serve as the first component of the CAMPER pipeline, predicting the antimicrobial activity of peptides against S. aureus. To construct the ML model, we collected 3,961 peptide sequences with MIC measurements against S. aureus from the Database of Antimicrobial Activity and Structure of Peptides (DBAASP). The database included 2,280 sequences we labeled as active (MIC ≤ 32 µg/mL) and 1,431 as inactive (MIC ≥ 64 µg/mL), excluding those with MIC values between these thresholds. After removing sequences containing non-standard amino acids, we retained a final dataset of 3,660 unique peptide sequences.

To assess model performance on unseen data, we first created a stratified 80 to 20 random split of the dataset. The 20% portion served as a holdout set. The training set contained 2,928 peptides, with 1,674 active peptides (57.2%) and 1,254 inactive peptides (42.8%). The holdout set contained 732 peptides, with 419 active peptides (57.2%) and 313 inactive peptides (42.8%). These proportions match the class distribution of the full dataset, which consisted of 2,093 active peptides (57.2%) and 1,567 inactive peptides (42.8%). Next, we generated a total of 66 physicochemical descriptors to characterize each peptide and train the model on properties that may influence antimicrobial activity (Table S1). Additionally, we computed the Morgan fingerprint (2048-bit, radius 4) to represent the substructural features of each peptide, resulting in a combined feature set of 2,114 descriptors.

Model Training, Evaluation, and Robustness
We assessed performance using the homology aware 5-fold cross-validation procedure described in the Methods. Based on the sensitivity analysis, we selected an 80% similarity threshold as the most stable and well-balanced cutoff for homology-aware evaluation (Figure S1). We evaluated several combinations of feature representations and classifiers; specifically, sequence-derived n-grams, and ProtBERT embeddings paired with random forest, XGBoost, and logistic regression. Across these combinations, models built on physicochemical descriptors consistently outperformed those trained on sequence-derived n-grams and ProtBERT embeddings, and random forests produced the strongest overall discrimination (Figure 2A-B, Table S2). The corresponding confusion matrices for all feature–model combinations are shown in Figure S2, which illustrates the distribution of correct and incorrect predictions under homology-aware cross-validation.

The RF model achieved an accuracy of 78% on the holdout set, with a precision of 79%, recall of 83%, F1 score of 81%, and AUC of 82%. Under the homology-aware 5-fold cross-validation scheme, the RF classifier showed lower overall discrimination than in the random split, with a mean cross-validated AUC of 70% (Figure 2C). This decrease reflects the stricter evaluation created by grouping peptides with substantial sequence similarity into the same fold, which reduces the amount of overlapping sequence information across training and validation subsets. The confusion matrix shows the distribution of true positive, false positive, true negative, and false negative predictions in the holdout set (Figure 2E). As shown in the calibration plot in Figure S3, the RF classifier produces reasonably calibrated probability estimates, with a Brier score of 0.156 indicating good overall alignment between predicted probabilities and observed frequencies.

To validate model reliability, we performed y-randomization, shuffling the activity labels in the training data while preserving the feature values. After 100 iterations, the randomized models achieved an average accuracy of 52%, significantly lower than the 80% accuracy of the original model, confirming that the observed performance is not due to random associations within the data.

We evaluated feature importance within the random forest classifier by ranking features according to their mean impurity-based importance scores (Figure 2F). GRAVY ranked highest, followed by MolLogP and BertzCT. Several BCUT2D descriptors, along with molar refractivity and the Kappa shape indices, also appeared among the top contributors. Hydrophobic moment was likewise ranked highly. Overall, the model relied most on descriptors related to hydrophobicity, charge distribution, polarizability, and amphipathic structure.

Biophysical Scorer Integration
To refine the selection of peptides predicted by the first component of CAMPER, we integrated a biophysical scoring function alongside the RF classifier to generate the final ranking. The biophysical scoring function was designed to prioritize α-helical, membrane-active peptides targeting S. aureus by ranking candidates based on four key physicochemical properties: net charge, hydrophobicity, hydrophobic moment, and helicity. Detailed rationale for property selection and the mathematical transformations applied are described in the Methods section, with hyperparameter and weights of the biophysical scorer provided in Supplementary Results.

We calculated performance metrics on the held-out test set. As noted above, peptides with MIC ≤ 32 µg/mL were labeled as active, and those with MIC ≥ 64 µg/mL as inactive, with peptides in the intermediate range excluded from evaluation. Compared to the standalone RF model, which achieved a precision of 81%, CAMPER (RF combined with Biophysical Scorer) improved precision to 86%, representing a 5 percentage point increase in correctly identifying true positives among predicted actives (Table S2). Notably, the number of false positives (defined as peptides predicted as active by the model but labeled as inactive based on their MIC) decreased substantially from 92 in the RF model to just 21 with CAMPER, representing a 77% reduction (Figure 2E). This reduction aligns with the intended use of CAMPER as a prioritization framework that screens large, custom peptide libraries and aims to minimize synthesis and testing of non-active peptides. CAMPER shows lower recall and a lower AUC compared to the standalone RF model (Figure 2C, Table S2), but this decrease in recall and AUC primarily reflects the prioritization introduced by the biophysical scoring function which favors peptides consistent with an α-helical, membrane-disruptive mechanism rather than optimizing discrimination across all antimicrobial mechanisms. Analysis of peptides in the external evaluation set that were experimentally labeled as active in the dataset, predicted as active by the RF model, but deprioritized by CAMPER showed that 70% (140/200) were not predominantly α-helical based on AlphaFold2 structure predictions. These peptides and their predicted secondary structure classes are provided in Supplementary Data 1.

To assess the biophysical characteristics of peptides prioritized by CAMPER, we selected the top 50 test set predictions ranked by (i) the RF classifier alone, (ii) the biophysical scoring function alone, and (iii) the combined CAMPER score. Compared to RF-only predictions, CAMPER-ranked peptides showed higher median helicity and hydrophobic moment with tighter distributions around values characteristic of α-helical, membrane-active peptides targeting Gram-positive bacteria (Figure S4). Additionally, the integration of the biophysical scoring function within CAMPER led to improvements in prediction precision within specific regions of the biophysical feature space. For example, among peptides with hydropathy (GRAVY) between -1.19–0.26, the precision increased from 72% (RF-only) to 95% with the CAMPER dual-component model (Figure 2G), and for the 0.20–0.40 helicity range precision increased from 75% to 97% (Figure 2H). Similar improvements in precision were observed in additional regions of the biophysical feature space. Precision gains were also observed across various grand average of charge and hydrophobic moment bins (Figure 2G-I). At the same time, we observed a clear drop in precision in the highest charge interval, which includes only a small number of peptides and represents an extreme physicochemical regime that is not characteristic of most membrane-acting sequences (Figure 2I). This pattern highlights that CAMPER performs best in well-represented and biologically relevant regions of the feature space while showing reduced precision in sparse, extreme outlier bins.

Application of CAMPER to a Rationally Designed Peptide Library
To demonstrate the ability of CAMPER to prioritize potent and membrane-active AMPs, we applied the pipeline to a candidate pool derived from a natural 14-mer peptide template, using it as a proof of concept for targeted in silico screening. Details on the peptide library generation are provided in the Methods section. We screened the peptide library with CAMPER and prioritized the five top-ranked candidates for synthesis to enable rapid and efficient experimental assessment. WP-CAMPER1, the top-ranked candidate, exhibited a favorable physicochemical profile for membrane-targeting activity against Gram-positive bacteria with a GRAVY of 1.21, a hydrophobic moment of 0.538, net charge of 2.55 and a helical propensity of 83.33%. The physicochemical profiles of all five synthesized candidates are summarized in Table 1.

In vitro antimicrobial potency of the top-ranked peptides.
We evaluated the antimicrobial activity of WP-CAMPER1 followed by the next four top-ranked AMPs (WP-CAMPER2 to WP-CAMPER5), selected from the rationally designed peptide library described in the preceding section, using MIC assays against S. aureus strain MW2 (Table 1). WP-CAMPER1 exhibited a potent MIC of 4 µg/mL against S. aureus MW2. We also explored the antimicrobial robustness of the WP-CAMPER1 peptide by evaluating its antimicrobial activity under physiological salt conditions, varying pH levels, and in the presence of serum. The MIC of WP-CAMPER1 remained unchanged in the presence of monovalent and divalent salt (150 mM NaCl, 150 mM MgSO4) and at pH levels of 7.4 and 8.0, except at pH 6.8, where it increased twofold. Similarly, the MIC increased only twofold in the presence of 5% human serum (Table S3).

Validation of WP-CAMPEAR1 by traditional quantitative-structure activity relationship (QSAR)-based peptide design
We conducted a QSAR analysis of WP-CAMPER1 to evaluate the hypothesis that CAMPER effectively ranks peptide sequences for membrane-disruptive antimicrobial activity by optimizing charge, hydrophobicity, and amphipathicity. In this series of analyses, we introduced substitutions of both hydrophilic and hydrophobic amino acids on different faces of WP-CAMPER1 (Figure S5). We explored three conditions: increasing the charge, enhancing hydrophobicity, and altering both as detailed in the Methods.

The variants displayed MICs ranging from 4 to 8 µg/mL, depending on modifications to charge and hydrophobicity (Table S4). When only the charge or hydrophobicity was increased in WP-CAMPER1 (variants WP-CAMPER1-6K and WP-CAMPER1-5L8L), the antimicrobial potency was reduced, resulting in a higher MIC of 8 µg/mL. However, variants incorporating both increased charge and hydrophobicity (WP-CAMPER1-6K5L8L and WP-CAMPER1-6K3W5W7W11W) exhibited an MIC of 4 µg/mL against S. aureus MW2, comparable to that of WP-CAMPER1. These results indicate that further enhancement of charge or hydrophobicity does not improve activity, supporting the conclusion that CAMPER selected a sequence with physicochemical properties that are near-optimal for membrane-targeting antimicrobial efficacy.

Comparison of WP-CAMPER1 with other Mastoparan peptides
To demonstrate the utility of CAMPER in identifying peptides with enhanced antimicrobial potency, we synthesized three diverse mastoparan peptides (mastoparan-S, dominulin A and mp_mastoparan) and evaluated their antimicrobial activity against S. aureus MW2, as well as their cytotoxicity in human liver-derived hepatocellular carcinoma (HepG2) cells and hemolysis of human red blood cells (Table S5). WP-CAMPER1 exhibited a MIC of 4 µg/mL against S. aureus MW2, while mastoparan-S (MIC= >32 µg/mL), dominulin A (MIC= 32 µg/mL) and mp_mastoparan (MIC= 32 µg/mL), showed reduced antimicrobial potencies. WP-CAMPER1 had an lethal dose 50% (LD50) of 128 µg/mL in HepG2 cells and caused 50% hemolysis at 64 µg/mL, whereas the mastoparans showed no toxicity to HepG2 cells (over 99% survival at 128 µg/mL) (Figure S6A) and < 40%, < 20% and < 10% hemolysis for dominulin A, mp_mastoparans and mastoparan-S, respectively at 128 µg/mL (Figure S6B). Although these peptides exhibit lower cytotoxicity, they show minimal or no antimicrobial activity against S. aureus MW2, limiting their relevance to CAMPER design goals and translational aims. In contrast, CAMPER enabled the design of WP-CAMPER1 from the same mastoparan scaffold, resulting in markedly superior antimicrobial potency, highlighting the utility of CAMPER in AMP design.

Potency of WP-CAMPER1 towards drug-resistant S. aureus exponential and stationary phase persister cells
To confirm the membrane-targeting and antimicrobial potential of WP-CAMPER1, we first assessed its ability to inhibit the growth of exponential-phase S. aureus strains MW2 and VRS1, before testing its efficacy against more resilient forms such as biofilms and persisters. WP-CAMPER1 rapidly suppressed the growth of exponentially growing S. aureus MW2 bacterial cells compared to control antibiotics such as gentamicin and vancomycin (Figure 3A,S7A and S8A). Whereas vancomycin was not active against S. aureus VRS1 (Figure S9A).

Subsequently, in time-kill kinetics assays, WP-CAMPER1 (40 µg/mL) eliminated 1 × 10⁸ CFU of exponential and stationary phase persister cells of S. aureus MW2 within 120 minutes of exposure (Figure 3B,C). We observed a similar result of complete cell killing within 120 mins for 1 × 106 CFU of exponential and stationary phase persister cells of S. aureus VRS1 (Figure S7B,C). WP-CAMPER1 (4 µg/mL) reduced S. aureus MW2 exponential and stationary phase persister bacterial counts by 3.2 log10 and 3.5 log10, respectively (Figure 3B,C). Under the same conditions, the control antibiotic ciprofloxacin (at 10 µg/mL) reduced S. aureus MW2 exponential cells by only 0.5 log10 and showed no activity against stationary phase persister cells, confirming the presence of a drug-tolerant persister state (Figure 3B,C). Similarly, WP-CAMPER1 (4 µg/mL) reduced S. aureus VRS1 exponential and stationary phase persister cell counts by approximately 2 log10 and 1.2 log10, respectively (Figure S7B,C). The control antibiotic linezolid (at 100 µg/mL) reduced S. aureus VRS1 exponential cells by 1.0 log10 but was inactive against the persister form of S. aureus VRS1 (Figure S7B,C). Bithionol was active against both exponential and persister populations of S. aureus MW2 and S. aureus VRS1 (Figure S8B,C and S9B,C). Compared with untreated controls, bithionol reduced the exponential-phase bacterial load by approximately 3.0 log10 CFU in S. aureus MW2 and 1.5 log10 CFU in S. aureus VRS1 (Figure S8B, S9B). In persister assays, bithionol decreased the bacterial burden by approximately 2.0 log10 CFU in S. aureus MW2 and 0.5 log10 CFU in S. aureus VRS1 (Figure S8C, S9C).

Anti-biofilm activity of WP-CAMPER1 against drug-resistant S. aureus biofilms
Next, we tested the antibiofilm activity of WP-CAMPER1 against S. aureus MW2 and VRS1 biofilms by evaluating both biofilm inhibition (Figure 3D,E and S7D,E) and disruption stages (Figure 3F,G and S7F,G). WP-CAMPER1 inhibited biofilm formation by S. aureus MW2 and VRS1, inhibited 50% of S. aureus MW2 live cells at 2 μg/mL, and reduced biofilm biomass by 50% at 6 μg/mL (MBIC50) (Figure 3D,E). For S. aureus strain VRS1, WP-CAMPER1 achieved 50% killing of live cells at 6 μg/mL and a 50% reduction in biofilm mass at 4 μg/mL (MBIC50) (Figure S7D,E). Additionally, WP-CAMPER1 disrupted 24-hour pre-established biofilm, killed 50% of live S. aureus MW2 biofilm cells and reduced biofilm biomass by 50% (MBEC50) at 12 μg/mL (Figure 3F,G). For S. aureus VRS1, WP-CAMPER1 killed 50% of biofilm-embedded cells at 16 μg/mL and reduced biofilm biomass by 50% (MBEC50) at 12 μg/mL (Figure S7F,G).

To further evaluate the antibiofilm activity of WP-CAMPER1, we included three additional S. aureus clinical isolates (BF1, BF2, and BF3) (Figure S10). WP-CAMPER1 effectively disrupted 24-hour mature biofilms formed by these isolates, achieving 50% biomass reduction (MBEC50) at 8 μg/mL, 16 μg/mL, and 12 μg/mL, respectively. Similarly, WP-CAMPER1 inhibited biofilm formation in these clinical isolates, reducing biomass by 50% (MBIC50) at 1 μg/mL (BF1), 5 μg/mL (BF2), and 3 μg/mL (BF3). These results highlight the broad-spectrum antibiofilm efficacy of WP-CAMPER1 across diverse clinical S. aureus backgrounds.

To visually assess the antibiofilm potential of WP-CAMPER1, we performed fluorescence imaging on 24-hour established S. aureus MW2 and VRS1 biofilms treated with WP-CAMPER1 (Figure 3H,I and S7H,I). Untreated control biofilms displayed predominantly SYTO9 green fluorescence (Figure 3H,S7H), indicating the presence of mostly live cells. In contrast, biofilms treated with WP-CAMPER1 (32 μg/mL) showed a pronounced propidium iodide red fluorescence, suggesting most cells were dead (Figure 3I,S7I).

We then evaluated the ability of WP-CAMPER1 to disrupt 24-hour established S. aureus MW2 and VRS1 biofilms grown on solid membrane supports (Figure 3J,S7J). WP-CAMPER1 (40 µg/mL) reduced the bacterial burden in S. aureus MW2 biofilms by 2 log10 (p=0.017) and in S. aureus VRS1 biofilms by 2.2 log10 (p=0.016)(Figure 3J,S7J), compared to the untreated control. At 4 µg/mL, WP-CAMPER1 lowered the bacterial burden by 0.3 log10 (p=0.016) for S. aureus MW2 and 1.0 log10 (p=0.024) for S. aureus VRS1 compared to the untreated control. In contrast, vancomycin at 10 μg/mL did not reduce bacterial burden in S. aureus MW2 or VRS1 strains.

Confirmation of Membrane Targeting
Because the biophysical scoring function was designed to prioritize peptides with physicochemical properties associated with membrane activity, it was essential to experimentally confirm that WP-CAMPER1, the top-ranked peptide, exerts its antimicrobial effect through direct interaction with the bacterial membrane. To this end, we conducted a series of peptide-membrane interaction studies.

To evaluate the initial membrane interaction and structural conformation of WP-CAMPER1 in a bacterial membrane-mimetic environment, we conducted circular dichroism (CD) spectroscopy in the presence of sodium dodecyl sulfate (SDS) micelles. The CD spectra exhibited characteristic minima at 208 and 222 nm, indicative of an α-helical conformation (Figure S11). Furthermore, 2D NMR spectroscopy in the presence of SDS micelles revealed that WP-CAMPER1 adopted an α-helical structure with a distinct hydrophobic surface essential for membrane interaction (Figure 4A-D). Extended structural calculations on the NMR structural restraints, hydrogen bonds, and Ramachandran plot statistics are provided in the Table S6 and Supplementary Results.

Molecular dynamics (MD) simulations demonstrated that WP-CAMPER1 bound to and disrupted a DOPC/DOPG(7:3) model membrane, with key residues tryptophan 1 and lysine 2, 9 and 10 forming stable hydrogen bonds and perturbing membrane integrity (Figure 4E-H). Table S7 and Supplementary Results provide details of the simulation outcomes, including hydrogen bond occupancies between DOPG/DOPC and interacting amino acids, as well as membrane water perturbation following WP-CAMPER1 insertion.

To determine whether WP-CAMPER1 could disrupt a membrane model that more closely mimics the S. aureus lipid composition, we performed MD simulations of WP-CAMPER1 with a POPG:CL (7:3) bilayer. WP-CAMPER1 rapidly associated with the membrane, binding within the first 10 ns and remaining embedded throughout the 500-ns simulation, indicating a stable and energetically favorable peptide-membrane interaction (Figure S12A). The partial density profile further demonstrated peptide insertion into the bilayer (Figure S12B).

In the next series of studies, we aimed to confirm that the membrane binding and insertion behavior of WP-CAMPER1 observed in silico reflects true membrane-targeting activity by conducting biochemical assays to assess its effects on live S. aureus MW2 cells (Figure 4I-R). Using a DiBAC4(3) fluorescence-based dye assay, we observed that WP-CAMPER1 (32 µg/mL) caused a rapid increase in fluorescence, signifying membrane depolarization (Figure 4I). Similarly, a Triton X-100 (1% v/v) control depolarized most cells, displaying maximum fluorescence intensity. Untreated bacterial cells displayed no change in DiBAC4(3) fluorescence, indicating an intact membrane potential.

As an additional test to confirm whether WP-CAMPER1 can permeabilize membranes, we performed dye permeation experiments using propidium iodide and SYTOX Green. Upon co-incubation of S. aureus MW2 cells with WP-CAMPER1 (at 4 µg/mL) and propidium iodide, we detected elevated fluorescence, suggesting WP-CAMPER1-induced membrane damage (Figure 4J). Similarly, WP-CAMPER1 (at 4 µg/mL) resulted in increased SYTOX Green fluorescence, further confirming membrane disruption (Figure 4K). Additionally, WP-CAMPER1 at 4 µg/mL led to higher ATP luminescence levels compared with untreated controls, suggesting ATP leakage due to compromised membrane integrity (Figure 4l).

To further establish the putative membrane-targeting nature of WP-CAMPER1, we employed a ΔfmtC transposon mutant from the Nebraska Transposon Mutant Library (NTML). This mutant lacks the ability to transfer a lysyl group onto the bacterial membrane surface,34 increasing its susceptibility to membrane-targeting antimicrobial agents. Compared with the wild-type background JE2 strain, WP-CAMPER1 demonstrated a two-fold reduction in MIC against the ΔfmtC mutant, suggesting enhanced membrane-disruptive activity, likely due to the reduced positive charge from the absence of lysyl transfer (Table S8).

Next, to assess the membrane-associated physiological disruptions of live S. aureus MW2 cells, we used the fluorescent probe 2′,7′-Bis-(2-carboxyethyl)-5-(and-6)-carboxyfluorescein (BCECF) to monitor changes in the ΔpH component of the proton motive force (PMF). WP-CAMPER1 at 4 μg/mL significantly perturbed ΔpH levels (Figure 4M). Additionally, WP-CAMPER1 (32 μg/mL) induced a marked increase in intracellular reactive oxygen species (ROS), as evidenced by enhanced fluorescence from the ROS-sensitive 2′,7′-Dichlorodihydrofluorescein diacetate dye (DCFH-DA) (Figure 4N).

To determine whether ROS generation directly contributed to bacterial killing as an auxiliary mechanism in addition to peptide-induced membrane disruption, we performed kinetic growth and spot-titer CFU assays in the presence of the ROS scavenger N-acetylcysteine (NAC, 10 mM). In the kinetic growth assay, cultures with an initial inoculum of 1×106 CFU/mL showed a significant bacterial growth difference at 16 h when co-incubated with WP-CAMPER1 (1× MIC) and NAC (p = 0.046), whereas no growth recovery was observed in cultures treated with WP-CAMPER1 compared to untreated controls (p = 0.011) and NAC alone did not affect S. aureus MW2 growth (Figure S13A). In the spot-titer CFU assay, NAC partially restored S. aureus MW2 viability, reducing the bactericidal effect of WP-CAMPER1 by approximately 5 log10 CFU after 4 h of co-incubation (p = 0.0006), compared with the untreated control that reached 8 log10 CFU (Figure S13B). Similarly, using a higher inoculum (1×107 CFU/mL) treated with 5× MIC of WP-CAMPER1, initial bacterial killing was followed by partial regrowth in the presence of NAC, whereas complete eradication occurred with peptide treatment alone after 18 hours (Figure S13C). Together, these findings suggest that, in addition to direct membrane disruption, ROS generation contributed to the overall bactericidal mechanism of the CAMPER peptides.

To determine whether this oxidative stress led to lipid peroxidation, we employed a fluorescent based assay using C11-BODIPY dye. WP-CAMPER1 (4 μg/mL) treatment significantly increased C11-BODIPY oxidation, indicating lipid peroxidation and ROS-mediated membrane damage (Figure 4O).

Microscopic visualization of WP-CAMPER1- induced S. aureus membrane disruption
In order to visually confirm WP-CAMPER1-induced bacterial membrane damage, we employed scanning and cryo-transmission electron microscopy to examine S. aureus MW2 cells treated with the peptide WP-CAMPER1 at 40 µg/mL (Figure 4P). Untreated bacterial control cells displayed smooth and uniform surfaces, indicating intact cells (Figure 4P, upper panel). In contrast, WP-CAMPER1-treated cells exhibited prominent membrane blebbing, invaginations, and structural damage, indicating damage to bacterial membrane integrity caused by WP-CAMPER1 (Figure 4P, bottom panel). We also performed cryo-transmission electron microscopy (cryo-TEM) on S. aureus MW2 cells treated with WP-CAMPER1 (80 µg/mL) to visualize ultrastructural changes in the bacterial membrane. Compared to the control cells (Figure 4Q, upper panel), which displayed a homogeneous and intact membrane surface, WP-CAMPER1-treated cells exhibited membrane disruption, including invaginations, vacuole-like structures, shrunken cytoplasm near the cell periphery (Figure 4Q, bottom panel), suggesting compromised membrane integrity.

Transcriptional effect of WP-CAMPER1
We performed RNA-seq analysis on WP-CAMPER1-treated S. aureus to help elucidate the mechanism by which WP-CAMPER1 impacts bacterial membrane and thus affects S. aureus persisters and biofilms. We found that treatment with WP-CAMPER1 (2 µg/mL) significantly altered gene expression, causing 852 upregulated and 843 downregulated genes (p < 0.05) (Supplementary Data 2,3). Notably, WP-CAMPER1 significantly affected the expression of S. aureus pathways involved in bacterial virulence, oxidative phosphorylation, folate biosynthesis, and energy metabolism (Figure 4R). These transcriptional changes reflect bacterial stress responses to WP-CAMPER1 and support our hypothesis that CAMPER-designed peptides selectively disrupt membrane integrity while triggering broader physiological effects. Detailed analysis of gene expression changes across multiple regulatory pathways is provided in the Supplementary Results.

