Identification of antimicrobial peptides from ancient gut microbiomes

Domain: chemistry
Published: 2026-01-14
Editorial outcome: major_revision

Abstract
Fecal coprolites preserve ancient microbiomes and are a potential source of extinct but highly efficacious antimicrobial peptides (AMPs). Here, we develop AMPLiT (AMPLightweight Identification Tool), an efficient tool deployable to portable hardware for AMP screening in metagenomic datasets. AMPLiT demonstrates AUPRC performances of 0.9486 ± 0.0003 and reasonable overall training time of 3200 ± 53 s. By computationally utilizing AMPLiT, we analyze seven ancient human coprolite metagenomes, identifying 160 AMP candidates. Of 40 representative peptides synthesized, 36 (90%) peptides demonstrate measurable antimicrobial activity at 100 μM or less in vitro. Strikingly, approximately two-thirds of these peptides are sourced from Segatella copri, a dominant ancient gut commensal that is conspicuously underrepresented in modern populations, particularly those with Westernized lifestyles. Representative S. copri-derived AMPs exhibit disruptions against membranes of pathogenic bacteria, coupled with low cytotoxicity and hemolytic risk. In vivo, lead peptides demonstrate potent antibacterial and wound-healing efficacy comparable to traditional antibiotics, especially in combating gram-positive pathogens. Our findings highlight the ancient gut microbiomes as sources of novel AMPs, offering valuable insights into the historical role of S. copri in human health and its decline in contemporary populations.

Introduction
The gut microbiome is increasingly recognized as a vast repository of biologically active antimicrobial peptides (AMPs), offering a critical resource for addressing the global antibiotic resistance crisis1–4. Beyond their role in modulating and maintaining host microbial community equilibrium5, AMPs employ diverse mechanisms—such as membrane disruption, intracellular division inhibition, and immunomodulation to suppress pathogens, thereby enhancing the adaptive resilience of hosts6,7. However, contemporary research remains disproportionately focused on AMPs from modern human microbiomes1,3, which coexist with pathogens that have evolved sophisticated resistance mechanisms8. This arms race underscores the limitations of mining modern microbiomes for novel AMPs, as pathogens may be equipped with resistance.

Ancient gut microbiomes, preserved in coprolites, dental calculus, and paleofeces, serve as paleogenomic “time capsules” that chronicle microbial evolution under pre-industrial diets, natural antimicrobial exposures, and negligible antibiotic pressure9,10. Notably, these communities exhibit compositional signatures distinct from modern microbiomes. One of these signatures is the dominance of Segatella copri (previously Prevotella copri)11. This taxon has been depleted in most of the modern gut microbiomes, possibly driven by processed foods, clinical antibiotics, and hygienic practices12–14. Given the unique compositions of pre-industrial microbiota and the fast evolution rates of microbial strains, ancestral microbial ecosystems might harbor AMPs whose targets have no modern analogs15. Critically, these ancient AMPs were forged in biological conflicts against pathogens lacking contemporary resistance mechanisms, creating a potential evolutionary mismatch: their molecular targets may remain vulnerable in modern pathogenic strains16. By resurrecting AMPs from microbial lineages untainted in the industrial era, we tap into a pre-resistance arsenal with unparalleled therapeutic potential.

Here, we bridge evolutionary biology and antimicrobial discovery by leveraging the ancient gut microbiome for combating modern pathogenic microorganisms. Combining an AI-driven metagenomic mining tool (AMPLiT) with experimental validation, we identified huge amounts of S. copri-derived AMPs from 7 ancient human coprolites17. This finding is consistent with previous observations that bacteria from the Prevotella genus are prolific producers of AMPs3,18. These AMPs, characterized by unique sequential patterns absent in modern databases19, demonstrated broad-spectrum efficacy against pathogens in vitro and promising effectiveness in murine infection models, comparable to conventional antibiotics like vancomycin and polymyxin B. Overall, this work contributes to a growing body of studies mining ancient samples for novel peptide antibiotics3,4,18.

Results
AMPLiT: a framework for time-efficient training on resource-constrained devices

Computational approaches for accurately identifying AMPs in microbiome (e.g., ancient coprolite metagenomes) demand substantial computational resources for deployment and model training1,18,20. To overcome this, we first developed AMPLiT (Antimicrobial Peptide Lightweight Identification Tool), a computational framework optimized for portable hardware. By re-engineering the architecture of our previous AMP identifier5, we minimized reliance on computationally intensive convolutional kernels through three innovative computational blocks (Supplementary Figs. 1 and 2), without sacrificing predictive performances (Table 1).

The first block, inspired by paradigmatic multi-head attention mechanisms, significantly reduced training time by 43% (Model C vs. AMP identifier). The second module, building on prior mathematical principles, synergized with the first to further streamline training costs (3206 ± 68 s) and improved overall performance (Model E, AUPRC: 0.9222 ± 0.0031), compared to the comparison framework (Model A, AUPRC: 0.8697 ± 0.0025). The third module introduced dynamic feature refinement to suppress noise in peptide sequences, achieving a 36.8% reduction in training time and an AUPRC of 0.8927 ± 0.0086 (Model B) compared to the original AMP identifier5. The integration of the 3 blocks into AMPLiT (Model G) reduced approximately 80% training time (3200 ± 53 s), while matching its state-of-the-art accuracy (AUPRC: 0.9486 ± 0.0003). Remarkably, the performance was attained using consumer-grade hardware (Intel i7-10875H CPU), eliminating dependence on high-performance computing infrastructure.

Mining and in vitro validation of AMP candidates from ancient gut microbiomes

We next analyzed seven published ancient Homo sapiens coprolite (1000–2000 years of history) metagenomes17 using this tool (Fig. 1a). To identify ancient AMPs candidates, we screened the seven published ancient Homo sapiens coprolite (1000–2000 years of history) metagenomes17 using AMPLiT (Fig. 1a). Following decontamination to remove environmentally derived sequences, we extracted raw open reading frames (ORFs) across all samples (Fig. 1b). Applying a stringent prediction threshold (probability ≥ 0.9), AMPLiT deployable on standard hardware was used to identify putative AMP candidates.

Comparative analysis of physicochemical properties (isoelectric point, aromaticity, charge at pH 7.0, and hydrophobicity) between AI-predicted sequences and 8794 experimentally validated AMPs revealed alignment in feature patterns (Fig. 1c–f), confirming the tool’s capacity to capture features of AMPs. Intriguingly, the AI-predicted candidates exhibited significantly lower molecular weights (median ~2 kDa vs. 2.8 kDa), which might be confounded by the vertebrate-derived AMPs with longer sequences in the reference database. To prioritize candidates with synthetic feasibility and ecological relevance, we implemented a multi-step filtration strategy: sequences were first filtered by ORF length (39–78 nucleotides, corresponding to 13–26 amino acids) and sample coverage (occurred in at least 5 stool samples), narrowing the list to 160 non-redundant candidates. Subsequent cytotoxicity prediction21 removed sequences with potential host toxicity, resulting in 41 high-confidence candidates for experimental validation.

Among the 41 filtered candidates, 40 peptides were successfully synthesized for in vitro validation (Fig. 1g and Supplementary Table 1). Initial screening against model Gram-positive (Bacillus subtilis 23857, Staphylococcus aureus 6538) and Gram-negative (Escherichia coli 25404, Pseudomonas aeruginosa 27853) pathogens revealed that 36 peptides (90% of those synthesized) exhibited measurable growth inhibition at 100 μM (Fig. 1h), underscoring the precision of AMPLiT. Strikingly, around two-thirds of the predicted AMPs originated from S. copri, a keystone commensal highly enriched in ancient gut microbiomes (Supplementary Fig. 3a) but depleted at levels of prevalence and abundance among modern industrialized populations12,13. Approximately 50% of the S. copri-derived AMPs showed potent antimicrobial activities (with at least 50% inhibitory rate to at least one tested bacterial strain), suggesting S. copri may play a critical role in maintaining microbial equilibrium through AMP production, a function potentially eroded during human modernization.

Genomic origins and conservation of ancestral antimicrobials in S. copri

To elucidate the genomic origins of AMPs from S. copri, we analyzed the loci encoding AMPs with potent activity in vitro (Fig. 1h), revealing interesting evolutionary and functional insights (Fig. 2a). Notably, around 77% of those ORFs represented fragments of larger genes encoding functionally diverse proteins, including topoisomerase-primase domain-containing proteins, aminoacyl-histidine dipeptidase, cell division protein FtsZ, TonB receptors, glutamate synthase large subunits, and etc. (Fig. 2a and Supplementary Fig. 4). This pattern aligns with recent reports of resource-efficient evolutionary strategies in which microbes repurpose “trans-membrane” fragments or motifs of larger housekeeping genes to generate novel AMPs, a mechanism thought to minimize metabolic costs while expanding antimicrobial repertoires8,9,18,22. In contrast, AMP9, AMP24, and AMP38 ORFs were not embedded within any annotated functional genes, suggesting alternative origins such as noncoding regions or horizontal gene transfer.

Due to DNA degradation in the ancient samples, the precise binning for these ancient AMP ORFs into particular phylogenetic lineages and clades of S. copri is currently difficult. Nonetheless, multiple ancient ORFs were consistently identified across multiple publicly available S. copri genomes assembled from rural populations (Fig. 2b). Some ORFs encoding broad-spectrum antimicrobials (AMP8, AMP13, AMP31, AMP38) were even conservatively shared among at least six distinct S. copri strains, indicating striking conservation. Notably, more than 67% of the ORFs detected in S. copri were not shared with other species, suggesting exclusive distribution patterns. Furthermore, sequence space clustering revealed limited overlap between these ancient AMPs and currently known AMPs (Fig. 2c), highlighting their unique signatures. Together, these findings suggest that S. copri evolved specialized AMPs potentially through both gene fragment recycling and de novo innovation. The exclusive distributions of these peptides seemed to imply unique roles of S. copri in strain-specific colonization or niche interactions with host cells, a dynamic now obscured by its decline in modern microbiomes.

A dose-response assay further benchmarked the therapeutic potential of the top-performing S. copri-derived AMPs against multiple pathogens (Fig. 2d). For Gram-positive pathogens, six peptides (AMP8, AMP13, AMP24, AMP30, AMP31, and AMP38) effectively suppressed B. subtilis 23857 and S. aureus 6538 at 25–100 μM. For Gram-negative strains, eight candidates (AMP8, AMP9, AMP13, AMP24, AMP28, AMP30, AMP31, and AMP38) showed potent inhibitory efficacy against E. coli 25404 at 5–100 μM, while five peptides (AMP13, AMP24, AMP30, AMP31, and AMP38) revealed inhibitory efficacy against P. aeruginosa 27853 at 5–100 μM. Moreover, we observed a non-monotonic concentration-dependent antimicrobial effect for certain resurrected AMPs, wherein inhibitory activity decreased at higher concentrations. This phenomenon aligns with previously reported behavior of AMPs, which may undergo self-association or aggregation at elevated concentrations7,23,24, thereby influencing their observed efficacy. The results of lead peptides were further tested by CFU plate spreading methods (Supplementary Fig. 3b, and Supplementary Table 2). Overall, these observations demonstrated broad-spectrum inhibitory efficacy by S. copri-derived AMPs, highlighting the functional decline of protective commensals in modern gut ecosystems.

