Discovery of antimicrobial peptides targeting Acinetobacter baumannii via a pre-trained and fine-tuned few-shot learning-based pipeline

Domain: neuroscience
Published: 2026-02-07
Editorial outcome: major_revision

Abstract
Acinetobacter baumannii, a robust Gram-negative bacterium known for causing nosocomial infections, exhibiting multidrug resistance, and lacking antimicrobial peptides that target it, remains hard to treat. Here, we report a few-shot learning pipeline integrating classification, ranking, and regression modules. Each module is trained via a few-shot learning strategy involving pre-training and multiple fine-tuning steps, incorporating similar and true data for fine-tuning, to identify potent AMPs against Acinetobacter baumannii. This pipeline effectively scans complete libraries of hexapeptides, heptapeptides, and octapeptides (encompassing tens of billions of candidates) despite the extreme scarcity of training data. Results show it discovers AMPs active against Acinetobacter baumannii and Candida albicans, with low off-target toxicity and negligible drug resistance susceptibility. Additionally, EME7(7) controls Acinetobacter baumannii pneumonia in mice without kidney injury, a contrast to the observed effects of polymyxin B. This work provides a paradigm for addressing challenges of limited data availability.

Introduction
The rapid proliferation of infections caused by antibiotic-resistant pathogens has emerged as a significant determinant impacting human health, resulting in a global estimate of 700 thousand annual fatalities1. Furthermore, this issue is projected to escalate dramatically, reaching an alarming toll of 10 million deaths per year by 20502. Among the various pathogens responsible for such infections, Gram-negative bacteria, particularly A. baumannii, pose a substantial threat by inducing severe clinical complications, accounting for approximately 67% of infected patients3. A. baumannii, a nosocomial pathogen, predominantly afflicts critically ill individuals with ventilator-associated infections, consistently exhibiting multidrug resistance due to its resilient outer membrane and the rapid mutability of its adaptive genome in response to adverse conditions and stress4–6. In addition, A. baumannii infection is associated with a range of severe clinical conditions, including ventilator-associated pneumonia, septicemia, sepsis, necrotizing pneumonia, and septic shock. In critical cases, these complications can ultimately lead to patient mortality. Hence, the World Health Organization (WHO) has classified it as the first rank of critical strains7. While polymyxin B serves as the last line of defense against A. baumannii, its use is associated with the risk of kidney injury and the gradual development of drug resistance8–10. The urgent need for new antimicrobial agents has become evident. Unfortunately, the recent decades witness only limited new antibiotics discovery and development11,12. Addressing this critical issue is essential to combat the growing threat of antibiotic-resistant infections and to safeguard public health.

Antimicrobial peptides (AMPs) are a class of small peptides typically composed of 10-50 amino acid residues, exhibiting amphipathic properties and possessing a net positive charge13. Given their membrane lytic killing mechanism, and reduced likelihood of inducing resistance, AMPs have emerged as promising alternatives to conventional antibiotics and potential candidates for next-generation antimicrobial agents14,15. Notably, a considerable number of AMPs have been designed to target pathogens such as P. aeruginosa, E. coli, and S. aureus, with thousands of variants currently available. While dozens of AMPs are currently undergoing evaluation in clinical trials16. However, the discovery of novel AMPs against A. baumannii has proven challenging through conventional screening approaches. The number of AMPs identified for A. baumannii is less than one-tenth compared to those discovered for the above pathogens. Consequently, the development of ideal AMPs specifically targeting A. baumannii with attributes such as low production costs, minimal off-target toxicity, and good solubility, remains a significant hurdle17.

Currently, the advent of machine learning (ML) techniques, including natural language processing, pattern insertion, evolutionary algorithms, binary classifiers, and deep generative models, has provided opportunities to swiftly navigate the extensive sequence space of AMPs through in silico exploration5,18–24. Notably, we have recently developed a sequential model ensemble pipeline (SMEP) that incorporates classification, ranking, and regression modules, resulting in a notable improvement in AMP mining accuracy, reaching 98.2% (54/55), validated by wet-laboratory experiments25,26. Nevertheless, it is important to acknowledge that the efficacy of these powerful ML models relies heavily on the availability of large quantities of high-quality training data (1762 positive AMPs). Unfortunately, the reported data for A. baumannii remains limited, comprising only hundreds of sequences [148 sequences in GRAMPA dataset (dataset 1)], far less than that number of AMPs against other strains. This scarcity of data poses a major challenge in the training of robust ML models tailored specifically to address the characteristics and properties of AMPs against A. baumannii.

ML methods have shown robust accuracies in predicting functions of drugs or materials with a large number of data, but they are likely to yield suboptimal predictions without further tuning or regularization27–30. To address the problem in this study, we introduce a few-shot learning-based sequential ensemble model pipeline (FSLSMEP). Particularly, we propose a pre-training and multiple fine-tuning strategy to tackle the challenge of working with a dataset of only 148 sequences (Fig. 1a). At a macroscopic level, we employed the SMEP funnel to progressively narrow down the search space. At a microscopic level, we selected the unsupervised pre-training model ESM-1b31, which was trained on a vast collection of 250 million protein sequences, owing to its exceptional performance in predicting the biological activity of sequences after fine-tuning32–34. Our approach encompassed two stages of fine-tuning: using a relatively large dataset of AMPs targeting P. aeruginosa first and utilizing a smaller dataset of AMPs specifically against A. baumannii second. By employing this strategy, we observed improved performance in the classification, ranking, and regression models compared to models trained solely on 148 sequences. Consequently, we successfully identified a series of highly potent AMPs from the comprehensive search space encompassing whole hexapeptide, heptapeptide, and octapeptide libraries (Fig. 1c).

Wet-laboratory experiments confirmed the noteworthy efficacy of the three leading AMPs (EME4(6), EME7(7), and EME11(8)) against A. baumannii and other strains, exhibiting similar performance to that of polymyxin B. Importantly, all three leading AMPs demonstrated low off-target toxicity (Fig. 1d) and did not induce drug resistance in A. baumannii even after 10 days of bacterial culture. Furthermore, when administered as an aerosolized formulation, EME7(7) exhibited comparable therapeutic efficacy to polymyxin B in a bacterial pneumonia mouse model, while mitigating the risk of kidney injury associated with the antibiotic (Fig. 1d). In addition to identifying anti-A. baumannii AMPs, this methodology was also successfully applied to discover AMPs effective against another pathogen, C. albicans. Collectively, the discovered AMPs exhibit great potential as next-generation antibiotics, and our findings underscore the viability of the FSLSMEP framework for the screening of potential AMPs across the entire combinatorial search space, even with limited data.

Results
Overview of the FSLSMEP
Within the vast search space of hexapeptides, we present our data-driven approach aimed at identifying optimal peptide candidates. Our recent study confirms the effectiveness of a machine learning pipeline consisting of classification, ranking, and regression models. Building upon this concept, we propose a plug-and-play deep learning pipeline with the same modules. FSLSMEP takes peptide sequences and physicochemical descriptors as input and generates predictions regarding their classification as an antimicrobial peptide (AMP), antimicrobial ranking, and minimum inhibitory concentration (MIC) score. However, owing to the limited availability of AMP data specifically targeting A. baumannii [only 148 sequences in GRAMPA dataset, (dataset 1)], training models with high predictive performance is challenging.

To address this data deficiency problem, we employed a few-shot learning strategy of pre-training and multiple fine-tuning (Fig. 1a,b). Notably, we selected ESM as the pre-training model due to its outstanding performance in predicting the biological properties of peptides after fine-tuning31–33. Before fine-tuning the model with AMPs targeting A. baumannii, we considered utilizing similar data. As thousands of AMPs have been reported against various strains, determining the optimal subset for the first step of model fine-tuning becomes crucial. Fortunately, we observed that an AMP often targets multiple strains. Thus, we analyzed the antibacterial properties (MICs) of the same AMP against different strains, including A. baumannii. Supplementary Fig. 1 illustrated that MICs of AMPs targeting P. aeruginosa (1186 sequences) and K. pneumoniae (317 sequences) exhibited the closest similarity to those against A. baumannii. Taking data quality and quantity into account, we selected AMPs targeting P. aeruginosa for the initial phase of fine-tuning. Subsequently, the model underwent further fine-tuning using AMPs specific to A. baumannii. This strategy was employed to train the classification, ranking, and regression models, respectively. Furthermore, based on insights from our previous work, we recognized the significance of incorporating physicochemical descriptors, especially for the ranking task. Thus, we utilized the same 676 descriptors to train a neural network, prioritizing with anti-P. aeruginosa AMPs during the first stage and fine-tuning with A. baumannii AMPs during the second stage for the ranking and regression models.