In vivo therapeutic efficacy of WP-CAMPER1 on a murine skin infection model
To evaluate whether the bacterial membrane-targeting activity of WP-CAMPER1 translates into protective efficacy in vivo, we next tested its ability to reduce bacterial burden in a skin infection model that mimics early-stage or prophylactic intervention. We employed an abraded murine skin infection model, infecting mice with 107 CFU of S. aureus MW2 to evaluate the in vivo efficacy of WP-CAMPER1 (Figure 5A). A single application of the 2% (w/w) WP-CAMPER1 formulation, administered 10 minutes after infection (representing a prophylactic model), led to a significant reduction in S. aureus MW2 bacterial load (evaluated after 24 h), achieving a 2.5 log10 decrease (p = 0.0002) (95% CI = 2.15 – 2.85) compared to the vehicle treated mice groups (Figure 5B). The 1% (w/w) formulation also demonstrated efficacy, reducing the bacterial load by 1.4 log10 (p = 0.0005) (95% CI = 0.84 – 1.42). As a positive control, mupirocin ointment (2% w/w) resulted in a 2.2 log10 reduction in S. aureus MW2 burden (p = 0.0002) (95% CI = 1.82 – 2.48) compared to the vehicle treated mice. In addition to decreased bacterial burden, the skin of mice treated with the WP-CAMPER1 (2% w/w) formulation exhibited significantly lower levels of proinflammatory cytokines and chemokines compared to the vehicle treated skin, including TNF-α (14-fold reduction, p=0.029), IL-1β (4-fold, p=0.028), IFN-γ (2.2-fold, p=0.004), MCP-1 (2-fold, p=0.023), and IL-6 (5-fold, p=0.002) (Figure S14).

Class A peptidomimetics of WP-CAMPER1
After confirming the membrane-targeting mechanism, in vitro potency, and topical efficacy of WP-CAMPER1, we sought to explore its efficacy in a murine thigh infection model of persistent S. aureus, which involves systemic administration of the study agent. Because WP-CAMPER1, like other α-helical peptides could be vulnerable to enzymatic degradation by host and bacterial proteases,14 we hypothesized that its D-enantiomeric variant could retain comparable antimicrobial activity, due to preservation of the key physicochemical properties prioritized by CAMPER (charge, hydrophobicity, helicity, and amphipathicity), while offering resistance to proteolytic degradation.

We synthesized WP-CAMPER1-d alongside a set of Class A peptidomimetics that preserve the overall peptide backbone structure of WP-CAMPER1 but incorporate substitutions at lysine residues with non-proteinogenic amino acids to avoid recognition and degradation by proteases. We evaluated the resulting peptide variants for activity against a panel of antibiotic-resistant S. aureus strains (Table 2). As hypothesized, WP-CAMPER1-d retained antimicrobial activity comparable to that of the parent peptide WP-CAMPER1 (4–8 μg/mL), mirroring the intact physicochemical profile prioritized by CAMPER. In contrast, analogs containing diaminobutyric acid (Dab), diaminopimelic acid (Dap), and ornithine (Orn) substitutions, which disrupted the physicochemical properties prioritized by CAMPER, showed reduced potency (MIC 8–32 μg/mL), and the cyclic variant was inactive (MIC > 32 μg/mL).

Next, we tested the stability of WP-CAMPER1-d to α-chymotrypsin and its cytotoxicity against various human cell lines. WP-CAMPER1-d remained intact after 24 hours of α-chymotrypsin exposure, whereas linear WP-CAMPER1 was fully degraded within 2 hours (Figure S15). Hemolysis assays showed comparable toxicity (Figure S16). Against other human cell lines, the LD50 of WP-CAMPER1 was 120 µg/mL in HaCaT, 128 µg/mL in HepG2, and 55 µg/mL in HKC-8 cells. For WP-CAMPER1-d, the LD50 values were 65 µg/mL in HaCaT, 50 µg/mL in HepG2, and 35 µg/mL in HKC-8 cells (Figure S17). Overall, WP-CAMPER1-d exhibited a promising anti-staphylococcal profile compared to other lysin substituted peptidomimetics, showed proteolytic stability and a defined therapeutic window.

We also evaluated whether S. aureus MW2 could develop resistance to WP-CAMPER1 and WP-CAMPER1-d. No resistance was observed after 21 serial passages, as MIC values remained unchanged (Figure 5C). In contrast, ciprofloxacin exhibited a >4-fold MIC increase by day 15 and a >16-fold increase by day 21, indicating the emergence of resistance.

To assess the persistence of bacterial growth suppression following peptide exposure, we evaluated the post-antibiotic effect (PAE) of WP-CAMPER1 and WP-CAMPER1-d against S. aureus MW2. The PAE values for WP-CAMPER1, WP-CAMPER1-d, and ciprofloxacin, relative to the untreated control, were 1.68 ± 0.19 h (95% CI= 1.26–2.10) (p= 0.0009), 2.45 ± 0.31 h (95% CI= 1.67–3.23) (p < 0.0001), and 0.91 ± 0.17 h (95% CI= 0.53–1.29) (p= 0.0047), respectively. Notably, the PAE of WP-CAMPER1-d was significantly longer than that of WP-CAMPER1 (p = 0.018), confirming that D-enantiomerization prolongs post-antibiotic suppression relative to the parent peptide. These findings demonstrate that both CAMPER-derived peptides exhibit prolonged bacterial growth inhibition compared with ciprofloxacin, with WP-CAMPER1-d showing the strongest and most durable effect.

Anti-persister activity of WP-CAMPER1-d against exponential phase S. aureus persisters
To assess ability of WP-CAMPER1 and WP-CAMPER1-d against persisters cells in an environment that is closer to the in vivo conditions, we used the Mother Machine, a single-cell microfluidic assay. Using this platform, we monitored ~5,520 trenches, each containing ~20 exponential-phase S. aureus USA300 cells, and exposed them to a bactericidal concentration of gentamicin (3.75 µg/mL) (Figure 5D,E). During first gentamicin treatment, out of 5,520 trenches analyzed across four biological replicates, only 4 trenches contained surviving lineages after treatment with survival frequency: (4/5520, 0.072%; 95% CI: 0.019% to 0.185%), exhibiting a persister-like phenotype (Figure 5D). These results correspond to a survival frequency of 0.069% based on lineage-level analysis. However, during the second gentamicin treatment (3.75 µg/mL) in which, the four surviving lineages from the first gentamicin treatment were exposed to a second challenge: two each for retreatment with gentamicin or with the WP-CAMPER1-d peptide (60 µg/mL) (Figure 5D) (Supplementary Video 1). In gentamicin retreatment: both lineages survived (2/2, 100%; 95% CI: 15.811%–100%) (Supplementary Video 2,3), while during peptide retreatment with WP-CAMPER1-d, both lineages were eradicated (0/2, 0%; 95% CI: 0%–84.188%) (Supplementary Video 4,5), indicating a differential response to the two treatment regimens (Figure 5D,E).

In vivo efficacy of WP-CAMPER1-d in a murine biofilm and a persister infection model
Following the single-cell evidence of anti-persister activity of WP-CAMPER1-d against exponential-phase MRSA persisters, we sought to evaluate its efficacy in capturing both surface-associated and deep-tissue infections. We employed two murine infection models: a biofilm-associated skin infection and a deep-seated thigh infection model (Figure 5F,H). In the established biofilm model, a single topical dose of WP-CAMPER1-d (2% w/w in white petroleum jelly), applied 24 hours post-infection, significantly reduced S. aureus MW2 burden by 1.37 log10 CFU (p < 0.0001) (95% CI = 0.90–1.84) compared to the vehicle treated animal group (Figure 5G). In comparison, WP-CAMPER1 (2% w/w) reduced the bacterial load by 1.22 log10 CFU (p < 0.0001) (95% CI = 0.73–1.71), which was only 0.15 log10 CFU lower than WP-CAMPER1-d, indicating similar efficacy between L- and D- forms of WP-CAMPER1, since the difference was not statistically significant (p = 0.496). Meanwhile the 2% mupirocin ointment control achieved a reduction of 2.06 log10 CFU (p < 0.0001) (95% CI = 1.54–2.58) compared to the control mice treated with white petroleum jelly (Figure 5G). Bacterial reduction by mupirocin was 0.69 log10 CFU (p = 0.0198) (95% CI = 0.13–1.26) greater than that achieved by WP-CAMPER1-d and approximately 0.84 log10 CFU (p = 0.0080) (95% CI = 0.26–1.42) greater than WP-CAMPER1.

In the deep-seated thigh infection model, WP-CAMPER1-d (10 mg/Kg) reduced the bacterial load of stationary-phase S. aureus MW2 persister cells by 1.6 log10 CFU (p < 0.0001) (95% CI: 1.33–1.87), whereas WP-CAMPER1 achieved a 1.05 log10 reduction (p < 0.001) (95% CI = 0.81–1.29), compared to the vehicle treated animal group (Figure 5I). The observed additional ~0.55 log10 reduction in S. aureus MW2 burden with WP-CAMPER1-d compared to WP-CAMPER1 (p = 0.0029), suggests that the enhanced enzymatic stability of the D-form may play a critical role, ultimately affecting the extent of bacterial clearance. In contrast, treatment with the control antibiotic vancomycin (30 mg/Kg) was ineffective, confirming the presence of antibiotic-tolerant persisters and validating the bacterial persistence in the deep-seated thigh infection model. We also evaluated the renal toxicity of WP-CAMPER1-d and WP-CAMPER1 by measuring serum levels of blood urea nitrogen (BUN). Neither WP-CAMPER1 nor WPCAMPER1-d led to a statistically significant increase in BUN levels compared to the vehicle-treated control group (Figure S18).

Methods
Approvals
All animal studies were performed in accordance with all relevant ethical regulations and were approved by the Institutional Animal Care and Use Committee (IACUC) at the Houston Methodist Research Institute (Houston, Texas, USA; protocol numbers IS00008777 and IS00008451).

Data Collection and Preprocessing
We collected peptide sequences with MIC measurements against Staphylococcus aureus from the Database of Antimicrobial Activity and Structure of Peptides (DBAASP) (https://dbaasp.org/)68 and converted all sequences to uppercase and removed duplicates to maintain consistency. We excluded sequences containing non-standard amino acid symbols. Specifically, we removed sequences that included X (any amino acid, unknown, or unspecified), B (asparagine or aspartic acid, ambiguous), Z (glutamine or glutamic acid, ambiguous), J (leucine or isoleucine, ambiguous), U (selenocysteine), and O (pyrrolysine).

In data preprocessing, we labeled peptides with MIC values of 32 µg/mL or lower as active and those with MIC values of 64 µg/mL or greater as inactive, excluding peptides with MIC values between 32 and 64 µg/mL. This binning strategy was selected to support the primary goal of CAMPER, which is to identify high-confidence peptide candidates with strong activity. MIC values between 32 and 64 µg/mL often fall into an ambiguous range that can vary by experimental conditions and including them can introduce label noise. Broth MIC measurements commonly vary by approximately one two-fold dilution, so values that differ by a single dilution step fall within expected assay variability.69 Because our aim was to design a selective prioritization tool to maximize precision and reduce the number of non-active peptides forwarded for synthesis and testing, we used conservative thresholds to retain only peptides with clear activity or clear inactivity. For peptides with multiple MIC measurements, we averaged their binary activity labels (0 for inactive, 1 for active) and classified peptides with a mean activity score greater than 0.5 as active, reflecting a predominance of active measurements.

Feature Engineering
We evaluated three feature-engineering strategies to characterize peptide sequences: Physicochemical descriptors and fingerprints. To calculate peptide descriptors, we used the RDKit70 (version 2023.03.3) and Biopython71 (version 1.83) libraries (Python 3.11.3). We computed a comprehensive set of physicochemical descriptors,72 including Grand Average of Hydropathy (GRAVY), helicity, hydrophobic moment, charge, topological polar surface area (TPSA), LogP, fraction of sp3 carbons, molar refractivity, hydrogen bond donors and acceptors, molecular weight, partial charges, aromatic ring count, and various Chi and Kappa indices.73 We added each descriptor as a feature in the dataset (Table S1). Additionally, we calculated Morgan fingerprints (2048-bit, radius 4) to represent substructure features in a binary vector format.74 A detailed rationale for the relevance of each descriptor class to peptide biophysics and antimicrobial activity is provided in the Supplementary Methods.

Sequence n-grams: We generated k-mer frequency vectors from the primary amino-acid sequence to capture short-range compositional patterns. We evaluated several n-gram sizes and normalized counts before model training.

ProtBERT embeddings: We extracted contextual embeddings for each peptide using the ProtBERT language model.75 We averaged token embeddings to produce a fixed-length representation of each sequence, which we used directly as input to downstream classifiers.

Model Training and Evaluation
We created a stratified 80 to 20 outer split, reserving the 20% portion as a fixed external holdout set. We used the remaining 80% of the data for model development and evaluation. We trained a random forest classifier and optimized model performance using 5-fold cross validation to select the best-performing hyperparameters. Random forest is an ensemble learning method that constructs multiple decision trees during training and outputs the class that is the majority vote among the trees, enhancing accuracy and reducing overfitting.76 To compare performance with other commonly used classifiers, we trained XGBoost77, and logistic regression with L1 Regularization (Lasso)78 classifiers (using scikit-learn version 1.5.2) on each feature-engineering strategy described in the feature engineering section of the Methods.

To prevent near-duplicate sequences from leaking across folds, we applied homology-aware 5-fold cross-validation inside the 80% training set. We computed pairwise sequence identity using the Levenshtein ratio,79 clustered peptides at the chosen identity threshold, and assigned entire clusters to the same fold. This approach ensured that homologous peptides did not appear on opposite sides of a training–validation split. To identify an appropriate sequence-identity threshold, we performed a sensitivity analysis in which we repeated the clustering and homology-aware cross-validation across multiple similarity cutoffs and compared both the resulting cluster structure and performance metrics of the RF classifier at each threshold.

We computed accuracy, precision, recall (also known as sensitivity), and F1-score to assess predictive performance. In the following formulas, TP is the number of true positives, TN is the number of true negatives, FP is the number of false positives, and FN is the number of false negatives.

Accuracy measures the proportion of correctly classified instances out of the total instances and is defined as:
Accuracy = (TP + TN) / (TP + TN + FP + FN) (1)

Precision indicates the proportion of true positive predictions among all positive predictions, providing insight into the model’s accuracy for the positive class:
Precision = TP / (TP + FP) (2)

Recall (sensitivity), measures the proportion of actual positive instances that the model correctly identifies:
Recall = TP / (TP + FN) (3)

The F1-score, which is the harmonic mean of precision and recall, provides a balance between the two and is especially useful when classes are imbalanced:
F1 Score = 2 * (Precision * Recall) / (Precision + Recall) (4)

These metrics offer a comprehensive assessment of the model’s predictive performance, considering both its overall accuracy and its effectiveness in correctly classifying positive cases and making reliable positive predictions.

To assess model robustness, we performed y-randomization80 by randomly shuffling the response variable (active/inactive) in the training dataset while keeping the feature variables unchanged. We repeated this process 100 times, evaluating model performance after each iteration. Comparing the performance of the original model to these randomized models allowed us to assess whether the observed accuracy was likely due to meaningful patterns in the data rather than random chance.

CAMPER (Constraint-driven AMP Engineering with Ranking)
To enhance the prediction of antimicrobial activity against S. aureus, we developed CAMPER (Constraint-driven AMP Engineering with Ranking), a hybrid framework that combines the RF model with a mechanistic biophysical scoring function that incorporates key physicochemical determinants of peptide-membrane interactions. This approach extended the ML model beyond statistical pattern recognition by explicitly modeling the mechanistic basis of antimicrobial activity against Gram-positive bacteria. Four critical physicochemical properties were selected based on their established roles in antimicrobial peptide (AMP) function:

Charge. Net positive charge is essential for initial electrostatic attraction to the negatively charged bacterial surface of S. aureus, which contains teichoic acids and negatively charged phospholipids such as phosphatidylglycerol.8 Unlike mammalian cell membranes, which are primarily zwitterionic, the negative charge density of Gram-positive bacterial surfaces creates a selective targeting mechanism for cationic peptides. However, excessive cationic character can potentially prevent membrane insertion by creating overly strong surface interactions and trap the peptide at the membrane surface, preventing deeper insertion and pore formation.8,11 Thus, membrane-active peptides require a specific range of positive charge for optimal activity, with both insufficient and excessive charge reducing effectiveness.14 We modeled this range with a Gaussian function:
ChargeScore = exp(-(q - q_opt)^2 / (2 * sigma^2)) (5)
Where:
• q is the peptide’s net charge.
• q_opt is the optimal charge
• sigma is the width parameter that determines how quickly the score decreases as charge deviates from q_optimal.
This function produces a maximum value of 1.0 when q= q_opt and decreases symmetrically as charge moves away from optimal in either direction, capturing the domain knowledge that both insufficient and excessive charge reduce antimicrobial effectiveness.

Helicity. The ability to form helical structures upon membrane contact is a crucial determinant of antimicrobial activity against Gram-positive bacteria, including S. aureus.47 This structural transition typically occurs at the bacterial membrane interface and enables the peptide to organize its amino acids into an amphipathic arrangement. Structure-activity relationship studies demonstrate that peptides with helicity below a critical threshold fail to adopt the necessary conformation for membrane disruption, resulting in substantially reduced antibacterial activity.24,47 This suggests a non-linear, threshold-dependent relationship between helicity and antimicrobial potency. We modeled this threshold effect using a sigmoid function:
HelicityScore = 1 / (1 + exp(-k_h * (h - h_threshold))) (6)
Where:
• h is the peptide’s helical propensity (0 to 1 scale).
• h_threshold is the inflection point where activity begins to increase rapidly
• k_h is the steepness parameter that controls how rapidly the function transitions from low to high values.
The sigmoid function produces a smooth S-shaped curve that maps helicity to a score between 0 and 1. When helicity is well below the threshold (h ≪ h_threshold), the score approaches 0. As helicity increases and crosses the threshold, the score rises rapidly, eventually approaching 1 as helicity becomes sufficiently high (h ≫ h_threshold). This formulation reflects the understanding that increased helicity improves the likelihood of stable membrane interaction, supporting the peptide’s ability to insert and disrupt the bilayer.81

Hydrophobicity. Gram-positive bacterial membranes contain a higher proportion of branched-chain fatty acids compared to mammalian membranes, creating distinct hydrophobic environments.8 Sufficient hydrophobicity, measured by the Grand Average of Hydropathy (GRAVY), is essential for peptide insertion into these membrane environments. AMPs with higher hydrophobicity values can penetrate deeper into the hydrophobic core of the bacterial membrane.82 Similar to helicity, hydrophobicity exhibits a threshold-dependent effect, where values below a certain level prevent effective membrane insertion regardless of other properties.82 We modeled this using another sigmoid function:
GravyScore = 1 / (1 + exp(-k_g * (G - G_threshold))) (7)
Where:
• G is the GRAVY value of the peptide.
• G_threshold is the minimum effective GRAVY value.
• k_g is the steepness parameter controlling the transition rate.
This mathematical representation aligns with biophysical evidence that peptides must achieve sufficient hydrophobicity to overcome the energetic barrier of inserting into the lipid bilayer.

Amphipathicity. The spatial segregation of hydrophobic and hydrophilic residues, quantified by the hydrophobic moment (μ_H), enables peptides to simultaneously interact with both the polar headgroups and hydrophobic core of bacterial membranes.24 For Gram-positive targets such as S. aureus, where peptidoglycan layers may partially shield membranes, strong amphipathicity facilitates deeper penetration and subsequent membrane disruption.8 This property mediates multiple mechanisms of action including barrel-stave, toroidal pore, and carpet models of membrane permeabilization,8 each requiring a minimum threshold of amphipathicity to be effective. We modeled this using a sigmoid function:
AmphipathicityScore = 1 / (1 + exp(-k_a * (μ_H - μ_H,threshold))) (8)
Where:
• μ_H is the hydrophobic moment, measuring the segregation of hydrophobic and hydrophilic residues.
• μ_H,threshold is the minimum effective hydrophobic moment.
• k_a is the steepness parameter controlling the transition rate.
Like the other sigmoid functions, this mathematical formulation produces a score that transitions from near-zero to near-one as the amphipathicity crosses the threshold value. This function models the biophysical requirement for sufficient segregation of hydrophobic and hydrophilic residues to interact effectively with bacterial membranes.

Membrane-active peptides operate through a multi-step mechanism involving initial electrostatic binding to membrane surfaces, hydrophobic insertion into the lipid bilayer, and subsequent pore formation or membrane permeabilization.82 Each step requires simultaneous contribution from multiple physicochemical properties: charge facilitates initial membrane binding and selectivity, hydrophobicity drives insertion into the lipid core, helicity ensures proper orientation and depth, and amphipathicity interfacial alignment and peptide clustering.8,11,24,81 This creates interdependencies where deficiency in one property cannot be compensated by excess in others, as each step requires a minimum threshold of all relevant properties to proceed effectively. We mathematically model this cooperative nature through two mechanisms:

Cooperative Effect (Minimum Function). The minimum function captures the rate-limiting step concept in peptide-membrane interactions:
CooperativeEffect = min(ChargeScore, HelicityScore, GravyScore, AmphipathicityScore) (9)
This function returns the lowest value among all input scores, imposing a strict constraint that the overall effectiveness cannot exceed that of the weakest component. This reflects the sequential, multi-step nature of peptide-membrane interactions, where deficiency in any step creates a bottleneck for the entire process.

Cooperative Boost (Threshold Function). We also implemented a threshold-based cooperative boost that captures the synergistic enhancement observed when all properties exceed minimum requirements:
CooperativeBoost = β * I(all scores > τ) (10)
Where:
• β is the boost magnitude.
• τ is the quality threshold.
• I(⋅) is the indicator function that equals 1 when the condition is satisfied and 0 otherwise.
This creates a step function that adds a constant bonus (β) when all component scores exceed the threshold τ and contributes nothing otherwise.

Final Score Calculation. The component scores were combined into a comprehensive biophysical score, which was then normalized and integrated with the ML prediction:
RawBiophysicalScore = ∑(w_i * Component_i) + CooperativeBoost (11)
Where w_i are the component weights for each property and cooperative term.
To ensure the biophysical score remains within the [0,1] range for consistent interpretation, we apply normalization:
BiophysicalScore = RawBiophysicalScore / (∑ w_i + β) (12)
We combined the ML probability and the biophysical score using a simple weighted sum to generate the final CAMPER score:
FinalScore = w_prob * MLProbability + (1 - w_prob) * BiophysicalScore (13)
Where w_prob controls the balance between data-driven and mechanism-based predictions.

Optimization of the Biophysical Scoring Function
To define meaningful threshold values for the biophysical scoring function, we utilized peptide sequences from the Antimicrobial Peptide Database (APD) (https://aps.unmc.edu/) that were active against Gram-positive bacteria and experimentally confirmed to adopt an α-helical structure.83 For each of the four biophysical features used in the scorer (net charge, predicted helicity, hydrophobicity, and amphipathicity) we calculated the median value across this curated set. These medians were then used as the threshold parameters in the transformation functions previously defined (sigmoid for helicity, hydrophobicity, and amphipathicity; Gaussian for charge).

We then performed a grid search with five-fold cross-validation to tune the remaining hyperparameters of the scoring function. This included the steepness of the transformation functions, a threshold and weight controlling the contribution of cooperative effects between features, individual weights for each biophysical component, and a mixing weight that balances the biophysical score with the predicted probability of the random forest model. Cross-validation was conducted exclusively on the training set of the random forest classifier, ensuring that the test set remained untouched during parameter selection. For each hyperparameter combination, a biophysical scorer was fit to the training folds and applied to the validation fold. Performance was evaluated using ranking metrics, with precision in the top 10% of ranked peptides as the primary optimization target. The best-performing parameter set was selected based on the average cross-validated performance across all folds.

Precision Analysis Across Biophysical Property Space
To evaluate how prediction precision varied across key biophysical properties, we conducted a stratified analysis of test set performance for the RF model and CAMPER. For each property, GRAVY, helicity, net charge, and hydrophobic moment the test set was divided into eight equal-width bins spanning the observed range. Bins with fewer than 10 samples were excluded to ensure statistical robustness.
Within each bin, we calculated precision for both the RF model and the biophysically adjusted predictions. We then computed the absolute improvement in precision introduced by the biophysical adjustment. These values were used to identify regions of the property space where the biophysical integration improved prediction reliability.

Rational Design of a Peptide Pool Library
To demonstrate the ability of CAMPER to prioritize potent, membrane-active AMPs, we applied the pipeline to a candidate pool derived from a natural 14-mer template, using it as a proof of concept for targeted in silico screening. To generate the pool, we selected a mastoparan template, a 14-mer natural peptide from the wasp family, a group of amphipathic, cationic α-helical peptides found in the venom of insects of the Hymenoptera order.84 While CAMPER can be applied to any linear peptide sequence to enhance peptide-membrane interact through ideal amino acid substitutions, we selected this peptide template because it exhibited a distinct pattern of conserved and non-conserved amino acid positions (Figure S19). We specifically targeted the non-conserved residues, as they were not restricted to any specific class of amino acids and could either contribute to increased net charge for electrostatic interactions or participate in hydrophobic interactions with the membrane.

While linear peptides of various lengths can be used, shorter AMP sequences are generally more cost-effective for therapeutic applications.11 Therefore, we first optimized the sequence by removing amino acids that were unlikely to contribute to bacterial membrane binding. Specifically, to shorten the mastoparan peptide, we removed the first two N-terminal residues, isoleucine and asparagine, since they do not participate in the α-helical structure, as shown by the NMR structure of mastoparan-L in detergent micelles mimicking bacterial membranes (PDB ID: 1D7N),85 and thus are not involved in membrane interactions critical for peptide activity (Figure S20). This truncation yielded a 12-residue peptide template retaining three helical turns.

Next, we determined which amino acid positions in this 12-residue template could be substituted to enhance its antimicrobial potential. We aligned all known mastoparan peptide sequences to identify conserved and non-conserved amino acid positions (Figure S19). The sequence of our 12-residue wasp derived (WP) template was (WKXIAXXAKKXL), where X represents the non-conserved residues (positions 3, 6, 7, and 11) (Figure S19). We substituted these four positions with all possible amino acid combinations, generating a pool of 160,000 (20^4) peptides.