AMPs from commensal S. copri displayed low cytotoxicity risks

Given the historical symbiosis between S. copri and ancient humans, we hypothesized that these AMPs would exhibit low cytotoxicity—a trait evolutionarily optimized for coexistence with mammalian hosts. To assess this, we prioritized seven candidates (AMP8, AMP13, AMP24, AMP28, AMP30, AMP31, and AMP38) showing high growth inhibitory rate (≥70%) and broad-spectrum activities (≥2 strains) for further safety profiling. Hemolysis assays revealed minimal or undetectable red blood cell lysis across all tested concentrations (5–100 μM), with no candidate exceeding a 10% average hemolytic rate (Fig. 3a). Similarly, CCK-8 cytotoxicity assays using intestinal epithelial cells showed no significant viability reduction at concentrations 5–100 μM for most peptides (Fig. 3b–h), except for slight cytotoxicity by AMP30 at 100 μM and AMP38. Scanning electron microscopy (SEM) further demonstrated the peptides’ mechanistic selectivity: all seven candidates disrupted the membrane integrity of pathogenic Gram-positive (S. aureus) and Gram-negative (E. coli and P. aeruginosa) pathogens (Fig. 3c). The discriminatory actions suggests that S. copri fine-tuned these AMPs to balance antimicrobial efficacy with biocompatibility, a dual functionality rarely achieved by synthetic peptides25.

In vivo effectiveness of AMPs from S. copri

Building on their efficacy against S. aureus 6538 and P. aeruginosa 27853 in vitro (Fig. 2d and Supplementary Fig. 3c), five AMPs (AMP8, AMP13, AMP30, AMP31, and AMP38) were advanced to in vivo test using murine wound infection models (Fig. 4a and Supplementary Fig. 5a). Mice infected with either pathogen were treated topically with AMPs (100 μM) at Days 1, 2, 3, 6, and 9, with vancomycin (100 μM) and polymyxin B (100 μM) serving as antibiotic controls. By day 12, multiple AMP treatment groups in combating gram-positive pathogens showed observable reductions in bacterial load (Fig. 4b) and accelerated wound closure (Fig. 4c and d). For S. aureus-infected wounds, AMP30, AMP31, and AMP38 showed antimicrobial efficacy comparable to vancomycin, achieving near-complete wound healing (Figs. 2e, f and 4c). Histological investigations of wound tissues using H&E and Masson staining showed severe immune cell infiltration in the negative control group, whereas experimental groups of AMP8, AMP30, and AMP38 showed lower immune cell infiltration similar to vancomycin treatment group (Fig. 4d and Supplementary Fig. 5b). For P. aeruginosa-infected wounds, AMP31 and AMP38 treatment groups showed improvement in antimicrobial efficacy (Fig. 2e). Improved wound healing and lower immune cell infiltration in AMP38 and polymyxin B-treated groups were also observed, indicating slightly improved tissue recovery (Figs. 2f and 4c). These results demonstrated the efficacy of S. copri-derived AMPs, especially in combating gram-positive pathogenic bacteria.

Methods
Biological data availability and metagenome assembly

The quality control steps follow the procedures of the previous study17, and 7 metagenomics samples among 15 palaeofaeces samples that have been previously confirmed with low contaminations17 were curated for the subsequent study. In details, 7 ancient human metagenomic stool samples (SRR12557704, SRR12557705, SRR12557706, SRR12557722, SRR12557711, SRR12557707, and SRR12557733), and 3 environmental metagenomic samples (SRR12557702, SRR12557703, and SRR12557732)17 were collected from NCBI for AMPs mining. First, the reads simultaneously detected in stool samples and environmental samples were regarded as environmental contaminations and were removed. Then, human host DNA sequences were excluded by using KneadData v.0.6.1 and the Homo sapiens reference database (build hg19)34. Lastly, short reads fewer than 30 bps were also removed. The metagenome assembly was conducted by SPADes (v4.0.0)35, with modes of “meta”, and automatically optimized k-mer parameters.

ORFs mining from ancient metagenomic samples

To avoid false positives, the assembled contigs contain “N” in sequences. Next, an in-house script named “ORF_hunter.py” created in our previous study5 was used for mining raw ORFs (33-150 nt). The max length cutoff range was set in accordance with the peptide length scope in the dataset used for model training and in consideration of downstream chemical synthesis applicability. The “ORF_hunter.py” functionally detects the ORFs within assembled contigs and converts them to the corresponding amino acid sequences, with 1,838,633 peptide sequences reserved.

Model training and peptide prediction

The AMPLiT was created by using Python 3.9, with the original code publicly available at Github: https://github.com/ChenSizhe13893461199/AMPLiT. The data used for model training were curated from UniProt36, and AMP databases7,19,29,30,37, with procedures for constructing training dataset, validation dataset, and test dataset strictly consistent with our previous study5. Overall, 8859 AMPs and 79,675 non-AMPs with length less than 50 amino acids were reserved, with the training dataset (7065 AMPs and 36,339 non-AMPs), the validation dataset (804 AMPs and 5006 non-AMPs), and the test dataset (990 AMPs and 38,263 non-AMPs), respectively. As outlined in the previous study5, the imbalanced ratios of AMPs/non-AMPs cases don’t signify the imbalanced featural identification between AMPs/non-AMPs, as performances in sensitivity and specificity (Table 1) are balanced. The introduction of many more non-AMPs has been validated to be necessary, as insufficient coverage of non-AMPs (molecular features are diverse) in model training is risky in leading to false positives. Therefore, compared to the paradigm of the “focal loss” method, the binary cross-entropy strategy was selected and showed superior effectiveness in model training. The measures of decreasing model “over-fitting” dilemma are conformed to the methods used in our latest article5. Multiple tests on the performances of AMPLiT confirmed the optimal model structures and parameters (Fig. S2 and Table 1). The core model structure includes two nine-layer dense blocks, with filter kernel sizes of 1×15, 1×75, and 1×15, respectively. The initial learning rate was set to 0.003 using the Adam optimizer with default parameters. The dropout rate and dropout density were both set to be 0.2, with batch sizes of 512. Weight decay was set as 1×10−6, with the growth rate of dense blocks as 16. The aforementioned hyperparameters were confirmed by model fine-tuning over the validation dataset and final performance assessment on the test dataset. Compared to AMPidentifier 1.0 embedded with computational block 2 (inspired by transformer frameworks), we additionally designed two new computational blocks to decrease computational burdens while retaining the robust performances. The first block, inspired by paradigmatic multi-head attention mechanisms, significantly reduced training time by 43% (Model C vs. AMP identifier). The second module, building on prior mathematical principles, synergized with the first to further streamline training costs (3206 ± 68 s) and improved overall performance (Model E, AUPRC: 0.9222 ± 0.0031), compared to the comparison framework (Model A, AUPRC: 0.8697 ± 0.0025). The third module introduced dynamic feature refinement to suppress noise in peptide sequences, achieving a 36.8% reduction in training time and an AUPRC of 0.8927 ± 0.0086 (Model B) compared to the original AMP identifier5. The integration of these 3 blocks into AMPLiT (Model G) achieved an approximately 80% reduction in training time (3200 ± 53 s), while matching its state-of-the-art accuracy (AUPRC: 0.9486 ± 0.0003). Remarkably, this performance was attained using consumer-grade hardware (Intel i7-10875H CPU), eliminating dependence on high-performance computing infrastructure. AMPLiT generated parallel performances to AMPidentifier 1.0, with overall fitting parameters and time costs decreased by approximately 56 and 80%, respectively. All tests were conducted on standard laptops (Intel i7-10875H CPU), demonstrating reasonable resource requirements. For peptide descriptors, the methods of using one-hot code and physicochemical descriptors were the same as the methods outlined in our previously published article5. The one-hot matrices contained n × 20 elements (where n = 50, representing the maximum amino acid numbers in peptide sequences) to represent sequence compositions. The protein physicochemical descriptors were calculated by Propy3 packages (https://propy3.readthedocs.io/en/latest/UserGuide.html), which generated a mathematical vector containing 1547 unique features of a given protein sequence. To represent the intrinsic relationships between amino acids within a particular peptide, the Word2Vec language framework was trained on short peptides (less than 50 amino acids) curated from the UniProt database36 by setting the “subcellular location” filter. All the same cases shared with the validation and test datasets were excluded. By extensive tests, the method of skip-gram and “hierarchical softmax” were selected, with optimal parameters of context window, vector size for each amino acid, and minimum word count confirmed as 50, 100 and 1, respectively. Overall, the model input of AMPs/non-AMPs was simultaneously represented by matrices of one-hot codes, physicochemical descriptors and the aforementioned Word2Vec matrices, respectively. The validities of all these descriptors were tested through the performance evaluations.

By setting the threshold of AI score as 0.9, the AMPLiT was used for identifying potential AMPs among the 1,838,633 raw peptide sequences. Due to the challenges associated with chemical synthesis, only the ORF length between 39 and 78 base pairs were reserved, and this range is also consistent with most of the AMPs in databases7,19,25,29,30. Next, we reserved the sequences with ORFs detected in contigs of at least 5 ancient stool metagenomics samples, generating 160 non-redundant potential sequences. Then, the 160 sequences were further filtered by cytotoxicity prediction21. Finally, 41 peptides (40 peptides successfully synthesized) were reserved for experimental validations. For particular assembled metagenomic contigs containing these AMP ORFs, they were putatively assigned to the ancient prevalent gut microbiota S. copri, through alignment-based taxonomic binning against reference genomes available in NCBI. Although the inherent erosion of ancient microbial DNA precluded full genomic reconstruction and limited the resolution to the species level, the high sequencing coverage of the corresponding contigs supports their assembly reliability and preservation quality. These contigs exhibited strikingly high identity to modern S. copri reference genomes without tracing to other species, confirming the phylogenetic origin of the S. copri clade.