The entire pipeline commenced with a pre-filtering process known as empirical selection, aimed at eliminating the least promising peptides from the sequence library (Fig. 1a). Since AMPs typically exhibited amphipathic properties and a net positive charge, these two structural characteristics were employed as criteria for pre-filtering34. As a result, the search space was remarkably reduced, yielding a candidate pool of 3,931,718 peptides. This pool then underwent virtual screening utilizing the FSLSMEP, encompassing the classification, ranking, and regression modules, respectively. Ultimately, we identified 10 predicted leading AMPs for subsequent validation through wet-laboratory experiments.

Detailed analyses of model performance
The dataset utilized for model training was divided into two parts: positive and negative data. The positive data consisted of aligned peptide-MIC pairs obtained from a published work dubbed GRAMPA35, which incorporated open-source datasets such as APD36, DADP37, DBAASP38, DRAMP39, and YADAMP40. To create the final positive datasets, we specifically selected C-terminal amidated peptides with MICs targeting P. aeruginosa and A. baumannii, resulting in 1,186 and 148 peptide-MIC pairs, respectively. The collection of negative data portion followed the approach from our recent work25, encompassing 5,898 pairs. As previously mentioned, the FSLSMEP comprised classification, ranking, and regression modules. To ascertain the predictive capabilities of the final pipeline, we constructed various computational proximal scores tailored to each module, carefully selecting the best-performing models. More specifically, once we finished the inference pass on the validation dataset, we ran the predicted results, aligned with the ground-truth results, to an evaluation metric calculative module without relying on the wet-lab experiments. Overall, the FSLSMEP aims to generalize its findings to the entire peptide sequence library, after empirical selection, and identify potent AMPs targeting A. baumannii.

The primary objective of the classification module was to perform rough filtering by excluding a substantial number of the least possible peptides from the pool. To achieve this, we developed a binary classifier using the available dataset and removed predicted negative candidates. Given the limited data, we employed a few-shot learning strategy involving pre-training and multiple fine-tuning. Specifically, we selected ESM as the pre-training model and evaluated four approaches for fine-tuning. The first approach solely employed anti-A. baumannii AMPs, while the second approach employed anti-all_strains AMPs, that against various strains, and anti-A. baumannii AMPs, in sequence. The third approach utilized anti-all_strains AMPs, anti-P. aeruginosa AMPs, and anti-A. baumannii AMPs. The fourth approach utilized anti-P. aeruginosa and anti-A. baumannii AMPs. Fig. 2a and Supplementary Table 3 demonstrated that including anti-all_strains AMPs in the training set sharply reduced model precision. Notably, the fourth approach, involving multiple fine-tuning steps with ESM using anti-P. aeruginosa and anti-A. baumannii AMPs in sequence [ESM(-P.a.+A.b.)], exhibited the best performance across the four indices. This result evidenced the superiority of multiple fine-tuning compared with singular fine-tuning. The fourth approach was finalized to perform the classification task. As a result, 122,673 samples were retained from this initial module and proceeded to the subsequent stages.

Moving along the pipeline, we implemented a ranking module to identify potent AMP candidates from the selected positive samples. This module focused on ranking peptides based on their MIC values, providing robustness against dataset bias and noise. Particularly, we removed data of anti-all_strains AMPs from multiple fine-tuning steps as their adverse impact on model performance was evidenced in the classification module. Instead, we used the parameters from the classification model fine-tuned by anti-P. aeruginosa AMPs to initialize parameters for the pre-training model and followed the multiple fine-tuning steps. Additionally, given the superior performance of the ranking model trained with physicochemical descriptors in our previous work25, we employed the same set of 676 descriptors as features to train a multilayer perceptron (MLP) using anti-P. aeruginosa and anti-A. baumannii AMPs, in sequence. Fig. 2b and Supplementary Table 4 presented the predictive performance of different approaches following the FSLSMEP. The ESM(P.a.+A.b.)* and MLP(P.a.+A.b.) approaches outperformed the others, warranting their selection for the ranking task to identify the top-500 peptides in the pool.

In the final stage, we incorporated a regression module to predict the MIC value of a peptide, effectively concluding the pipeline. The regression model demonstrated heightened sensitivity to subtle distinctions within the optimized candidate pool, and the positive training data gathered at this stage tended to cluster in the small-MIC score range, as depicted in Supplementary Fig. 2. Remarkably, the incorporation of a regression module served as a refined filtering mechanism to identify the top-10 candidates from the top-500 candidates predicted by ranking model. To train the model, we employed the standard L2-loss and considered all approaches from the ranking module. Consistent with the previous modules, we found that multiple fine-tuning approaches outperformed singular fine-tuning approaches. As demonstrated in Fig. 2c and Supplementary Table 5, the ESM(P.a.+A.b.)* and MLP(P.a.+A.b.) approaches exhibited superior performance. Consequently, we selected these two approaches to execute the regression task, finalizing the selection of the top-10 peptides for subsequent wet-laboratory validation.

Wet-lab validation
We synthesized the top 10 peptides predicted from the entire hexapeptide library, comprising 64,000,000 sequences, by a single inference from each of the four pipelines, respectively: ESM (based on BERT)-ESM-ESM (EEE), ESM-MLP-ESM (EME), ESM-MLP-MLP (EMM), and ESM-fusion of ESM and MLP-ESM (EFE). These peptides were obtained via solid-phase synthesis, and their MICs against A. baumannii 19606 were tested. Fig. 3 and Supplementary Tables 10-13 present a comparison of the MIC values for peptides from each pipeline, in detail. The results indicated that the EEE pipeline identified one peptide with modest and three peptides with weak antimicrobial activity. Similarly, the EMM pipeline identified one modest and six weak AMPs. Notably, the EME pipeline demonstrated improved performance, as all 10 peptides exhibited antimicrobial activity, including one potent peptide with MIC of 16 µg mL-1 and three peptides with modest antimicrobial activity. Building on the superior performance of ESM(P.a.+A.b.)* in the ranking module, we attempted to fuse ESM(-P.a.+A.b.)* and MLP(P.a.+A.b.) in this module and tested the MICs of the top-10 AMPs predicted using the EFE pipeline. Unfortunately, the pipeline almost failed to pick out AMPs, with only one peptide with weak antimicrobial activity (MIC = 128 µg mL-1) being identified. In the evaluation of the four pipelines, the EME pipeline emerged as the clear frontrunner, outperforming the other three alternatives. Furthermore, to underscore the importance of the limited true data, we also examined the pipeline without the inclusion of anti-A. baumannii peptides during fine-tuning, referred to as the EME* pipeline. The results, as depicted in Fig. 3 and Supplementary Table 14, demonstrated that the EME* pipeline could only identify 5 AMPs with modest activity, where the minimal MIC recorded was 128 µg mL-1.

Besides deep learning approaches, we also compared the performance of a tree-based method, XGBoost, following the same training methodology. Specifically, we developed a XGBoost-XGBoost-LSTM (XXL) pipeline, which was directly trained on 148 anti-A. baumannii peptides, to identify the top-10 AMPs from the entire hexapeptide library (Supplementary Table 6). However, as shown in Supplementary Table 15, only 20% (2/10) of the peptides exhibited antimicrobial activity, with the most active peptide showing a MIC of 64 µg mL-1. To further leverage XGBoost’s warm-start and fine-tuning capabilities, we trained the model using anti-P. aeruginosa peptides and subsequently fine-tuned it with anti-A. baumannii peptides. Nevertheless, the model’s performance remained suboptimal (Supplementary Table 7). These findings collectively demonstrated that XGBoost was inadequate for AMP discovery in a data-limited scenario.

In addition, to systematically assess the impact of training sample size on model performance, we trained the EME pipeline using the full set of anti-P. aeruginosa peptides along with either 50 or 100 anti-A. baumannii peptides. We then evaluated the antimicrobial activity of the top-10 predicted peptides by measuring their MIC values (Supplementary Table 8). The results demonstrated a reduction in the number of anti-A. baumannii training samples significantly impaired the predictive performance of the pipeline (Fig. 3b, Supplementary Tables 16 and 17). Based on these findings, we confidently selected the EME pipeline as the optimal choice for AMP mining and subsequently fixed and adopted the associated models for subsequent tests to assess their generation capability and robustness.

Exploring AMPs in expanded peptide Libraries using FSLSMEP process
We employed the FSLSMEP process to discover potential AMPs against A. baumannii from the whole hexapeptide library, encompassing 64 million candidates. To assess the generation capability and robustness of the process (the EME pipeline), we deployed the fully automated computational framework in the exponentially growing libraries of longer peptides, including whole hepta- and octapeptides with over 25 billion candidates (Fig. 4a). Notably, we applied the process for visual screening of longer peptides directly without making any modifications or additional training. Subsequently, within each of the two libraries, we synthesized the top-15 predicted AMPs generated by the EME pipeline and measured their MIC values targeting A. baumannii 19606. The selection of focusing on hexapeptides to octapeptides for screening was guided by two primary rationales. (i) Short peptides, relative to their longer counterparts, exhibit lower synthesis costs and reduced inherent biotoxicity, collectively positioning them as candidates with enhanced translational potential. (ii) Whole library scanning of short peptides incurred substantially lower computational costs than that of longer peptides.