Validation of WP-CAMPER1 by traditional quantitative-structure activity relationship (QSAR)-based peptide design
To assess whether CAMPER successfully prioritized peptide sequences with charge, hydrophobicity, and amphipathicity profiles optimized for membrane-disruptive antimicrobial activity, we conducted a QSAR analysis of WP-CAMPER1, the top-scoring peptide from the CAMPER screen. This involved introducing targeted substitutions of hydrophilic and hydrophobic residues on distinct helical faces of the peptide to evaluate how changes in physicochemical properties affected antimicrobial potency. To assess the effect of increasing charge, we replaced leucine (non-conserved position) at position 6 on the hydrophilic face with lysine, creating WP-CAMPER1-6K. To enhance the hydrophobicity of this charged variant, we substituted alanine residues (conserved positions) at positions 5 and 8 with leucine, generating WP-CAMPER1-6K5L8L. To further increase the hydrophobicity of WP-CAMPER1-6K, we substituted tryptophan residues at positions 3, 7, and 11 (non-conserved positions) to generate WP-CAMPER1-6K3W7W11W and at positions 3, 5, 7, 8, and 11 (all hydrophobic positions) to produce WP-CAMPER1-6K3W5W7W11W. Finally, we enhanced the hydrophobicity of the hydrophobic face while keeping the overall charge of the ML-selected peptide unchanged. Starting with WP-CAMPER1 we replaced the alanine (conserved positions) at positions 5 and 8 with leucine, resulting in WP-CAMPER1-5L8L.

Bacterial strains growth conditions and peptide synthesis
We employed a panel of S. aureus strains exhibiting diverse antibiotic susceptibility profiles, as detailed in Table S9. All S. aureus strains were grown in tryptic soy broth (TSB) (BD, Franklin Lakes, NJ, USA) under standard aerobic conditions at 37°C with shaking unless otherwise stated.

Synthesis of peptides
We synthesized the peptides using solid-phase chemistry (GenScript Inc., Piscataway, NJ, USA). These peptides had a purity of 95% or higher (List of peptides and their characterization data can be found in Figure S21).

Minimal inhibitory concentration (MIC) assay
We determined the MIC of candidate peptides using the broth microdilution method described by the Clinical and Laboratory Standards Institute.86 We prepared serial dilutions of 10× concentrated peptides (10 µL) in duplicate in 96-well plates (Cat no. 3595, Corning, NY, USA). To these dilutions, we added 90 µL of logarithmic-phase bacteria at 1 × 106 CFU/mL (in TSB) and incubated the plates at 37°C for 18 hours. We then measured AOD600 on a Spectra Max i3x spectrophotometer (Molecular Devices, CA, USA) and determined the MIC as the lowest concentration of peptides that completely inhibited bacterial growth. To assess peptide activity under various salt conditions, we incorporated physiologically relevant concentrations of salts and serum into the TSB medium, adding 150 mM NaCl, 2.5 mM CaCl2, 8 µM ZnSO4, 1 mM MgSO4, and 5-10% human serum to medium for MIC assays.

S. aureus persister cell and time-kill assays
We generated antibiotic-induced S. aureus MW2 and VRS1 persister cells by following an established protocol.87 We grew 25 ml cultures of S. aureus strains MW2 and VRS1 to stationary phase, then treated the cells with gentamicin at 20 μg/mL for an additional 4 hours. After washing the bacterial cultures three times with 25 ml of phosphate-buffered saline (PBS), we adjusted the cultures to ~1 × 108 CFU/mL for S. aureus MW2 and ~1 × 107 CFU/mL for S. aureus VRS1 in PBS. To assess the killing kinetics of WP-CAMPER1 in persister cells, we added 1 mL of cell suspension to wells in a 2-ml deep-well assay block (Cat no. 3960, Corning, NY, USA) containing 10× MIC or 1× MIC of the peptide. We also included ciprofloxacin (at 10 μg/mL) for S. aureus MW2 and linezolid (100 μg/mL) for S. aureus VRS1 as antibiotic controls. We incubated the plates at 37°C with shaking at 225 rpm. At specific time points (0, 15, 30, 60, and 120 minutes), we collected 10 μL samples, performed serial dilutions, and plated them on tryptic soy agar (TSA) (BD Difco, NJ, USA) plates. The TSA plates were incubated at 37°C for 18 hours, and then we counted resulting colonies. We conducted these experiments in duplicate. For comparison, we prepared exponential-phase cells of S. aureus MW2 and VRS1 in PBS and analyzed their killing kinetics in the same way as described for persister cells.

Biofilm viability assay on solid support
We generated gentamicin-induced S. aureus MW2 and VRS1 persister cells as outlined above. Afterward, we diluted the persister cell culture 1:200 in TSB medium supplemented with 0.2% glucose.88 To form biofilms on substrates, we applied 1 ml of diluted bacterial culture onto a 13-mm Millipore mixed cellulose ester membrane (Cat no. HAWP01300, Millipore, MA, USA) at the bottom of a 12-well plate (Cat no. 353043, Falcon, MA, USA) and incubated it statically at 37°C for 24 hours. After incubation, we removed the planktonic cells by washing the membranes twice and transferred them to a fresh 12-well plate. We then added 1 ml of PBS containing 10× MIC concentrations of peptides and antibiotics to each well and incubated the plates under static conditions at 37°C for another 24 hours. Following this, we washed the membranes twice with PBS, placed them in 1 ml of PBS, and sonicated them in an FS 30 ultrasonic bath (Fisher Scientific, MA, USA) for 10 minutes. The sonicated samples were serially diluted with PBS in a 96-well plate, spot-plated onto TSA plates, and incubated overnight at 37°C. The following day, we counted the viable bacterial colonies.

Prevention of S. aureus biofilm formation
We assessed the ability of the designed wasp-derived peptides to inhibit biofilm formation using an established protocol with minor modifications.87 Briefly, we grew exponential cultures of S. aureus MW2 and VRS1, adjusted to OD600 = 0.01, in TSB medium supplemented with 0.2% glucose, starting from overnight cultures. We then added 90 µL of this bacterial culture to 10 µL of serially diluted 10× peptide solutions in flat-bottomed 96-well polystyrene microtiter plates (Cat no. Corning 3595, NY, USA) and incubated the plates at 37°C for 24 hours. TSB medium containing bacteria with water served as the positive control, while TSB medium with sterile water acted as the negative control. After incubation, we carefully removed the TSB medium and washed the wells with PBS (Gibco, MD, USA) to eliminate loosely attached planktonic cells. We stained the biofilms with crystal violet to quantify the biomass and determined the concentration of peptide that inhibited 50% of biofilm formation (MBIC50). To measure the live-cell count within the biofilms, we used XTT dye [2,3-bis(2-methyloxy-4-nitro-5-sulfophenyl)-2H-tertazolium-5-carboxanilide] (ATCC, VA, USA). The percentage of biofilm growth was calculated by normalizing the biofilm growth in peptide-treated wells to that in wells containing untreated bacteria.89

Disruption of S. aureus established biofilms
We evaluated the ability of the designed peptides to disrupt established biofilms following an established protocol with modifications.87 We prepared exponential cultures of S. aureus MW2 and VRS1 and adjusted to 1 × 106 CFU/mL in TSB medium supplemented with 0.2% glucose from overnight cultures. We then added 100 µL of this bacterial culture to flat-bottomed 96-well polystyrene microtiter plates (Cat no. 3595, Corning, NY, USA) and incubated the plates statically at 37°C for 24 hours to allow biofilm formation. After incubation, we carefully removed the TSB medium and gently washed the wells with PBS (Gibco, MD, USA) to eliminate loosely attached planktonic cells. We treated the established biofilms with 10 µL of serially diluted 10× peptide solutions and added 90 µL of TSB medium supplemented with 0.2% glucose to each well. The plates were incubated for an additional 24 hours at 37°C before processing the biomass and live-cell content. We then calculated the concentration of peptide required to disrupt 50% of the established biofilms (MBEC50).

Fluorescence microscopy of WP-CAMPER1-treated S. aureus established biofilms
We performed fluorescence microscopy on WP-CAMPER1-treated S. aureus biofilms using a previously described protocol with modifications.24 In brief, we prepared exponential-phase cultures of S. aureus MW2 and VRS1, adjusting the bacterial count to 1 × 106 CFU/mL in fresh TSB medium. To establish biofilms, we added 200 µL of the bacterial culture to the chambers of Millicell EZ Slides (Millipore Sigma, MA, USA) and incubated them statically at 37°C for 24 hours. After incubation, we carefully removed the TSB medium and gently washed the Millicell wells with PBS (Gibco, MD, USA) to eliminate loosely attached planktonic cells. We then treated the established biofilms with 20 µL of WP-CAMPER1 peptide (at 10× MIC) and added 180 µL of TSB medium. The Millicell slides were incubated for an additional 24 hours at 37°C. Following this, we gently washed the Millicell slides with PBS to remove planktonic cells and stained the biofilms with 50 µL of staining reagent from a LIVE/DEAD kit (Life Technologies, OR, USA) according to the manufacturer’s instructions. We visualized the biofilms using a Nikon ECLIPSE Ti fluorescent microscope (Melville, NY, USA) and processed the images with NIS-Elements AR 4.00.07 software (Nikon, Tokyo, Japan).

Mammalian cell cytotoxicity assays
We assessed the ability of the peptides to induce hemoglobin leakage in human red blood cells using a previously established method.90 First, we washed human erythrocytes (Rockland Immunochemicals, PA, USA) three times with an equal volume of PBS and resuspended them in a 4% solution. We then added 100 µL of this cell suspension to 100 µL of a 2× peptide solution in a 96-well microtiter plate (Cat no. 3595, Corning, NY, USA) and incubated the plates at 37°C for 1 hour. Following incubation, we centrifuged the plates at 500× g for 5 minutes, transferred 100 µL of the supernatant to a new 96-well plate, and measured the absorbance at 540 nm. We calculated the percent hemolysis using the formula: (A540 nm of peptide solution − A540 nm of PBS) / (A540 nm of 1% Triton X-100 − A540 nm of PBS) × 100, where 100% hemolysis was defined by 1% Triton X-100 and 0% hemolysis was defined by PBS.

We used liver-derived HepG2 cells (Cat. No. HB-8065, American Type Culture Collection, Manassas, VA, USA), human keratinocytes (HaCaT) (Cat. No. T0020001, Fisher Scientific, MA, USA), and human kidney cortical (HKC-8) cells (courtesy of Dr. Racusen of John Hopkins University, Baltimore, MD) to evaluate the cytotoxicity of AMP based on an established protocol.90 We cultured HepG2, HaCaT, and HKC-8 cells at 37°C with 5% CO2 in Dulbecco’s Modified Eagle Medium (DMEM) supplemented with 10% heat-inactivated fetal bovine serum and 1% penicillin/streptomycin (Gibco, MA, USA). At ~85% confluence, cells were harvested, resuspended in fresh DMEM medium and seeded at 1 x 106 cells/well in a 96-well plate (Cat no. 3598, Corning, NY, USA) for 24 h at 37°C with 5% CO2. Cells were subsequently treated with 100 μL two-fold serially diluted peptides in DMEM for an additional 24 h. After treatment, 70 μL of the XTT-electron coupling reagent mixture (CyQUANT™ XTT cell viability assay kit (Fisher Scientific, MA, USA) was added to each well and incubated for 4 h. Absorbance was measured at 450 nm and 660 nm using the SpectraMax 250 plate reader (Molecular Devices, San Jose, CA, USA).

Circular dichroism (CD)
We recorded the CD spectra of WP-CAMPER1 (0.2 mg/mL) in the presence and absence of 50 mM sodium dodecyl sulfate (SDS) using a Jasco J-815 Circular Dichroism (CD) Spectropolarimeter (Jasco, OK, USA) according to a previously described method.91 We placed the samples in a thermostat cell holder set to 25°C, using a quartz cell with a 1-mm path length. The CD data were expressed as mean residue ellipticity. We obtained baseline scans under the same conditions for samples containing only buffer, micelles, and vesicles, and subtracted these baseline scans from the data obtained with peptide-containing samples.

Nuclear magnetic resonance (NMR)
We investigated the membrane-targeting mechanism of WP-CAMPER1 using two-dimensional nuclear magnetic resonance (2D-NMR) spectroscopy, following a previously published method with minor modifications.59 We collected the NMR data at 25ºC on a Bruker Avance II 500 MHz NMR spectrometer (Bruker, MA, USA), using 600 µL of 2 mM WP-CAMPER1 dissolved in 80 mM deuterated SDS (SDS-d25; pH 5.5). We selected SDS micelles to mimic an anionic, membrane-like environment and to stabilize the peptide for structural analysis as SDS provides high spectral resolution for backbone assignments and has been widely used in NMR studies to determine the structures of AMPs.14,92,93 We acquired homonuclear 2D-total correlation spectroscopy (TOCSY) with an 80 ms mixing time and nuclear Overhauser effect spectroscopy (NOESY) with a 300 ms mixing time, 5000 Hz spectral widths in both dimensions, and 512 increments in the indirect dimension. We also collected natural abundance 2D-13C-heteronuclear single quantum coherence (HSQC) and 13C-HSQC-TOCSY spectra with 128 scans to obtain 13C chemical shifts, which we used to derive the dihedral angles of the peptide using TALOS+.94 We processed and analyzed all NMR data using Topspin software version 3.2 (Bruker, MA, USA) and Sparky software version 3.115 (UCSF, CA, USA). A semi-automated assignment of NOESY cross-peaks was conducted using CYANA version 3.98.15,95 and all assigned peaks were manually verified for accuracy in Sparky. We performed the initial peptide structure calculation with 254 NOE-derived distance restraints and 22 dihedral angles. In the final calculation, we included 14 hydrogen bond restraints supported by NOESY patterns. We used 200 structures for the final calculation and selected the 20 lowest-energy structures to represent the WP-CAMPER1 structure ensemble.

Molecular dynamics (MD) simulation
We conducted MD simulations using membrane bilayers composed of DOPC (1,2-dioleoyl-sn-glycero-3-phosphocholine) and DOPG (dioleoyl phosphatidylglycerol) at a 7:3 molar ratio, with each bilayer containing 64 lipids.12 We generated the initial WP-CAMPER1 structure using the AlphaFold2 Colab server96 and positioned the peptide parallel to the membrane surface, at least 1.5 nm above the upper leaflet. The simulations proceeded in two stages.97 First, we performed coarse-grained (CG) MD simulations using the MARTINI 2.2P force field for 500 ns to capture membrane-peptide association.98 We then selected representative CG frames corresponding to key events, specifically the initial membrane contact and the fully engaged state at 500 ns, and converted them into all-atom models. These all-atom systems were subsequently simulated for 10 ns using the CHARMM36 force field.98 All simulations were carried out using the GROMACS 2020.1-1 software package (Groningen, Netherlands), with system input files generated through the CHARMM-GUI server. We visualized the trajectories using Visual Molecular Dynamics (VMD)99 and the trial version of BIOVIA Discovery Studio Visualizer (Accelrys Inc., SD, USA). We also simulated WP-CAMPER1 in membrane composed of POPG (1-palmitoyl-2-oleoyl-sn-glycero-3-phospho-(1’-rac-glycerol) and CL (cardiolipin) (7:3 molar ratio), following the same protocol used for the DOPC:DOPG system, to evaluate its interactions with a bilayer enriched in PG and CL, the major anionic phospholipids of S. aureus membranes.100

Membrane depolarization
We measured bacterial membrane potential following an established protocol with minor modifications.90 We prepared an exponential phase culture of S. aureus MW2 in fresh TSB medium, washed the culture twice with PBS, and then resuspended it in twice the volume of PBS. To energize the bacterial cells, we added 25 mM glucose and incubated them for 15 minutes at 37°C. Next, we introduced 500 nM of DiBAC4(3) (bis-(1,3-dibutylbarbituric acid) trimethine oxonol) (Thermo Fisher Scientific, ON, Canada), a potential-sensitive probe that enters depolarized cells, binds to intracellular proteins or membranes, and exhibits enhanced fluorescence. We distributed 90 µL of the prepared bacterial culture into each well of a black, clear-bottomed 96-well plate (Cat. no. 3904, Corning, NY, USA) and monitored the fluorescence using a SpectraMax i3x (Molecular Devices, CA, USA) for 20 minutes, with excitation at 485 nm and emission at 520 nm, until a stable baseline was established. Afterward, we added 10 µL of serially diluted peptide solutions and recorded the fluorescence for an additional 40 minutes. We used Triton X-100 (1%) as a positive control.

SYTOX-based cell membrane permeability assay
We prepared exponential-phase S. aureus MW2 cells and diluted them in PBS to an OD600 of 0.2. We then added SYTOX Green (Molecular Probes, Life Technologies, Oregon, USA) to the cell suspension, achieving a final concentration of 5 μM, and incubated the mixture for 30 minutes at room temperature in the dark, following a previously established protocol.89 We distributed 90 µL of this dye/bacteria mixture into 10 μL of serially diluted 10× peptide solutions in black 96-well plates and measured the fluorescence for 1 hour at room temperature, using excitation and emission wavelengths of 485 nm and 525 nm, respectively.

Propidium iodide-based membrane permeability
We utilized a propidium iodide (PI) fluorescence-based bacterial permeation assay to investigate membrane-targeting interactions with the designed peptides, following a previously established protocol.89 We prepared exponential-phase S. aureus MW2 bacteria as described for the SYTOX assay, except that the final bacterial density was OD600 = 0.4 in PBS. We added PI dye to the bacterial suspension to achieve a final concentration of 2 µM. We measured fluorescence using excitation at 584 nm and emission at 620 nm for 1 hour at room temperature.

ATP release assay
To assess the ATP leakage potential of the peptides, we used a luciferase-based assay.101 We began by washing exponential-phase S. aureus MW2 bacteria three times with PBS (pH 7.4) and then adjusted the cell suspension to an OD600 of 0.4 in PBS. After treating the bacteria with 32 µg/mL of WP-CAMPER1 peptide at 37°C for 30 minutes, we centrifuged the cells at 14,000× g for 5 minutes. We transferred 50 µL of each supernatant to black, clear-bottomed 96-well plates containing 50 µL of BacTiter-Glo reagent (Promega, WI, USA) and measured the luminescence after 5 minutes of incubation at room temperature, as previously described.101

ΔpH measurement
We cultured S. aureus MW2 bacteria to mid-logarithmic phase, centrifuged the exponential-phase bacterial suspensions, and washed the cells three times with PBS (Gibco, MA, USA). We then resuspended the cells to an OD600 of 0.4 and incubated them with BCECF (1 µM) in the dark for 30 minutes at 37 °C. After staining, we treated the cells with peptides for 1 hour at 37 °C. We measured fluorescence intensity at 525 nm (excitation at 485 nm) using a Cytation 3 imaging reader (BioTek Instruments, VT, USA).

Reactive Oxygen Species (ROS) detection assay
We quantified ROS levels in peptide-treated S. aureus MW2 using the fluorescent probe 2′,7′-dichlorofluorescein diacetate (DCFH-DA), a commonly used indicator of intracellular ROS.57 We cultured S. aureus MW2 to mid-logarithmic phase, washed the cells three times, and resuspended them in PBS (Gibco, MA, USA) to an OD600 of 0.4. We then stained the cells with DCFH-DA (100 μM) and co-incubated them with peptides at 10× MIC for 30 minutes at 37°C. We measured fluorescence intensity at 525 nm (excitation at 485 nm) using a Cytation 3 imaging reader (BioTek Instruments, VT, USA).

ROS-associated growth inhibition and spot titer assays
We evaluated the role of ROS in the antibacterial activity of peptides using spot titer and kinetic growth curve assays, following a previously established protocol57 with minor modifications. In a microplate growth curve assay, exponential-phase cultures of S. aureus MW2 were adjusted to 1×106 CFU/mL and incubated in 96-well plates (100 µL per well) with WP-CAMPER1 (1× MIC) in the presence or absence of NAC (10 mM). The optical density at 600 nm was recorded every 2 h to monitor bacterial growth kinetics using the SpectraMax 250 plate reader (Molecular Devices, San Jose, CA, USA). A decrease in bacterial killing efficiency in the presence of ROS scavengers indicated that ROS played a contributory role in the antibacterial mechanism of WP-CAMPER1.57

In a complimentary spot titer assay, exponential-phase S. aureus MW2 cultures were grown in TSB and diluted to approximately 1×106 CFU/mL. The bacterial suspensions were exposed to WP-CAMPER1 at 1× MIC in the presence or absence of the ROS scavenger N-acetylcysteine (NAC, 10 mM). Cultures were incubated at 37 °C, and samples were collected at 0, 4, and 8 h, serially diluted, and plated on Remel™ TSA plates (Fisher Scientific, MA, USA) to determine surviving CFU after overnight incubation.

To assess the effect of higher bacterial and peptide concentrations, we also exposed 1×107 CFU/mL cultures to WP-CAMPER1 at 5× MIC, with or without NAC (10 mM). Samples were collected at 0, 4, and 18 h, and 20 µL from the culture was serially diluted, and plated on Remel™ TSA plates (Fisher Scientific, MA, USA) to quantify bacterial survival.

Lipid peroxidation assay
We assessed lipid peroxidation in S. aureus MW2 following peptide treatment using the fluorescent probe C11-BODIPY. We cultured the bacteria to mid-logarithmic phase, washed the cells three times, and resuspended them in PBS (Gibco, MA, USA) to an OD600 of 0.4. The cells were then stained with C11-BODIPY (2 μM) and co-incubated with peptides at 10× MIC for 30 minutes at 37°C. Fluorescence intensity was recorded at 525 nm (excitation at 485 nm) using a Cytation 3 imaging reader (BioTek Instruments, VT, USA).

Scanning electron microscopy (SEM)
We employed a modified established protocol to perform SEM imaging of S. aureus MW2 bacteria treated with the WP-CAMPER1 peptide.102 We treated 2 mL of an exponential-phase S. aureus MW2 culture (OD600 = 0.4, washed and resuspended in PBS) with 10× MIC of WP-CAMPER1 for 1 hour at 37°C. Following treatment, we centrifuged the bacterial cultures at 13,000× g for 10 minutes at room temperature. We fixed the bacteria with 50 µL of a fixing solution containing 2.5% glutaraldehyde in PBS buffer (pH 7.3) for over 1 hour at room temperature. After fixation, we placed a 20 µL droplet of the sample onto a (3-Aminopropyl) triethoxysilane (ANPTES)-functionalized silicon wafer and incubated it for 1 hour. We then thoroughly washed the silicon substrate in PBS (pH 7.3). To dehydrate the samples, we sequentially exposed the substrate to ethanol solutions of 20%, 40%, 60%, 90%, and 100% (v/v in water) for 5 minutes each. We then immersed the samples in tert-butyl alcohol (50% v/v in ethanol) for 5 minutes and allowed them to air dry. We mounted the samples on an aluminum SEM sample holder using double-sided carbon conductive tape. To improve the contrast of the SEM images, we coated the samples with a 5 nm thick Pt/Pd film using a magnetron 208HR High Resolution sputtering coater. We visualized the samples using a Nava Nano SEM 230 with a working distance of 5 mm, operating at room temperature and high vacuum (2E-6 Torr). During imaging, we set the electron beam diameter to 3 nm and the acceleration high voltage to 5 kV.

Cryo-electron microscopy (cryo-EM)
We treated a 10 ml exponential-phase S. aureus MW2 culture (OD600 = 0.4, washed and resuspended in PBS) with WP-CAMPER1 at 20× MIC for 1 hour at 37 °C. For cryo-electron microscopy, bacterial samples were vitrified using a Leica EM-GP2® plunger (Leica Microsystems, Wetzlar, Germany) on R2×2 Quantifoil® carbon holey film grids (Micro Tools GmbH, Jena, Germany), following established protocols.103 In brief, 4 µL of the treated suspension was applied to plasma-cleaned grids (Gatan Solarus 950), blotted with filter paper, and plunge-frozen in liquid ethane. Grids were stored in liquid nitrogen until imaging. For microscopy, grids were loaded into a JEM 2200FS electron microscope (JEOL Ltd, Akishima, Japan) operating at 200 keV, equipped with a field emission gun, an omega-type in-column energy filter, and a DE20 direct electron detector (Direct Electron Inc., CA, USA). Images were acquired using a 20 eV energy slit width, with a total electron dose of ~15 e⁻/Å² and a pixel size of 3.66 Å at the specimen level.

Use of S. aureus transposon mutants
We utilized S. aureus transposon mutants obtained from the Nebraska Transposon Mutant Library (NTML).104 This library includes 1,952 strains, each carrying a single mutation in a nonessential gene of the USA300 strain, a community-associated MRSA isolate.104 We specifically chose a mutant with a transposon insertion in the fmtc gene, which is responsible for transferring a lysyl group from L-lysyl-tRNA to membrane-bound phosphatidylglycerol (PG) to form lysylphosphatidylglycerol (LPG).59 LPG reduces the negative charge on the membrane, thereby enhancing bacterial virulence by providing a resistance mechanism against cationic AMPs.59

RNAseq assays
For RNA sequencing studies, we adhered to the protocol recommended by Novogene (Sacramento, CA, USA).105 We started with exponential cultures of S. aureus MW2 grown in TSB medium and adjusted to OD600 = 0.4. We treated 10 ml of this bacterial culture with 0.5× MIC of WP-CAMPER1 or water as a negative control for 30 minutes at 37°C with shaking at 180 rpm. Following treatment, we washed the bacteria three times with PBS and stored the pellet at -80°C. We extracted total RNA from the pellet and assessed its purity using a NanoPhotometer® spectrophotometer. RNA integrity and concentration were evaluated with the RNA Nano 6000 Assay Kit using a Bioanalyzer 2100 system. We also checked RNA degradation and contamination on 1% agarose gels. For transcriptome sequencing, we prepared libraries using 1 µg of RNA per sample with the NEBNext® UltraTM RNA Library Prep Kit for Illumina® (NEB, MA, USA). We started by purifying mRNA from total RNA with poly-T oligo-attached magnetic beads and then fragmented it using divalent cations in NEBNext First Strand Synthesis Reaction Buffer (5×). We synthesized cDNA using random hexamer primers and M-MuLV Reverse Transcriptase for the first strand, followed by DNA Polymerase I and RNase H for the second strand.106 Overhangs were converted to blunt ends with exonuclease/polymerase activities. We then adenylated the 3’ ends of the DNA fragments and ligated NEBNext Adaptors with hairpin loop structures for hybridization. To select cDNA fragments of the preferred length (150-200 bp), we purified the library fragments using the AMPure XP system (Beckman Coulter, CA, USA). Next, to the size-selected, adaptor ligated cDNA, we added 3 µL USER Enzyme (NEB, MA, USA) at 37°C for 15 min followed by 5 min at 95°C before carrying out the polymerase chain reaction (PCR). We performed the PCR with Phusion High-Fidelity DNA polymerase, Universal PCR primers, and Index (X) Primer. We purified the PCR products with the AMPure XP system (Beckman Coulter, CA, USA) and assessed library quality using the Agilent Bioanalyzer 2100 system (Agilent Technologies, CA, USA).