Model prediction evaluations

The AI deep learning model performances were mathematically evaluated by indicators of Sensitivity, Specificity, and AUPRC with formulas shown below:

Sensitivity = True positive number / (True positive number + False negative number) (1)

Specificity = True negative number / (True negative number + False positive number) (2)

The AUPRC is the indicator quantifies the area under the precision-recall curve (PRC). PRC plots Precision against Recall. The formula of Precision and Recall are shown below:

Precision = True positive number / (True positive number + False positive number) (3)

Recall = True positive number / (True positive number + False negative number) (4)

Chemical synthesis of the selected peptides

There are 40 peptides successfully synthesized by Gill Biotech’s solid-phase approach38, with one peptide excluded because of failure in chemical synthesis. The method began with resin functionalization, where the resin was treated with a 20% piperidine solution for 30 min to remove the N-terminal Fmoc group. The deprotection step was confirmed by using a colorimetric reagent that turned color of blue. Next, by adding a coupling reagent, a base, and Fmoc-X (any of the 20 standard amino acids)-OH, the reaction was initiated for an hour. The reaction completion was determined when the reagents showed no color change. The deprotection step was then repeated: the resin was again treated with 20% piperidine solutions for 30 min to remove the newly added Fmoc group. The effectiveness of the step was also confirmed by using the colorimetric detection reagent. Likewise, the aforementioned reactions were repeated until the ultimate peptide sequences were generated. Finally, the resin was carefully dried and cut to yield the crude peptide powder. For quality control, high-performance liquid chromatography and mass spectrometry were applied to determine the compositions and confirmed the molecular purities 95% at least.

In vitro validations of antimicrobial activities

The antimicrobial activities of the selected peptides against B. subtilis 23857 (ATCC 23857), S. aureus 6538 (ATCC 6538), E. coli 25404 (ATCC 25404), and P. aeruginosa 27853 (ATCC 27853) were quantified according to the broth microdilution guidelines from NCCLS39 and previously published article1,5,9,20. Specifically, the single colonies of the aforementioned bacteria were cultured in liquid Luria Broth (LB) medium to the exponential stage (OD600 = 0.6) at 37°C, 250 rpm. Next, the medium was diluted to a density of approximately 5 × 105 cfu/mL. The 100 μL aliquots were further transferred into a 96-well microtiter plate containing 100 μL AMP solutions (experimental groups), 100 μL kanamycin or polymyxin B solutions (positive control groups), and 100 μL deionized water (negative control groups). Notably, for tests against B. subtilis 23857, E. coli 25404, and S. aureus 6538, kanamycin was used as positive control groups. For tests against P. aeruginosa 27853, polymyxin B was used as positive control groups. The initial tests were conducted by using the final concentrations of 100 μM for both AMP experimental groups and positive control groups. The plates were incubated at 37°C for 12 h, after which the OD600 absorbance was determined by a microplate reader. All measurements were independently repeated 3 times. Subsequently, those candidate peptides from S. copri showing potent antimicrobial activities were further selected for antimicrobial tests in vitro under diverse concentration gradients of 0, 5, 10, 25, 50, and 100 μM, with the same experimental procedures and principles aforementioned.

Hemolysis rates against red blood cells

The hemolytic tests were conducted according to experimental protocols applied in previously published articles1,5,9. The blood cells from healthy male rabbits (2–2.5 kg) were collected, and were centrifugated at 3000 rpm, 8 min for separating the plasma. Next, the upper plasma layer was aspirated and PBS solutions were added to wash the remaining red blood cells for 3 times, centrifugated at 3000 rpm for 10 min each time. Then, the remaining red blood cells were suspended to prepare 4% (v/v) solutions in 15 mL Eppendorf tube, and centrifugated at 1000 × g for 10 min to obtain cell pellets, with supernatant discarded. The particular AMPs were dissolved in saline and cultivated with red blood cell pellets at final concentrations of 5 μM, 10 μM, 25 μM, 50 μM and 100 μM, respectively. Red blood cell pellets cultivated in 1 mL of saline and 1 mL of distilled water were regarded as a negative control and a positive control, respectively. All experimental groups were incubated at 37°C for 2 h with three replicates for each group. Finally, all experimental groups were centrifuged at 1000 × g for 10 min, collecting supernatants into a 96-well plate (100 μL each well) to measure OD540 absorbance at 540 nm for confirming hemolysis rates. The formula for calculating the hemolysis rate is as follows:

Hemolysis (%) = (OD Sample - OD Negative control) / (OD Positive control - OD Negative control) × 100% (5)

Cytotoxicity against mammalian cells

Commercialized Caco-2 intestine model cells, widely used for mimicking the in vivo small intestinal epitheliums40–43, were cultivated in T25-bottle containing 4 mL DMEM (20% Fetal Bovine Serum, and 1% Penicillin/Streptomycin) at 37°C, 5% CO2 atmosphere for 5 days. Subsequently, the Caco-2 cells were washed with PBS solutions for 3 times (20–30 s each time), followed by adding of 1 mL pancreatic enzymes for 3–5 min at 37°C. Then, the suspended cells were centrifuged at 1000 × g for 5 min. The cell pellet was resuspended in the DMEM (20% Fetal Bovine Serum, and 1% Penicillin/Streptomycin) to approximately 1 × 104 cells/mL and cultivated in 96-well plates (100 µL) for 36 h. Next, the cultivated solutions were added with 0.5% PBS, or 100 µM particular AMPs for 12 h. Finally, 10 µL of CCK-8 reagents (BS350E, purchased from Biosharp Life Sciences, CHN) were added to each well for 4 h at 37°C. Each experimental group contain 3 replicates. The OD450 of each well was measured after the incubation, and the cell survival rates were calculated based on these results.

Scanning electron microscopy observation

The destruction effectiveness of particular AMPs to bacterial membranes was confirmed via using SEM (Thermofisher Helios 5 CX). The S. aureus 6538 (ATCC 6538), E. coli 25404 (ATCC 25404), and P. aeruginosa 27853 (ATCC 27853) were all cultivated in LB medium to the exponential phase at 37°C, and suspended to the load of 1 × 108 cfu/mL, co-culturing with particular 100 μM AMP solutions at 37°C for 24 h. Next, with 0.5% glutaraldehyde processed for 2 h, 10 µL liquid sample was added onto copper wafers. Finally, the pre-processed samples were sent to the SEM center at Kunming Medical University for generating SEM images.

In vivo treatment using AMPs

All experiments using animals were consistent with the ethical policies, and the involved experimental protocols were approved by the Laboratory Animal Welfare & Ethics Committee at Health Science Center of Kunming Medical University (New Permit Number: 2025DF007). At Day 0, a total of 108 6–8 weeks healthy female Sprague-Dawley (SD) rats (190–220 g, purchased from Beijing Huafukang Biological Technology Co., Ltd.) were fully anesthetized and shaved on the back to establish infection models. Only female rats were used in this study to minimize sex-related variability in wound healing and immune response. A full-thickness wound with 10 mm in circular diameters was created by using a hole puncher. Then, all mice were randomly separated into two groups, and the mice wound of either group were further dropped with suspensions of either S. aureus ATCC: 6538 or P. aeruginosa ATCC: 27853 (1 × 107 cfu/mL, 100 µL), respectively. Following the 24 h infection, the mice infected by the same pathogens (S. aureus ATCC: 6538 or by P. aeruginosa ATCC: 27853) were randomly assigned again to one of six groups, respectively. The six groups contain: (1) PBS (negative control), (2) vancomycin (100 μM, for group infected by S. aureus) or polymxin B (100 μM, for group infected by P. aeruginosa) (positive control), (3) AMP8 (100 μM, for group infected by S. aureus) or AMP13 (100 μM, for group infected by P. aeruginosa), (4) AMP 30 (100 μM), (5) AMP31 (100 μM), (6) AMP38 (100 μM). Following the steps, the mice wounds were administered with 100 µL of PBS, AMP8 or AMP13, AMP30, AMP31, AMP38, and vancomycin or polymxin B on Days 1, 2, 3, 6, and 9, respectively. Three mice in each group were euthanized on Days 1, 6, and 12, with wound granulation tissues split into two sections for H&E/Masson staining and CFU load observations, respectively. The tissue portion for CFU load observations were firstly immersed with 2 mL PBS solutions for 2 h and then spread onto an LB agar plate at 37°C for 14 h before assessment. In addition to the CFU load observations, the supernatant solutions immersed with the infected tissues were centrifuged at 1000 × g for 5 min, and the OD600 values of the supernatant solutions were quantified to further assess and calculate the decreased bacterial load.

Statistics and reproducibility

No statistical method was used to predetermine sample size. The exact choices of ancient metagenomic samples (n = 7) was determined based on the availability of published, pre-determined low-contamination coprolite datasets17. The data used for model training were curated from UniProt36, and AMP databases7,19,29,30,37, with procedures for constructing training dataset, validation dataset, and test dataset strictly consistent with our previous study5. Overall, 8859 AMPs and 79,675 non-AMPs with length less than 50 amino acids were reserved, with the training dataset (7065 AMPs and 36,339 non-AMPs), the validation dataset (804 AMPs and 5006 non-AMPs), and the test dataset (990 AMPs and 38,263 non-AMPs), respectively. No experimental data were excluded from the analyses during the computational and experimental process. The experiments were not randomized. The study did not include any interventions and thus the conventional blinding was not applicable to this study.

Ethic statement

This research complies with the relevant ethical regulations. Experiments using animals were consistent with the ethical policies, and the involved experimental protocols were approved by the Laboratory Animal Welfare & Ethics Committee at Health Science Center of Kunming Medical University (New Permit Number: 2025DF007).