Fig. 4b and Supplementary Tables 18, 19 demonstrated that only one heptapeptide and one octapeptide displayed non-antimicrobial activity. In general, AMPs exhibited improved antimicrobial properties with longer sequences. Remarkably, one heptapeptide exhibited the strongest potency with a MIC of 8 µg mL-1, demonstrating highly competitive activity compared to polymyxin B (with a MIC of 4 µg mL-1 against the same strain, Table 1), which is a clinically used medication for curing A. baumannii infections9,10. Furthermore, we conducted a comparison of key molecular features, such as amino acid composition, charge, hydrophobicity, hydrophobic moment, and so on, between the top-100 predicted AMPs and the positive training dataset. As illustrated in Fig. 4c, d, and Supplementary Fig. 9, 12 the top-ranked predicted AMPs exhibited low sequence similarity with the trained AMPs while maintaining the essential physicochemical characteristics of cationic charge and amphiphilicity similar to the trained AMPs (Fig. 4e-n).

Exploring AMPs against C. albicans in the hexapeptide Library using FSLSMEP process
In addition to A. baumannii, we retrained and deployed the EME pipeline to discover AMPs targeting a kind of fungi, C. albicans (657 samples), to show the generalization ability of the approach. The selection of C. albicans was due to three reasons: (i) clinical relevance in therapeutic applications, (ii) limited availability of existing AMP data, and (iii) efficacy validation beyond bacteria. C. albicans is a significant pathogen responsible for a range of infections, which can escalate into life-threatening conditions and, in severe cases, may lead to death. We continued the pretraining and fine-tuning process to further evaluate the generative capability and robustness of the EME pipeline. Before the first fine-tuning step, we analyzed the MICs of the AMPs against P. aeruginosa and C. albicans. Supplementary Fig. 3 demonstrated that the MICs of AMPs targeting P. aeruginosa strongly correlated with those against C. albicans (R=0.6654). Hence, we first fine-tuned the ESM pre-trained model with anti-P. aeruginosa AMPs, but instead of fine-tuning with anti-A. baumannii peptides, we used the anti-C. albicans peptides to fine-tuning the pipeline (Supplementary Table 9). The updated pipeline was then deployed to mine AMPs against C. albicans from the complete heptapeptide library. As shown in Supplementary Table 20, 90% of the top 10 predicted heptapeptides (9/10) exhibited antimicrobial activity against C. albicans, including one potent peptide with a MIC of 16 µg mL-1.

In vitro evaluation of the leading peptides
We selected three peptides [EME4(6), EME7(7), and EME11(8)], representing the best-performing AMPs from the hexapeptide, heptapeptide, and octapeptide libraries, respectively, as the leading peptides for further wet-laboratory investigation. In addition to A. baumannii 19606, we included two other A. baumannii strains (17978 and XH197) to test their susceptibility to the leading peptides. As shown in Table 1, all three leading compounds, particularly EME7(7), exhibited highly competitive activity against all A. baumannii strains. Notably, EME7(7) displayed superior antimicrobial activity to polymyxin B against A. baumannii XH197, a mutant strain characterized by the loss of lipopolysaccharide (the target of polymyxin B) in the cell membrane41, with a MIC of 16 µg mL-1. Additionally, we evaluated the antibacterial activity of the three peptides. As shown in Fig. 5a and Supplementary Fig. 10, EME4(6), EME7(7), and EME11(8) reduced the viable count of A. baumannii by more than 99.5% within 20 min and 99.9% within 2 h, at concentrations of 4× MIC. Furthermore, we assessed the antimicrobial efficacy of these AMPs against other clinically relevant strains, including both Gram-positive and Gram-negative pathogens. Supplementary Table 21 showed that EME7(7) exhibited potent antimicrobial activity against S. aureus, P. aeruginosa, while displaying moderate activity against E. coli. Notably, the peptides demonstrated a half-life of approximately 3 h in whole blood, further supporting their therapeutic potential (Supplementary Fig. 4).

Minimizing off-target toxicity is a critical challenge for cationic AMPs. In our evaluation of the safety profile of the three leading AMPs, we conducted cytotoxicity and hemolysis measurements and compared them with those of the US Food and Drug Administration (FDA)-approved natural AMPs, polymyxin B and gramicidin. As depicted in Fig. 5b, c, gramicidin displayed high toxicity to both incubated human NIH3T3 cells and mice red blood cells at a concentration of 50 µg mL-1. In contrast, all three leading compounds, similar to polymyxin B, exhibited negligible cytotoxicity and hemolysis activities. We also measured the 50% inhibiting concentration (IC50, 50% cell viability) and 50% hemolytic concentration (HC50, 50% hemolysis) toxicity values of the leading AMPs (Table 1 and Supplementary Fig. 5, 6). In comparison to reported longer AMPs18, all the peptides demonstrated higher biocompatibility. Importantly, the substantial IC50 and HC50 to MIC ratios of the leading peptides, particularly EME7(7) and EME11(8), indicated their broad potential therapeutic windows. These findings underscore the promising biocompatibility of the leading AMPs, suggesting their potential as safe and effective antimicrobial agents for further clinical development.

Lysing bacterial membrane has long been considered the primary mechanism of action for most cationic AMPs13. Bacterial membranes, containing anionic lipids, lipopolysaccharides (in Gram-negative bacteria) or teichoic acids (in Gram-positive bacteria), exhibit higher electronegativity compared to those of plants and animals, rendering them more susceptible to the effects of cationic compounds. Through electrostatic interactions, cationic peptides with amphiphilic nature can bind to the bacterial membrane, resulting in membrane pore formation, leakage of cellular contents, and ultimately, bacterial cell death. Notably, the empirical selection process implemented a pre-filtering step to retain cationic and amphiphilic peptides. Thus, we speculated that the leading compounds followed the membrane lysis mechanism. To validate our hypothesis, we conducted scanning electron microscope (SEM) and transmission electron microscope (TEM) imaging of peptide-treated A. baumannii cells. The images revealed distinct membrane damage on the bacterial cell surface, providing strong evidence of the membrane lysis mechanism induced by the leading AMPs (Fig. 5d–i). In addition, Supplementary Fig. 11 showed that upregulated genes exhibited transcriptional features of bacterial cell membrane damage, including processes related to cell membrane repair (e.g., fatty acid β-oxidation via acyl-CoA dehydrogenase, protein folding chaperone activity, and chaperone-mediated protein folding) and oxidative stress responses (e.g., sequence-specific DNA binding, zinc ion binding, transcription factor binding, DNA-binding transcription factor activity, protein autophosphorylation, signal transduction via protein phosphorylation, mo-molybdenum-molybdopterin cofactor biosynthetic process, and regulation of DNA-templated transcription). These observations support the notion that the leading peptides exert their antimicrobial effects by targeting and disrupting the bacterial membrane, underscoring their potential as effective antimicrobial agents against A. baumannii.

As lysing the bacterial membrane does not involve a specific target, AMPs are considered less likely to induce bacterial resistance13. To investigate whether the leading peptides maintain this property, we conducted a resistance development study by culturing A. baumannii 17978 with sub-MIC levels of the peptides and measuring their MIC values at each round, with the clinical medication polymyxin B as a comparison. As depicted in Fig. 5j, A. baumannii did not develop resistance against the leading peptides even after 10 days. On the contrary, the bacteria developed resistance to polymyxin B after 3-4 days, resulting in a 4-fold increase in MIC growth after 10 days. Overall, the leading peptides demonstrated potent antibacterial properties against multiple A. baumannii strains, along with low off-target toxicities and a reduced tendency to promote bacterial resistance. These findings highlighted the high potential of the leading peptides in addressing the rapid emergence of antimicrobial resistance and tackling serious infections associated with A. baumannii. The ability of these peptides to circumvent resistance development further enhances their value as promising candidates for combating A. baumannii infections in a clinically challenging landscape of antimicrobial resistance.