Fabrication and surface treatment of microfluidic devices for exponential phase MRSA persister killing
A silicon wafer master mold and a polydimethylsiloxane (PDMS)-based microfluidic device were manufactured as previously described.61 To inhibit biofilm formation during imaging, the lanes of the microfluidic device were pretreated with 0.1 M EDTA (Sigma Aldrich, MO, USA) for 10 min, Biofloat Flex coating solution (facellitate) for 30 min, and rinsed with MOPS buffer.61 For bacterial culture and cell loading, S. aureus USA 300 strain was grown overnight in tryptic soy broth (TSB) supplemented with 800 µg/mL Pluronic® F108 (Sigma-Aldrich, MO, USA) at 37C and 220 rpm using an Excella E24 incubator shaker (New Brunswick scientific, NJ, USA). Cells were loaded into the microfluidic device using gel-loading tips (Fisherbrand™, NH, USA) by pipetting small volumes of culture directly into two lanes. To promote uniform cell loading into the device trenches, the chip was mounted onto a custom 3D-printed holder and centrifuged at 500 g for 5 minutes followed by 500 g for 15 seconds in the opposite direction, as previously described.61 After loading, and prior to imaging, excess inoculum was flushed out with fresh TSB containing 800 µg/mL Pluronic® F108.

For media delivery and antibiotic treatment, each lane was connected to a media reservoir flask with 0.02-inch internal diameter and 0.06-inch outer diameter Tygon tubbing (ND 100-80 Microbore Tubing, US plastic, OH, USA) and gauge 20, 1-inch, 90° stainless steel blunt Luer-lock needles (McMaster, GA, USA). A flow rate of 20 µL/min was maintained using an independent peristaltic pump in each lane (Longer Pump, Hebei Province, China).

The experiment followed a staged media exchange protocol. During the first 4 h, all device lanes were perfused with TSB containing 800 g/mL Pluronic® F108. Subsequently, the media was switched for 4 h to TSB with the same Pluronic® F108 concentration supplemented with 3.75 µg/mL of gentamicin which represented fifteen times the MIC for S. aureus USA 300 in a broth microdilution assay. Following exposure, the cells were incubated for an additional 16 h in fresh TSB with 800 g/mL Pluronic® F108 to assess gentamicin survival. Lanes with surviving trenches were further treated for 4 h with either TSB with 800 g/mL Pluronic® F108 and 60 g/mL WP-CAMPER1-d which also corresponds to fifteen times the bulk MIC or TSB with 800 g/mL Pluronic® F108 and 3.75 µg/mL gentamicin. This second treatment was followed by a final wash in TSB with 800 g/mL Pluronic® F108 to evaluate bacteria survival.

For microscopy, image acquisition and image analysis, phase contrast live-cell imaging was performed using the same microscope as described.61 Images were analyzed using a custom Python-based pipeline called TrenchCoat, as previously described.61 All image processing and data extraction were performed on the O2 High-Performance Computing (HPC) cluster at Harvard Medical School, MA, USA, which enabled parallel processing of large datasets. The resulting trench detection tables were used to generate kymographs using an additional custom-built Python script and FIJI macro.

For survival quantification and trench counts, the initial number of trenches was estimated by multiplying the number of FOVs imaged by 60 trenches per FOV. This method includes all trenches present in each FOV, regardless of whether a particular trench was empty at the beginning of imaging. Lineage survival was assessed visually after each first and second antibiotic treatment, based on the presence or absence of dividing cells in each trench. During non-antibiotic periods, we assumed that no cell death happened.

Serial passage assay
We evaluated the potential of WP-CAMPER1 and WP-CAMPER1-d to induce resistance in S. aureus MW2 using a serial passage assay adapted from a published protocol.107 On Day 1, we performed a standard MIC assay to establish baseline susceptibility. We defined bacterial growth as an OD600 ≥ 0.35. On each subsequent day, we initiated new cultures from the well that showed visible growth (OD600 ≥ 0.35) at the highest drug concentration. We diluted the selected culture 1:1000 in fresh TSB medium and performed a new MIC assay with a concentration gradient of the peptide or the control antibiotic. We repeated this procedure for 21 consecutive days. We included ciprofloxacin as a reference antibiotic. We recorded MIC values daily to track any changes in susceptibility over time.

Post-antibiotic effect (PAE)
We evaluated the post-antibiotic effect (PAE) of WP-CAMPER1, WP-CAMPER1-d, and ciprofloxacin against S. aureus MW2 following previously published methods108,109 with minor modifications. Briefly, overnight cultures of S. aureus MW2 were grown in TSB at 37 °C with shaking (220 rpm) and diluted in fresh TSB to obtain an inoculum of 2× 108 CFU/mL. We treated each 1 mL culture with WP-CAMPER1, WP-CAMPER1-d, or ciprofloxacin at 5× MIC for 1 h at 37 °C with shaking at 220 rpm. After treatment, we centrifuged the cultures at 16,000× g for 5 min to remove residual drug and washed the pellets three times with fresh TSB. The washed cultures were resuspended in drug-free TSB and incubated at 37°C with shaking (220 rpm) to allow regrowth. We collected samples immediately before and after drug removal and then at 1 h intervals. At each time point, we removed 10 µL of culture, performed serial dilutions in PBS, and plated them on TSA plates (BD Difco, NJ, USA). Plates were incubated overnight at 37 °C, and CFU were enumerated the following day. We calculated the PAE using the formula: PAE=T-C, where T was the time required for the treated culture to increase by 1 log10 CFU/mL from the count immediately after drug removal, and C was the corresponding time for the untreated control. Each assay, including its control, was performed in triplicate.

In vivo murine skin infection and treatment
We adapted a topical skin-abraded murine infection model with minor modifications to assess the in vivo efficacy of the WP-CAMPER1 designed peptides.110 Female C57BL/6 mice (6 weeks old; Jackson Laboratory, Bar Harbor, ME, USA) were anesthetized via intraperitoneal injection of ketamine (90 mg/Kg) and xylazine (10 mg/Kg). The dorsal skin (~2 cm²) was shaved and tape-stripped seven times using Tensoplast® tape (replacing the tape each time) to disrupt the skin barrier and create a visibly damaged surface. To initiate infection, we applied 1 × 10⁷ CFU of S. aureus MW2 (exponential phase, suspended in PBS) in a 50 µL inoculum directly onto the damaged skin. For treatment, we applied 30 mg of peptide ointment (1% or 2% w/w in white petroleum jelly; Fisher Scientific, Cat. No. 19-090-843) at either 10 minutes post-inoculation (prophylactic model) or 24 hours post-inoculation (established infection model). Control groups received either petroleum jelly alone (negative control) or 2% commercial mupirocin ointment (positive control). Buprenorphine SR (0.5 mg/Kg) was administered for analgesia.

After 24 hours of treatment, mice were euthanized, and approximately 2 cm² of infected skin was excised. The skin tissue was homogenized in 1 mL PBS (Gibco), and 100 µL of the homogenate was serially diluted, plated on Remel™ TSA plates (Fisher), and incubated at 37°C for 18 hours to quantify viable bacteria. For cytokine analysis, 10 µL of EDTA-free protease inhibitor cocktail (Set III, Millipore Sigma) was added to 500 µL of the homogenate. Cytokine concentrations were quantified by Cytokine Core, LLC (Indianapolis, IN, USA) using a Luminex® MagPix™ platform following their in-house protocol.111

Deep-seated mouse thigh infection model
We evaluated the in vivo efficacy of WP-CAMPER1 designed peptides against S. aureus MW2 persisters using a modified neutropenic mouse thigh infection model.12 We rendered six-week-old female CD-1 mice (~25 g, Charles River Laboratories, Wilmington, MA, USA) neutropenic by administering cyclophosphamide intraperitoneally at 150 mg/kg on day -4 and 100 mg/kg on day -1 before infection. On the day of infection, we washed overnight cultures of S. aureus MW2 three times with sterile saline and diluted them to ~10⁷ CFU/mL. We injected 50 µL of the bacterial suspension into the right thigh muscle of each mouse. At 24 hours post-infection, we treated mice intraperitoneally every 12 hours for 3 days with either sterile water (vehicle control), WP-CAMPER1 or WP-CAMPER1-d (10 mg/Kg), or vancomycin (30 mg/Kg). We euthanized the animals at 96 hours post-infection. We collected blood via cardiac puncture to assess renal toxicity and excised the infected thighs aseptically. We weighed and homogenized the thigh tissue in PBS. For bacterial quantification, we serially diluted the samples, plated them on Remel™ TSA plates (Fisher Scientific, MA, USA), and incubated them at 37 °C for 18 hours. We expressed bacterial load as CFU per gram of thigh tissue. We analyzed blood urea nitrogen (BUN) (Life Technologies, MD, USA) levels using commercial kits according to the manufacturers’ protocols to evaluate liver and kidney function.

Statistical analysis
We used two-sided one-way ANOVA to compare multiple experimental groups or two-tailed unpaired Student’s t-test to compare values between two groups. We considered a p-value of less than 0.05 as statistically significant for all experiments.

Data availability
The RNA-seq data generated in this study have been deposited in the National Center for Biotechnology Information (NCBI) Sequence Read Archive (SRA) under accession code PRJNA1282775 (https://www.ncbi.nlm.nih.gov/sra/PRJNA1282775). The RNA-seq data are freely available without restriction. Values for all data points presented in the figures are provided in the Source Data file. The processed data generated in this study are included in the Source Data file and Supplementary Information associated with this article.

Code availability
The code generated in this study has been deposited in Zenodo and is available at: https://doi.org/10.5281/zenodo.17781367.