Peer review rounds
[{"round": 1, "reviewer_comments": {"reviewer_1": "The manuscript presents a compelling study that utilizes portable hardware-compatible AI (AMPLiT) to mine novel antimicrobial peptides (AMPs) from ancient human coprolite metagenomes, predominantly derived from Segatella copri. The identification and subsequent validation of active AMPs against both Gram-positive and Gram-negative pathogens through in vitro and in vivo assays is commendable. The evolutionary rationale for exploring ancient microbiomes as a strategy to overcome modern resistance mechanisms is particularly insightful.\nHowever, it is essential to clarify that the identified AMPs are not traditional secreted bacterial peptides or host-derived AMPs, but rather fragments of various functional bacterial proteins. The rationale behind this approach requires further explanation. Additionally, the study did not include antimicrobial-resistant (AMR) strains in its analyses; consequently, the discovered AMPs did not demonstrate potent activity against AMR pathogens. This limitation should be explicitly acknowledged.\nTo enhance the manuscript’s rigor and impact, the authors should benchmark AMPLiT against existing AMP discovery methods to contextualize its performance and advantages. While the manuscript is generally well-structured, several key points—such as the methodology, rationale, and experimental scope—would benefit from clarification and expansion. Furthermore, the figure legends are currently insufficiently detailed and difficult to read; improving their clarity and comprehensiveness would greatly aid reader understanding.\nMinor comments:\n1. While the in vitro cytotoxicity results are presented, the potential for these evolutionarily distant AMPs to trigger heightened immunogenicity in a modern mammalian host (e.g., humans) warrants serious consideration. If feasible, preliminary cytokine profiling using human immune cells in vitro would significantly strengthen the safety profile assessment. At minimum, this must be highlighted as a key consideration for future development.\n2. The paper focuses on pathogen suppression but ignores a key aspect: potential collateral damage to the host's beneficial microbiota. The antimicrobial peptides (AMPs) discovered by the authors are broad-spectrum, and their impact on modern commensal bacteria community is unclear.\n3. Several references appear to be incorrectly cited, for example in Line 104, reference 19 is not relevant to toxicity prediction. In Line 123, references 7 and 17 do not seem to support the author's point of view. Please verify and replace with an appropriate citation.\n4. More details could be added on the development of AMPLiT, such as to specify the transformer architecture used.\n5. Did the authors predict AMPs from the metagenome assembled contigs or MAGs? And in Fig 1h and Line 112, how did the authors assign hosts to these AMPs? Please add this detail in the methods.\n6. Line 241, the instability index is not shown in the figure.\n7. Figure 2d, why does AMP31 or other AMPs have an inhibition rate of 100% at a concentration of 25 mM, but the inhibition rate decreases at higher concentrations?\n8. The in vivo results are presented qualitatively, such as representative images. The lack of quantification severely limits interpretation and statistical validation.", "reviewer_2": "This manuscript from Chen, Yuan, et al. presents several AMPs extracted from ancient gut microbiome data. They validate several candidates in vitro, with a high hit rate (albeit by using a high threshold of 100µM to define positive, see #3 below) and test the top candidate in vivo (but see #6 below).\nOverall, I find that the work could be very interesting but several details are not clear enough to make a full assessment and some claims should be toned down.\nMAJOR COMMENTS\n1. Could the authors provide the codes in a more organized fashion? The authors mention that \"All code to reproduce data analyses in this Brief Communication is provided in the Supplementary Information\", but there is still a bit of a need to follow links to previous papers and repositories and nowhere is everything put together. At least a single document with instructions would be very helpful. In particular, I failed to find the code that produces produces the alignments shown in Fig 2A.\n2. The argument of computational efficiency is not very convincing. In all these pipelines, the bottleneck is—by far—the assembly and not the AMP prediction step. For example, I can run the MACREL tool (which the authors indirectly cite as support for their claim as it is the tool behind [17]) on thousands of peptides per second (single CPU, using a laptop that was modern in 2021).\n3. A threshold of 100µM is high for AMP determination. For the ones that the authors follow up on, they do use lower thresholds, but it would be good to either see the data for all AMPs or at least mention the high threshold already in the abstract (when the authors mention the 90% hit rate).\n4. The lower molecular weight of the peptides here compared to the previously reported ones could be due to differences in origin species. Several peptides in the databases are from vertebrate origin, which which may influence the comparison. I am also do not find the inference that this may indicate potential membrane-penetration activity (L100) very convincing. Most AMPs are anyway membrane-disrupting, so is the argument here that the ancient ones are even more likely to be active in this way? In any case, the relationship with low microbial weight is not a strong one.\n5. Also, if I understand the functionality of ORF_finder.py correctly (I'm reading the version here https://github.com/ChenSizhe13893461199/Fast-AMPs-Discovery-Projects/blob/2e1147625a96a6133875c309e5418882b9663d44/ORF_hunter.py which may not be the right version, but see #1), the authors actually consider the shortest possible ORFs even inside another longer ORF! It is such an odd choice that I would tend to consider it a bug in the code. Also, if there is a out-of-frame START, it will reset the scanning to consider a different frame.\nThis also affects any claims of novelty. For example, taking just their first sequence (KERKKYGLKAARRAPQFSKR), this matches a longer peptide in AMPSphere: https://ampsphere.big-data-biology.org/amp?accession=AMP10.387_945 but given the way that ORFs are called here, only the shorter fragment is considered.\n6. The claims related to the distribution of the S. copri AMPs in different strains (L130) are potentially very interesting, but it is not clear where the strain genomes are coming from. If the authors are using modern genomes, then this partly disproves the value of using ancient data (although it is very interesting from an evolutionary perspective to see that these genes have been conserved). In any case, the authors should provide more details on the genomes used.\n7. The in vivo results are potentially very important, but there is a lack of quantification. The data is shown as an extended figure and it is, frankly, hard to be perform all the necessary comparisons by eye (and only a single replicate of each condition is shown). The authors should present the CFU counts as well and quantify the wound sizes in a systematic way.\nMINOR COMMENTS\n8. Finding S. copri as a high producer of AMPs is consistent with previous literature, see DOI 10.1016/j.cell.2024.05.013 or DOI 10.1016/j.cell.2024.07.027 (which use the name Prevotella copri).\n9. Figs 1c-e: I would ask that the authors use box and dot plots instead of the dynamite ones which do not show the distribution of data well.\n10. L113: what is the criterion behind calling some AMPs \"highly potent\"?", "reviewer_3": "This paper by Chen et al. introduces AMPLiT, a deep-learning pipeline that can be run on consumer-grade CPUs to mine metagenomic assemblies for short antimicrobial peptide (AMP) open-reading frames. Applying AMPLiT to seven well-curated ≈ 1,000- to 2,000-year-old human coprolite metagenomes, the authors shortlisted 160 AMP candidates. Forty peptides were synthesized and 36 (90%) inhibited the growth of four pathogens in vitro; roughly two-thirds of the hits originated from Segatella copri, a commensal strongly enriched in ancient but not modern guts. Seven S. copri peptides with broad-spectrum activity showed ≤10 % hemolysis and negligible Caco-2 toxicity, and five leads (i.e., AMP8, AMP13, AMP30, AMP31, AMP38) accelerated wound closure and reduced bacterial burden in a topical murine infection model at 100 µM, achieving efficacy comparable to vancomycin against Staphylococcus aureus. The interesting study adds to emerging literature in this field and positions ancient microbiomes as an untapped reservoir of AMPs.\nSpecific comments:\n• Activity was screened at 100 µM and the same high dose was used in vivo; This is a very high dose for peptides, limiting their therapeutic potential.\n• MICs should be shown quantitatively (through CFU/mL measurements).\n• All in vivo data should be quantified (and shown in the main figures), and most (if not all) qualitative results should be moved to the SI.\n• This work contributes to a growing body of studies mining ancient samples (e.g., doi: 10.1038/s41551-024-01201-x) and microbiomes (e.g., doi: 10.1016/j.cell.2024.07.027, describing the discovery of prevotellin-2; doi: 10.1016/j.cell.2024.05.013) for novel peptide antibiotics and needs to be described in the context of the existing literature.\n• In vitro testing covers only four laboratory strains, and in vivo efficacy was assessed against S. aureus and P. aeruginosa wound infections. Though not strictly required, testing for activity against multidrug-resistant clinical isolates would add to the study.\n• Please move SEM images showing membrane disruption to the SI.\n• AMPLiT is only compared to the authors’ previous method (AMPidentifier). This comparison should be extended to other methods (e.g., APEX, AMPSPhere, etc.).\n• The authors should better highlight the novelty of AMPLiT over AMPidentifier.\n• Candidates were limited to 13–26 amino acids and canonical residues; no effort was made to improve stability (e.g., D-amino acids, cyclization) or to test synergistic combinations suggested by the authors. This needs to be mentioned as a limitation.\n• What was the rationale for limiting sequences to 13–26 amino acid residues?\n• Several comparisons lack details of statistical tests or corrections for multiple hypotheses, and raw activity data (IC₅₀/MIC tables, hemolysis datasets) have not been deposited in a public repository.\n• The manuscript should be thoroughly revised for grammar issues.\n• I suggest changing the title to “Ancient gut microbiomes harbor unexplored antimicrobial peptides”."}, "author_response": "POINT-BY-POINT REPLY TO EDITORS AND REVIEWERS\nDear Rev iewers,\nThank you very much for the valuable comments to our manuscript. We have provided a\npoint-by-point response to the reviewers’ comments, together with a tracked and clean\nversion of the revised manuscript. All changes are highlighted.\nWe appreciate the opportunity to resubmit and hope you find this version acceptable for\npublication.\nYours sincerely\nQi Su on behalf of co-authors\nReviewer #1:\nRemarks to the Author:\n1. The manuscript presents a compelling study that utilizes portable hardware-compatible AI\n(AMPLiT) to mine novel antimicrobial peptides (AMPs) from ancient human coprolite\nmetagenomes, predominantly derived from Segatella copri. The identification and subsequent\nvalidation of active AMPs against both Gram-positive and Gram-negative pathogens through in\nvitro and in vivo assays is commendable. The evolutionary rationale for exploring ancient\nmicrobiomes as a strategy to overcome modern resistance mechanisms is particularly insightful.