Additionally, we further investigated the mechanism underlying the low propensity of the AMPs to induce bacterial resistance. The antimicrobial mechanism of AMPs primarily involves electrostatic adsorption to negatively charged bacterial membranes, followed by self-assembly and membrane disruption, ultimately leading to bacterial lysis. However, in low Mg2+ environments, bacteria can activate two-component regulatory systems, resulting in the phosphorylation and modification of lipopolysaccharides (LPS). This modification reduces the membrane’s negative charge, weakens AMP binding, and diminishes its bactericidal efficacy. As shown in Supplementary Table 22, supplementation with a low concentration of Mg2+ (10 μM) significantly reduced the antibacterial activity of the AMPs by 75%–94%. qPCR analysis revealed that Mg2+ treatment upregulated the expression of pmrA, pmrB, and pmrC genes by 2–3 fold. In contrast, treatment with EME7(7) did not significantly alter the expression levels of these genes (Supplementary Fig. 7). Moreover, RNA-seq analysis showed that EME7(7) had minimal impact on the expression of known antimicrobial resistance genes in A. baumannii, with only a slight downregulation observed for mdfA (Fig. 5k). Together, these findings supported the proposed mechanism for the low resistance induction potential of EME7(7).

Therapeutic efficacy in vivo
As previously mentioned, A. baumannii poses a great threat in causing serious ventilator-associated infections. To assess the therapeutic efficacy of the leading compound, we established a mice acute bacterial pneumonia model. In this model, mice were first induced with neutropenia by cyclophosphamide injection and subsequently administered with A. baumannii 17978 [1×105 colony-forming units (c.f.u.) per mouse] through intranasal administration. The treatment involved four doses of EME7(7), polymyxin B or saline delivered via a vibrating mesh nebulizer, as it is one of the most efficient devices for aerosol drug delivery. After treatment, we examined the lungs, blood, and kidneys to assess the remaining bacterial count and characterize any lung and kidney injuries (Fig. 6a). As depicted in Fig. 6b, both EME7(7) and polymyxin B treated groups demonstrated approximately 93% reduction in A. baumannii count in the lungs, with no significant difference between the two groups at 24 h after infection, as compared to the saline group. Tissue histology images of the lungs revealed that lungs treated with EME7(7) and polymyxin B exhibited only slight injury and maintained clear alveolar morphology, while the saline group showed severe damage due to A. baumannii infection (Fig. 6c). Notably, polymyxin B, considered as a human’s last line of defense42, is known to induce kidney injury after treatment. As shown in Fig. 6d, the concentrations of blood urea nitrogen (BUN) significantly increased after polymyxin B treatment, while the EME7(7) group’s BUN levels remained similar to those of the saline group. Furthermore, kidney histological analysis revealed severe glomerular damage, brush border disappearance, and cyst formation after polymyxin B treatment (Fig. 6e). While kidney in EME7(7) group remained similar to those of the saline group.

A rigorous approach was employed to further investigate the renal toxicity of the compound in vivo. Mice were intraperitoneally injected, a method known for its precision in controlling drug concentrations, with PBS, EME7(7), and polymyxin B, twice daily for three consecutive days. Following the treatment period, comprehensive examinations of both blood and kidney tissues were conducted to assess potential kidney injuries (Fig. 7a). As elucidated in Fig. 7b, c, the concentrations of BUN (upregulating ~44%) and creatinine (Cr) (upregulating ~46%) exhibited a significant increase in response to polymyxin B administration. In contrast, the BUN and Cr levels in the EME7(7) group remained comparable to those observed in the saline-treated group. Furthermore, detailed kidney histological analysis unveiled severe glomerular damage, brush border disappearance, and cyst formation in response to polymyxin B treatment (Fig. 7d). In stark contrast, the kidneys from the EME7(7) treated group closely resembled those from the saline-treated group.

These findings indicate that EME7(7) showed comparable efficacy to polymyxin B in reducing A. baumannii in the lungs while demonstrating a remarkable advantage in avoiding kidney injury. The histological images and biochemical markers support the lower toxicity profile of EME7(7) compared to polymyxin B, positioning it as a promising therapeutic option for A. baumannii infections without compromising kidney function.

In addition, we conducted molecular biology studies to investigate the host response during AMP treatment. Given the potential immunomodulatory effects of cyclophosphamide, we employed an alternative mouse pneumonia model that excluded its use (Supplementary Fig. 8). In this model, mice were intranasally infected with A. baumannii 17978 (1×105 CFU per mouse) on Day 1. Subsequently, they received three doses of treatment via a vibrating mesh nebulizer on Days 4, 5, and 6 once daily. Following treatment, we analyzed the lungs to quantify residual bacterial burden, assess tissue histopathology, and evaluate gene and protein expression changes (Supplementary Fig. 8a). As shown in Supplementary Fig. 8b, EME7(7) treatment resulted in a ~95% reduction in bacterial load in the lungs, demonstrating strong therapeutic efficacy. Histological examination revealed that lungs from EME7(7)-treated mice exhibited only mild injury and retained well-defined alveolar architecture (Supplementary Fig. 8c). Furthermore, analysis of inflammatory markers of lungs indicated significant downregulation of pro-inflammatory cytokines (IL-1β, TNF-α, IL-6, IL-2) and upregulation of anti-inflammatory cytokines (IL-4, IL-10) in the EME7(7)-treated group (Supplementary Fig. 8d, e), suggesting an immunomodulatory effect associated with bacterial clearance.

Methods
Model Development
To screen out a few most promising AMP candidates from massive candidate peptides, we adopt the strategy of coarse-to-fine screening and design a pipeline consisting of classification, ranking, and regression. In previous research, traditional machine learning solutions as well as deep learning methods based on convolutional neural networks (CNNs) or RNN (such as BiLSTM) have been attempted for AMP identification-related tasks7.

However, it is too difficult to start training models from scratch for recognizing anti-A. baumannii peptides when only a small amount of annotated data related to A. baumannii is available. To achieve this goal, we are based on one large-scale pre-trained model: ESM-1b. Considering that protein pre-training models have shown excellent performance in various downstream tasks, we believe that the hidden information in sequence can be very effective in building the structural recognition of effective antibiotic peptide sequences. This information can also be transferred to the antimicrobial peptide recognition task through fine-tuning. In our pipeline, we choose one of the best-performing pre-training models in this field, ESM-1b11, a protein pre-training model that uses the Roberta architecture and is pre-trained on Uniref5013 database, to perform fine-tuning for downstream tasks. Furthermore, considering that there is only a tiny amount of labeled data for anti-A. baumannii peptide, every module (classification, ranking, and regression) in our pipeline adopts a two-step fine-tuning strategy. The first step uses representative Gram-negative bacteria P. aeruginosa as the training data for fine-tuning. We hope that this step can learn domain knowledge relevant to Gram-negative bacteria. In the second step of fine-tuning, we use sequences related to A. baumannii, transferring the model’s domain knowledge to the target domain.

Our pipeline is illustrated in Fig. 1b, providing an overview of its components. The following sections provide detailed descriptions of each module in the pipeline. (For each model, epochs: 40, early stopping: 5 epochs, learning rate: 1e-4, random seeds: 42, batch_size: 32).

The Classification Model. As the first step of our pipeline for identifying antimicrobial peptides, the classification task aims to perform coarse-grained filtering, significantly reducing the number of sequences in our vast candidate library to manageable levels. For the classification model, we fine-tune the protein pre-training model ESM-1b11 with a frozen bottom layer up to layer 32, while only adjusting the parameters of the last Bert layer (layer 33). Specifically, the final representation of a sequence is obtained from the [CLS] token of ESM-1b’s last layer, which is then fed into a classification head composed of multiple MLP layers. Adam optimizer is chosen with a learning rate of 1e-4, more details can be found in our code. For this binary classification task, we employ the binary-cross-entropy loss function L_CE:

L_CE = - \sum_{i=1}^{N} [y_i log(p_i) + (1-y_i) log(1-p_i)] (1)

where p_i represents the categorical probability output of the classification model with the input x, and y_i is the corresponding ground truth class label.

The Ranking Model. The ranking task plays a crucial role in the entire process; it constitutes the core of the entire pipeline, handling the most critical tasks. While the classification model aids in the initial screening phase by filtering out approximately 80% of candidate sequences that lack antimicrobial potential, the subsequent ranking model takes it a step further. Its purpose is to precisely select a few hundred peptides with the highest antimicrobial performance, discarding nearly 99.99% of the data, thus alleviating the predictive burden of the final regression task.

In terms of technical details, our ranking model is an improvement on LambdaRank. We employ an incremental ranking strategy, wherein each iteration, we randomly select a batch of samples from the dataset and optimize the model parameters by maximizing their NDCG@k (Normalized Discounted Cumulative Gain).

NDCG@k = DCG@k / IDCG@k (2)

DCG@k = \sum_{i=1}^{k} [2^{rel_i} - 1] / log_2(i+1) (3)

IDCG@k = \sum_{i=1}^{k} [2^{max(rel_i, 0)} - 1] / log_2(i+1) (4)

Here, rel_i represents the relevance score of the item at rank i. DCG@k is the Discounted Cumulative Gain at position k, and IDCG@k is the Ideal Discounted Cumulative Gain at position k.