Peer review rounds
[{"round": 1, "reviewer_comments": {"reviewer_1": "Shehadeh and Mishra et al. developed CAMPER, a hybrid AI pipeline combining a random forest classifier trained on ~3,600 AMP sequences with a mechanistic biophysical scoring functon(charge, hydrophobicity, helicity, amphipathicity) to prioritize α-helical, membrane-active peptides against antibiotic-resistant Staphylococcus aureus. The authors identified WP-CAMPER1, a mastoparans-derived 12-mer peptide with potent activity (MIC 4 µg/mL) against drug-resistant S. aureus strains. The team then carried out extensive characterizations and showed that WP-CAMPER1 kills exponential and stationary-phase persisters and disrupts biofilms. They also carried out RNA-seq analysis and revealed transcriptional changes across hundreds of genes with broad stress responses. D-enantiomer of this peptide (WP-CAMPER1-d) showed promising activity while providing a substratial advantage of proteolytic stability. The in vivo experiments with both WP-CAMPER1 and WP-CAMPER1-d in different contexts are unique strengths of the study. This combined method of ML-based S. aureus-specific model and the biophysical scoring is a promising approach toward the discovery of pathogen-specific AMPs, natural or synthetic. This work demonstrates how collecting pathogen-specific data and training an ML model could result in potent AMPs against a target pathogen. The manuscript is very well written, and the progression of experiments is logical and easy to follow. I have minor concerns that should be addressed before considering this work for publication: -In L152-154, the authors mention the improvement in CAMPER performance compared to RF model in false positives. This is misleading when the false negative has a dramatic increase (60 to 256 from RF to CAMPER). -In line with the previous comment, in Figure 2, comparisons of the models, CAMPER should also be added and the authors should provide a solid justification why, despite a decrease in AUC for CAMPER, this is still beneficial. -The work introduces mastoparan peptides (supplementary figure 9) as the design template but provides no direct comparisons of them with CAMPER peptides. Without testing mastoparans in the same conditions, how can one evaluate the degree of improvement achieved by CAMPER? For the in vitro assays (MIC, cytotoxicity and hemolysis) three diverse mastoparans from supplementary figure 9 should be tested.", "reviewer_2": "In this study, the authors propose a biophysics-based scoring function for antimicrobial peptides and integrate it with machine learning predictions to prioritize membrane-targeting peptides effective against persister and biofilm forms of methicillin-resistant Staphylococcus aureus. While the authors have conducted extensive biological characterization, the manuscript needs some revision to be further considered for publication in Nature Communications. In particular, a more thorough literature review should be provided to better reflect recent advances in antimicrobial peptide prediction. 1. The novelty of this work primarily lies in incorporating biophysical properties of antimicrobial peptides (such as hydrophobicity, net charge, and α-helical propensity) into machine learning-based prediction. However, many existing studies have already considered these factors as part of their screening processes (Meher PK et al, Predicting antimicrobial peptides with improved accuracy by incorporating the compositional, physico-chemical and structural features into Chou’s general PseAAC. Porto WF et al, CS-AMPPred: an updated SVM model for antimicrobial activity prediction in cysteine-stabilized peptides. Bhadra P, AmPEP: Sequence-based prediction of antimicrobial peptides using distribution patterns of amino acid properties and random forest). The authors should thoroughly review these prior works in the Introduction and better clarify the unique contributions of their study. 2. Some of these previous studies have used such biophysical features directly as input to ML models. It is strongly recommended that the authors concatenate the features used in their scoring function into the input of an XGBoost/RF model as a baseline model, and compare performance on the test set. This would more convincingly highlight the value of the proposed scoring function. 3. Figure 1 could be improved to more clearly convey the overall workflow and enhance visual clarity. For example, the numerous arrows in the scoring function module may be confusing to readers, and the sources of the input sequences at the top should be labeled more specifically. 4. In the confusion matrix for XGBoost in Figure 2B, the total number of actual negatives is 224, which differs from Random Forest and Logistic Regression (both showing 314). The authors should carefully verify whether the data shown are correct. 5. In Figures 2G–J, the meaning of “n” and the percentages is unclear. Please clarify these elements in the figure legend. 6. Some regions in Figures 2G–J show a marked drop in precision. It would be beneficial to include a discussion of this observation in the main text to help interpret the potential causes. 7. It is confusing that different control groups were used for different experiments in Figure 3. 8. Figure 3 is too large, and the results for MRSA and VRSA are quite similar. It is recommended that the VRSA data be moved to the Supplementary Information for clarity. 9. In the screening process described above, all training data were L-form peptides. I wonder why D-form peptides were used in the animal experiments. Although D-forms improve stability, WP-CAMPER1-d did not appear to exhibit significantly better efficacy in Figure 5; on the contrary, it showed increased toxicity, as indicated by a significantly lower LD50.", "reviewer_3": "This manuscript by Shehadeh et al. introduces CAMPER, a dual-component framework for antimicrobial peptide (AMP) discovery against S. aureus that combines (i) a random-forest classifier trained on DBAASP-curated peptides and (ii) a mechanistic biophysical scoring function emphasizing charge, hydrophobicity, hydrophobic moment, and helicity. The DBAASP set is binarized (MIC ≤32 µg/mL = active; ≥64 µg/mL = inactive) and split 80/20 for model evaluation; the RF achieves ~80% accuracy (precision 81%, recall 86%, AUC 85%) and integrating the biophysical scorer raises precision to 86% and reduces false positives (84→27). CAMPER prioritized a mastoparan-derived peptide, WP-CAMPER1, and its D-enantiomer (WP-CAMPER1-d), which show membrane-targeting activity supported by NMR/MD and cell-based assays, broad transcriptional effects by RNA-seq, anti-biofilm/anti-persister activity including single-cell “Mother Machine” eradication of gentamicin-induced persisters, and efficacy in murine skin and deep-thigh infection models; toxicity was profiled in vitro (RBC hemolysis, HepG2/HaCaT/HKC LD50s) and by blood urea nitrogen analysis in vivo. Overall, this is a potentially promising study. However, the following comments need to be addressed prior to further consideration. Specific comments: 1. The activity labeling excludes MICs in 32–64 µg/mL, which can inflate apparent separability and precision. The authors should justify this binning and provide sensitivity analyses with alternative thresholds or ordinal/continuous modeling. 2. The authors should use a homology-aware split (e.g., cluster at ≥70–80% sequence identity and split clusters) to prevent near-duplicate sequences across train/test. They should report performance under such splits and with strict per-source or per-strain splits, and add an external holdout set. (Current split is random 80/20.) 3. Feature engineering choices need justification and ablation. For example, the authors compute 66 physicochemical descriptors plus 2048-bit Morgan fingerprints (features designed for small molecules rather than peptides). The authors should justify the peptide relevance of each class and include ablations comparing: (a) sequence embeddings (e.g., ESM/ProtBERT), (b) learned n-gram/CNN/RNN/Transformer baselines, and (c) the current RDKit/Biopython+Morgan approach. Those features driving gains should be reported. 4. ROC and PR curves with AUROC/AUPRC and 95% Cis should be shown; including calibration plots and class-balance details. Clarify threshold selection and report variance over multiple random seeds/splits. 5. When combining the RF with the biophysical scorer, the authors should detail how the two components are integrated (weighted sum? rank aggregation?). Include a formal ablation showing each property’s marginal utility and hyper-parameter sweeps for the sigmoid thresholds. 6. NMR structures were determined in 80 mM SDS micelles (pH 5.5); SDS can exaggerate helicity and is not a close mimic of the S. aureus membrane. The authors are encouraged to add spectra in milder, more physiologic mimetics (e.g., DPC micelles, lipid vesicles with PG/CL, etc.) and/or lipid titrations. 7. MD simulations use a DOPC:DOPG (7:3) bilayer. The authors should provide rationale and, ideally, additional runs with S. aureus–like compositions (higher PG/CL; lower PC) and longer all-atom trajectories (>2 ns AA) to support stable insertion conclusions. 8. The authors note uncertainty whether ROS arises secondarily from membrane damage. I feel like this should be explored further. For example, the authors could test ROS involvement (e.g., with ROS scavengers) and clarify causality. 9. The Mother Machine experiment reports ~4,320 trenches and eradication of three surviving lineages with WP-CAMPER1-d vs. gentamicin. I feel like this should perhaps be repeated across biological replicates, quantify CI on survival frequencies, and test additional antibiotics/conditions to generalize the effect. Authors should provide raw lineage-tracking data/videos as SI. 10. Minor: For biofilms, MBEC/MBIC determinations should be done against multiple clinical isolates and matrix characterization (eDNA/protein/polysaccharide). 11. In vivo experiments: The authors should report full dosing regimens, randomization/blinding procedures, group sizes per arm, and effect sizes with CIs for skin and thigh models. 12. It does not make sense to me that the positive control in the thigh infection model (i.e., vancomycin) did not reduce the bacterial load (e.g., Fig. 5H). 13. The authors could assess resistance potential (serial passage) and post-antibiotic effect. A “resistance-proof” implication should be avoided unless tested experimentally. 14. The authors should provide: (i) the exact train/validation splits, (ii) the labeled DBAASP sequence table with all preprocessing, (iii) scripts to reproduce figures/tables, and (iv) a model card with limitations. 15. The authors should clarify the definition of “Class A peptidomimetics,” and ensure sequences for WP-CAMPER1/analogs (and any non-standard residues) are disclosed in the main text or SI. The LM and D-forms’ therapeutic windows should be tabulated alongside MICs, hemolysis, and LD50s for direct comparison."}, "author_response": "Response to Reviewers\nReviewer #1:\nShehadeh and Mishra et al. developed CAMPER, a hybrid AI pipeline combining a random forest classifier\ntrained on ~3,600 AMP sequences with a mechanistic biophysical scoring functon(charge, hydrophobicity,\nhelicity, amphipathicity) to prioritize α-helical, membrane-active peptides against antibiotic-resistant\nStaphylococcus aureus. The authors identified WP-CAMPER1, a mastoparans-derived 12-mer peptide with\npotent activity (MIC 4 µg/mL) against drug-resistant S. aureus strains. The team then carried out extensive\ncharacterizations and showed that WP-CAMPER1 kills exponential and stationary-phase persisters and\ndisrupts biofilms. They also carried out RNA-seq analysis and revealed transcriptional changes across\nhundreds of genes with broad stress responses. D-enantiomer of this peptide (WP-CAMPER1-d) showed\npromising activity while providing a substratial advantage of proteolytic stability. The in vivo experiments\nwith both WP-CAMPER1 and WP-CAMPER1-d in different contexts are unique strengths of the study.\nThis combined method of ML-based S. aureus-specific model and the biophysical scoring is a promising\napproach toward the discovery of pathogen-specific AMPs, natural or synthetic. This work demonstrates\nhow collecting pathogen-specific data and training an ML model could result in potent AMPs against a\ntarget pathogen. The manuscript is very well written, and the progression of experiments is logical and\neasy to follow.\nI have minor concerns that should be addressed before considering this work for publication:\nComment 1: In L152-154, the authors mention the improvement in CAMPER performance compared to\nRF model in false positives. This is misleading when the false negative has a dramatic increase (60 to 256\nfrom RF to CAMPER).\nResponse: We have clarified this point in the manuscript. CAMPER is not intended to function as a general-\npurpose classifier; rather, it is a design and prioritization framework optimized to identify membrane-acting\npeptides that target gram-positive bacteria. The primary objective is to reduce the number of peptides\npredicted as active that do not show activity when tested (false positives) so that fewer non-active peptides\nare selected for synthesis and testing, which substantially reduces the time and experimental costs\nassociated with screening large custom peptide libraries. We computed performance metrics on an\nevaluation set composed of peptides downloaded from public AMP databases, which include peptide\nsequences with diverse and often unclear mechanisms of action. Because CAMPER is intentionally biased\ntoward selecting membrane-active candidates, false negatives in this context could reflect peptides outside\nthis mechanistic class and do not necessarily correspond to missed membrane-active hits. We have revised\nthe text to clarify this point and to emphasize that the key performance characteristics relevant to the\nintended use case of CAMPER.\nRevised Results:\n“Notably, the number of false positives (defined as peptides predicted as active by the model but labeled as\ninactive based on their MIC) decreased substantially from 92 in the RF model to just 21 with CAMPER,\nrepresenting a 77% reduction (Figure 2E). This reduction aligns with the intended use of CAMPER as a\nprioritization framework that screens large, custom peptide libraries and aims to minimize synthesis and\ntesting of non-active peptides. CAMPER shows lower recall and a lower AUC compared to the standalone\nRF model (Figure 2C, Table S2), but this decrease in recall and AUC primarily reflects the prioritization\nintroduced by the biophysical scoring function which increases the ranking of peptides with membrane-\nactive profiles rather than achieving broad discriminative performance across all mechanisms of action.\nGiven the heterogeneous and sometimes unclear modes of action in in the external evaluation set, many\nresulting false negatives and globally misranked peptides may reflect sequences that do not match\nmembrane-active profiles.” (Page 11, Lines 178-189)\nalso be added and the authors should provide a solid justification why, despite a decrease in AUC for\nCAMPER, this is still beneficial.\nResponse: As clarified above, CAMPER serves as a design and prioritization framework rather than a\ngeneral-purpose classifier. Its value lies in its ability to identify high-confidence membrane-active peptide\ncandidates while minimizing false positive peptide leads, thereby directly reducing the synthesis and testing\ncosts associated with screening large peptide libraries.\nWe now include CAMPER in the model comparison in Figure 2C-D and clarify in the text why lower\nAUC does not undermine its utility (See response to Comment 1). AUC reflects global ranking performance\nacross all peptides (PMID: 7063747), including those with mechanisms unrelated to membrane disruption.\nCAMPER is intentionally modeled to prioritize physicochemical signatures associated with membrane\nactivity, so the ranking shifts toward peptides that match these profiles. This reduces incorrect predictions\nin the regions of the physicochemical space most relevant for membrane-disruptive AMPs, even though it\nlowers global AUC in evaluation sets that include peptides with many other mechanisms of action.\nRevised Figure 2C-D:\nFigure 2. Classifier performance, homology-aware evaluation, feature importance, and CAMPER\nprecision across the biophysical feature space. (C) ROC comparison of Random Forest and CAMPER\nunder the homology-aware 5-fold cross-validation versus a stratified random 80/20 split. (D) Precision-\nrecall comparison of Random Forest and CAMPER under the homology-aware 5-fold cross-validation\nversus a stratified random 80/20 split.\nReferences cited in this response to address the reviewer’s comments, which are not in the main manuscript\ntext:\n J A Hanley, B J McNeil. The meaning and use of the area under a receiver operating characteristic\n(ROC) curve. Radiology. 1982 Apr;143(1):29-36. doi: 10.1148/radiology.143.1.7063747. PMID:\n7063747\nComment 3: The work introduces mastoparan peptides (supplementary figure 9) as the design template\nbut provides no direct comparisons of them with CAMPER peptides. Without testing mastoparans in the\nsame conditions, how can one evaluate the degree of improvement achieved by CAMPER? For the in vitro\nassays (MIC, cytotoxicity and hemolysis) three diverse mastoparans from supplementary figure 9 should\nbe tested.\nResponse: To address this concern, we synthesized three diverse mastoparan peptides; mastoparan-S,\ndominulin A and mp_mastoparan from Figure S20 (previous Figure S9) and tested them under identical\nconditions for antimicrobial activity, cytotoxicity, and hemolysis. Although originating from the same\nmastoparan family, mastoparan-S, dominulin A, and mp_mastoparan represent a highly diverse set of\npeptides. While they share a common amphipathic and cationic α-helical characters, their sequences differ\n2\nmarkedly in length, hydrophobic residue placement, and the positioning of lysine-rich motifs. Mastoparan-\nS is the least hydrophobic and carries the highest positive charge, whereas dominulin A displays higher\nhydrophobicity but a lower net positive charge, and mp_mastoparan exhibits intermediate charge and\nhydrophobicity relative to the other two peptides. This sequential diversity provides a robust framework for\ncomparing how distinct arrangements of hydrophobic and cationic residues modulate membrane binding,\ninsertion, and antimicrobial potency. In our antimicrobial assays, WP-CAMPER1 consistently showed\nsuperior antimicrobial activity against S. aureus MW2 compared to the other mastoparans, at least 8-fold\nhigher MICs. In addition, mastoparan-S, dominulin A and mp_mastoparan exhibited slightly lower\ncytotoxicity and hemolysis compared to WP-CAMPER1, these findings are less relevant in the absence of\ndetectable antimicrobial activity. These results are included in new Supplementary Table S5 and Figure\nS6, and a revised Results section.\nRevised Results:\n“Comparison of WP-CAMPER1 with other mastoparan peptides\nTo demonstrate the utility of CAMPER in identifying peptides with enhanced antimicrobial potency, we\nsynthesized three diverse mastoparan peptides (mastoparan-S, dominulin A and mp_mastoparan) and\nevaluated their antimicrobial activity against S. aureus MW2, as well as their cytotoxicity in liver-derived\nhepatocellular carcinoma (HepG2) cells and hemolysis of human red blood cells (Table S5). WP-\nCAMPER1 exhibited a MIC of 4 µg/mL against S. aureus MW2, while mastoparan-S (MIC= >32 µg/mL),\ndominulin A (MIC= 32 µg/mL) and mp_mastoparan (MIC= 32 µg/mL), showed reduced antimicrobial\npotencies. WP-CAMPER1 had an lethal dose 50% (LD ) of 128 µg/mL in HepG2 cells and caused 50%\n50\nhemolysis at 64 µg/mL, whereas the mastoparans showed no toxicity to HepG2 cells (over 99% survival at\n128 µg/mL) (Figure S6A) and < 40%, < 20% and < 10% hemolysis for dominulin A, mp_mastoparans and\nmastoparan-S, respectively at 128 µg/mL (Figure S6B). Although these peptides exhibit lower cytotoxicity,\nthey show minimal or no antimicrobial activity against S. aureus MW2, limiting their relevance to\nCAMPER design goals and translational aims. In contrast, CAMPER enabled the design of WP-CAMPER1\nfrom the same mastoparan scaffold, resulting in markedly superior antimicrobial potency, highlighting the\nutility of CAMPER in AMP design.” (Page 14, Lines 246-261)\nNew Supplementary Table S5:\nTable S5. Physical parameters of mastoparan peptides and their minimal inhibitory concentrations (MIC)\nagainst S. aureus MW2.\nPeptide NCb Hyc Hmd Boman rTf MIC\nindexe (min) (µg/m\nSequencea\nL)\nSAg\nMastoparan-S LRLKSIVSYAKKVL 4 0.46 0.39 0.63 10.480 > 32\nDominulin A INWKKIAEVGGKILSSL 2 0.48 0.49 0.13 16.525 32\nMp_mastoparan INWLKLGKKMMSAL 3 0.59 0.51 -0.19 15.075 32\nWP-CAMPER1 WKLIALLAKKLL 3 0.850 0.51 -1.56 18.925 4\nCiprofloxacin N.A. N.A. N.A. N.A. N.A. N.A. 0.5\naPeptide sequences have free N-terminus and amidated at C-terminus\nbNC denotes net charge\n3\nc Hy:Hydrophobicity\ndHm:hydrophobic moment of respective peptides calculated from HeliQuest analysis\n(https://heliquest.ipmc.cnrs.fr/)\neProtein-binding potential (Boman index) in kcal/mol\nf HPLC retention time (rT) in minutes on a C18 reverse-phase column\ngdenotes S. aureus MW2\nNew Supplementary Figure S6:\nFigure S6. Analysis of three sequentially diverse mastoparan peptides. Mastoparan-S, dominulin A,\nand mp_mastoparan, with differing charge and hydrophobicity, showing their induced toxicity toward (A)\nhuman hepatocellular carcinoma cells (HepG2) and (B) human red blood cells (hRBCs).\nReviewer #2:\nIn this study, the authors propose a biophysics-based scoring function for antimicrobial peptides and\nintegrate it with machine learning predictions to prioritize membrane-targeting peptides effective against\npersister and biofilm forms of methicillin-resistant Staphylococcus aureus. While the authors have\nconducted extensive biological characterization, the manuscript needs some revision to be further\nconsidered for publication in Nature Communications. In particular, a more thorough literature review\nshould be provided to better reflect recent advances in antimicrobial peptide prediction.\nComment 1: The novelty of this work primarily lies in incorporating biophysical properties of antimicrobial peptides (such as\nhydrophobicity, net charge, and α-helical propensity) into machine learning-based prediction. However, many existing\nstudies have already considered these factors as part of their screening processes (Meher PK et al, Predicting antimicrobial\npeptides with improved accuracy by incorporating the compositional, physico-chemical and structural features into Chou’s general PseAAC.\nPorto WF et al, CS-AMPPred: an updated SVM model for antimicrobial activity prediction in cysteine-\nstabilized peptides. Bhadra P, AmPEP: Sequence-based prediction of antimicrobial peptides using\ndistribution patterns of amino acid properties and random forest). The authors should thoroughly review\nthese prior works in the Introduction and better clarify the unique contributions of their study.\nResponse: We revised the Introduction to cite the requested prior studies and to clarify how our approach\nrelates to earlier feature-based ML models. The updated text notes that previous methods incorporate\nphysicochemical and structural properties solely as input features, whereas our framework uses these\nproperties both as input features in the ML component and as explicit mechanistic constraints in the\nbiophysical scoring function. This distinction is important because applying mechanistic constraints helps\nensure that top-ranked peptides match the biophysical requirements of the membrane-disruption\nmechanism, improving the relevance and interpretability of the predictions for peptide design.\n“ML-based approaches provide important statistical associations but overlook key mechanistic features,\nsuch as membrane selectivity and structural feasibility, limiting their biological relevance.15-28 Several prior\nstudies have incorporated physicochemical and structural features into ML classifiers, including\nhydrophobicity, net charge, and helix-forming propensity, as part of their feature sets.17,18,29 While these\napproaches capture statistical associations between biophysical properties and activity, they treat these\nproperties as input variables for supervised learning rather than as explicit mechanistic constraints. As a\nresult, they overlook the biophysical requirements that define membrane-active AMP behavior, such as\nselective membrane interaction, amphipathic folding, and the structural feasibility of helical\nconformations.”(Page 7, Lines 70-78)\nComment 2: Some of these previous studies have used such biophysical features directly as input to ML\nmodels. It is strongly recommended that the authors concatenate the features used in their scoring function\ninto the input of an XGBoost/RF model as a baseline model, and compare performance on the test set.\nThis would more convincingly highlight the value of the proposed scoring function.\nResponse: The biophysical features that form the basis of the CAMPER scoring function were already\nincluded as input features in the random forest, XGBoost and logistic regression baseline models. We have\nrevised the Feature Engineering section of the Methods to state clearly that these biophysical descriptors\nwere part of the features used to train the baseline models.\nRevised Methods:\n“To calculate peptide descriptors, we used the RDKit70 and Biopython libraries.71 We computed a\ncomprehensive set of physicochemical descriptors,72 including Grand Average of Hydropathy (GRAVY),\nhelicity, hydrophobic moment, charge, topological polar surface area (TPSA), LogP, fraction of sp3\ncarbons, molar refractivity, hydrogen bond donors and acceptors, molecular weight, partial charges,\naromatic ring count, and various Chi and Kappa indices.73 ” (Page 35, Lines 719-724)\nComment 3: Figure 1 could be improved to more clearly convey the overall workflow and enhance visual\nclarity. For example, the numerous arrows in the scoring function module may be confusing to readers,\nand the sources of the input sequences at the top should be labeled more specifically.\nResponse: We updated Figure 1 for visual clarity. The updated figure removes the dense and overlapping\narrows in the scoring function module and replaces them with simplified, direct paths that make the flow\nof information easier to follow. We also reorganized the layout into a clean workflow and added labeling\nof the input source.\nRevised Figure 1:\nPeptide sequences are first evaluated by a Random Forest (RF) classifier trained to predict antimicrobial\nactivity, producing a probability score for each sequence. The sequences are also independently assessed\nby a biophysical scoring module, which evaluates GRAVY, hydrophobic moment, helicity, and net charge.\nThe RF probability and biophysical score are then combined to produce a final CAMPER score, which is\nused to prioritize candidates for in vitro and in vivo validation.\nComment 4: In the confusion matrix for XGBoost in Figure 2B, the total number of actual negatives is\n224, which differs from Random Forest and Logistic Regression (both showing 314). The authors should\ncarefully verify whether the data shown are correct.\nResponse: We have updated the confusion matrices and verified all cell counts. The XGBoost confusion\nmatrix appears together with the corresponding matrices for the other models in the new Figure S2. We\nalso revised the Results to direct readers to the full set of cross-validated confusion matrices shown in\nFigure S2.\nRevised Results:\n“We evaluated several combinations of feature representations and classifiers; specifically, sequence-\nderived n-grams, and ProtBERT embeddings paired with random forest, XGBoost, and logistic regression.\nAcross these combinations, models built on physicochemical descriptors consistently outperformed those\ntrained on sequence-derived n-grams and ProtBERT embeddings, and random forests produced the\nstrongest overall discrimination (Figure 2A-B, Table S2). The corresponding confusion matrices for all\nfeature–model combinations are shown in Figure S2, which illustrates the distribution of correct and\nincorrect predictions under homology-aware cross-validation.” (Page 9, Lines 135-142)\nFigure S2. Confusion matrices comparing all feature-model combinations. RDKit + Biopython\ndescriptors, ProtBERT embeddings and n-gram features with Random Forest (RF), XGBoost, and Logistic\nRegression classifiers, using homology aware 5-fold cross-validation.