\nHowever, it is essential to clarify that the identified AMPs are not traditional secreted bacterial\npeptides or host-derived AMPs, but rather fragments of various functional bacterial proteins. The\nrationale behind this approach requires further explanation. Additionally, the study did not include\nantimicrobial-resistant (AMR) strains in its analyses; consequently, the discovered AMPs did not\ndemonstrate potent activity against AMR pathogens. This limitation should be explicitly\nacknowledged.\nTo enhance the manuscript’s rigor and impact, the authors should benchmark AMPLiT against\nexisting AMP discovery methods to contextualize its performance and advantages. While the\nmanuscript is generally well-structured, several key points—such as the methodology, rationale, and\nexperimental scope—would benefit from clarification and expansion. Furthermore, the figure\nlegends are currently insufficiently detailed and difficult to read; improving their clarity and\ncomprehensiveness would greatly aid reader understanding.\nResponse: We thank the reviewer for the positive comments. We have revised the manuscript\nas suggested.\n1. Major Comments\n1) Many AMPs were found to be fragments of functional proteins.\nResponse: We agree that this is an important point to clarify. Indeed, many of the putative AMPs\nidentified in our study are fragments derived from functional bacterial proteins. This phenomenon\nis not uncommon in AMP discovery. The underlying rationale is that certain regions of bacterial\nproteins can be ancestors of AMP genes and may exhibit intrinsic antimicrobial activity. This has\nbeen previously observed and reported in other studies of novel AMP discovery [1], in which modern\nand extinct human proteins were found to harbor antimicrobial subsequences. These cryptic\nantimicrobial regions may serve as an evolutionary reservoir for host defense or inter-microbial\ncompetition [2]-[3]. Specifically, the reference [2] involves a whole section named “Mutations in\nlarger genes generate c_AMPs as independent genomic entities” to support the evolutionary\norigin of AMPs from larger functional proteins and authors stated that “we hypothesized that\nsome of these have originated from larger proteins by fragmentation at the genomic level”.\nThese previous studies indicated the underlying rationale of the AMPs’ origin.\nIn the context of our study, which focuses on ancient microbiome data, it is plausible that such\nfragments could have represented a form of microbial arsenal or regulatory molecule in complex\nancestral communities. Their identification through AMPLiT suggests that our model recognizes\nsuch sequences with potential antimicrobial properties.\n[1] Molecular de-extinction of ancient antimicrobial peptides enabled by machine learning, Cell\nHost & Microbe, 2023, 31, 8, 1260-1274.e6.\n[2] Discovery of antimicrobial peptides in the global microbiome with machine learning. Cell, 2024,\n187, 3761-3778.e3716.\n[3] Antimicrobial host defence peptides: functions and clinical potential. Nature Reviews Drug\nDiscovery, 2020, 19, 311-332.\n2) AMPLiT against existing AMP discovery methods to further contextualize advantages\nResponse: Thank you again for your insightful suggestion, and we have supplemented this part to\nfurther contextualize advantages of AMPLiT. By using the same training and testing dataset from\nthe recently published study [2], the comparison between AMPLiT and a series of the recently\nproposed state-of the-art models [2] has been included in Table 1. AMPLiT with low computational\nburdens showed robust performances in multiple indicators comparable to PGAT-ABPp and other\nmethods (Table 1). PGAT-ABPp highly relies on the accuracy of the predicted peptide structures,\npotentially biased by inaccurate structural prediction risks and high computational burdens.\nWhile PGAT-ABPp push the boundaries of performances, it requires significant GPU resources\nand are not validated by in vitro and in vivo experiments. Based on these results, AMPLiT\ndemonstrates a compelling combination of reasonable computational efficiency and high\npredictive performances.\nTable 1. Comparison of the AMPLiT and PGAT-ABPp using the same training/testing data from [1]\nMethod Specificity (%) Precision (%) Accuracy (%) AUC References\nAMPLiT 97.26 ± 0.67 95.38 ± 1.03 96.69 ± 0.46 0.9910 ± 0.0017 In this study\nAMPidentifier 96.46 ± 0.86 94.03 ± 0.99 95.40 ± 0.77 0.9872 ± 0.0014 [1]\nPGAT-ABPp 96.72 ± 0.38 95.31 ± 0.51 96.49 ± 0.21 0.9936 ± 0.0007 [2]\nOnehot-GAT 96.49 ± 0.21 87.33 ± 3.90 92.15 ± 0.90 0.9776 ± 0.0039 [2]\nWord2vec-GAT 82.85 ± 5.62 79.83 ± 5.17 88.25 ± 3.26 0.9623 ± 0.0142 [2]\nProtT5-CNN 94.37 ± 0.28 92.30 ± 0.36 95.61 ± 0.16 0.9924 ± 0.0005 [2]\n[1] Screening and identification of antimicrobial peptides from the gut microbiome of cockroach\nBlattella germanica. Microbiome, 2, 272.\n[2] PGAT-ABPp: harnessing protein language models and graph attention networks for antibacterial\npeptide identification with remarkable accuracy. Bioinformatics, 40, 8, btae497.\n3) Explanations to other mentioned concerns\nResponse: According to the valuable suggestions from reviewer, we have thoroughly revised the\nmanuscript to provide more clarity and details regarding the methodology, rationale, and\nexperimental scope. We sincerely hope these efforts could enhance the transparencies and clarities\nof the manuscript. Assessing the activity of our lead AMPs against multidrug-resistant (AMR)\nclinical isolates is an important aspect of our future researches. We fully acknowledge the critical\nimportance of experimentally evaluating the efficacy of these peptides against contemporary AMR\npathogens, and we have explicitly highlighted it as a key and necessary goal for our future research\n(Line 185-187). Accordingly, our on-going project plans to expand the screening of identified\npeptides to a broader panel of clinically relevant multidrug-resistant strains.\nThe description of the AMPLiT framework, including its architecture, training process, and hyper-\nparameters optimization steps, to improve reproducibility, has been further supplemented and\nexplained (Methods section). The evolutionary and ecological premises for mining AMPs from\nancient microbiomes have been more thoroughly articulated in the manuscript (Line 179-194). All\nfigure legends have been revised to be more comprehensive, with improved explanations of\nexperimental designs, statistical analyses, and panel interpretations to enhance readability and\ncontextual understanding (Lin 403-464).\nMinor comments:\n1. While the in vitro cytotoxicity results are presented, the potential for these evolutionarily distant\nAMPs to trigger heightened immunogenicity in a modern mammalian host (e.g., humans) warrants\nserious consideration. If feasible, preliminary cytokine profiling using human immune cells in vitro\nwould significantly strengthen the safety profile assessment. At minimum, this must be highlighted\nas a key consideration for future development.\nResponse: Thank you for the important suggestion and we sincerely agreed with the importance\nof emphasizing “cytokine profiling using human immune cells” as a key consideration for\nfuture development. According to your requests, we have revised our manuscript and include\nthe discussion of necessities of conducting the preliminary cytokine profiling using human\nimmune cells in future researches and investigations (Line 187-189).\n2. The paper focuses on pathogen suppression but ignores a key aspect: potential collateral damage\nto the host's beneficial microbiota. The antimicrobial peptides (AMPs) discovered by the authors\nare broad-spectrum, and their impact on modern commensal bacteria community is unclear.\nResponse: We highly appreciate this valuable suggestion and we agreed with the mentioned\nconsideration of key aspect in AMPs’ influences to host’s beneficial microbiota. Although the AMPs\nwe discovered from S. copri exhibited broad-spectrum antimicrobial activity, these AMP ORFs have\nalso been conservatively reserved in genomes of S. copri assembled from modern populations living\nin rural areas. These phenomena indirectly highlight the potential low safety concerns to modern\ncommensal community, as they still exist in modern humans despite their low prevalence. Lastly,\nwe sincerely acknowledged the limitations of not assessing the potential influences of these AMPs\nto commensal community in this study, and we have clarified it as a key goal for future research\nin the manuscript (Line 187-189).\n3. Several references appear to be incorrectly cited, for example in Line 104, reference 19 is not\nrelevant to toxicity prediction. In Line 123, references 7 and 17 do not seem to support the author's\npoint of view. Please verify and replace with an appropriate citation.\nResponse: Thank you so much for the kind reminds, and please allow us to clarify.\nIn details, the reference 19 proposed the tool of “ToxinPred2”, which enables the prediction of\npeptide cytotoxicity, and we have utilized it in this study for excluding those peptides with\npotential cytotoxicity.\nFor references of 7 and 17, these 2 literatures indirectly support the view of “AMPs origin”.\nFor example, the reference 17 include a whole section named “Mutations in larger genes\ngenerate c_AMPs as independent genomic entities” to support the evolutionary origin of AMPs\nfrom larger functional proteins. And authors of reference 17 stated that “we hypothesized that\nsome of these have originated from larger proteins by fragmentation at the genomic level”. In\nreference 7, modern and extinct human proteins were found to harbor antimicrobial subsequences.\nThese cryptic antimicrobial regions may serve as an evolutionary reservoir for host defense or inter-\nmicrobial competition.\nTo further alleviate concerns from the reviewer, we have supplemented 2 additional references\nto support our viewpoints. In literature [1]-[2], they mention that the AMP gene may originate\nfrom a fragment of a larger gene. Combined with the original references of 7 and 17, these two\nreferences have also been supplemented in the Line 126 of the manuscript. And we hope these\nefforts can alleviate the concerns from reviewer.\n[1] Antimicrobial host defence peptides: functions and clinical potential. Nat Rev Drug Discov,\n19, 311–332.\n[2] Cryptic Antimicrobial Peptides: Identification Methods and Current Knowledge of their\nImmunomodulatory Properties. Curr Pharm Des, 24, 1054-1066.\n4. More details could be added on the development of AMPLiT, such as to specify the transformer\narchitecture used.\nResponse: Thank you so much for the important suggestion, and we have supplemented more\ntechnical details of the AMPLiT architecture in the manuscript according to your suggestion.\nFor improving the transparency of the manuscript, we have also added more technical details\nin the “Method” section (Line 195-369).\n5. Did the authors predict AMPs from the metagenome assembled contigs or MAGs? And in Fig 1h\nand Line 112, how did the authors assign hosts to these AMPs? Please add this detail in the methods.\nResponse: Thank you so much for this valuable question, and we have revised this detail in the\nMethod section (Line 252-264). And please allow us to answer your questions in details.\n1) Are these AMPs from the metagenome assembled contigs or MAGs?\nThese AMPs were identified within the assembled metagenomic contigs, which were assigned\nto ancient prevalent gut microbiota S. copri.\n2) how did we assign hosts to these AMPs?\nAccording to the figure (left panel) shown below, these contigs are assigned to host S. copri by\nexplicitly aligning to those assembled contigs to the reference genomes deposited in NCBI. Due to\nerosion of the microbial DNA in ancient samples (left panel in the figure below), their contigs were\nnot able to fully recover the complete genome of the S. copri and can only annotate at the species\nlevel (S. copri). Fortunately, the ancient S. copri-derived contigs containing these AMP’s ORFs\npossess sufficient sequencing reads coverage (right panel below), indicating high assembly quality\nand good preservation of these contigs despite the long history of samples (1000-200 years ago).