For example, during a specific iteration, K sequences were randomly sampled from the dataset. Subsequently, these sequences were ranked based on their Minimum Inhibitory Concentration (MIC) values, with lower MIC values corresponding to higher rankings. In cases where negative samples shared identical MIC values, they were assigned the same rank. This procedure allowed the derivation of relative scores rel_i for each sequence.

In the context of model selection for the ranking task, we leverage protein feature descriptors embodying the physicochemical attributes of peptides as the model input. The structural framework of the ranking model is established through the adoption of a multi-layer perceptron (MLP). This strategic decision is substantiated by two primary considerations: firstly, antecedent investigations underscore the utility of traditional structured data modeling in facilitating the discernment of the structural nuances inherent in peptides. Secondly, given the computational intricacies associated with ranking models, the employment of a comparatively parsimonious model structure is conducive to heightened inference speed, thereby facilitating the handling of voluminous datasets.

Given the presence of a limited sample size in our overall task, employing traditional machine learning models is not conducive to multi-step fine-tuning. Therefore, we opted for a straightforward yet efficient neural network model as the foundational structure for the ranking model. The ultimate results from our wet lab experiments also indicate that employing such a trained ranking model in the pipeline leads to antimicrobial peptides with overall superior antibacterial performance against Acinetobacter baumannii.

The Regression Model. After the ranking phase, several hundred candidate peptides remain. To refine our selection and identify the top ten peptides with the most potent antibacterial properties, we train a regression model that can predict minimum inhibitory concentration (MIC) values accurately. As the final stage in our pipeline, the model must minimize prediction bias in MIC. Through experimental comparison, we opt for the same fine-tuning strategy applied in the classification stage, using ESM-1b pre-trained model, freezing the parameters in the first 32 layers, and adjusting only the last layer. The final representation of a sequence is obtained from the [CLS] token of ESM-1b’s last layer, which is then input into a regression head composed of multiple MLP layers. Our rationale behind choosing this model architecture is that it achieves the lowest K-MSE score and delivers the best wet-lab results among all options explored. Details regarding hyperparameters can be found in our code. The employed loss function is defined as:

f*_regression = argmin_w L_L2 (f_regression(x), y) (5)

where y denotes the label corresponding to input x and w is the intermediate parameters of the regressing model f_regression.

Materials
Peptides with purity >95% were custom synthesized by Shanghai Toppeptide. Ethanol and dimethyl sulfoxide (DMSO) were obtained from Shanghai Titan Scientific (Greagent). Polymyxin B was obtained from Aladdin. Gramicidin was purchased from RuiBiotech. Cell Counting Kit-8 (CCK-8) was obtained from Sigma-Aldrich. Milli-Q water was obtained using a Millipore Milli-Q Academic Water Purification System.

Bacteria
Bacterial strains utilized in this work are listed in Supplementary Table 23. Briefly, A. baumannii 19606 and 17978, S. aureus 6538, P. aeruginosa 27023, E. coli 25922, K. pneumoniae 13883, C. albicans 2.178 were purchased from Guangzhou institute of microbiology. A. baumannii XH197 was the laboratory-evolved mutant of 1797839.

Mice
All animal studies were performed in compliance with the guide for the care and use of laboratory animals and adopted the protocol that has been approved by the Medical Experimental Animal Care Commission of Zhejiang University (IACUC-24-S104). Mice studies were carried out with 3-week-old male Institut for Cancer Research (ICR) mice. They were cultured in an environment with 12h/12h dark/light cycle, humidity of 50% and temperature of 27°C.

Nomenclature rules of AMP
The three capital letters successively represent the models used in the classification, ranking, and regression tasks. For instance, EME represents the pipeline using ESM classification model, MLP ranking model and ESM regression model. 7 represents the 7-th ranked peptide by the pipeline, while (7) represents the heptapeptide. So, EME7(7) represents the 7-th ranked AMP predicted by ESM-MLP-ESM pipeline from the whole heptapeptide library.

Antimicrobial activity assays
Briefly, bacteria were cultured overnight in LB at 37°C, 0.8 g to log phase. Cells were then collected, washed twice, and suspended in the assay medium at a density of 5×105 CFU/mL. The peptides were dissolved into PBS with an initial concentration of 1024 μg/mL and followed by 2-fold serial dilutions. Then, 50 μL of each peptide solution was added to 50 μL of the bacteria suspension in a 96-well plate. Plates were incubated at 37°C for 12 h. The MIC was determined as the minimal peptide concentration at which no visible bacterial growth was observed.

Analysis of cytotoxicity
For the cytotoxicity test, 1×104 NIH3T3 cells were seeded into each well of the 96-well plates and cultured at 37°C overnight. Then the culture medium was removed and the peptides at the desired concentrations in the media were added to each well (n=5). The cells were cultured for another 24 h, followed by replacing the media with 10% CCK-8 containing fresh DMEM and another 2 h incubation. The absorbance of the solution was measured at 450 nm by a microplate reader. Cell viability was calculated using the following equation.

%cell viability = (absorbance_sample - absorbance_blank) / (absorbance_negative - absorbance_blank) × 100 (6)

Hemolysis test
Red blood cells (RBCs) from mice were used for this test. Hemolytic activity of the peptides was measured by first mixing the RBCs with the peptide solutions (n=5). The mixtures were then incubated at 37°C for 1 h. RBCs in PBS were used as the negative control, and 0.5% (w/v) Triton X-100 treated RBCs were used as the positive control. Afterward, the samples were centrifuged at 1500g for 8 minutes. The absorbance of the supernatant was measured at 570 nm by a microplate reader. The percentage of hemolysis was calculated using the following equation.

%hemolysis = (absorbance_sample - absorbance_negative) / (absorbance_positive - absorbance_negative) × 100 (7)

Drug resistance Assay
Resistance development assays were performed following a previous report25. The initial MIC values (MIC0) for EME4(6), EME7(7), EME11(8), and polymyxin B were listed in Table 1 (against A. baumannii 17978). Drug resistance of A. baumannii 17978 was tested by treating the bacteria repeatedly with peptides and penicillin at the sub-MIC concentration (1/2 MIC). MIC values of the bacteria after every day were tested using the previously described method.

Peptide Sequence Analysis
The physicochemical properties including hydrophobic moment, aliphatic index, aromaticity, instability index, Eisenberg hydrophobicity, isoelectric point, hydrophobic ratio, charge density, and charge were estimated using the Global Analysis method in modLAMP. Protparam tool from Expasy (https://web.expasy.org/protparam) was used to estimate the grand average of hydropathicity (GRAVY) score. Needleman–Wunsch algorithm in the function “needleall” and Smith-Waterman algorithm in the function “glsearch36” from the Jemboss software package (version 1.5) were used to estimate the similarity between our top AMPs and AMPs in the training set.

In vivo experiments
(i) 15 mice were divided randomly into 3 groups. To establish the acute pneumonia model, mice were injected intraperitoneally with cyclophosphamide 3-d and 1-d before the bacterial administration at the concentrations of 150 mg/kg and 100 mg/kg, respectively, to induce neutropenia. Mice were anesthetized by isoflurane and infected with A. baumannii 17978 (1×105 CFU in 100 μL PBS) by tracheal instillation via a catheter. For the treatments, mice were administered with EME7(7), polymyxin B, and PBS control, at a concentration of 0.5 mg/mL in PBS via a vibrating mesh nebulizer connected to a whole-body chamber. The dosage was 2.5 mg peptides for a group of 5 animals per chamber. Mice were sacrificed 24-h post-treatment, the lung, kidney tissues and serum were collected for analysis. The CFU of A. baumannii per lung was calculated by plating dilutions (0.25 g tissue in 10 mL PBS) of lung homogenates on agar plates and counting colonies. The serum was used for BUN measurement.

(ii) 15 mice were divided randomly into three groups. To establish the in vivo toxicity study, mice were injected intraperitoneally with EME7(7), polymyxin B, and PBS at concentrations of 10 mg/kg twice a day for three days. They were sacrificed 24 h after the last drug administration. Kidney tissues were collected for further analysis. The serum was collected for BUN and Cr measurements.

(iii) 15 mice were divided randomly into 3 groups. To establish the pneumonia model, mice were anesthetized by isoflurane and infected with A. baumannii 17978 (1×105 CFU in 100 μL PBS) on day 1 by tracheal instillation via a catheter. On days 4, 5, and 6, mice were administered EME7(7), polymyxin B, and PBS once daily, at a concentration of 0.5 mg/mL in PBS via a vibrating mesh nebulizer connected to a whole-body chamber. The dosage was 2.5 mg peptides for a group of five animals per chamber. Mice were sacrificed on day 7, and the lungs were collected for analysis. The CFU of A. baumannii per lung was calculated by plating dilutions (0.25 g tissue in 10 mL PBS) of lung homogenates on agar plates and counting colonies. The lungs were collected for RNA-seq and western blotting analysis.