\nComment 5: In Figures 2G–J, the meaning of “n” and the percentages is unclear. Please clarify these\nelements in the figure legend.\nResponse: To improve clarity, we updated Figures 2G–J by removing the “n” and percentage annotations.\nInstead, we display the number of true positives (TP) and false positives (FP) directly within each bin.\nThis modification makes the underlying counts explicit and avoids any ambiguity regarding how these values\nrelate to the precision shown in each bar.\nRevised Figure 2G-J:\nprecision across the biophysical feature space. (G–J) Comparison of prediction precision between\nRandom Forest and CAMPER across binned values of GRAVY (G), helicity (H), charge (I), and\nhydrophobic moment (J).\nComment 6: Some regions in Figures 2G–J show a marked drop in precision. It would be beneficial to\ninclude a discussion of this observation in the main text to help interpret the potential causes.\nResponse: We revised the Results section to directly address the drop in precision observed in certain\nregions of the biophysical feature space in Figures 2G–J. The updated text highlights where CAMPER\nincreases precision relative to the RF-only model and explains that the decreases occur primarily in the\nhighest charge interval, which contains very few peptides and reflects an extreme physicochemical regime\nthat is not typical of membrane-active sequences.\nRevised Results:\n”Additionally, the integration of the biophysical scoring function within CAMPER led to improvements in\nprediction precision within specific regions of the biophysical feature space. For example, among peptides\nwith hydropathy (GRAVY) between -1.19–0.26, the precision increased from 72% (RF-only) to 95% with\nthe CAMPER dual-component model (Figure 2G), and for the 0.20–0.40 helicity range precision increased\nfrom 75% to 97% (Figure 2H). Similar improvements in precision were observed in additional regions of\nthe biophysical feature space. Precision gains were also observed across various grand average of charge\nand hydrophobic moment bins (Figure 2G-I). At the same time, we observed a clear drop in precision in\nthe highest charge interval, which includes only a small number of peptides and represents an extreme\nphysicochemical regime that is not characteristic of most membrane-acting sequences (Figure 2I). This\npattern highlights that CAMPER performs best in well-represented and biologically relevant regions of the\nfeature space while showing reduced precision in sparse, extreme outlier bins.” (Page 12, Lines 195-207)\nComment 7: It is confusing that different control groups were used for different experiments in Figure 3.\nResponse: We revised Figure 3 to include only one representative antibiotic control in the main panel for\nclarity. The additional control data from selective sub-panel have been moved to Supplementary Figure\nS7-9. We have updated Figure 3, revised its legend accordingly, updated Results and also added the new\nSupplementary Figures S7-9 to make it less confusing.\n“WP-CAMPER1 rapidly suppressed the growth of exponentially growing S. aureus MW2 bacterial cells\ncompared to control antibiotics such as gentamicin and vancomycin (Figure 3A,S7A and S8A). Whereas\nvancomycin was not active against S. aureus VRS1 (Figure S9A).” (Page 15, Lines 266-269)\n“Bithionol was active against both exponential and persister populations of S. aureus MW2 and S. aureus\nVRS1 (Figure S8B,C and S9B,C). Compared with untreated controls, bithionol reduced the exponential-\nphase bacterial load by approximately 3.0 log CFU in S. aureus MW2 and 1.5 log CFU in S. aureus\n10 10\nVRS1 (Figure S8B, S9B). In persister assays, bithionol decreased the bacterial burden by approximately\n2.0 log CFU in S. aureus MW2 and 0.5 log CFU in S. aureus VRS1 (Figure S8C, S9C).” (Page 16,\n10 10\nLines 282-287)\nRevised Figure 3:\nFigure 3. In vitro growth-inhibitory and bactericidal activity of WP-CAMPER1 against exponential,\npersister, and biofilm-associated S. aureus MW2. (A) Growth curve of S. aureus MW2 in the presence\nof WP-CAMPER1 and gentamicin (gen) at 32 µg/mL (n=3). (B-C) Bactericidal kill kinetics of WP-\nCAMPER1 against S. aureus MW2 in exponential (B), and gentamicin-induced stationary persister states\n9\n(C) at 4 and 40 µg/mL, compared to untreated bacterial control (UT). Ciprofloxacin (10 µg/mL) was\nincluded as antibiotic control. CFU counts were measured over 120 minutes (n = 2). (D-E) Inhibition of S.\naureus MW2 biofilm formation by WP-CAMPER1 (4–32 µg/mL), evaluated after 24 hours using metabolic\nactivity reduction (XTT assay) (D), and biomass quantification (crystal violet staining) (E) (n = 3). (F-G)\nDisruption of 24-hour established S. aureus MW2 biofilms by WP-CAMPER1 (4–32 µg/mL), measured\nby reduction in live-cell viability (XTT assay) (F), and decrease in biomass (crystal violet staining) (G)\n(n = 3). (H-I) Fluorescence microscopy images of 24-hour S. aureus MW2 biofilms stained with live/dead\nstain: untreated control (H), and WP-CAMPER1 treated biofilms at 32 µg/mL (I). Scale bars: 0.05 mm. (J)\nDisruption of 24-hour established biofilms of S. aureus MW2 by WP-CAMPER1 at 4 and 40 µg/mL,\nassessed by log reduction in bacterial loads on solid membranes (n = 3) (* is p < 0.05 by comparing two\ngroups by unpaired student t-test). Vancomycin (10 µg/mL) was included as an antibiotic control.\nNew Supplementary Figure S7:\nFigure S7. In vitro growth-inhibitory and bactericidal activity of WP-CAMPER1 against exponential,\npersister, and biofilm-associated S. aureus strain VRS1. (A) Growth curve of S. aureus VRS1 in the\npresence of WP-CAMPER1 and gentamicin (gen) at 32 µg/mL (n=3). (B-C) Bactericidal activity of WP-\nCAMPER1 against S. aureus VRS1 in exponential (B), and stationary persister states (C) at 4 and\n40 µg/mL. Linezolid (100 µg/mL) was included as antibiotic control (n = 2). (D-E) Inhibition of S. aureus\nactivity reduction (XTT assay) (D), and biomass quantification (crystal violet staining) (E) (n = 3). (F-G)\nDisruption of 24-hour established S. aureus VRS1 biofilms by WP-CAMPER1 (4-32 µg/mL), measured by\nreduction in live-cell viability (XTT assay) (F), and decrease in biomass (crystal violet staining) (G) (n = 3).\n(H-I) Fluorescence microscopy images of 24-hour S. aureus VRS1 biofilms stained with live/dead stain: untreated control (H), and WP-CAMPER1 treated biofilms at 32 µg/mL (I). Scale bars: 0.05 mm. (J)\nDisruption of 24-hour established biofilms of S. aureus VRS1 by WP-CAMPER1 at 4 and 40 µg/mL,\nassessed by log reduction in bacterial loads on solid membranes (n = 3) (* is p < 0.05 by comparing two\ngroups by unpaired student t-test). Vancomycin (10 µg/mL) was included as an antibiotic control.\nNew Supplementary Figure S8:\nFigure S8. In vitro growth-inhibitory and bactericidal activity of control antibiotics against\nexponential and persister cells of S. aureus strain MW2. (A) Growth curve of S. aureus MW2 in the\npresence of vancomycin at 32 µg/mL (n=3). (B-C) Bactericidal kill kinetics against S. aureus MW2 in\nexponential (B), and gentamicin-induced stationary persister states (C) in presence of bithionol (10 µg/mL)\ncompared to untreated bacterial control (UT). CFU counts were measured over 120 minutes (n = 2).\nNew Supplementary Figure S9:\nFigure S9. In vitro growth-inhibitory and bactericidal activity of control antibiotics against\nexponential and persister cells of S. aureus strain VRS1. (A) Growth curve of S. aureus VRS1 in the\npresence of vancomycin at 32 µg/mL (n=3). (B-C) Bactericidal kill kinetics against S. aureus VRS1 in\nexponential (B), and gentamicin-induced stationary persister states (C) in presence of bithionol (10 µg/mL)\ncompared to untreated bacterial control (UT). CFU counts were measured over 120 minutes (n = 2).\nComment 8: Figure 3 is too large, and the results for MRSA and VRSA are quite similar. It is recommended\nthat the VRSA data be moved to the Supplementary Information for clarity.\nResponse: We moved the VRSA data from Figure 3 to a new two Supplementary Figure S7,S9 to\nimprove clarity as elaborated in the response to Reviewer 2 comment 7.\nComment 9: In the screening process described above, all training data were L-form peptides. I wonder\nwhy D-form peptides were used in the animal experiments. Although D-forms improve stability, WP-\nincreased toxicity, as indicated by a significantly lower LD50.\nResponse: We used the D-form of WP-CAMPER1 for in vivo experiments because of its enhanced\nresistance to proteolytic degradation. Although WP-CAMPER1-d resulted in no significant improvement\nover WP-CAMPER1 in the established biofilm model (~0.15 log CFU reduction; p = 0.496), it\n10\ndemonstrated significantly enhanced antibacterial activity in the deep-seated persister thigh infection\nmodel, yielding an additional ~0.55 log (p = 0.0029) CFU reduction relative to the L-form.\nbetween the L- and D-forms of WP-CAMPER1 along with their corresponding p-values in both the\nestablished biofilm and thigh infection models. We have also revised Figures 5E and 5F to display the p-\nvalues, and updated their figure legends to specify the statistical methods used to determine the significance\nof comparisons between WP-CAMPER1 and WP-CAMPER1-d, which now reads:\nRevised Results:\n“Following the single-cell evidence of anti-persister activity of WP-CAMPER1-d against exponential-\nphase MRSA persisters, we sought to evaluate the efficacy of WP-CAMPER1 and WP-CAMPER1-d in\nboth surface-associated and deep-tissue infections. We employed two murine infection models: a biofilm-\nassociated skin infection and a deep-seated thigh infection model (Figure 5E,G). In the established biofilm\nmodel, a single topical dose of WP-CAMPER1-d (2% w/w in white petroleum jelly), applied 24 hours post-\ninfection, significantly reduced S. aureus MW2 burden by 1.37 log CFU (p < 0.0001) (95% CI = 0.90–\n10\n1.84) compared to the vehicle treated animal group (Figure 5E). In comparison, WP-CAMPER1 (2% w/w)\nreduced the bacterial load by 1.22 log CFU (p < 0.0001) (95% CI = 0.73–1.71), which was only 0.15 log\n10 10\nCFU lower than WP-CAMPER1-d, indicating similar efficacy between L- and D- forms of WP-CAMPER1,\nsince the difference was not statistically significant (p = 0.496). Meanwhile the 2% mupirocin ointment\ncontrol achieved a reduction of 2.06 log CFU (p < 0.0001) (95% CI = 1.54–2.58) compared to the control\n10\nmice treated with white petroleum jelly (Figure 5E). Bacterial reduction by mupirocin was 0.69 log CFU\n10\n(p = 0.0198) (95% CI = 0.13–1.26) greater than that achieved by WP-CAMPER1-d and approximately 0.84\nlog CFU (p = 0.0080) (95% CI = 0.26–1.42) greater than WP-CAMPER1.” (Page 25, Lines 498-512)\n10\n“In the deep-seated thigh infection model, WP-CAMPER1-d (10 mg/Kg) reduced the bacterial load of\nstationary-phase S. aureus MW2 persister cells by 1.6 log CFU (p < 0.0001) (95% CI: 1.33–1.87), whereas\n10\ntreated animal group (Figure 5H). The observed additional ~0.55 log reduction in S. aureus MW2 burden\n10\nwith WP-CAMPER1-d compared to WP-CAMPER1 (p = 0.0029), suggests that the enhanced enzymatic\nstability of the D-form may play a critical role, ultimately affecting the extent of bacterial clearance.” (Page\n26, Lines 513-519)\nRevised Figures 5E and F:\naureus strain MW2 in murine skin, single channel-based microfluidics and deep thigh infection\nmodels. (A) Schematic overview of the prophylactic skin-abraded murine infection model used to evaluate\nearly intervention efficacy. (B) Quantification of bacterial burden in skin tissues from mice infected with\nexponential-phase S. aureus MW2 and treated 10 minutes post infection with WP-CAMPER1 (1% and 2%\nw/w), or mupirocin (2% w/w) as a topical antibiotic control (n = 12;***p < 0.001, by one-way ANOVA\nfollowing Dunnett’s test). (C) Schematic kymograph of a single-channel microfluidic device designed to\ntrap S. aureus USA300 gentamicin-induced persister cells and enable dynamic peptide treatment. (D)\nSurvival plot of exponential phase S. aureus USA300 persister cells within the microfluidic device treated\nwith WP-CAMPER1-d or gentamicin replicated, showing loss of viability over time with the\npeptidomimetics treatment. (E) Schematic of the established murine skin infection model where treatment\nis initiated 24 hours post-infection. (F) Bacterial burden in infected skin tissue treated with WP-CAMPER1\nor WP-CAMPER1-d (2% w/w), compared with mupirocin (2% w/w) and vehicle control (n = 8; ****p\n< 0.0001 by one-way ANOVA following Dunnett’s test; comparison of WP-CAMPER1 with WP-\nCAMPER1-d by unpaired t-test, n.s. non-significant). (G) Schematic of the neutropenic deep thigh infection\nmodel used to evaluate systemic efficacy. (H) Bacterial burden in thigh muscle tissue infected with\nstationary-phase S. aureus MW2 persister cells (IM), and treated intraperitoneally (IP) with vehicle control\n13\n(n=8) with WP-CAMPER1-d (n=8) or WP-CAMPER1 (10 mg/Kg) (n=7), or vancomycin (30 mg/Kg)\n(n=8) as an antibiotic control (n = 8; ****p < 0.0001 by one-way ANOVA following Dunnett’s test;\ncomparison of WP-CAMPER1 with WP-CAMPER1-d by unpaired t-test, **p < 0.005).\nReviewer #3:\nThis manuscript by Shehadeh et al. introduces CAMPER, a dual-component framework for antimicrobial\npeptide (AMP) discovery against S. aureus that combines (i) a random-forest classifier trained on\nDBAASP-curated peptides and (ii) a mechanistic biophysical scoring function emphasizing charge,\nhydrophobicity, hydrophobic moment, and helicity. The DBAASP set is binarized (MIC ≤32 µg/mL =\nactive; ≥64 µg/mL = inactive) and split 80/20 for model evaluation; the RF achieves ~80% accuracy\n(precision 81%, recall 86%, AUC 85%) and integrating the biophysical scorer raises precision to 86% and\nreduces false positives (84→27). CAMPER prioritized a mastoparan-derived peptide, WP-CAMPER1, and\nits D-enantiomer (WP-CAMPER1-d), which show membrane-targeting activity supported by NMR/MD and\ncell-based assays, broad transcriptional effects by RNA-seq, anti-biofilm/anti-persister activity including\nsingle-cell “Mother Machine” eradication of gentamicin-induced persisters, and efficacy in murine skin\nand deep-thigh infection models; toxicity was profiled in vitro (RBC hemolysis, HepG2/HaCaT/HKC\nLD50s) and by blood urea nitrogen analysis in vivo.\nOverall, this is a potentially promising study. However, the following comments need to be addressed prior\nto further consideration.\nSpecific comments:\nComment 1: The activity labeling excludes MICs in 32–64 µg/mL, which can inflate apparent separability\nand precision. The authors should justify this binning and provide sensitivity analyses with alternative\nthresholds or ordinal/continuous modeling.\nResponse: The decision to exclude MIC values in the 32–64 µg/mL range was intentional and is aligned\nwith the specific purpose of CAMPER. Our objective is to develop a highly selective prioritization\nframework that identifies peptide candidates with the greatest likelihood of strong antimicrobial activity.\nBroth MIC measurements commonly vary by one two-fold dilution, so a shift of a single dilution step falls\nwithin expected assay variability.69 As a result, MIC values of 32 or 64 µg/mL may not reliably distinguish\nactive from inactive peptides and can dilute the model. Including them as either active or inactive would\nintroduce substantial noise into the training set and would reduce the ability of the model to achieve high\nprecision, which is the main performance criterion for minimizing synthesis of non-active peptides in\npractical library screening. Although alternative thresholds or continuous modeling approaches are\nappropriate for some applications, the goal of CAMPER is to enrich for high-confidence membrane-active\npeptides rather than to model the full MIC distribution. For this reason, we used conservative thresholds\nthat reflect clear separation between strong activity and non-activity. We clarified this rationale in the\nMethods section.\nRevised Methods:\n“This binning strategy was selected to support the primary goal of CAMPER, which is to identify high-\nconfidence peptide candidates with strong activity. MIC values between 32 and 64 µg/mL often fall into an\nambiguous range that can vary by experimental conditions and including them can introduce label noise.\nBroth MIC measurements may vary by one or two -fold dilution, so values that differ by a single dilution\nstep fall within expected assay variability.69 Because our aim was to design a selective prioritization tool to\nmaximize precision and reduce the number of non-active peptides forwarded for synthesis and testing, we\nused conservative thresholds to retain only peptides with clear activity or clear inactivity.” (Page 34, Lines\n706-716)\nand split clusters) to prevent near-duplicate sequences across train/test. They should report performance\nunder such splits and with strict per-source or per-strain splits, and add an external holdout set. (Current\nsplit is random 80/20.)\nResponse: We created a stratified outer random split to generate a 20% holdout set and then evaluated all\nmodels using homology-aware 5-fold cross-validation within the remaining 80 percent of the data. We\ncomputed pairwise sequence identity using the Levenshtein ratio and grouped peptides into clusters based\non this similarity measure so that each cluster contained sequences with high sequence similarity. For the\nhomology-aware 5-fold cross-validation, we assigned whole clusters to folds: in each iteration, one set of\nclusters formed the validation fold and the remaining clusters formed the training folds, so no cluster\ncontributed peptides to both training and validation within the same fold. We updated the Methods and\nResults accordingly.\nRevised Methods:\n“We created a stratified 80 to 20 outer split, reserving the 20% portion as a fixed external holdout set. We\nused the remaining 80% of the data for model development and evaluation. We trained a random forest\nclassifier and optimized model performance using 5-fold cross validation to select the best-performing\nhyperparameters. Random forest is an ensemble learning method that constructs multiple decision trees\nduring training and outputs the class that is the majority vote among the trees, enhancing accuracy and\nreducing overfitting.76 To compare performance with other commonly used classifiers, we trained\nXGBoost77, and logistic regression with L1 Regularization (Lasso)78 classifiers on each feature-engineering\nstrategy described in the feature engineering section of the Methods.\nTo prevent near-duplicate sequences from leaking across folds, we applied homology-aware 5-fold cross-\nvalidation inside the 80% training set. We computed pairwise sequence identity using the Levenshtein\nratio,79 clustered peptides at the chosen identity threshold, and assigned entire clusters to the same fold.\nThis approach ensured that homologous peptides did not appear on opposite sides of a training–validation\nsplit. To identify an appropriate sequence-identity threshold, we performed a sensitivity analysis in which\nwe repeated the clustering and homology-aware cross-validation across multiple similarity cutoffs and\ncompared both the resulting cluster structure and performance metrics of the RF classifier at each\nthreshold.” (Page 36, Lines 735-751)\nRevised Results:\n“We assessed performance using the homology aware 5-fold cross-validation procedure described in the\nMethods. Based on the sensitivity analysis, we selected an 80 percent similarity threshold as the most stable\nand well-balanced cutoff for homology-aware evaluation (Figure S1). We evaluated several combinations\nof feature representations and classifiers; specifically sequence-derived n-grams, and ProtBERT\nembeddings paired with random forest, XGBoost, and logistic regression. Across these combinations,\nmodels built on physicochemical descriptors consistently outperformed those trained on sequence-sequence-derived\nn-grams and ProtBERT embeddings, and random forests produced the strongest overall discrimination\n(Figure 2A-B, Table S2).” (Page 9, Lines 133-140)\nNew Figure S1:\nsummarizing the effect of varying the sequence-identity threshold on data structure and model performance\nduring homology-aware cross-validation. Top left: Size of the largest cluster as a function of the similarity\nthreshold. Bottom left: Number of clusters formed at each threshold. Top right: Mean ROC AUC of the\nRandom Forest classifier across thresholds. Bottom right: Precision in the top 10% of ranked peptides as a\nfunction of threshold. Together, these plots guided the choice of an 80% similarity threshold for the\nhomology-aware evaluation.\nComment 3: Feature engineering choices need justification and ablation. For example, the authors\ncompute 66 physicochemical descriptors plus 2048-bit Morgan fingerprints (features designed for small\nmolecules rather than peptides). The authors should justify the peptide relevance of each class and include\nablations comparing: (a) sequence embeddings (e.g., ESM/ProtBERT), (b) learned n-\ngram/CNN/RNN/Transformer baselines, and (c) the current RDKit/Biopython+Morgan approach. Those\nfeatures driving gains should be reported.\nResponse: In the revised manuscript, we expanded the Methods and Results sections to clarify the rationale\nfor the physicochemical descriptors and Morgan fingerprints and to incorporate the additional comparative\nanalyses. More specifically, we evaluated two additional feature representations, sequence-derived n-grams\nand ProtBERT embeddings, and compared them to our descriptor-based approach across multiple\nclassifiers. These new ablation experiments, now summarized in the Results and shown in Figure 2A,B\nand Table S2, demonstrate that physicochemical descriptor models consistently outperformed models\ntrained on n-grams or ProtBERT embeddings. Finally, we performed a feature importance analysis to\n16\nidentify which physicochemical descriptors contributed the most information, providing context for why\nthese descriptor-based models outperformed n-gram and ProtBERT models in the ablation experiments.\nRevised Methods:\n“We evaluated three feature-engineering strategies to characterize peptide sequences: Physicochemical\ndescriptors and fingerprints. To calculate peptide descriptors, we used the RDKit70 and Biopython\nlibraries.71 We computed a comprehensive set of physicochemical descriptors,72 including Grand Average\nof Hydropathy (GRAVY), helicity, hydrophobic moment, charge, topological polar surface area (TPSA),\nLogP, fraction of sp3 carbons, molar refractivity, hydrogen bond donors and acceptors, molecular weight,\npartial charges, aromatic ring count, and various Chi and Kappa indices.73 We added each descriptor as a\nfeature in the dataset (Table S1). Additionally, we calculated Morgan fingerprints (2048-bit, radius 4) to\nrepresent substructure features in a binary vector format.74 A detailed rationale for the relevance of each\ndescriptor class to peptide biophysics and antimicrobial activity is provided in the Supplementary\nMethods.\nSequence n-grams: We generated k-mer frequency vectors from the primary amino-acid sequence to\ncapture short-range compositional patterns. We evaluated several n-gram sizes and normalized counts\nbefore model training.\nProtBERT embeddings: We extracted contextual embeddings for each peptide using the ProtBERT\nlanguage model.75 We averaged token embeddings to produce a fixed-length representation of each\nsequence, which we used directly as input to downstream classifiers.” (Page 35, Lines 718-733)\nNew Supplementary Methods:\n“The RDKit descriptors presented in Table S1 were selected because they summarize global molecular\nproperties that are tightly linked to antimicrobial peptide activity. TPSA, LabuteASA,\nNumHDonors/NumHAcceptors, and ExactMolWt capture overall polarity, size, and hydrogen-bonding\ncapacity, which influence peptide solubility and partitioning between aqueous and lipid phases.12 MolLogP,\nFractionCSP3, and the SlogP_VSA terms describe lipophilicity. NumRotatableBonds and MolMR describe\nthree-dimensional shape. The ring and shape indices (RingCount, Kappa1–3, BalabanJ, BertzCT) describe\nconformational flexibility. All of these properties modulate helix stability and the ability to form\namphipathic, membrane-active surfaces.13 Partial-charge descriptors (Min/MaxPartialCharge and\nBCUT2D_CHGHI/CHGLO) quantify the distribution of positive and negative charge, complementing the\nnet charge feature and reflecting electrostatic interactions with anionic bacterial membranes. Fragment\ncounts for guanidinium, and amine groups (fr_guanido, fr_NH0–2, fr_Ndealkylation1–2), as well as\naromatic and heteroaromatic rings (for example fr_benzene, fr_imidazole), encode the density of cationic\nand aromatic side chains that are hallmarks of many membrane-active AMPs.14 Finally, the explicitly\npeptide-oriented descriptors (GRAVY, helicity, charge, and hydrophobic_moment) capture classical AMP\ndesign principles by summarizing hydrophobicity, secondary-structure propensity, net cationic character,\nand amphipathicity at the sequence level.15 Together, these features provide a chemically interpretable\nbridge between residue-level composition and the global physicochemical profile thought to underlie\npeptide antimicrobial activity.” (Supplementary Methods, Page 6, Lines 112-130)\nRevised Results:\n“We assessed performance using the homology aware 5-fold cross-validation procedure described in the\nMethods. Based on the sensitivity analysis, we selected an 80 percent similarity threshold as the most stable\nand well-balanced cutoff for homology-aware evaluation (Figure S1). We evaluated several combinations\nof feature representations and classifiers; specifically, sequence-derived n-grams, and ProtBERT\nembeddings paired with random forest, XGBoost, and logistic regression. Across these combinations,\nmodels built on physicochemical descriptors consistently outperformed those trained on sequence-derived\nn-grams and ProtBERT embeddings, and random forests produced the strongest overall discrimination\n(Figure 2A-B, Table S2).” (Page 9, Lines 113-140)\n17\n“We evaluated feature importance within the random forest classifier by ranking features according to their\nmean impurity-based importance scores (Figure 2F). GRAVY ranked highest, followed by MolLogP and\nBertzCT. Several BCUT2D descriptors, along with molar refractivity and the Kappa shape indices, also\nappeared among the top contributors. Hydrophobic moment was likewise ranked highly. Overall, the model\nrelied most on descriptors related to hydrophobicity, charge distribution, polarizability, and amphipathic\nstructure.” (Page 11, Lines 159-164)\nRevised Figure 2A-B:\nFigure 2. Classifier performance, homology-aware evaluation, feature importance, and CAMPER\nprecision across the biophysical feature space. (A) Receiver operating characteristic (ROC) curves\ncomparing all feature-model combinations; RDKit + Biopython descriptors, ProtBERT embeddings and n-\ngram features with for Random Forest (RF), XGBoost, and Logistic Regression classifiers, using homology\naware 5-fold cross-validation. (B) Precision–recall (PR) curves comparing all feature-model combinations;\nRDKit + Biopython descriptors, ProtBERT embeddings and n-gram features with for Random Forest (RF),\nXGBoost, and Logistic Regression classifiers, using homology aware 5-fold cross-validation.\nNew Figure 2F:\nFigure 2. Classifier performance, homology-aware evaluation, feature importance, and CAMPER\nprecision across the biophysical feature space. (F) Top 10 most important features in the RF model,\nranked by mean impurity-based importance scores.\nComment 4: ROC and PR curves with AUROC/AUPRC and 95% Cis should be shown; including\ncalibration plots and class-balance details. Clarify threshold selection and report variance over multiple\nrandom seeds/splits.\nResponse: We now provide ROC and precision recall curves with AUROC and AUPRC values and 95%\nconfidence intervals for all evaluation sets (Figure 2A–B). We added a calibration plot (Figure S3) to\n18\nillustrate the reliability of the predicted probabilities. Because our workflow uses continuous probability\nestimates that are combined with the biophysical scorer to rank peptides for screening, fixed decision\nthresholds are not part of the analysis. For a homology-aware evaluation, the revised analysis uses a\nstratified 80 to 20 outer split followed by homology-aware cross-validation within the training portion, and\nwe report performance under this scheme in Table S2. We report class-balance details in the Results.