\nFurthermore, the contig (containing those identified AMP ORFs) alignment also shared astonishing\nhigh conservations to those S. copri genome isolated from modern rural populations (~94-99%\nsimilarity, Figure below) in NCBI. These observations clearly traced the potential molecular origins\nof these AMPs from S. copri.\n6. Line 241, the instability index is not shown in the figure.\nResponse: Thank you so much for the careful checking, and we have deleted this typing error\nin the current version of the manuscript accordingly (Line 411-413).\n7. Figure 2d, why does AMP31 or other AMPs have an inhibition rate of 100% at a concentration\nof 25 mM, but the inhibition rate decreases at higher concentrations?\nResponse: Thank you so much for this valuable question, and we also noticed these interesting\nphenomena. As a natural property observed in many peptides (including AMPs [1]), we\nspeculate that the AMP molecules may undergo self-aggregation or form oligomers under high\ndosage concentrations. The self-aggregation may cause these peptides not enable to show the\nsame antimicrobial killing efficacies as they were under lower dosage, thereby potentially\nresulting in a decreased result.\n[1] Computational Methods and Tools in Antimicrobial Peptide Research. Journal of Chemical\nInformation and Modeling, 61, 3172-3196.\n8. The in vivo results are presented qualitatively, such as representative images. The lack of\nquantification severely limits interpretation and statistical validation.\nResponse: Thank you so much for this valuable question. We have conducted the in vivo\nquantitative analysis before, and here we supplemented the results below. These are results of\nthe decreased bacteria load in infected tissues.\nAs previously stated in “Methods” section, we immersed the wound granulation tissues by PBS\nsolutions, and split them into two sections for H&E/Masson staining and CFU load observations.\nIn addition to the CFU load observations (Extended Figure 4b), the supernatant solutions\nimmersed with the infected wound granulation tissues were further centrifugated, and we\nquantified the OD values of the supernatant solutions to further assess and calculate the\n600\ndecreased bacteria load (supplemented in Figure 2 and Methods). The quantitative results\nsupported the potential therapeutic efficacy of these identified AMPs.\nIn addition, the quantitative results of measuring wound closure size at Day 12 across different\nexperimental groups are also supplemented here (Figure above, supplemented in Figure 2 and\nMethods). Taken together, these results also suggested potential therapeutic efficacy of AMP13,\nAMP30, and AMP38 in vivo.\nHope our efforts aforementioned can alleviate your concerns, and thank you so much for the\nvaluable suggestions.\nReviewer #2 (Remarks to the Author):\nThis manuscript from Chen, Yuan, et al. presents several AMPs extracted from ancient gut\nmicrobiome data. They validate several candidates in vitro, with a high hit rate (albeit by using a\nhigh threshold of 100µM to define positive, see #3 below) and test the top candidate in vivo (but\nsee #6 below).\nOverall, I find that the work could be very interesting but several details are not clear enough to\nmake a full assessment and some claims should be toned down.\nResponse: We thank the reviewer for the positive comments. We have revised the manuscript\nas suggested.\nMAJOR COMMENTS\n1. Could the authors provide the codes in a more organized fashion? The authors mention that \"All\ncode to reproduce data analyses in this Brief Communication is provided in the Supplementary\nInformation\", but there is still a bit of a need to follow links to previous papers and repositories and\nnowhere is everything put together. At least a single document with instructions would be very\nhelpful. In particular, I failed to find the code that produces the alignments shown in Fig 2A.\nResponse: Thank you so much for this valuable suggestion. According to your suggestions, we\nhave revised our manuscript accordingly to include more technical and coding details in\n“Methods” section (Line 195-357) for avoiding the need to follow links to previous papers and\nrepositories. In addition, we have also revised the coding repository at\nhttps://github.com/ChenSizhe13893461199/AMPLiT to put everything together with expanded\ninstructions. For information and the alignments shown in Fig 2A, we are sorry for the\ninconvenience this has caused. Here are no particular codes used for this analysis, as it was\nmainly finished by using online tools from public websites. For your convenience, here we\nprovided the full ORF sequences of each AMPs that were analyzed in Figure 2 at the\naforementioned coding repository (AMP_S.copri_ORF.xlsx). By using these data, the figure\nand data can be easily reproduced via tools provided by public websites (e.g. NCBI BLAST).\n2. The argument of computational efficiency is not very convincing. In all these pipelines, the\nbottleneck is—by far—the assembly and not the AMP prediction step. For example, I can run the\nMACREL tool (which the authors indirectly cite as support for their claim as it is the tool behind\n[17]) on thousands of peptides per second (single CPU, using a laptop that was modern in 2021).\nResponse: We sincerely thank the reviewer for this insightful comment. We fully agree that\nmetagenomic assembly is indeed a computationally intensive step that often constitutes the\nbottleneck in traditional AMP discovery pipelines, especially for ancient or highly fragmented\nsamples.\nThe primary motivation behind developing AMPLiT was not to accelerate the assembly step,\nbut rather to provide a lightweight, hardware-accessible, and efficient tool for rapid model\ntraining, and accurate downstream AMP screening. While tools like MACREL seems to be\nefficient for peptide-level screening, MACREL only utilized 22 physiochemical features for\nmodel training and AMPs screening. As a comparison, AMPLiT enables rapid training within\n3200 ± 53 seconds and incorporates more than 3,547 physiochemical features for each input\npeptide sequences (1,000 features for one-hot code compositional information, 1,547 features\nfor physiochemical properties, 1,000 features for Word2Vec embedding features), which is 161\ntimes greater in scale than the previous tool MACREL. This potentially enhanced the screening\nperformances of AMPLiT.\nTable 1. Comparison of the AMPLiT and PGAT-ABPp using the same training/testing data from [1]\nMethod Specificity (%) Precision (%) Accuracy (%) AUC References\nAMPLiT 97.26 ± 0.67 95.38 ± 1.03 96.69 ± 0.46 0.9910 ± 0.0017 In this study\nAMPidentifier 96.46 ± 0.86 94.03 ± 0.99 95.40 ± 0.77 0.9872 ± 0.0014 [1]\nPGAT-ABPp 96.72 ± 0.38 95.31 ± 0.51 96.49 ± 0.21 0.9936 ± 0.0007 [2]\nOnehot-GAT 96.49 ± 0.21 87.33 ± 3.90 92.15 ± 0.90 0.9776 ± 0.0039 [2]\nWord2vec-GAT 82.85 ± 5.62 79.83 ± 5.17 88.25 ± 3.26 0.9623 ± 0.0142 [2]\nProtT5-CNN 94.37 ± 0.28 92.30 ± 0.36 95.61 ± 0.16 0.9924 ± 0.0005 [2]\nIn addition, we have also supplemented the comparison results with other recently proposed state-\nof the-art models [2] to further contextualize advantages of AMPLiT (Table 1 above). By using the\nsame training and testing dataset from the recently published study [2], the comparison between\nAMPLiT and a series of those recently proposed state-of the-art models [2] has been included in\nTable 1. AMPLiT with low computational burdens showed robust performances in multiple\nindicators comparable to PGAT-ABPp and other methods (Table 1). PGAT-ABPp highly relies on\nthe accuracy of the predicted peptide structures, potentially biased by inaccurate structural\nprediction risks and high computational burdens. While PGAT-ABPp push the boundaries of\nperformances, it requires significant GPU resources and are not validated by in vitro and in vivo\nexperiments. Based on these results, AMPLiT demonstrates a compelling combination of\nreasonable computational efficiency and high predictive performances.\n[1] Screening and identification of antimicrobial peptides from the gut microbiome of cockroach\nBlattella germanica. Microbiome, 2, 272.\n[2] PGAT-ABPp: harnessing protein language models and graph attention networks for antibacterial\npeptide identification with remarkable accuracy. Bioinformatics, 40, 8, btae497.\n3. A threshold of 100 µM is high for AMP determination. For the ones that the authors follow up on,\nthey do use lower thresholds, but it would be good to either see the data for all AMPs or at least\nmention the high threshold already in the abstract (when the authors mention the 90% hit rate).\nResponse: Thank you so much for this valuable suggestion, and we have revised the abstract\nto include the threshold of 100 µM dosage in the abstract accordingly.\n4. The lower molecular weight of the peptides here compared to the previously reported ones could\nbe due to differences in origin species. Several peptides in the databases are from vertebrate origin,\nwhich may influence the comparison. I am also do not find the inference that this may indicate\npotential membrane-penetration activity (L100) very convincing. Most AMPs are anyway\nmembrane-disrupting, so is the argument here that the ancient ones are even more likely to be active\nin this way? In any case, the relationship with low microbial weight is not a strong one.\nResponse: Thank you for this insightful comment. We agree that molecular weight alone is not\na strong predictor of function and that the comparison to the reference database could be\nconfounded by origin. Therefore, we have revised the manuscript to remove any speculative\nlink between molecular weight and membrane-penetration activity. The sentence now simply\nreports the lower molecular weight as an objective characteristic of the filtered candidate set,\nwithout implying a functional consequence.\n5. Also, if I understand the functionality of ORF_finder.py correctly (I'm reading the version here\nhttps://github.com/ChenSizhe13893461199/Fast-AMPs-Discovery-\nProjects/blob/2e1147625a96a6133875c309e5418882b9663d44/ORF_hunter.py which may not be\nthe right version, but see #1), the authors actually consider the shortest possible ORFs even inside\nanother longer ORF! It is such an odd choice that I would tend to consider it a bug in the code. Also,\nif there is a out-of-frame START, it will reset the scanning to consider a different frame. This also\naffects any claims of novelty. For example, taking just their first sequence\n(KERKKYGLKAARRAPQFSKR), this matches a longer peptide in AMPSphere:\nhttps://ampsphere.big-data-biology.org/amp?accession=AMP10.387_945 but given the way that\nORFs are called here, only the shorter fragment is considered.\nResponse: We thank the reviewer for this insightful comment and for closely examining our\nORF detection methodology. The reviewer has accurately identified a key feature of our tool,\nORF_finder.py: it is indeed designed to consider short ORFs nested within longer ones and to\ninitiate new ORF scanning upon encountering a start codon in any reading frame.\n1) We wish to clarify that this is not a bug but a deliberate design choice, tailored specifically\nfor the de novo discovery of AMPs from ancient metagenomes. This approach is grounded in\nthe established evolutionary biology of AMPs. A growing body of research indicates that novel\nAMP genes frequently originate from the fragmentation and exaptation of larger housekeeping\ngenes [1]-[5]. For instance, it has been shown that human proteins harbor cryptic antimicrobial\nregions [1], potentially contributing to novel AMP’s origin. And a recent study dedicated a\nsection “Mutations in larger genes generate c_AMPs as independent genomic entities” and they\nstated that “we hypothesized that some of these have originated from larger proteins by\nfragmentation at the genomic level” [2].\n2) This strategy is also crucial for analyzing potentially fragmented ancient DNA entities\n(~1000-2000 years old), where the recovery of full-length genes is exceedingly rare compared\nto modern samples. By design, our method aims at maximizing the probability of detecting all\npossible translation initiation events, thereby identifying short, complete ORFs that have a\nhigher chance of encoding functional peptides.