Statistical analysis
The results were expressed as the mean ± standard deviation (SD). The statistical differences were analyzed by one-way ANOVA.

Peer review rounds
[{"round": 1, "reviewer_comments": {"reviewer_1": "The revision addresses most of the prior concerns; no critical point was entirely ignored. Remaining partial gaps are mainly the following:\n1. Incremental methodological novelty – still borderline.\n2. Statistical robustness of evaluation – test-set remains very small; cross-validation or additional external datasets would help strengthen the authors’ claims.\n3. In vivo efficacy magnitude – one-log CFU drop is modest despite explanation of delivered dose.", "reviewer_3": "I appreciate the additional work that went into addressing my prior critiques. I have a few remaining questions and comments.\n1. From Comment 1 - I think that a tSNE or UMAP representation of the peptide space explored in (a) the pre-trained ESM-1b model; (b) the 3,930,000-peptide training dataset; (c) the 1,186 anti-Pa peptide training dataset; (d) the 148 anti-Ab peptide training dataset; and (e) the peptide set that was tested in vitro should be included in the paper. A visual representation of the relative similarities of these peptide sets will help readers understand what the model was trained on and how it generalized.\n2. From Comment 4 - In the manuscript, the authors should more thoroughly discuss how EME performed relative to EEE, EMM, and EFE. They should also provide some hypotheses regarding their relative in silico performance, as they have done in their response to the original Comment 4.\n3. Figure 5k. RNA sequencing is a common method to generate hypotheses regarding the mechanisms of action of novel molecules (including AMPs). The authors did not robustly explore potential mechanistic insight that could be gleaned from this RNA sequencing experiment. How do EME7(7)-induced transcriptional changes suggest that this peptide is functioning in the cell? Are there transcriptional signatures that are consistent with membrane disruption, for example? This needs much elaboration in the paper.\n4. Supplementary Table 21. The authors state \"Supplementary Table 21 demonstrated that the AMPs exhibited potent antimicrobial activity against S. aureus 6538, P. aeruginosa 27023, and E. coli 25922, while demonstrating moderate activity against K. pneumoniae 13883.\" However, this is an overly positive assessment based on the data shown in this table. MICs of 8 ug/ml are a reasonable starting point, at best. 16 ug/ml - 32 ug/ml is modest. Any MIC values into the hundreds of ug/ml are poor and would not be suitable for further consideration.", "reviewer_5": "The authors have proposed the SMEP pipeline for antimicrobial classification and MIC regression in 2023 and have now extended it to FSLSMEP, incorporating a few-shot learning framework. The work addresses the key challenge of data scarcity (only 148 known anti-A. baumannii AMPs) by leveraging a few-shot learning strategy: pre-training on a large corpus via the ESM model, followed by a two-stage fine-tuning process on a broader but related dataset (1,186 anti-P. aeruginosa AMPs) and finally on the limited A. baumannii dataset. The pipeline systematically filters a massive virtual library of 64 million peptides through empirical selection, classification, ranking, and regression modules, yielding a top-10 candidate list for wet-lab synthesis and validation.\nBelow are my detailed comments regarding the authors’ response to Reviewer #2, along with additional concerns:\nResponse to Reviewer #2\nQ2.1.\nThe authors’ reply reiterates the filtering pipeline but compares the entire GRAMPA corpus to external databases rather than focusing on the A. baumannii subset. The reported numbers remain inconsistent. Please reconcile these figures using updated and subset-specific sources to ensure clarity and reproducibility.\nQ2.2.\nThis issue is addressed, but the manuscript would be strengthened by discussing strategies to mitigate heavy computational costs. For example, pre-filtering the full sequence space with clustering approaches such as Linclust (https://www.nature.com/articles/s41467-018-04964-5) could improve efficiency. Without such pre-filtering, the FSLSMEP paradigm may struggle when scaling to longer AMPs or broader modification spaces—areas where generative models typically excel.\nQ2.3.\nThe core evaluation issues remain unresolved. The classifier is still evaluated on ~29 positives within a highly imbalanced dataset, using single-split Recall/Precision/F1 metrics without confidence intervals, repeated splits, or power analysis—this yields essentially noisy estimates. Limited data is understandable, but this is precisely why robust resampling strategies (e.g., repeated k-fold CV or bootstrapping) are necessary.\nAdditionally, the “Top-k MSE” metric is methodologically flawed. As currently described, it selects the ground-truth top-k peptides and computes the MSE over them. This introduces label leakage and only answers “how well do we fit already-known good samples?” rather than “how good are the sequences the model recommends for testing?” For drug discovery, the latter is the critical question. Instead, the top-k predictions from the test set should be evaluated against the ground-truth top-k. Please justify or revise the “Top-k MSE” approach accordingly, as the current formulation risks inflating performance estimates.\nA re-evaluation with proper statistics is needed, including repeated k-fold CV on the entire pipeline, especially in the absence of a dedicated validation set.\nQ2.4.\nThe authors have clearly explained why they did not adjust the MIC threshold for positive/negative labeling. This rationale is acceptable.\nQ2.5.\nThe reviewer suggested exploring correlations between experimentally tested peptide properties (e.g., MW, net charge) and antimicrobial activity, rather than merely listing descriptors in Supplementary Table 2. A targeted analysis of these correlations would better reveal potential structure-activity relationships.\nQ2.6.\nThe authors provided predicted vs. experimental MIC values (Supplementary Tables 10–20), which show underfitting and high bias. While this is understandable given the limited training data, and some peptides did show acceptable MICs, please address the concerns raised in Q2.3 to avoid over-optimistic estimation of model performance.\nQ2.7.\nThe ablation issue remains insufficiently addressed. Although prior work included wet-lab ablation studies, here an in silico ablation on the test set with the comprehensive metrics outlined in Q2.3 would strengthen the manuscript. Since FSLSMEP replaces the previous ML backbone with ESM, the noise characteristics and learned features differ; therefore, results cannot simply be assumed to transfer.\nQ2.8.\nThe authors used Needleman–Wunsch global alignment to report that most predicted AMPs share <55% identity with the positive training set. However, since the training peptides range from 5–80 aa while predictions are only 6–8 aa, global alignment may artificially penalize shorter sequences. Please provide the length distributions of query and training peptides and discuss whether global alignment is appropriate. For example, RWWKIW and RWWKIWXXXXXXXX share only 42.86% identity under Needleman–Wunsch but 100% under Smith–Waterman.\nOther concerns from Reviewer #2 are adequately addressed.\nAdditional Major Concerns\nComparison with Generative Models\nThere are now numerous generative models for AMP design, including diffusion-based (https://www.science.org/doi/10.1126/sciadv.adp7171) and autoregressive models (https://doi.org/10.1038/s41467-023-36994-z, https://www.nature.com/articles/s41467-024-51933-2). In contrast, FSLSMEP relies on exhaustive screening with a relatively simple pipeline. The authors should discuss the trade-offs between brute-force screening and generative approaches, highlighting when FSLSMEP is preferable and where it may fall short.\nModel Choice\nFacebook Research has released the ESM-2 suite (https://www.science.org/doi/10.1126/science.ade2574), which reportedly outperforms ESM-1. Please clarify why ESM-1 was chosen as the backbone despite the availability of an updated model.\nDataset Provenance\nGRAMPA is a classic MIC dataset primarily designed for E. coli. However, DBAASP lists 939 A. baumannii entries, which reduce to 346 after filtering (e.g., amidation, canonical amino acids), nearly doubling the 148 sequences used here. While some reduction due to clustering (CD-HIT, mmseq2) is expected, the manuscript lacks a clear explanation of data processing steps. Please detail how the final A. baumannii dataset was derived.\nComparative Baselines\nThe only non-deep models compared are those from the original SMEP pipeline, which is not a fair benchmark against an ESM-based backbone. Prior studies (https://doi.org/10.1186/s12915-025-02253-4, Table 1) show significant MCC differences between ML and DL models. Why not train tree-based models (e.g., XGBoost, RF) directly on ESM embeddings for a more rigorous baseline?\nMinor Issues\nLine 93: Instead of stating ML models “always” fail on limited data, use softer phrasing such as “they are likely to yield suboptimal predictions without further tuning or regularization.”\nLine 193–195: If empirical filtering discards a large portion of chemical space upfront, it is misleading to claim FSLSMEP “generalizes to the entire peptide library.” Please revise this claim.\nLine 244: The manuscript states that the ranking module selects the top 500 peptides, then the regression module refines the top 10—yet it also says the regression module “replaces” the ranking model. These statements contradict each other and need clarification.\nFigure 1B/C: Consider representing the ESM module with multiple Transformer blocks rather than simple FCLs. While the classification head may be an MLP, the focus should reflect ESM’s Transformer architecture.