\nRevised Results:\n“The training set contained 2,928 peptides, with 1,674 active peptides (57.2%) and 1,254 inactive peptides\n(42.8%). The holdout set contained 732 peptides, with 419 active peptides (57.2%) and 313 inactive\npeptides (42.8%). These proportions match the class distribution of the full dataset, which consisted of\n2,093 active peptides (57.2%) and 1,567 inactive peptides (42.8%).” (Page 9, Lines 123-127)\n“The RF model achieved an accuracy of 78% on the holdout set, with a precision of 79%, recall of 83%,\nF1 score of 81%, and AUC of 82%. Under the homology-aware 5-fold cross-validation scheme, the RF\nclassifier showed lower overall discrimination than in the random split, with a mean cross-validated AUC\nof 70% (Figure 2C). This decrease reflects the stricter evaluation created by grouping peptides with\nsubstantial sequence similarity into the same fold, which reduces the amount of overlapping sequence\ninformation across training and validation subsets. The confusion matrix shows the distribution of true\npositive, false positive, true negative, and false negative predictions in the holdout set (Figure 2E). As\nshown in the calibration plot in Figure S3, the RF classifier produces reasonably calibrated probability\nestimates, with a Brier score of 0.156 indicating good overall alignment between predicted probabilities\nand observed frequencies.” (Page 10, Lines 143-153)\nNew Figure S3:\nFigure S3. Calibration curve for the Random Forest classifier. Reliability diagram illustrating the\nrelationship between predicted probabilities and the observed fraction of positive samples for the Random\nForest classifier. The dashed line represents perfect calibration.\nComment 5: When combining the RF with the biophysical scorer, the authors should detail how the two\ncomponents are integrated (weighted sum? rank aggregation?). Include a formal ablation showing each\nproperty’s marginal utility and hyper-parameter sweeps for the sigmoid thresholds.\nscore using a weighted sum. We revised the relevant Methods section to make this step clearer in the\nrevised manuscript. The full hyperparameter values and selected weights already appear in Supplementary\nResults. To make this connection clearer, we revised the Results section so that it directs readers more\nexplicitly to the Supplementary Results for the complete set of hyperparameters and biophysical-scorer\nweights.\nRevised Methods:\n“To ensure the biophysical score remains within the [0,1] range for consistent interpretation, we apply\nnormalization:\nRawBiophysicalScore\nBiophysicalScore = (12)\n∑ 𝑤 +β\n𝑖 𝑖\nWe combined the ML probability and the biophysical score using a simple weighted sum to generate the\nfinal CAMPER score:\nFinalScore = 𝑤 ×MLProbability+(1−𝑤 )×BiophysicalScore (13)\nprob prob\nWhere 𝑤 controls the balance between data-driven and mechanism-based predictions.” (Page 43, Lines\nprob\n887-893)\nRevised Results:\n“Detailed rationale for property selection and the mathematical transformations applied are described in the\nMethods section, with hyperparameter and weights of the biophysical scorer provided in Supplementary\nResults.” (Page 11, Lines 170-172)\nSupplementary Results:\n“To determine optimal transformation thresholds and weighting parameters for the biophysical scoring\nfunction, we conducted five-fold cross-validation on the training set using a grid search approach. The\nthresholds for each biophysical property were initialized using the median values derived from\nexperimentally validated α-helical Gram-positive-active peptides in the APD database. These were set to:\n Charge threshold (𝑞 ): 1.5\nopt\n Helicity threshold (ℎ ): 0.38\nthreshold\n GRAVY threshold (𝐺 ): 0.8\nthreshold\n Hydrophobic moment threshold (𝜇 ): 0.52\n𝐻,threshold\nDuring optimization, the following hyperparameters were tuned:\n 𝜎 (width of Gaussian function for charge): 1.0\n𝑞\n 𝑘 , 𝑘 , 𝑘 (steepness of sigmoid for helicity, GRAVY, and amphipathicity): 8.0, 8.0, 4.0\nℎ 𝑔 𝑎\n 𝑤 , 𝑤 , 𝑤 , 𝑤 (component weights): 0.1, 0.1, 0.1, 0.1\n𝑞 ℎ 𝑔 𝑎\n β (cooperative boost magnitude): 0.3\n τ (quality threshold for cooperative boost): 0.4\n 𝑤 (mixing weight with RF probability): 0.5” (Supplementary Results, Page 3, Lines 36-51)\nprob\nComment 6: NMR structures were determined in 80 mM SDS micelles (pH 5.5); SDS can exaggerate\nhelicity and is not a close mimic of the S. aureus membrane. The authors are encouraged to add spectra in\nmilder, more physiologic mimetics (e.g., DPC micelles, lipid vesicles with PG/CL, etc.) and/or lipid\ntitrations.\nResponse: Indeed, SDS micelles can sometimes induce minor structural deviations. Our primary goal in\nthis study was to confirm whether WP-CAMPER1 retains its helical conformation under membrane-\nmimetic conditions. In this regard, SDS was selected because it provides high spectral resolution for\nbackbone assignment and has been widely used in numerous NMR studies for determining AMPs\nstructures.14,92,93 In addition, studies with many AMPs such as LL-37, GI-20, LL-23 and RP-1 have shown\n20\nthat SDS often yields comparable secondary structures to DPC and PG micelles (PMID: 18818205,\n18712851, 22185690). For clarity regarding the use of SDS micelles, we have revised and updated the\nMethods section.\nUpdated Methods:\n“We collected the NMR data at 25ºC on a Bruker Avance II 500 MHz NMR spectrometer (Bruker, MA,\nUSA), using 600 µL of 2 mM WP-CAMPER1 dissolved in 80 mM deuterated SDS (SDS-d25; pH 5.5). We\nselected SDS micelles to mimic an anionic, membrane-like environment and to stabilize the peptide for\nstructural analysis as SDS provides high spectral resolution for backbone assignments and has been widely\nused in NMR studies to determine the structures of AMPs.14,92,93” (Page 52, Line 1096-1101)\nReferences cited in this response to address the reviewer’s comments, which are not in the main manuscript\ntext:\n Guangshun Wang. Structures of human host defense cathelicidin LL-37 and its smallest\nantimicrobial peptide KR-12 in lipid micelles. J Biol Chem. 2008 Nov 21;283(47):32637-43. doi:\n10.1074/jbc.M805533200. PMID: 18818205\n Sarah Bourbigot, Erin Dodd, Chrystal Horwood, Nichole Cumby, Liam Fardy, William H Welch,\nZachary Ramjan, Shantanu Sharma, Alan J Waring, Michael R Yeaman, Valerie Booth.\nAntimicrobial peptide RP-1 structure and interactions with anionic versus zwitterionic micelles.\nBiopolymers. 2009 Jan;91(1):1-13. doi: 10.1002/bip.21071. PMID: 18712851\n Guangshun Wang, Melissa Elliott, Anna L Cogen, Edward L Ezell, Richard L Gallo, Robert E W\nHancock. Structure, dynamics, and antimicrobial and immune modulatory activities of human LL-\n23 and its single-residue variants mutated on the basis of homologous primate cathelicidins.\nBiochemistry. 2012 Jan 17;51(2):653-64. doi: 10.1021/bi2016266. PMID: 22185690\nComment 7: MD simulations use a DOPC:DOPG (7:3) bilayer. The authors should provide rationale and,\nideally, additional runs with S. aureus–like compositions (higher PG/CL; lower PC) and longer all-atom\ntrajectories (>2 ns AA) to support stable insertion conclusions.\nResponse: We initially selected the DOPC:DOPG (7:3) bilayer to model a bacterial membrane containing\nboth anionic and zwitterionic lipids. Our choice is supported by the fact that many clinically relevant\npathogens possess substantial amounts of phosphatidylglycerol (PG), the major anionic lipid in bacterial\nmembranes, for example, Staphylococcus aureus (~58% PG), Pseudomonas aeruginosa (~21% PG),\nEscherichia coli (~15% PG), and Klebsiella pneumoniae (~5% PG) (PMID: 18822270). Collectively, these\nvalues indicate that approximately one-third of a bacterial membrane mimetic can be composed of\nnegatively charged lipids. In this model, DOPG represents the anionic PG component, while DOPC\nprovides a zwitterionic counterpart that stabilizes the bilayer (PMID: 15011033). The DOPC:DOPG (7:3)\nsystem reproduces experimentally validated structural parameters, including lipid volume and area per\nlipid, and therefore is widely used in peptide-membrane MD simulations (PMID: 28445757, 30042491,\n17944489, 25093761).\nBased on your recommendation, we conducted an additional 10 ns all-atom simulation using the same\nDOPG:DOPC (7:3) membrane model. The results from the 10 ns DOPG:DOPC simulation were\ncomparable to the original 2 ns dataset, showing similar amino acid interactions with only minor differences\nin interaction occupancies, confirming stable peptide-membrane insertion. In addition, to more accurately\nrepresent the S. aureus membrane, known to be enriched in phosphatidylglycerol and cardiolipins (PMID:\n18822270), we further performed simulations on a POPG:CL (7:3) bilayer.\nWe have now replaced the original 2 ns all-atom data with the updated 10 ns dataset in Supplementary\nTable S7 and Figure 4G,H. Additionally, we have included a new Supplementary Figure S12 presenting\nthe simulation results for the POPG:CL (7:3) system. Both the Methods and the Results section has been\nupdated accordingly and now reads:\nUpdated Methods:\n21\n“Molecular dynamics (MD) simulation\nWe conducted MD simulations using membrane bilayers composed of DOPC (1,2-dioleoyl-sn-glycero-3-\nphosphocholine) and DOPG (dioleoyl phosphatidylglycerol) at a 7:3 molar ratio, with each bilayer\ncontaining 64 lipids.12 We generated the initial WP-CAMPER1 structure using the AlphaFold2 Colab\nserver96 and positioned the peptide parallel to the membrane surface, at least 1.5 nm above the upper leaflet.\nThe simulations proceeded in two stages.97 First, we performed coarse-grained (CG) MD simulations using\nthe MARTINI 2.2P force field for 500 ns to capture membrane-peptide association.98 We then selected\nrepresentative CG frames corresponding to key events, specifically the initial membrane contact and the\nfully engaged state at 500 ns, and converted them into all-atom models. These all-atom systems were\nsubsequently simulated for 10 ns using the CHARMM36 force field.98 All simulations were carried out\nusing the GROMACS 2020.1-1 software package (Groningen, Netherlands), with system input files\ngenerated through the CHARMM-GUI server. We visualized the trajectories using Visual Molecular\nDynamics (VMD)99 and the trial version of BIOVIA Discovery Studio Visualizer (Accelrys Inc., SD, USA).\nWe also simulated WP-CAMPER1 in membrane composed of POPG (1-palmitoyl-2-oleoyl-sn-glycero-3-\nphospho-(1’-rac-glycerol) and CL (cardiolipin) (7:3 molar ratio), following the same protocol used for the\nDOPC:DOPG system, to evaluate its interactions with a bilayer enriched in PG and CL, the major anionic\nphospholipids of S. aureus membranes.100” (Page 53, Lines 1115-1132)\nUpdated Results:\n“To determine whether WP-CAMPER1 could disrupt a membrane model that more closely mimics the S.\naureus lipid composition, we performed MD simulations of WP-CAMPER1 with a POPG:CL (7:3) bilayer.\nWP-CAMPER1 rapidly associated with the membrane, binding within the first 10 ns and remaining\nembedded throughout the 500-ns simulation, indicating a stable and energetically favorable peptide-\nmembrane interaction (Figure S12A). The partial density profile further demonstrated peptide insertion\ninto the bilayer (Figure S12B)”. (Page 19, Lines 342-347)\nUpdated Supplementary Results:\n“Key residues forming hydrogen bonds with DOPC were tryptophan 1 and lysine 2 and lysine 10, with\noccupancies of 10.36%, 9.46% and 3.87%, respectively and for DOPG, lysine 10, 2, and 9, formed\nhydrogen bonds, with occupancies of 48.20%, 13.69%, and 80.9%, respectively (Table S7).”\n(Supplementary Results, Page 4, Lines 68-71)\nUpdated Table S7:\nTable S7. Hydrogen bond interactions of WP-CAMPER1 as derived from molecular dynamics simulations\nwith a DOPC/DOPG(7:3) model membrane at the end of 500 ns simulation.\nWith nearest DOPC With nearest DOPG\nDONOR ACCEPTOR OCCUPANCY DONOR ACCEPTOR OCCUPANCY\nTRP1-Side DOPC60-Side 10.36% LYS10-Side DOPG14-Side 48.20%\nLYS2-Side DOPC53-Side 9.46% LYS10-Side DOPG16-Side 35.23%\nLYS2-Main DOPC60-Side 3.87% LYS2-Side DOPG15-Side 13.69%\nLEU12-Side DOPC59-Side 3.06% LYS9-Side DOPG13-Side 8.92%\nLYS2-Side DOPC56-Side 2.97% LYS9-Side DOPG18-Side 7.39%\nLEU3-Main DOPC60-Side 1.80% LYS10-Side DOPG13-Side 6.04%\nLYS2-Side DOPC55-Side 0.27% LYS9-Side DOPG14-Side 1.53%\nLYS9-Side DOPC59-Side 0.18% DOPG15-Side LYS2-Main 0.45%\nDOPG13-Side LEU12-Main 0.45%\nTRP1-Main DOPG17-Side 0.18%\nRevised Figures 4G and H:\nFigure 4. (G) Alterations in membrane thickness upon WP-CAMPER1 interaction. (H) Increase in average\nlipid surface area per molecule following WP-CAMPER1 binding to the bacterial membrane mimicking\nbilayer (****, * is p < 0.001 and < 0.05 by comparing two groups by unpaired student t-test).\nNew Supplementary Figure S12:\nCAMPER1 into a POPG:CL (7:3) bilayer at 500 ns.\n(B) Lipid density changes and membrane perturbation induced by WP-CAMPER1-mediated water\nintrusion.\nReferences cited in this response to address the reviewer’s comments, which are not in the main manuscript\ntext:\n Richard M Epand, Raquel F Epand. Lipid domains in bacterial membranes and the action of\nantimicrobial agents. Biochim Biophys Acta. 2009 Jan;1788(1):289-94. PMID: 18822270\n K Balali-Mood, T A Harroun, J P Bradshaw. Molecular dynamics simulations of a mixed\nDOPC/DOPG bilayer. Eur Phys J E Soft Matter. 2003 Nov:12 Suppl 1:S135-40. PMID: 15011033\n Joseph E Faust, Pei-Yin Yang, Huey W Huang. Action of Antimicrobial Peptides on Bacterial and\nLipid Membranes: A Direct Comparison. Biophys J. 2017 Apr 25;112(8):1663-1672. PMID:\n28445757\n Nelson G O Júnior, Marlon H Cardoso, Elizabete S Cândido, Daniëlle van den Broek, Niek de\nLange, Nadya Velikova, J Mieke Kleijn, Jerry M Wells, Taia M B Rezende, Octávio Luiz Franco,\nRenko de Vries. An acidic model pro-peptide affects the secondary structure, membrane\ninteractions and antimicrobial activity of a crotalicidin fragment. Sci Rep. 2018 Jul 24;8(1):11127.\ndoi: 10.1038/s41598-018-29444-0. PMID: 30042491\n Christopher J Arnusch, Hilbert Branderhorst, Ben de Kruijff, Rob M J Liskamp, Eefjan Breukink,\nRoland J Pieters. Enhanced membrane pore formation by multimeric/oligomeric antimicrobial\npeptides. Biochemistry. 2007 Nov 20;46(46):13437-42. doi: 10.1021/bi7015553. PMID: 17944489\n Yen-Fei Chen, Tzu-Lin Sun, Yen Sun, Huey W Huang. Interaction of daptomycin with lipid\nbilayers: a lipid extracting effect. Biochemistry. 2014 Aug 26;53(33):5384-92. doi:\n10.1021/bi500779g. PMID: 25093761\nComment 8: The authors note uncertainty whether ROS arises secondarily from membrane damage. I feel\nlike this should be explored further. For example, the authors could test ROS involvement (e.g., with ROS\nscavengers) and clarify causality.\nResponse: We investigated whether ROS generation occurs as a secondary effect of membrane disruption\nor represents an associated bactericidal mechanism of WP-CAMPER1. To clarify causality, we performed\nspot titer experiments using the ROS scavenger N-acetylcysteine (NAC) (10 mM) that do not directly affect\nbacterial growth.57 We also measured bacterial growth curves with peptide treatment in the presence or\nabsence of the ROS. NAC was used to determine whether neutralizing ROS could rescue bacterial viability\nduring peptide exposure. We observed that co-treatment with NAC partially restored bacterial survival\nfollowing WP-CAMPER1 treatment, indicating that ROS production contributes to the bactericidal activity\nin addition to membrane disruption.\nWe have added these new findings as Supplementary Figures S13A-C, which display the kinetic growth\ncurves, bacterial CFU counts and spot titer images of S. aureus MW2 in presence of WP-CAMPER1 and\nthe presence or absence of NAC. The corresponding Methods subsection has been updated under “ROS-\nassociated growth inhibition and spot titer assays”, and the Results section now includes the following\nrevised text:\nUpdated Methods:\n“ROS-associated growth inhibition and spot titer assays\nWe evaluated the role of ROS in the antibacterial activity of peptides using spot titer and kinetic growth\ncurve assays, following a previously established protocol57 with minor modifications. In a microplate\ngrowth curve assay, exponential-phase cultures of S. aureus MW2 were adjusted to 1×106 CFU/mL and\nincubated in 96-well plates (100 µL per well) with WP-CAMPER1 (1× MIC) in the presence or absence of\nNAC (10 mM). The optical density at 600 nm was recorded every 2 h to monitor bacterial growth kinetics\nusing the SpectraMax 250 plate reader (Molecular Devices, San Jose, CA, USA). A decrease in bacterial\nkilling efficiency in the presence of ROS scavengers indicated that ROS played a contributory role in the\nantibacterial mechanism of WP-CAMPER1.57\nIn a complimentary spot titer assay, exponential-phase S. aureus MW2 cultures were grown in TSB and\ndiluted to approximately 1×106 CFU/mL. The bacterial suspensions were exposed to WP-CAMPER1 at 1×\nMIC in the presence or absence of the ROS scavenger N-acetylcysteine (NAC, 10 mM). Cultures were\nincubated at 37 °C, and samples were collected at 0, 4, and 8 h, serially diluted, and plated on RamelTM\nTSA plates (Fisher Scientific, MA, USA) to determine surviving CFU after overnight incubation.\nTo assess the effect of higher bacterial and peptide concentrations, we also exposed 1×107 CFU/mL cultures\nto WP-CAMPER1 at 5× MIC, with or without NAC (10 mM). Samples were collected at 0, 4, and 18 h,\nand 20 µL from the culture was serially diluted, and plated on RamelTM TSA plates (Fisher Scientific, MA,\nUSA) to quantify bacterial survival.” (Page 56, Lines 1185-1203)\nUpdated Results:\n“To determine whether ROS generation directly contributed to bacterial killing as an auxiliary mechanism\nin addition to peptide-induced membrane disruption, we performed kinetic growth and spot-titer CFU\nassays in the presence of the ROS scavenger N-acetylcysteine (NAC, 10 mM). In the kinetic growth assay,\ncultures with an initial inoculum of 1×106 CFU/mL showed a significant bacterial growth difference at 16\nh when co-incubated with WP-CAMPER1 (1× MIC) and NAC (p=0.046), whereas no growth recovery was\nobserved in cultures treated with WP-CAMPER1 compared to untreated controls (p=0.011) and NAC alone\ndid not affect S. aureus MW2 growth (Figure S13A). In the spot-titer CFU assay, NAC partially restored\nS. aureus MW2 viability, reducing the bactericidal effect of WP-CAMPER1 by approximately 5 log CFU\n10\nafter 4 h of co-incubation (p = 0.0006), compared with the untreated control that reached 8 log CFU\n10\n(Figure S13B). Similarly, using a higher inoculum (1×107 CFU/mL) treated with 5× MIC of WP-\nCAMPER1, initial bacterial killing was followed by partial regrowth in the presence of NAC, whereas\ncomplete eradication occurred with peptide treatment alone after 18 hours (Figure S13C). Together, these\nfindings suggest that, in addition to direct membrane disruption, ROS generation contributed to the overall\nbactericidal mechanism of the CAMPER peptides.” (Page 20, Lines 378- 392)\nMW2. (A) Growth kinetics of exponential-phase S. aureus MW2 cultures treated with WP-CAMPER1 (1×\nMIC) in the presence or absence of the ROS scavenger N-acetylcysteine (NAC, 10 mM). Statistical\nsignificance was determined by one-way ANOVA followed by Tukey’s multiple-comparison test (B) Spot-\ntiter CFU assay of S. aureus MW2 (1×106 CFU/mL) treated with WP-CAMPER1 (1× MIC) ± NAC at 0\nand 4 h. Statistical significance was determined by Student t-test (C) Spot-titer CFU assay of S. aureus\nMW2 (1×107 CFU/mL) treated with WP-CAMPER1 (5× MIC) ± NAC at 0, 4, and 18 h.\nComment 9: The Mother Machine experiment reports ~4,320 trenches and eradication of three surviving\nlineages with WP-CAMPER1-d vs. gentamicin. I feel like this should perhaps be repeated across biological\nreplicates, quantify CI on survival frequencies, and test additional antibiotics/conditions to generalize the\neffect. Authors should provide raw lineage-tracking data/videos as SI.\nResponse: We have expanded the Mother Machine dataset to include two independent biological replicates\nfor each treatment conditions, the gentamicin and WP-CAMPER1-d, increasing the total number of\nanalyzed trenches from approximately 4,320 to 5,520 (1200, 1200, 1620, 1500 trenches per experiment).\nThese updated results are reflected in the revised figure (Figure 5D).\nThe rarity of exponential-phase persister cells in the mother machine setup is a major practical limitation\nfor increasing sample size. Each experiment involves one full day of microfluidic device preparation,\nfollowed by more than 48 hours of experiment set-up, high-resolution time-lapse microscopy and analysis.\nEven with thousands of trenches per experiment, persister events are infrequent. While additional replicates\ncould modestly narrow the confidence intervals (CIs) for such rare-event survival frequencies, the inherent\n26\nstatistical uncertainty remains high, and further replication is unlikely to alter the principal interpretation of\nour findings.\nAs a result of this rarity, the CIs for the second treatment are extremely wide due to the very small sample\nsize (n=2 for each condition). Typically, we observe at most one persister event per replicate, after sampling\nthousands of trenches per experiment, severely restricting the number of lineages available for subsequent\nanalysis. To reach n=3 for both the gentamicin and peptide retreatment conditions, we would require at\nleast an additional successful rare-event experiment for each treatment arm, effectively doubling the\nexperimental workload, instrument usage, and analysis time. Although this would narrow the confidence\nintervals somewhat (for example, to 29.2%–100% for 3/3, and 0%–70.8% for 0/3), the intervals would still\nremain relatively wide due to the low number of events, a statistical limitation inherent to studies of this\nkind. This challenge underscores the practical and statistical challenges in studying rare persister cells, even\nwith maximal sampling in our system. Nevertheless, our results are consistent across all biological\nreplicates. We hope this clarifies our approach and addresses the Reviewer’s concern regarding sample size.\nRegarding the suggestion to test additional antibiotics and experimental conditions, we agree that broader\ncomparisons could further generalize the effects of WP-CAMPER1-d. However, in this study we\ndeliberately focused on gentamicin-induced S. aureus persister cells, as this model is well established and\nwidely validated in the field (PMID: 39289656, 34384900) and allows direct correlation with our\nstationary-phase persister assays performed in bulk culture. Testing additional antibiotics would not\nsubstantially strengthen the mechanistic conclusions we aim to draw. We believe that maintaining this\nfocused approach ensures consistency across both our single-cell and bulk-level S. aureus persister assays.\nFor survival quantification and trench counts, in this study, rather than employing lineage tracking, we\nquantified trench survival manually. We have updated the Methods section which now reads:\nUpdated Methods:\n“For survival quantification and trench counts, the initial number of trenches was estimated by multiplying\nthe number of FOVs imaged by 60 trenches per FOV. This method includes all trenches present in each\nFOV, regardless of whether a particular trench was empty at the beginning of imaging. Lineage survival\nwas assessed visually after each first and second antibiotic treatment, based on the presence or absence of\ndividing cells in each trench. During non-antibiotic periods, we assumed that no cell death happened.”\n(Page 62, Lines 1312-1317)\nIn addition, we have provided the requested survival frequencies and Clopper–Pearson 95% confidence\nintervals (CIs) for both the first and second treatments and updated our Results sections which now reads:\nUpdated Results:\nUsing this platform, we monitored ~5,520 trenches, each containing ~20 exponential-phase S. aureus\nUSA300 cells, and exposed them to a bactericidal concentration of gentamicin (3.75 µg/mL) (Figure\n5C,D). During first gentamicin treatment, out of 5,520 trenches analyzed across four biological replicates,\nonly 4 trenches contained surviving lineages after treatment with survival frequency: (4/5520, 0.072%; 95%\nCI: 0.019% to 0.185%), exhibiting a persister-like phenotype (Figure 5C). These results correspond to a\nsurvival frequency of 0.069% based on lineage-level analysis. However, during the second gentamicin\ntreatment (3.75 µg/mL) in which, the four surviving lineages from the first gentamicin treatment were\nexposed to a second challenge: two each for retreatment with gentamicin or with the WP-CAMPER1-d\npeptide (60 µg/mL) (Figure 5C). In gentamicin retreatment: both lineages survived (2/2, 100%; 95% CI:\n15.811%–100%), while during peptide retreatment with WP-CAMPER1-d, both lineages were eradicated\n(0/2, 0%; 95% CI: 0%–84.188%), indicating a differential response to the two treatment regimens (Figure\n5C,D).” (Page 25, Lines 484-496)\ntreatments in the microfluidic mother-machine device. The plot shows the percentage of viable single-cell\nlineages over time across two each biological replicates during primary gentamicin treatment and\nsubsequent secondary treatment with either gentamicin or WP-CAMPER1-d. Persister lineages that\nsurvived the initial gentamicin exposure sustained survival during gentamicin retreatment and showing\ncomplete loss of viability over time following WP-CAMPER1-d treatment.\nWe have provided the following as Supplementary Information section: (1) raw HDF5 files from one\nrepresentative experiment each for the second gentamicin and WP-CAMPER1-d treatments; (2) videos of\nthe FOV with the persister trench from each condition; and (3) videos highlighting a first gentamicin-\nsurviving trench from both gentamicin and WP-CAMPER1-d second treatment conditions, as well as a\nnon-persister trench. The total size of these files is approximately 350 GB.\nReferences cited in this response to address the reviewer’s comments, which are not in the main manuscript\ntext:\n Augustin Konkobo, Abdoul Karim Ouattara, Amana Mètuor Dabiré, Jacques Simporé. Exploring\nantibiotic-induced persister formation and bacterial persistence genes in clinical isolates from\nBurkina Faso. BMC Infect Dis. 2024 Sep 17;24(1):994. doi: 10.1186/s12879-024-09906-9. PMID:\n39289656\n Samira Karimaei, Seyed Mohammad Kazem Aghamir, Abbas Rahimi Foroushani, Mohammad\nReza Pourmand. Antibiotic tolerance in biofilm persister cells of Staphylococcus aureus and\nexpression of toxin-antitoxin system genes. Microb Pathog. 2021 Oct:159:105126. doi:\n10.1016/j.micpath.2021.105126. PMID: 34384900\nComment 10: Minor: For biofilms, MBEC/MBIC determinations should be done against multiple clinical\nisolates and matrix characterization (eDNA/protein/polysaccharide).\nResponse: We performed MBEC/MBIC determinations using three different S. aureus clinical isolates.\nWe updated the Results section and added a new Supplementary Figure S10 presenting the corresponding\nbiofilm biomass data quantified by crystal violet staining.\nUpdated Results:\n“To further evaluate the antibiofilm activity of WP-CAMPER1, we included three additional S. aureus\nclinical isolates (BF1, BF2, and BF3) (Figure S10). WP-CAMPER1 effectively disrupted 24-hour mature\nbiofilms formed by these isolates, achieving 50% biomass reduction (MBEC ) at 8 μg/mL, 16 μg/mL, and\n50\n12 μg/mL, respectively. Similarly, WP-CAMPER1 inhibited biofilm formation in these clinical isolates,\nreducing biomass by 50% (MBIC ) at 1 μg/mL (BF1), 5 μg/mL (BF2), and 3 μg/mL (BF3). These results\n50\nhighlight the broad-spectrum antibiofilm efficacy of WP-CAMPER1 across diverse clinical S. aureus\nbackgrounds.” (Page 17, Lines 299-306)\nFigure S10. Antibiofilm activity of WP-CAMPER-1 against S. aureus clinical isolates. (A-C) Disruption\nof 24-hour established biofilms formed by three S. aureus clinical isolates (BF1–BF3) following treatment\nwith WP-CAMPER1 (4–32 µg/mL), quantified as a reduction in biofilm biomass using crystal violet\nstaining (n = 4). (D–F) Inhibition of biofilm formation by the same S. aureus clinical isolates (BF1–BF3)\nin the presence of WP-CAMPER1 (4–32 µg/mL), evaluated after 24 hours by biomass quantification using\ncrystal violet staining (n = 4).\nComment 11: In vivo experiments: The authors should report full dosing regimens,\nrandomization/blinding procedures, group sizes per arm, and effect sizes with CIs for skin and thigh\nmodels.