\n3) Lastly, the AMP1 identified by AMPLiT is a subset of the longer AMP10.387_945 (40 aa)\nfound in AMPSphere. However, our peptide is sourced from an ancient human commensal\nwhile the AMPSphere's entry is derived from Ruminococcaceae genus of the modern cattle gut.\nThis phylogenetic and temporal distinction is significant. And we experimentally validated the\nantimicrobial activity of AMP1 in vitro. This confirms that this specific fragment is itself a\nfunctional antimicrobial unit, irrespective of the activity of the longer peptide it is embedded\nwithin. AMPLiT potentially provides insights in identifying active fragments that may not be\nannotated in modern databases focused on full-length proteins. It reveals that this specific\nbioactive fragment was encoded and potentially functional over a thousand years ago in a\ndifferent host environment, providing a new evolutionary perspective that is not captured by\nmodern databases alone.\nWe hope these explanations could alleviate the reviewer's concerns, and thank you again for\nyour valuable comment.\n[1] Molecular de-extinction of ancient antimicrobial peptides enabled by machine learning, Cell\nHost & Microbe, 2023, 31, 8, 1260-1274.e6.\n[2] Discovery of antimicrobial peptides in the global microbiome with machine learning. Cell, 2024,\n187, 3761-3778.e3716.\n[3] Antimicrobial host defence peptides: functions and clinical potential. Nature Reviews Drug\nDiscovery, 2020, 19, 311-332.\n[4] Antimicrobial host defence peptides: functions and clinical potential. Nat Rev Drug Discov,\n19, 311–332.\n[5] Cryptic Antimicrobial Peptides: Identification Methods and Current Knowledge of their\nImmunomodulatory Properties. Curr Pharm Des, 24, 1054-1066.\n6. The claims related to the distribution of the S. copri AMPs in different strains (L130) are\npotentially very interesting, but it is not clear where the strain genomes are coming from. If the\nauthors are using modern genomes, then this partly disproves the value of using ancient data\n(although it is very interesting from an evolutionary perspective to see that these genes have been\nconserved). In any case, the authors should provide more details on the genomes used.\nResponse: Thank you for this valuable question and comment, and we have revised to\nsupplement more details in the Method section (Line 252-264). These AMPs were identified\nwithin the assembled metagenomic contigs, which were assigned to anciently prevalent gut\nmicrobiota S. copri. According to the figure (left panel) shown below, these contigs are assigned\nto host S. copri by explicitly aligning to those assembled contigs to the reference genomes deposited\nin NCBI. Due to erosion of the microbial DNA in ancient samples (left panel in the figure below),\ntheir contigs were not able to fully recover the complete genome of the S. copri and can only\nannotate at the species level (S. copri). Fortunately, the S. copri-derived contigs containing these\nAMP’s ORFs possess sufficient sequencing reads coverage (right panel), indicating high assembly\nquality and good preservation of these contigs despite the long history (1000-200 years ago).\nFurthermore, the contigs (containing those identified AMP ORFs) alignment also shared astonishing\nhigh conservations to those S. copri genome isolated from modern rural populations (~94-99%\nsimilarity, Figure below) in NCBI. These observations clearly traced the potential origins of these\nAMPs from S. copri.\n7. The in vivo results are potentially very important, but there is a lack of quantification. The data is\nshown as an extended figure and it is, frankly, hard to be perform all the necessary comparisons by\neye (and only a single replicate of each condition is shown). The authors should present the CFU\ncounts as well and quantify the wound sizes in a systematic way.\nResponse: Thank you so much for this valuable question. Though the CFU load observations\ncan effectively prove the efficacy of our AMPs, the high density of the bacteria colonies in\nnegative control groups makes it currently hard to obtain quantitative counting comparisons.\nAnd here we supplemented additional quantitative results of assessing the decreased bacteria\nload in infected tissues (Figure below).\nAs previously stated in “Methods” section, in our previous experiments, we immersed the\nwound granulation tissues by 2 mL PBS solutions, and split them into two sections for\nH&E/Masson staining and CFU load observations. In addition to the CFU load observations\n(Extended Figure 4b), the supernatant solutions immersed with the infected wound granulation\ntissues were further centrifugated, and we quantified the OD values of the supernatant\n600\nsolutions to further assess and calculate the decreased bacteria load (supplemented in Figure 2\nand Methods). The quantitative results supported the potential therapeutic efficacy of these\nidentified AMPs.\nIn addition, the quantitative results of measuring wound closure size at Day 12 across different\nexperimental groups are also supplemented here (Figure above, supplemented in Figure 2 and\nMethods). Taken together, these results also suggested potential therapeutic efficacy of AMP13,\nAMP30, and AMP38 in vivo.\nHope our efforts aforementioned can alleviate your concerns, and thank you so much for the\nvaluable suggestions.\nMINOR COMMENTS\n8. Finding S. copri as a high producer of AMPs is consistent with previous literature, see DOI\n10.1016/j.cell.2024.05.013 or DOI 10.1016/j.cell.2024.07.027 (which use the name Prevotella\ncopri).\nResponse: Thank you so much for this valuable remind and comment, and we have supplemented\nthese references and clearly claimed their discoveries in our manuscript (Line 66-73). These\nliteratures identified AMPs from Prevotella genus (e.g. Prevotella jejuni, and Prevotella\nmelaninogenica, implying that S. copri might also be a high producer of AMPs.\n9. Figs 1c-e: I would ask that the authors use box and dot plots instead of the dynamite ones which\ndo not show the distribution of data well.\nResponse: Thank you so much for this valuable question, and we have revised the manuscript\naccording to your requests for showing the data distribution. Due to the huge amounts of the data\nsize, we choose to draw the data by box and violin plots, which might be clearer in representing data\ndistribution pattern (Figure 1c-1e).\n10. L113: what is the criterion behind calling some AMPs \"highly potent\"?\nResponse: Thank you so much for this valuable question and remind. Here we have revised the\nmanuscript to include the criterion behind calling some AMPs “potent” (Line 115-118). Specifically,\nwe define those AMPs with at least 50% inhibitory rate to at least one tested bacterial strain to be\npotent candidates, and we have included this explanation in the current manuscript.\nReviewer #2 (Remarks on code availability):\nSee the remark on the main review: the code is only partially available or‚ at least it was not clear\nto me how to access all of it. Some important questions are not clear from the Methods/Figure\nlegends and without the code, it was impossible to fully assess the paper.\nResponse: Thank you so much for this valuable question, and here we have revised the manuscript\n“Methods” section (Line 195-357) and the coding repository at the GitHub\n(https://github.com/ChenSizhe13893461199/Fast-AMPs-Discovery-Projects/) to include more\ntechnical details of AMPLiT and put everything together without the need to follow links to\nprevious papers. The revised content includes more details of utilization guidance, framework\nmechanisms, model training and assessment methods, and full access to codes. We sincerely hope\nour efforts can alleviate the concerns from reviewer.\nReviewer #3 (Remarks to the Author):\nThis paper by Chen et al. introduces AMPLiT, a deep-learning pipeline that can be run on consumer-\ngrade CPUs to mine metagenomic assemblies for short antimicrobial peptide (AMP) open-reading\nframes. Applying AMPLiT to seven well-curated ≈ 1,000- to 2,000-year-old human coprolite\nmetagenomes, the authors shortlisted 160 AMP candidates. Forty peptides were synthesized and 36\n(90%) inhibited the growth of four pathogens in vitro; roughly two-thirds of the hits originated from\nSegatella copri, a commensal strongly enriched in ancient but not modern guts. Seven S. copri\npeptides with broad-spectrum activity showed ≤10 % hemolysis and negligible Caco-2 toxicity, and\nfive leads (i.e., AMP8, AMP13, AMP30, AMP31, AMP38) accelerated wound closure and reduced\nbacterial burden in a topical murine infection model at 100 µM, achieving efficacy comparable to\nvancomycin against Staphylococcus aureus. The interesting study adds to emerging literature in this\nfield and positions ancient microbiomes as an untapped reservoir of AMPs.\nSpecific comments:\n1. Activity was screened at 100 µM and the same high dose was used in vivo; This is a very high\ndose for peptides, limiting their therapeutic potential.\nResponse: We thank the reviewer for raising this important point regarding the peptide\nconcentration used in our studies, and please allow us to explain in details.\nFor in vitro screening, the use of a single, relatively high concentration (e.g. 100 µM) for the\ninitial primary in vitro screening is a frequently-utilized strategy [1]-[2], allowing for the efficient\nidentification of peptides from a large pool of candidates (40 synthesized peptides in our case).\nAs the reviewer rightly implies, we did not stop at the 100 µM screen and we performed\ncomprehensive dose-response assays for the most promising leads (Fig. 2d), providing the\ncritical data on potency and therapeutic potential.\nFor in vivo experiment, we conducted our in vivo wound healing experiment based on the\nfollowing considerations:\n1) The AMPs identified in our study were locally administrated and this manner allows for the\nuse of higher concentrations [1], [3] that would be unsuitable for systemic delivery, as the local\nadministration minimizes the risk of systemic toxicity (and we have also proved their minimal\nhemolytic activity and low cytotoxicity in Extended Data Figure 3 a-h).\n2) We benchmarked our peptides against standard antibiotics (vancomycin and polymyxin B)\nand lead peptides performed comparably to these antibiotics under identical conditions\nhighlights their efficacy and potential.\n3) Here we attempted to provide a proof-of-concept for AMPs derived from ancient gut\nmicrobiota. We fully acknowledge that future work needs to focus on chemical engineering and\npeptide design to further optimize the therapeutic dosages of these identified AMPs. These\nefforts will contribute to the required therapeutic dosage and improve the clinical potency.\nThank you again for the valuable comment and suggestion.\n[1] Identification of antimicrobial peptides from the human gut microbiome using deep learning.\nNature Biotechnology, 40, 921-931.\n[2] Molecular de-extinction of ancient antimicrobial peptides enabled by machine learning. Cell\nHost & Microbe, 31, 1260-1274.e1266.\n[3] A sonosensitive diphenylalanine-based broad-spectrum antimicrobial peptide. Nature\nBiomedical Engineering, 9, 1349–1365.\n2. MICs should be shown quantitatively (through CFU/mL measurements).\nResponse: Thank you so much for this important comment, and here we further conducted MIC\nmeasurement experiments by CFU/mL methods (Figure S1c), with results shown below:\nMIC of representative lead peptides measured by CFU plate spreading method (μM)\nAMP E. coli 25404 S. aureus 6538 P. aeruginosa 27853\nAMP 8 25 100 -\nAMP13 10 - 100\nAMP30 25 5 100\nAMP31 100 >100 >100\nAMP38 25 50 >100\nThe relevant experimental results have now been thoroughly incorporated into the manuscript\n(Figure S1c and Table S3). Together, these results suggested that most of the experimental\nresults by CFU methods showed consistency with the previous OD-based assays.