\nTraining Details: Please include essential details (epochs, early stopping, module sequencing, random seeds) in the Methods rather than hiding them in the Supplementary file.\nDescriptors Section: The detailed descriptor listing in the Supplementary is not informative as-is. Consider summarizing key features instead of pasting raw code output.\nCode Availability: Please provide a GitHub repository link rather than a static attachment. This allows readers to raise issues, reproduce results, and contribute improvements.\nOverall Recommendation\nThis manuscript has potential for publication in Nature Communications given the valid wet-lab results and a sound conceptual framework. However, the current version suffers from critical statistical and methodological weaknesses in evaluation, incomplete data provenance, and insufficient discussion of alternative approaches. Addressing the issues above —particularly proper model evaluation (Q2.3), dataset clarification, and a more rigorous comparative analysis—will significantly improve the manuscript’s robustness and reproducibility.", "reviewer_6": "Huang et al. reported finely tuned deep learning models for the discovery of AMPs (specifically, hexapeptides, heptapetides, and octapeptides) targeting specific bacterial strains and fungal strains. This work inherited the previously reported ‘classification-ranking-regression’ pipeline and tuned it with transfer learning techniques. In the revised manuscript, I believe the majority of the comments from Reviewer 4 (23 in total) has been addressed and the clarity of the content enhanced, with three remaining concerns from me:\n1. The authors pointed out the AMPs exhibited a half-life of approximately 3 hours in whole blood. To achieve a satisfying antibacterial effect, what is the minimal time required for administration of these peptides? Is the half-lives (stability) concentration dependent?\nI see the in vitro antibacterial evaluation of EME4(6), EME7(7), EME11(8) at 4 times MIC concentrations and it may need more than 3 hours for EME7(7). I think this factor inhibited further translational applications.\n2. The comment #18 from Reviewer 4 indicated “only the peptide was injected in Figure 7”, I think it means ‘Drugs’ could be more clearly replaced with ‘EME7(7) or polymyxin B’, because we don`t know whether the AMP could be called a drug.\n3. While I appreciate the acute bacterial pneumonia mice model, a systematic infection like this (in comparison to the local infections of abdominal models) required compromised immune system in the animal, thus the cyclophosphamide injection. Could the authors provide evidence that neutropenia and other immunocompromised symptoms did happen in the mice?"}, "author_response": "[ANSWER] – We respect the reviewer’s perspective. This work seeks to develop a few-shot learning methodology to enable reliable predictive modeling under real-world data scarcity, with a focus on translational utility. We proposed a strategy involving pre-training followed by multiple fine-tuning steps, which allowed for training robust models despite limited data. This strategy was applied to train classification, ranking, and regression models, which were then integrated into the EME pipeline for AMP discovery. Notably, the proposed methodology incorporates the authors’ original biological insights and has successfully enabled AMP discovery in two distinct real-world scenarios, with success rates of 91.1% (41/45) and 90% (9/10), respectively.\n[ANSWER] – Thanks for comment. As suggested, we have re-evaluated the metrics of the classification, ranking, and regression models using 10-fold cross-validation (Supplementary Table 3-5). These results demonstrated that the models exhibited robust and high predictive performance. In addition, we used new data to redraw the Fig.2.\n[ANSWER] – In animal studies, a head-to-head comparison was conducted between EME7(7) and the clinically used polymyxin B, evaluating their in vivo bactericidal efficacy. The results demonstrated that the two agents exhibited comparable therapeutic effectiveness.\n[ANSWER] – Thanks for comment. As suggested, we have generated t-SNE representations of 1,186 anti-P. aeruginosa peptides, 148 anti-A. baumannii peptides, and 45 predicted peptides. The result revealed that the predicted peptides were distinct from those of both anti-P. aeruginosa and anti-A. baumannii peptides, while the embeddings of predicted peptides were not clustered with that of anti-A. baumannii peptides. This result demonstrated that the model generalized novel anti-A. baumannii peptides through learning from both anti-P. aeruginosa and anti-A. baumannii sequences.\nRegarding the peptide spaces explored in (a) the pre-trained ESM-1b model and (b) the 3,930,000-peptide set: The pre-trained ESM-1b model contained no peptides. Additionally, the 3,930,000 peptides did not constitute a training dataset; rather, they were selected via an empirical selection module from the whole hexapeptide library (640 million sequences). Accordingly, we did not generate t-SNE representations for (a) and (b).\n[ANSWER] – Thank you for the suggestion. As suggested, we have added relevant content to the Discussion section.\n[ANSWER] – Thanks for comment. The key transcriptional signatures of physical damage to the cell membrane were the upregulation of genes involved in cell membrane repair and oxidative stress-related pathways. As suggested, we re-evaluated the result of RNA sequencing. Supp. Fig. 10 showed the upregulated gene pathway enrichment of A. baumannii. These upregulated genes exhibited transcriptional features of bacterial cell membrane damage, including processes related to cell membrane repair (e.g., fatty acid β-oxidation via acyl-CoA dehydrogenase, protein folding chaperone activity, and chaperone-mediated protein folding) and oxidative stress responses (e.g., sequence-specific DNA binding, zinc ion binding, transcription factor binding, DNA-binding transcription factor activity, protein autophosphorylation, signal transduction via protein phosphorylation, mo-molybdenum-molybdopterin cofactor biosynthetic process, and regulation of DNA-templated transcription). These results substantiated the bactericidal mechanism of AMPs being cell membrane lysis. In addition, we have incorporated corresponding revisions into the Results section of the Manuscript.\n[ANSWER] –As suggested, we have revised the corresponding content in the Results section.\n[ANSWER] – As suggested, we have uploaded the codebase on GitHub. (https://github.com/AI-X-explorers/anti_bau)\n[ANSWER] – We appreciate the reviewer's positive comment.\n[ANSWER] – As suggested, we have checked and revised the “requirement.txt” on GitHub (https://github.com/AI-X-explorers/anti_bau).\n[ANSWER] – As suggested, we have added scripts for obtaining esm_embedding and structured data, along with accompanying documentation on GitHub (https://github.com/AI-X-explorers/anti_bau).\n[ANSWER] – we have added corresponding information in pipeline.sh on GitHub (https://github.com/AI-X-explorers/anti_bau).\n[ANSWER] – Thanks for comment. We collected anti-A. baumannii AMPs from the GRAMPA dataset, last updated in 2019. To ensure clarity and reproducibility of our work, we are able to provide the GRAMPA dataset; thus, the reviewer and other researchers can follow our screening strategy to obtain the 148 sequences. Additionally, we will upload these 148 sequences in the supplementary materials to further confirm the reproducibility of our works.\nWe acknowledge that the 148 data points, collected from GRAMPA in 2019, do not represent all anti-A. baumannii AMPs; the currently reported volume of such data may be twice or even three times this number. Nonetheless, it is noteworthy that we successfully trained a high-performance predictive model using only these 148 data points, with wet-lab experiments confirming the therapeutic potential of the identified AMPs. This work thus achieves proof-of-concept for training high-performance predictive models under few-sample conditions. To avoid potential misinterpretation by readers, we have accordingly used the phrasing \"hundreds of sequences, 148 sequences in GRAMPA dataset\" instead of \"148 sequences\" in the manuscript to provide a generalized description.\n[ANSWER] – Thanks for comment. Constrained by computational power, whole-library scanning approaches are challenging to implement in longer peptide sequence spaces. To address this issue, an AMP generation module could be incorporated prior to FSLSMEP. This module generates manageable-sized candidate libraries of AMPs—such as one-billion-scale peptides—followed by whole-library screening. As suggested, we have added relevant content to the Discussion section.\n[ANSWER] – Thanks for comment. As suggested, we have re-evaluated the metrics of the classification, ranking, and regression models using 10-fold cross-validation (Supplementary Table 3-5). In addition, we used new data to redraw the Fig.2. These results demonstrated that the models exhibited robust and high predictive performance. In the ranking task, we have used \"top-k prediction\" for model evaluation. Accordingly, we continue to employ \"top-k MSE\" for evaluation in the regression task. We acknowledge the reviewer's concern regarding \"how well we fit already known high-quality samples.\" To address this, in the \"top-k MSE\" prediction for the regression task, we did not directly compare the MSE of the top-k samples in the ground truth. Instead, we compared the MSE between the model-predicted top-k samples and their true values, which we believe addresses the reviewer's question regarding \"how good are the sequences the model recommends for testing?\"\n[ANSWER] – We appreciate the reviewer's positive comment.