\nResponse: We have incorporated details on the dosing regimen, randomization and blinding procedures,\nand group sizes in the Supplementary Methods section. Additionally, we have updated the Results section\nto include effect sizes with 95% confidence intervals (CIs) for both the skin and thigh infection models,\nwhich now reads:\nUpdated Supplementary Methods:\n“Dosing regimen, randomization and blinding procedures, group sizes for murine models\nMice received a single topical dose of 30 mg of either vehicle control (white petroleum jelly), peptide\nointment, or mupirocin, depending on the infection model.15 In the prophylactic skin infection model,\ntreatment consisted of vehicle control, 1% or 2% (w/w) WP-CAMPER1, or 2% (w/w) mupirocin, applied\n10 min post-inoculation. In the established skin infection model, treatment included vehicle control, 2%\n(w/w) WP-CAMPER1, 2% (w/w) WP-CAMPER1-d, or 2% (w/w) mupirocin, applied 24 h post-\ninoculation. In the deep-seated thigh infection model, mice received intraperitoneal injections of vehicle\ncontrol, WP-CAMPER1 or WP-CAMPER1-d (10 mg/Kg), or vancomycin (30 mg/Kg), administered every\n12 hours for a total of six doses.16,17 Mice were randomly assigned to treatment groups, and investigators\nperforming bacterial enumeration were blinded to treatment allocation. Each treatment arm consisted of\nn=12 mice in the prophylactic skin infection model, n=8 mice in the established skin infection model, and\nn=7-8 mice in the thigh infection model.” (Supplementary Methods, Page 7, Lines 132-143)\nUpdated Results:\n“A single application of the 2% (w/w) WP-CAMPER1 formulation, administered 10 minutes after infection\n(representing a prophylactic model), led to a significant reduction in S. aureus MW2 bacterial load\n(evaluated after 24 h), achieving a 2.5 log decrease (p < 0.0002) (95% CI = 2.15 – 2.85) compared to the\n10\nvehicle treated mice groups (Figure 5B). The 1% (w/w) formulation also demonstrated efficacy, reducing\n29\nthe bacterial load by 1.4 log (p < 0.0005) (95% CI = 0.84 – 1.42). As a positive control, mupirocin\n10\nointment (2% w/w) resulted in a 2.2 log reduction in S. aureus MW2 burden (p < 0.0002) (95% CI = 1.82\n10\n– 2.48) compared to the vehicle treated mice.” (Page 22, Lines 427-434)\n“Following the single-cell evidence of anti-persister activity of WP-CAMPER1-d against exponential-\nphase MRSA persisters, we sought to evaluate its efficacy in capturing both surface-associated and deep-\ntissue infections. We employed two murine infection models: a biofilm-associated skin infection and a\ndeep-seated thigh infection model (Figure 5E,G). In the established biofilm model, a single topical dose\nof WP-CAMPER1-d (2% w/w in white petroleum jelly), applied 24 hours post-infection, significantly\nreduced S. aureus MW2 burden by 1.37 log CFU (p < 0.0001) (95% CI = 0.90–1.84) compared to the\n10\nvehicle treated animal group (Figure 5E). In comparison, WP-CAMPER1 (2% w/w) reduced the bacterial\nload by 1.22 log CFU (p < 0.0001) (95% CI = 0.73–1.71), which was only 0.15 log CFU lower than\n10 10\nWP-CAMPER1-d, indicating similar efficacy between L- and D- forms of WP-CAMPER1, since the\ndifference was not statistically significant (p = 0.496). Meanwhile the 2% mupirocin ointment control\nachieved a reduction of 2.06 log CFU (p < 0.0001) (95% CI = 1.54–2.58) compared to the control mice\n10\ntreated with white petroleum jelly (Figure 5E). Bacterial reduction by mupirocin was 0.69 log CFU (p =\n10\n0.0198) (95% CI = 0.13–1.26) greater than that achieved by WP-CAMPER1-d and approximately 0.84\nlog CFU (p = 0.0080) (95% CI = 0.26–1.42) greater than WP-CAMPER1.” (Page 25, Lines 498-512)\n10\n“In the deep-seated thigh infection model, WP-CAMPER1-d (10 mg/Kg) reduced the bacterial load of\nstationary-phase S. aureus MW2 persister cells by 1.6 log CFU (p < 0.0001) (95% CI: 1.33–1.87), whereas\n10\ntreated animal group (Figure 5H). The observed additional ~0.55 log reduction in S. aureus MW2 burden\n10\nwith WP-CAMPER1-d compared to WP-CAMPER1 (p = 0.0029), suggests that the enhanced enzymatic\nstability of the D-form may play a critical role, ultimately affecting the extent of bacterial clearance.” (Page\n26, Lines 513-519)\nComment 12: It does not make sense to me that the positive control in the thigh infection model (i.e.,\nvancomycin) did not reduce the bacterial load (e.g., Fig. 5H).\nResponse: We used vancomycin as an antibiotic control rather than a positive control in the deep-seated\nthigh infection experiment. Vancomycin was included to verify that the S. aureus MW2 cells introduced in\nthe stationary phase in the murine thigh represented true persisters. As expected, vancomycin did not reduce\nthe bacterial burden, supporting the suitability of our model for testing treatments against antibiotic-tolerant\npersister cells. Consistent with our previous studies using the same model (PMID: 31358625, 29590091),\nvancomycin and other conventional antibiotics have shown limited efficacy against these persistent\ninfections. We have added a clarifying sentence in the Results section.\nUpdated Results:\n“In contrast, treatment with the control antibiotic vancomycin (30 mg/Kg) was ineffective, confirming the\npresence of antibiotic-tolerant persisters and validating the bacterial persistence in the deep-seated thigh\ninfection model.” (Page 26, Lines 519-522)\nReferences cited in this response to address the reviewer’s comments, which are not in the main manuscript\ntext:\n Wooseong Kim, Guijin Zou, Taylor P A Hari, Ingrid K Wilt, Wenpeng Zhu, Nicolas Galle,\nHammad A Faizi, Gabriel L Hendricks, Katerina Tori, Wen Pan, Xiaowen Huang, Andrew D\nSteele, Erika E Csatary, Madeline M Dekarske, Jake L Rosen, Noelly de Queiroz Ribeiro, Kiho\nLee, Jenna Port, Beth Burgwyn Fuchs, Petia M Vlahovska, William M Wuest, Huajian Gao,\nFrederick M Ausubel, Eleftherios Mylonakis. A selective membrane-targeting repurposed\n30\nantibiotic with activity against persistent methicillin-resistant Staphylococcus aureus. Proc Natl\nAcad Sci U S A. 2019 Aug 13;116(33):16529-16534. PMID: 31358625\n Wooseong Kim, Wenpeng Zhu, Gabriel Lambert Hendricks, Daria Van Tyne, Andrew D Steele,\nColleen E Keohane, Nico Fricke, Annie L Conery, Steven Shen, Wen Pan, Kiho Lee, Rajmohan\nRajamuthiah, Beth Burgwyn Fuchs, Petia M Vlahovska, William M Wuest, Michael S Gilmore,\nHuajian Gao, Frederick M Ausubel, Eleftherios Mylonakis. A new class of synthetic retinoid\nantibiotics effective against bacterial persisters. Nature. 2018 Apr 5;556(7699):103-107. doi:\n10.1038/nature26157. PMID: 29590091\nComment 13: The authors could assess resistance potential (serial passage) and post-antibiotic effect.\nA “resistance-proof” implication should be avoided unless tested experimentally.\nResponse: We conducted serial passage and post-antibiotic effect (PAE) experiments to assess both the\npotential for resistance development and the duration of post-treatment suppression of bacterial regrowth\nin Staphylococcus aureus strain MW2 following exposure to WP-CAMPER1 and WP-CAMPER1-d. No\nresistance was observed in S. aureus MW2 even after 21 consecutive days of exposure to WP-CAMPER1.\nIn contrast, the control antibiotic ciprofloxacin showed a >16-fold increase in MIC after 21 days, indicating\nresistance development. These results are now presented in Supplementary Figure S18, which shows the\ndaily MIC fold change values for both WP-CAMPER1 and ciprofloxacin against S. aureus MW2.\nIn addition, the PAE analysis revealed that both WP-CAMPER1 (1.68 ± 0.19 h) and WP-CAMPER1-d\n(2.45 ± 0.31 h) displayed significantly longer PAEs compared with ciprofloxacin (0.91 ± 0.17 h),\nhighlighting the enhanced persistence of bacterial growth suppression achieved by targeting the membrane\nof S. aureus MW2. Moreover, the PAE of WP-CAMPER1-d was significantly higher than that of WP-\nCAMPER1 (p = 0.018), confirming that D-enantiomerization prolongs post-antibiotic suppression relative\nto the parent peptide. The details of the experimental setup and results have been added to the Methods and\nResults sections, which now reads:\nUpdated Methods:\n“Serial passage assay\nWe evaluated the potential of WP-CAMPER1 and WP-CAMPER1-d to induce resistance in S. aureus MW2\nusing a serial passage assay adapted from a published protocol.107 On Day 1, we performed a standard MIC\nassay to establish baseline susceptibility. We defined bacterial growth as an OD600 ≥ 0.35. On each\nsubsequent day, we initiated new cultures from the well that showed visible growth (OD600 ≥ 0.35) at the\nhighest drug concentration. We diluted the selected culture 1:1000 in fresh TSB medium and performed a\nnew MIC assay with a concentration gradient of the peptide or the control antibiotic. We repeated this\nprocedure for 21 consecutive days. We included ciprofloxacin as a reference antibiotic. We recorded MIC\nvalues daily to track any changes in susceptibility over time.” (Page 62, Lines 1318-1327)\n“Post-antibiotic effect (PAE)\nWe evaluated the post-antibiotic effect (PAE) of WP-CAMPER1, WP-CAMPER1-d, and ciprofloxacin\nagainst S. aureus MW2 following previously published methods108,109 with minor modifications. Briefly,\novernight cultures of S. aureus MW2 were grown in TSB at 37 °C with shaking (220 rpm) and diluted in\nfresh TSB to obtain an inoculum of 2× 108 CFU/mL. We treated each 1 mL culture with WP-CAMPER1,\nWP-CAMPER1-d, or ciprofloxacin at 5× MIC for 1 h at 37 °C with shaking at 220 rpm. After treatment,\nwe centrifuged the cultures at 16,000× g for 5 min to remove residual drug and washed the pellets three\ntimes with fresh TSB. The washed cultures were resuspended in drug-free TSB and incubated at 37°C with\nshaking (220 rpm) to allow regrowth. We collected samples immediately before and after drug removal and\nthen at 1 h intervals. At each time point, we removed 10 µL of culture, performed serial dilutions in PBS,\nand plated them on TSA plates (BD Difco, NJ, USA). Plates were incubated overnight at 37 °C, and CFU\nwere enumerated the following day. We calculated the PAE using the formula: PAE=T-C, where T was the\ntime required for the treated culture to increase by 1 log CFU/mL from the count immediately after drug\n10\n31\nremoval, and C was the corresponding time for the untreated control. Each assay, including its control, was\nperformed in triplicate.” (Page 62, Lines 1328-1343)\nUpdated Results:\n“We also evaluated whether S. aureus MW2 could develop resistance to WP-CAMPER1 and WP-\nCAMPER1-d. No resistance was observed after 21 serial passages, as MIC values remained unchanged\n(Figure S18). In contrast, ciprofloxacin exhibited a >4-fold MIC increase by day 15 and a >16-fold increase\nby day 21, indicating the emergence of resistance.” (Page 24, Lines 467-470)\n“To assess the persistence of bacterial growth suppression following peptide exposure, we evaluated the\npost-antibiotic effect (PAE) of WP-CAMPER1 and WP-CAMPER1-d against S. aureus MW2. The PAE\nvalues for WP-CAMPER1, WP-CAMPER1-d, and ciprofloxacin, relative to the untreated control, were\n1.68 ± 0.19 h (95% CI= 1.26–2.10) (p= 0.0009), 2.45 ± 0.31 h (95% CI= 1.67–3.23) (p < 0.0001), and 0.91\n± 0.17 h (95% CI= 0.53–1.29) (p= 0.0047), respectively. Notably, the PAE of WP-CAMPER1-d was\nsignificantly longer than that of WP-CAMPER1 (p = 0.018), confirming that D-enantiomerization prolongs\npost-antibiotic suppression relative to the parent peptide. These findings demonstrate that both CAMPER-\nderived peptides exhibit prolonged bacterial growth inhibition compared with ciprofloxacin, with WP-\nCAMPER1-d showing the strongest and most durable effect.” (Page 24, Lines 471-480)\nSerial passage-S. aureus MW2\nIM\ne g1\n1\n2\nn a10\nh c 8\nd 6\nlo\nF 4\n2\n0\n0 7 14 21\nDAYS\nFigure S18. Evaluation of resistance induction in S. aureus MW2 by WP-CAMPER1 and WP-CAMPER1-\nd.\nComment 14: The authors should provide: (i) the exact train/validation splits, (ii) the labeled DBAASP\nsequence table with all preprocessing, (iii) scripts to reproduce figures/tables, and (iv) a model card with\nlimitations.\nResponse: We have deposited all requested materials on Zenodo, including the fully processed DBAASP\nsequence table with labels, all scripts used to generate the exact train and validation splits, scripts to\nreproduce every figure and table, detailed documentation for running the model to screen peptide libraries,\nand a model card outlining key assumptions and limitations. The Zenodo DOI has been added to the Data\nand Code Availability section of the manuscript and will become public upon acceptance. In the meantime,\nreviewers can access the full codebase and documentation here:\nhttps://zenodo.org/records/17781368?token=eyJhbGciOiJIUzUxMiJ9.eyJpZCI6ImJkNTk1NjA0LTg0Y\nWUtNDlmYy1hMDA1LWI4MGE4ZmU5YTA2MiIsImRhdGEiOnt9LCJyYW5kb20iOiJmMjBiMzAxM\nmE4OGJiNmFjZmFiMWY3Yjg4ZDAzMTU0NiJ9.3uvEygxAB49V4MJ_dbHy5km3C5FF9Ehv1Zw_cqI\nMKhZrU_wQshYVWh4DNpWJ4KECCnT0nTXSn8VORZwaFDvTUQ\nUpdated Data Availability\n“Values for all data points in graphs are reported in the Supporting Data Values file. Data are available\nupon request. Code was submitted to zenodo and is available here:\nhttps://doi.org/10.5281/zenodo.17781367. The RNA-Seq data is freely available under the National\nPRJNA1282775.”\nComment 15: The authors should clarify the definition of “Class A peptidomimetics,” and ensure\nsequences for WP-CAMPER1/analogs (and any non-standard residues) are disclosed in the main text\nor SI. The LM and D-forms’ therapeutic windows should be tabulated alongside MICs, hemolysis, and\nLD50s for direct comparison.\nResponse: We have revised the Discussion section to clarify the definition of Class A peptidomimetics.\nIn addition, we have updated the manuscript to include the sequences and details of the sequence modifications\nin WP-CAMPER1 and its peptidomimetic analogs in the updated Table 2. We also added comparative data\nsummarizing MICs, hemolysis, and LD values (HepG2 cells) for both L-, D-forms and other\n50\npeptidomimetics to facilitate direct evaluation of their therapeutic windows and the updated text now reads:\nUpdated Discussions:\n“To support systemic application, we synthesized WP-CAMPER1-d, the D-enantiomer of the parent\npeptide, together with a set of Class A peptidomimetic analogs. These peptidomimetic analogs preserved\nthe WP-CAMPER1 backbone while substituting lysine residues with non-proteinogenic α-amino acids to\nenhance protease resistance.58” (Page 31, Lines 631-634)\nUpdated Table 2:\nTable 2. Minimal inhibitory concentrations of WP-CAMPER1 based class A peptidomimetics against\ndrug-resistant S. aureus strains.\nPeptid MIC (µg/mL) HL j/ LD\n50 5\ne Hemo l/T\n%k ox\nc d\n%m\nWP- WKLI 4 4 4 4 8 4 4 4 64j 128l\nCAMP ALLA\nER1 KKLL\nWP- WKLI 4 4 4 4 8 4 4 8 64j 50l\nCAMP ALLA\nER1-d KKLL\nWP- WDab 16 8 8 16 32 8 16 16 14k 99m\nCAMP LIAL\nER1- LADa\nDab bDab\nLL\nWP- WDap 32 16 16 32 32 16 16 32 22k 99m\nCAMP LIAL\nER1- LADa\nDap pDap\nLL\nCAMP LIAL\nER1- LAOr\nOrn nOrn\nLL\nWP- > 32 > 32 > 32 > 32 > 32 > 32 > 32 > 32 2k 99m\nCAMP WKLI\nER1- ALLA\nCyc KKLL\nMeln GIGA 8 8 8 16 16 8 8 16 NA NA\nVLK\nVLTT\nGLPA\nLISW\nIKRK\nRQQ\naPeptide sequences have free N-terminus and amidated at C-terminus; italicized are all d-form of amino\nacids; bold and italicized are lysine residues substituted with unnatural amino acids; Dab- diaminobutyric\nacid, Dap- Diaminopimelic acid, Orn- Ornithine; Cyc- head to tail cyclization\nbMSSA ; S. aureus Newmann\nc MRSA ; S. aureus MW2\ndMRSA ; S. aureus USA300\neVISA ; S. aureus AR0215\nf VISA ; S. aureus AR0216\ngVRSA ; S. aureus VRS1\nhClinical isolate; S. aureus BF1\niClinical isolate; S. aureus BF2\nj HL ; 50% Hemolytic concentration to human RBCs\n50\nkHemolysis % ; Percentage lysis of human RBCs at 64 µg/mL peptide concentration\nlLD ; 50% Cytotoxic concentration to human HepG2 cells\n50\nmToxicity % ; Percentage toxicity of human HepG2 cells at 128 µg/mL peptide concentration\nnMel ; Melittin\nWe are pleased to submit the 2nd rev ision of our man uscript entitled “CAMPER: Mechanistic Artificial\nIntelligence fo r Designing Peptides that Target MRSA Persisters”. Fo r your convenience, we have copied\nthe reviewer’s comments in italic font, while we have highlighted our responses in blue. We have\nincorporated all of the suggestions and made the appropriate changes in our manuscript. Please find below a\npoint-by-point response to the reviewer’s comments.\nReviewer #1:\nThe authors have thoroughly addressed my comments by clarifying on model comparison as well as their\nexperimental comparison of WP-CAMPER1 with other Mastoparan peptides (mastoparan-S, dominulin A\nand mp_mastoparan)\nComment 1: My only concern remains with \"generic\" vs \"membrane-active\" that the authors mentioned\nin the response to my first two comments. It is widely known that \"most\" AMPs are membrane-active. The\nauthors should also address this using numbers from experimentally validated peptides in available AMP\ndata.\nResponse: We appreciate the Reviewer’s time and constructive feedback throughout the review process.\nWe agree with the reviewer that most antimicrobial peptides are membrane-active. To avoid ambiguity, we\nclarified in the revised manuscript that CAMPER does not aim to capture all membrane-associated\nmechanisms of action. Instead, it prioritizes peptides consistent with an α-helical, membrane-disruptive\nmechanism. To support this distinction, we analyzed peptides in the holdout evaluation set that were\nexperimentally labelled as active in the dataset, predicted as active by the RF model, but deprioritized by\nCAMPER. Structural predictions using AlphaFold2 showed that 70% (140/200) of these peptides did not\nadopt predominantly α-helical conformations. These sequences and their predicted secondary structures are\nprovided in Supplementary Table 3. This analysis clarifies that CAMPER deprioritizes peptides whose\nactivity likely arises from mechanisms outside the α-helical membrane-disruptive regime it is designed to\nenrich.\nRevised Results:\n“Notably, the number of false positives (defined as peptides predicted as active by the model but labeled as\ninactive based on their MIC) decreased substantially from 92 in the RF model to just 21 with CAMPER,\nrepresenting a 77% reduction (Figure 2E). This reduction aligns with the intended use of CAMPER as a\nprioritization framework that screens large, custom peptide libraries and aims to minimize synthesis and\ntesting of non-active peptides. CAMPER shows lower recall and a lower AUC compared to the standalone\nRF model (Figure 2C, Table S2), but this decrease in recall and AUC primarily reflects the prioritization\nintroduced by the biophysical scoring function which favors peptides consistent with an α-helical,\nmembrane-disruptive mechanism rather than optimizing discrimination across all antimicrobial\nmechanisms. Analysis of peptides in the external evaluation set that were experimentally labeled as active\nin the dataset, predicted as active by the RF model, but deprioritized by CAMPER showed that 70%\n(140/200) were not predominantly α-helical based on AlphaFold2 structure predictions. These peptides and\ntheir predicted secondary structure classes are provided in Table S3.” (Pages 11-12, Lines 179-191)\nNew Table S3:\nTable S3. Sequences and AlphaFold2-predicted secondary structure for peptides deprioritized by\nCAMPER in the holdout set\nSequence Secondary Structure\nKRFKKFFKKLKNSVKKRVKKFFRKPRVIGVTFPF α+non-αβ\nKRRVRWIIW Non-αβ\nVKLYPVKLYP β-sheet\nVQRWLIVWRIRK Non-αβ\nGLFKKLRRKIKKGF α-helical\nLFGFLIKLIPSLFGALSNIGRNRNQ Non-αβ\nYRLRVKWKW Non-αβ\nKKKKKKKKKKGIGKLFLHAAKKFAKAFVAEKMNS α+non-αβ\nGWWRRTVDKVRNAGRKVAGFASKACGALGH α-helical\nKLVKRFKKFFRKLKKS Non-αβ\nACQCPDAISGWTHTDYQCHGLENKMYRHVYAICMNGTQVYC β-sheet\nRTEWGSSC\nFAKKLAKKLKKLAKKLAKKWKL Non-αβ\nGLLSGILGAGKHIVCGLSGPCQSLNRKSSDVEYHLAKC β-sheet\nSSGWVCTLTIECGTVICAC β-sheet\nFIHHIIGGLFSVGKHIHSLIHRRRR Non-αβ\nPIPFPPY Non-αβ\nRRRRRRRWWWWW Non-αβ\nLRLKKRRWKYRVPW Cyclic peptide\nILPYKYPYYPYRR Non-αβ\nGFFLNALKNFAKTAGKRLKSLLNHASCKLSGQC α-helical\nLLRRVARLLKRFAK Non-αβ\nRRAAVVLIVIRR α-helical\nVTCDLLSFEAKGFAANHSLCAAHCLAIGRRGGSCERGVCICRR α+β\nRIWR Lipopeptide\nAKAGGIPPHVIPQIVPVRIRPLCGNV Non-αβ\nTKCFQWQRNMRKVRGPPVSCIKRDSPIQCI α+non-αβ\nKKK Lipopeptide\nWRRWRRRWRRRWRRW Non-αβ\nMSWLNFLKYIAKYGKKAVSAAWKYKGKVLEWLNVGPTLEW α+non-αβ\nVWQKLKKIAGL\nVWKPPYLPRPRPPRRIYNR Non-αβ\nRRWVRRVRRWVRRVVRVVRRWVRR Non-αβ\nRRLCRIVWVIRVCRR Cyclic peptide\nPFKISIHL Non-αβ\nGIGKFLHSAKKFGKAFVGEIMNSKKKKKKKKKK α+non-αβ\nVPKPPYLPRPRPPRRKYNR Non-αβ\nKFFKKLKNSVKKRAKKFFKKPRVIGVSIPF α+non-αβ\nLRLHKRRWKYRVPP Non-αβ\nGLLSVLGSVVKHVIPHVVPVIAEHL Non-αβ\nVLGLALIVGGALLIKKKQAKS α-helical\nRAWR Lipopeptide\nGANAAKKFANLIKKIFNYIW Non-αβ\nSLWETIKNAGKGFILNILDKIRCKVAGGCKT α+non-αβ\nFIKHFIHRFSGGRWRWRWF Non-αβ\nKKLFKKIGLRYL α+non-αβ\nKKLFKKILGRYK α+non-αβ\nRFRRLRWWTRKRLKKI Non-αβ\nGFFDRIKALTKNVTLELLNTITGKLPVTPP α+non-αβ\nLTKKLVVKVLYKVV α-helical\nFFGSLLSLGSKLLPSVFKLFQRKKE Non-αβ\nFLRSLLRGAKAIYRGARAGWRG Non-αβ\nGFMDTAKNVAKNVAATLLDKLKCKITGGC α+non-αβ\nSIRDKIKTIAIDLAKSAGMGILKTLICKLDKSC α+non-αβ\nKWSFRVCYRGICYRRAR Non-αβ\nILRWIWKIWRRW α-helical\nFFRNLWKGAKAAFRAGHAAWRA Non-αβ\nCRRKKC Lipopeptide\nAKAGGIGGHVIGQIVGVRIRGLGGNV Non-αβ\nLRRLRRRWRRRLRRL α+non-αβ\nFPLTWLKWWKWKK α-helical\nSYEKKINRHFKILKKNLKKK α-helical\nRRRRWWWW Non-αβ\nKWKLFKKKGTGAVLTVLTTGLPALIS α+non-αβ\nFLGGLIKWKWPWWPWRR α+non-αβ\nKKVVFKFKFK α-helical\nRVKRVWPLVIRTVIAGYNLYRAIKKK α+non-αβ\nKACFRVCYRGICYRRCR β-sheet\nFLGGLIKIVPAMICAVTKKCHHHHHH α+non-αβ\nINWLKIAKKVKGML α-helical\nGLPALISWIKRKRQ α+non-αβ\nWG Cyclic peptide\nWRIRIRWKRLRRPP α+non-αβ\nINWLKIKKKVAGML α-helical\nGLPALILWIKRKRQQ α-helical\nGLLNGLALRLGKRALKKIIKRLCR α-helical\nKVLGRLVKVLGRLV α-helical\nFIFHIIKGLF α-helical\nATCDLLSGTGINHSACAAHCLLRGNRGGYCNGKAVCVCRN α+β\nKIWVIRWR Non-αβ\nAGLFKKLARAIKKGFKA α-helical\nCVHGMTNTARTACIAP α+non-αβ\nVQRWLIVWRIRKG α-helical\nWKWKCTKSGCKWKW β-sheet\nKWKLFKKALKKLKKALKKAL α-helical\nCGRRPWWWRCRL Non-αβ\nINWKKIKKKVAGML α-helical\nKAYSMPRCKGGFRAVMCWL α+non-αβ\nCRKKKC Lipopeptide\nKIWWSGAWKRWRW Non-αβ\nAGLFKKLARAIAKGFKA α-helical\nGKVWDWIKSTAKKLWN α-helical\nGWGSIFKHGRHAAKHIGHAAVNHYL α-helical\nEVGGGGGSQQCPQERPKLSSCKDYVMERCFTMKDFPVTWPTK α+non-αβ\nWWKGGCEHEVREKCCKQLSQIAPQCRCDSIRRVIQGRLGGFLG\nIWRGEVFKQLQRAQSLPSKCNMGADCKFPSGYYW\nLRRLRRRWWWRRRLRRL α+non-αβ\nILVRWIRWRIQWG α-helical\nRRRRRRWWWW Non-αβ\nSHHRHFRSHQCRR Non-αβ\nRLARIVVIRVAR α-helical\nGIFGKILGVGKKTLCELSGMC α+non-αβ\nYSYFTVV Non-αβ\nDFGCARGMIFVCMRRCARMYPGSTGYCQGFRCMCDTHIPIRRP α+β\nPFIMG\nTRRKFWKKVLNGALKIA α-helical\nKQFRIRVRVIRK α-helical\nGRWLRKIRRWRPKRW α+non-αβ\nCKKRKC Lipopeptide\nKRIVQRIKDFLRAGPIKDFLR α+non-αβ\nCWTKSIPPKPCF Non-αβ\nCARLNCVPKGTSGNTETCPCYASLHSCRKYG α+non-αβ\nFSKYERQKDKRPYSERKNQYTGPQFLYPPERIPPQKVIKWNEE α+β\nGLPIYEIPGEGGHAEPAAA\nNALSMPRNKCNRALMCFG α+non-αβ\nDFGCGQGMIFMCQRRCMRLYPGSTGFCRGFRCMCDTHIPLRPP α+β\nFMVG\nGIWKTIKSMGKVFAGKILQNL α-helical\nLKGCWTKSIPPKPCF Non-αβ\nFAKKLAKLALKLAKL α-helical\nKKFLHSAKKWLKAFVKLFKNW α-helical\nGLFSVVTGVLKAVGKNVAKNVGGSLLEQLKCKISGGC α+non-αβ\nPRRR Lipopeptide\nLRWKKRRWKYRVPP Non-αβ\nNRWYCNSAAGGVGGAAVCGLAGYVGEAKENIAGEVRKGWG Non-αβ\nMAGGFTHNKACKSFPGSGWASG\nRKWWK Lipopeptide\nILPFKFPFFPFRR Non-αβ\nFIHHIIGGLFSVGKHIHRLIRRRRR α-helical\nGLFTLIKGAYKLDAPTVACN α+non-αβ\nGWGSFFKKAAHAGKHAGKAALTHYL α-helical\nGYGCPFNQYQCHSHCSGIRGYKGGYCKGTFKQTCKCY α+β\nKWKLWKKIEKWGQGIGAVLKWLTTWL α-helical\nTWLPAVIKIQAHWRGYRQRKIYL α+non-αβ\nKKWRKLLKKLKKLL α-helical\nKRRWRIWLV Non-αβ\nGWKKWLRKGAKHLGQAAIK α-helical\nKWKKFIKKIGIGAVLKVLTTGLPALKLTKK α+non-αβ\nRRIFKRLFKYLRRF α-helical\nAWLDKLKSLGKVVGKVALGVAQNYLNPQQ α+non-αβ\nRLKLLLLLRL α+non-αβ\nLTCDLLSFEAKGFAANHSLCAAHCLAIGRKGGACQNGVCVCR α+β\nR\nHKHWLWLW Non-αβ\nCKRKKC Lipopeptide\nKLLRHRLLVTLA α+non-αβ\nRVWR Lipopeptide\nWI Cyclic peptide\nRLFRRVKKVAGKIAKRIW α-helical\nIKYLLVKLQGASQKTITLMLRRN β-sheet\nILGKIWKPIKKLF α+non-αβ\nCKKKRC Lipopeptide\nGFMATAKNVAKNMDVTLLDNLKCKITKAC α+non-αβ\nSSLLEKGLDGAKKAVGGLGKLGKDAVEDLESVGKGAVHDVK α-helical\nDVLDSVL\nLRRLRRRLRRLRRRWRRRLRRLRRRLRRL α-helical\nGAFGNFLKGVAKKAGLKILSIAQCKLSGTC α+non-αβ\nL Non-αβ\nGIGAVLKVLTTGLPALIKRKRQQ α-helical\nGFMNTAKNVAKNVAVTLLDNLKCKITGGC α+non-αβ\nQRR Lipopeptide\nRRRRWWWWW Non-αβ\nFLPFIAGMAAKFLPKIFCAISKK α-helical\nLRKFWKKIRKFLKKI α-helical\nGLPLLISWIKRKRQQ α-helical\nKVVFKVKFK α-helical\nGIFTLIKGAAKLIGKTVAKEAGKTGLELMACKITNQC α+non-αβ\nFKCRRWQWRMKKLGAPS α+non-αβ\nGMASKAGAIAGKIAKVALKAL α-helical\nKSLRPRCWIKIKFRCKSLKF α+non-αβ\nKFFKKLKKAVKKGFKKFAKV α-helical\nKSKWLWLW Non-αβ\nGFGCNGPWSEDDLRCHRHCKSIKGYRGGYCAKGGFVCKCY α+β\nGLFTLIKGAYKNDAPTVACN α+non-αβ\nKGGK Lipopeptide\nSLFSLIKAGAKFLGKNLLKQGAQYAACKVSKEC α+non-αβ\nGFMDTAKNVAKNMAVTLLDNLKCKITKAC α+non-αβ\nFALGAVTKRLPSLFCLITRKC α+non-αβ\nYGRKKRRQRRRSISLL Non-αβ\nVRRFGWWWGFLRR α+non-αβ\nVIPIVSGLLSSLL α-helical\nKRLFKRLFKYLRRF α-helical\nKWKSFLKTFKSLKKTKLHTLLKLISS α-helical\nYKLLKLLLPKLKPLLFKL α-helical\nVQLRIRVRVIRK α-helical\nKRFKKFFMKLKKSVKKRVMKFFKKPMVIGVTFPF α+non-αβ\nRAGLQFPVGKLLKKLLKRLKR α+non-αβ\nGKKYRRFRWKFKGKLFLFG α-helical\nRRPWRWPWWPWRR Non-αβ\nFKCRRWQWRMKKLGAPSITCVRRAF α+non-αβ\nLRLKKRWWKYRVPP Non-αβ\nRGSRSGQSSRGERR Non-αβ\nVKRTKRGARRGLTKVLKKIFGSIVKKAVSKGV α+non-αβ\nIGGIISFFKRLF α-helical\nVDKPPYLPRPRPPRRFYNR Non-αβ\nRWWRLF Non-αβ\nGIGGALLNVGKVALKGLAKGLAEHFAN α-helical\nAHCLAIGRR α-helical\nKLLKHKLLVTLR α-helical\nGLFTLIKGAAKLIGKTVAKEAGKTGLELMACKITNQC α+non-αβ\nGLLRKGGEKIGEKLKKIGQKIKNFFQKLVPQPEQ α+non-αβ\nKLPKIWWIKVPK Non-αβ\nLRLKKLRWKYRVPP Non-αβ\nLKKWLKKWLKK α-helical\nKWKLFKKIRGGRLCYCRRRFCVCV β-sheet\nKILRGVSKKIMRTFLRRISKDILTGKK α-helical\nWPKWWKWKRRWGRKKAKKRRG α+non-αβ\nAGLFKKLARAIKKGFKK α-helical\nGLPALISWIKRKRQQ α-helical\nFLGSLLGLVGKVVPTLFCKISKKC α+non-αβ\nReviewer #2:\nThe authors have adequately addressed all of my concerns. I have no further questions.\n(Remarks on code availability)\nThe project is well organized.\nResponse: We thank the Reviewer and appreciate the Reviewer’s time and constructive feedback\nthroughout the process.\nComments on behalf of Reviewer #3:\nI have reviewed the authors’ responses to comments 7, 8, and 13 from Reviewer #3. In my opinion, these\nissues have been adequately addressed.\nFor Comment 13, I suggest adding error bars to the newly included Figure S18 and moving it into the main\ntext, as it provides important supporting evidence. Additionally, the red and blue labels in Figure S18\noverlap; using distinct symbols or adjusting the layout would improve readability\nResponse: We thank the Reviewer for their careful review and constructive suggestions. As requested, we\nadded error bars to the serial passage figure (previously Figure S18) and moved it from the Supplementary\nInformation into the main manuscript, where it now appears as Figure 5C. We also improved readability\nby updating the symbology of the figure.\nRevised Figure 5:\naureus MW2 in murine skin, single channel-based microfluidics and deep thigh infection models. (A)\nSchematic overview of the prophylactic skin-abraded murine infection model. (B) Quantification of\nbacterial burden in skin tissues from mice infected with exponential-phase S. aureus MW2 and treated 10\nminutes post infection with WP-CAMPER1 (1% and 2% w/w), or mupirocin (2% w/w). Data represent\nmean ± SD of n =12 biologically independent replicates. (C) Evaluation of resistance induction in S. aureus\nMW2 by WP-CAMPER1 and WP-CAMPER1-d. Data are presented as mean ± SD from three (n = 3),\nbiologically independent replicates. (D) Schematic kymograph of a single-channel microfluidic device\ndesigned to trap S. aureus USA300 gentamicin-induced persister cells. (E) Survival plot of exponential-\nphase S. aureus USA300 lineages during primary and secondary treatments in the microfluidic mother-\nmachine device. Persister lineages that survived the initial gentamicin exposure sustained survival during\ngentamicin retreatment and showing complete loss of viability over time following WP-CAMPER1-d\ntreatment. (F) Schematic of the established murine skin infection model where treatment is initiated 24\nhours post-infection. (G) Bacterial burden in infected skin tissue treated with WP-CAMPER1 or WP-\nCAMPER1-d (2% w/w), compared with mupirocin (2% w/w) and vehicle control. For each group, data\nrepresent mean ± SD of n = 8, biologically independent replicates. (H) Schematic of the neutropenic deep\n8\nthigh infection model. (I) Bacterial burden in thigh muscle tissue infected with stationary-phase S. aureus\nMW2 persister cells (IM), and treated intraperitoneally (IP) with vehicle control (n=8, biologically\nindependent replicates) with WP-CAMPER1-d (n=8 biologically independent replicates) or WP-\nCAMPER1 (10 mg/Kg) (n=7 biologically independent replicates), or vancomycin (30 mg/Kg) (n=8\nbiologically independent replicates) as an antibiotic control. For data in panels B, G and I, statistical\nsignificance for comparisons between untreated control and all treatment groups was determined using two-\nsided one-way ANOVA followed by Dunnett’s multiple-comparisons test. Comparisons between WP-\nCAMPER1 and WP-CAMPER1-d were performed using an unpaired two-tailed Student’s t-test. n.s.\ndenotes not significant. P values are indicated in the graph. Source data file provided.\n9"}]

Decision letter