\n[ANSWER] – Thanks for comment. From 676 features, we selected 123 features that are most likely to influence antimicrobial activity (including hydrophobicity, charge, polarity, and others). We further calculated the information gains of these 123 features with respect to the antimicrobial activity of AMPs. Supplemental Fig. 13 and Supplemental Table 2 indicated that Polarizability and Secondary structure of peptides exert a substantial influence on their antimicrobial activity.\n[ANSWER] – We appreciate the reviewer's positive comment.\n[ANSWER] – Thanks for comment. In our previous work, we proposed a three-step strategy (a classification-ranking-regression pipeline) to address the challenge of large-scale AMP screening. While, in theory, a highly accurate regression model alone could accomplish this task, careful investigation of AMP datasets revealed that training such a regression model is nearly impossible. This is because datasets typically only record highly active AMPs (MIC < 20 μM, Supplementary Fig. 2). Understandably, as only the discovery of highly active AMPs is publication-worthy. However, for training regression models, this introduces substantial data bias, preventing accurate prediction of MIC values for moderately or lowly active AMPs. This bias is particularly problematic in large-scale screening tasks, where candidate pools contain AMPs across the activity spectrum (high, moderate, and low), often leading to failed predictions. Notably, regression models trained on such data do exhibit more accurate predictions for highly active AMPs. Thus, we hypothesized that restricting regression models to datasets containing only highly active AMPs would significantly enhance their predictive value.\nTo achieve this, we first employed classification and ranking models for pre-screening of the whole peptide library to enrich the pool of highly active AMPs before regression-based prediction. In our previous work, wet-lab ablation studies detailed the consequences of removing each of the three modules, demonstrating that each is critical. It is this combinatorial predictive model with progressively refined granularity that achieved an AMP screening success rate of up to 98.2%.\nThe current work faces the same data bias (Supplementary Fig. 2). Thus, we directly adopted the three-module strategy. Additionally, we argue that dry-lab ablation studies would likely yield unconvincing results, as regression models—limited by small training datasets—produce inaccurate predictions. Wet-lab ablation studies, in our view, may not be necessary: the three-step strategy addresses inherent data bias, an issue that cannot be mitigated at the model level. Our development of this strategy aims to provide guidance for addressing such data bias (which arises across disciplines), ensuring that new knowledge is passed to subsequent researchers.\nIn summary, we suggest that the three-step strategy was thoroughly evaluated in our previous work, and redundant investigation in the current study may not be necessary.\n[ANSWER] – Thanks for comment. As recommended, we have calculated the identity of the predicted AMPs (the same sequences as those in Fig. 4d) using the Smith-Waterman algorithm. As shown in Supplementary Fig. 9, most of the predicted AMPs exhibit less than 75% similarity to previously reported AMPs in the positive training dataset.\n[ANSWER] – Thanks for comment. Screening models and generative models each possess distinct strengths and limitations. A key strength of screening models—particularly the three-step pipeline screening ensemble model reported herein—is their high success rates. For example, in this work and our previously reported study, the success rates of AMP screening via these models reached 91.1% and 98.2%, respectively. However, their limitations are notable: the whole-library screening paradigm demands substantial computational resources, restricting us to full-library screening of 8-mer peptides and precluding screening of longer AMPs. Thus, screening models are better suited for selecting AMPs from relatively limited candidate libraries.\nStandalone generative models are typically not readily applicable to direct AMP design. Instead, a screening module—such as molecular dynamics simulations or other screening methods—must often be incorporated post-sequence generation to enhance their efficacy in AMP design. In this way, the reported works exhibit high success rates in AMP design (62.5%, 96% and 90%) and offer an almost unlimited exploration space, far exceeding that of screening models. We posit that integrating the strengths of both generation and screening models could further enhance the potential of artificial intelligence in AMP discovery. For instance, leveraging generative models to construct a manageable-sized (ten-billion-scale) AMP library, followed by screening via screening models, would merge their respective advantages while mitigating limitations.\n[ANSWER] – Thanks for comment. The models in this work were trained and finalized in 2021, whereas ESM-2 was reported in 2023. In 2021, ESM-1 was the available backbone for model construction. By 2023, this work was nearly complete. Given that our objective was to train highly predictive models for AMP discovery under limited data scenarios—and that this goal had already been achieved by that time—we did not reconstruct the models using ESM-2.\n[ANSWER] – Thanks for comment. Regarding this question, we have provided a comprehensive explanation in Q2.1.\n[ANSWER] – Thanks for comment. As suggested, we trained the Random Forest-XGBoost-LSTM pipeline using ESM embeddings as input (Supplementary Table 25). The results showed that the performance of the Random Forest-XGBoost-LSTM pipeline outperformed that of the XXL pipeline (Supplementary Table 6) but was inferior to that of the EME pipeline (Supplementary Tables 3–5). These findings confirm the robust, high predictive performance of the EME pipeline.\n[ANSWER] –As suggested, we have revised the manuscript.\n[ANSWER] –As suggested, we have revised the manuscript.\n[ANSWER] –To avoid confusion, we have revised the manuscript.\n[ANSWER] – As suggested, we have redrawn the Fig. 1bc.\n[ANSWER] – As suggested, we have incorporated the \"model development\" section into the Methods section.\n[ANSWER] – As suggested, we calculate the information gain of potential 123 features on the antimicrobial activity of AMPs. Supplemental Fig. 13 and Supplemental Table 2 indicated that Polarizability and Secondary structure of peptides exert a substantial influence on their antimicrobial activity.\n[ANSWER] – As suggested, we have uploaded the codebase to GitHub (https://github.com/AI-X-explorers/anti_bau).\n[ANSWER] – Thanks for comment. In clinical or experimental settings, peptide usage concentrations typically operate within the first-order kinetic regime (i.e., below the saturation threshold of enzymes or transporters). Within this regime, half-life is primarily determined by intrinsic clearance rates—such as enzymatic degradation efficiency and renal excretion capacity—and is independent of concentration. Nonlinear clearance, which causes half-life to vary with concentration, may only occur under extremely high concentrations (e.g., experimental conditions involving supraphysiological doses).\nIn addition, we evaluated the bactericidal efficacy of AMPs over a shorter time course. As shown in Figure 5, at 20 minutes, EME4(6), EME7(7), and EME11(8) achieved 99.99%, 99.60%, and 99.97% bacterial killing, respectively. Their bactericidal action time is substantially shorter than the half-life of the peptides in whole blood (3 hours). This finding further supports the clinical application potential of these antimicrobial peptides.\n[ANSWER] – As suggested, we have revised the Fig. 7a.\n[ANSWER] – A classic approach to inducing mouse neutropenia involves intraperitoneal administration of cyclophosphamide at 150 mg/kg 3 days prior and 100 mg/kg 1 day prior. This method is widely employed in recent studies (Cell, 2024, 187, 5453; Nat. Biomed. Eng. 2023, 7, 797; Nat. Biomed. Eng. 2022, 6, 67; Cell Host & Microbe, 2023, 31, 1260). Additionally, our model has demonstrated significant in vivo bactericidal efficacy of EME7(7) compared with saline. Thus, we suggest that further assessment of immune system damage in mice following cyclophosphamide injection may not be necessary.\n[ANSWER] – We appreciate the reviewer's positive comment."}, {"round": 2, "reviewer_comments": {"reviewer_1": "The authors have mostly addressed my prior comments.", "reviewer_3": "All of my comments have been addressed. I congratulate the authors on an interesting study!", "reviewer_5": "I thank the authors for revising the manuscript; most of my concerns have been addressed. However, a discrepancy remains regarding the regression task. In the manuscript, Top-k MSE is reported based on model-predicted top-k samples, whereas the supplementary file (Model Evaluation Proxy, lines 168–175) describes Top-k MSE as evaluating the error “for the top K sequences with the best antibacterial performance,” which implies a ground-truth-based selection. Please reconcile this inconsistency between the manuscript and the supplementary material, and validate the codebase (e.g., antibact_reg.py, functions: calculate_matrix, evaluate, etc.) to ensure they are consistent with the intended definition.", "reviewer_6": "The authors have answered my questions, I have no further comments on this study."}, "author_response": "[ANSWER] – We thank the reviewers for their insights and guidance on the manuscript.\n[ANSWER] – We thank the reviewers for their insights and guidance on the manuscript.\n[ANSWER] – We thank the reviewers for their insights and guidance on the manuscript. As suggested, top-k MSE is reported based on model-predicted top-k samples. We have revised the descriptive errors in the Supplementary Information to align it with the content in the manuscript.\n[ANSWER] – We thank the reviewers for their insights and guidance on the manuscript."}]

Decision letter