\nSpecifically, AMP8, AMP13, AMP30, AMP31 and AMP38 exhibit bactericidal effects against\npathogenic E. coli 25404, with results consistent with the previous OD-based assays. For AMPs\nagainst S. aureus 6538, AMP8, AMP30, and AMP38 also showed bactericidal effects that are\nconsistent with the trends observed in OD-based assays. For AMPs against P. aeruginosa\n27853, results of AMP13, AMP30 and AMP 31 generally showed consistency with the previous\nOD-based assays. Interestingly, AMP31 against S. aureus 6538 and AMP38 against P.\naeruginosa 27853 exhibit reduced efficacy on solid media compared to the OD-based liquid\nculture. This could be due to the potential instability under certain environmental conditions,\nor it may primarily function as a bacteriostatic agent rather than a bactericidal one in this\nparticular context. Overall, CFU-derived results are in general alignment with the OD-based\ninhibitory data in our manuscript.\n3. All in vivo data should be quantified (and shown in the main figures), and most (if not all)\nqualitative results should be moved to the SI.\nResponse: Thank you so much for this valuable question. We have conducted the in vivo\nquantitative analysis before, and here we supplemented the results below. These are results of\nthe decreased bacteria load in infected tissues.\nAs previously stated in “Methods” section, we immersed the wound granulation tissues by PBS\nsolutions, and split them into two sections for H&E/Masson staining and CFU load observations.\nIn addition to the CFU load observations (Extended Figure 4b), the supernatant solutions\nimmersed with the infected wound granulation tissues were further centrifugated, and we\nquantified the OD values of the supernatant solutions to further assess and calculate the\n600\ndecreased bacteria load (supplemented in Figure 2 and Methods). The quantitative results\nsupported the potential therapeutic efficacy of these identified AMPs.\nIn addition, the quantitative results of measuring wound closure size at Day 12 across different\nexperimental groups are also supplemented here (Figure above, supplemented in Figure 2 and\nMethods). Taken together, these results also suggested potential therapeutic efficacy of AMP13,\nAMP30, and AMP38 in vivo.\nLastly, according to your suggestion, we have moved most qualitative results to SI accordingly.\nHope our efforts aforementioned can alleviate your concerns, and thank you so much for the\nvaluable suggestions.\n4. This work contributes to a growing body of studies mining ancient samples (e.g., doi:\n10.1038/s41551-024-01201-x) and microbiomes (e.g., doi: 10.1016/j.cell.2024.07.027, describing\nthe discovery of prevotellin-2; doi: 10.1016/j.cell.2024.05.013) for novel peptide antibiotics and\nneeds to be described in the context of the existing literature.\nResponse: Thank you so much for this kind suggestion, and we have supplemented these\nreferences for describing the context of the existing literature accordingly (Line 66-73).\n5. In vitro testing covers only four laboratory strains, and in vivo efficacy was assessed against S.\naureus and P. aeruginosa wound infections. Though not strictly required, testing for activity against\nmultidrug-resistant clinical isolates would add to the study.\nResponse: Thank you so much for this valuable suggestion. Assessing the activity of our lead\nAMPs against multidrug-resistant clinical isolates is a fascinating and important next step for\nour future research. The primary objective of this study was to determine whether ancient\nhuman coprolites, as an underexplored paleogenetic resource, could serve as a viable source for\nnovel and functional antimicrobial peptides. Our goal was to build the entire pipeline from\nancient DNA extraction to functional validation, establishing that these 'molecular fossils' can\nbe resurrected and exhibit bioactivity. According to your suggestions, we explicitly emphasized\nthe importance of assessing the efficacy of these peptides against contemporary antimicrobial-\nresistant (AMR) pathogens and we have clarified this as a key goal for future (Line 184-187). And\nwe will pursue these key aspects in future investigations.\n6. Please move SEM images showing membrane disruption to the SI.\nResponse: Thank you so much for this valuable suggestion, and we have revised to move SEM\nimages showing membrane disruption to the SI accordingly.\n7. AMPLiT is only compared to the authors’ previous method (AMPidentifier). This comparison\nshould be extended to other methods (e.g., APEX, AMPSPhere, etc.).\nResponse: Thank you again for your insightful suggestion, and we have supplemented this part to\nfurther contextualize advantages of AMPLiT. By using the same training and testing dataset from\nthe recently published study [2], the comparison between AMPLiT and a series of recently proposed\nstate-of the-art models [2] has been included in Table 1. AMPLiT with low computational burdens\nshowed robust performances in multiple indicators comparable to PGAT-ABPp and other methods\n(Table 1). PGAT-ABPp highly relies on the accuracy of the predicted peptide structures,\npotentially biased by inaccurate structural prediction risks and high computational burdens.\nWhile PGAT-ABPp push the boundaries of performances, it requires significant GPU resources\nand are not validated by in vitro and in vivo experiments. Based on these results, AMPLiT\ndemonstrates a compelling combination of reasonable computational efficiency and high\npredictive performances.\nTable 1. Comparison of the AMPLiT and PGAT-ABPp using the same training/testing data from [1]\nMethod Specificity (%) Precision (%) Accuracy (%) AUC References\nAMPLiT 97.26 ± 0.67 95.38 ± 1.03 96.69 ± 0.46 0.9910 ± 0.0017 In this study\nAMPidentifier 96.46 ± 0.86 94.03 ± 0.99 95.40 ± 0.77 0.9872 ± 0.0014 [1]\nPGAT-ABPp 96.72 ± 0.38 95.31 ± 0.51 96.49 ± 0.21 0.9936 ± 0.0007 [2]\nOnehot-GAT 96.49 ± 0.21 87.33 ± 3.90 92.15 ± 0.90 0.9776 ± 0.0039 [2]\nWord2vec-GAT 82.85 ± 5.62 79.83 ± 5.17 88.25 ± 3.26 0.9623 ± 0.0142 [2]\nProtT5-CNN 94.37 ± 0.28 92.30 ± 0.36 95.61 ± 0.16 0.9924 ± 0.0005 [2]\n[1] Screening and identification of antimicrobial peptides from the gut microbiome of cockroach\nBlattella germanica. Microbiome, 2, 272.\n[2] PGAT-ABPp: harnessing protein language models and graph attention networks for antibacterial\npeptide identification with remarkable accuracy. Bioinformatics, 40, 8, btae497.\n8. The authors should better highlight the novelty of AMPLiT over AMPidentifier.\nResponse: We thank the reviewer for this critical suggestion, and we have revised the manuscript\nto highlight the novelty of AMPLiT over AMPidentifier. We have supplemented our analysis with a\ncomprehensive comparison to other recently proposed state-of-the-art methods, including PGAT-\nABPp, Onehot-GAT, Word2vec-GAT, and ProtT5-CNN in the Table above (shown in the previous\nresponse). As shown, AMPLiT achieves robust performance, comparable to the current top-\nperforming method (PGAT-ABPp) and superior to several others, while maintaining its unique\nadvantages. AMPidentifier was a proof-of-concept model that, like PGAT-ABPp and ProtT5-CNN,\nrelied on computationally intensive architectures. PGAT-ABPp highly relies on the accuracy of the\npredicted peptide structures, potentially biased by inaccurate structural prediction risks and high\ncomputational burdens. Furthermore, PGAT-ABPp requires significant GPU resources and are not\nvalidated by in vitro and in vivo experiments. As a comparison, AMPLiT achieved an accessible\ntraining time costs on portable equipment, while maintaining its potent performances. We sincerely\nhope our efforts aforementioned can alleviate your concerns, and thank you so much for the\nvaluable suggestions.\n9. Candidates were limited to 13–26 amino acids and canonical residues; no effort was made to\nimprove stability (e.g., D-amino acids, cyclization) or to test synergistic combinations suggested by\nthe authors. This needs to be mentioned as a limitation.\nResponse: We sincerely appreciate the reviewer for this critical suggestion, and we have revised\nthe manuscript to clarify the aforementioned limitation accordingly (Line 189-191).\n10. What was the rationale for limiting sequences to 13-26 amino acid residues?\nResponse: We sincerely appreciate the reviewer for asking this insightful question, and we have\nrevised the manuscript to include more details of it (Line 103-107). The decision to limit sequences\nto certain length scope was a deliberate choice based on a combination of biological, practical, and\neconomic considerations. Peptide synthesis cost and yield are highly dependent on length, with\nlonger length becomes exponentially more expensive and suffers from lower yields and higher rates\nof synthesis errors [1]-[3]. Furthermore, the established antimicrobial peptide databases [4]-[7] reveals\nthat this length range encompasses the majority of known natural and synthetic antimicrobial\npeptides, effectively capturing functional motifs while avoiding the synthetic challenges associated\nwith very long sequences. Therefore, limiting sequences to certain length scope makes our approach\nfinancially feasible and practically executable, maximizing the chances of identifying active leads.\n[1] The Current State of Peptide Drug Discovery: Back to the Future? Journal of Medicinal\nChemistry, 61, 4.\n[2] Computational Methods and Tools in Antimicrobial Peptide Research. Journal of Chemical\nInformation and Modeling, 61, 3172-3196.\n[3] The multifaceted nature of antimicrobial peptides: current synthetic chemistry approaches and\nfuture directions. Chemical Society Reviews, 50, 7820-7880.\n[4] APD3: the antimicrobial peptide database as a tool for research and education. Nucleic Acids\nResearch, 44, D1087-D1093.\n[5] LAMP: A Database Linking Antimicrobial Peptides. PLOS ONE 8, e66557.\n[6] CAMPR4: a database of natural and synthetic antimicrobial peptides. Nucleic Acids Research,\n51, D377-D383.\n[7] DBAASP v3: database of antimicrobial/cytotoxic activity and structure of peptides as a resource\nfor development of new therapeutics. Nucleic Acids Research, 49, D1, 8.\n11. Several comparisons lack details of statistical tests or corrections for multiple hypotheses, and\nraw activity data (IC₅₀/MIC tables, hemolysis datasets) have not been deposited in a public\nrepository.\nResponse: Thank you so much for this suggestion, and we have revised the manuscript to include\ndetails of statistical tests and corrections (Line 411-416, 430-432, 455-458). For raw activity data\n(MIC values, hemolysis data, and cytotoxicity data), they have been prepared as supplementary files\n(antimicrobial_activity_data.xlsx) of the manuscript and have also been submitted to the Github\nrepository at https://github.com/ChenSizhe13893461199/AMPLiT. The hemolytic activity data\nhave also been submitted to public database “Hemolytik2” and will be publicly available soon after\nthe checking procedures by their website developing team. Thank you again for the valuable\ncomment.\n12. The manuscript should be thoroughly revised for grammar issues.\nResponse: Thank you so much for this kind remind, and we have thoroughly revised the\ngrammar issues of the manuscript accordingly.\n13. I suggest changing the title to “Ancient gut microbiomes harbor unexplored antimicrobial\npeptides”.\nResponse: We sincerely thank the reviewer for this insightful suggestion, and we have revised\nthe manuscript title accordingly."}, {"round": 2, "reviewer_comments": {"reviewer_1": "The revised manuscript has improved in clarity, and the authors have added relevant methodological details and study limitations. However, my primary concern regarding the ORF mining strategy remains unaddressed.\n1. The existing biological mechanisms of encrypted peptides generation (post-translational cleavage; genomic mutations) cited in the authors' references are inconsistent with their computational approach. The custom script (ORF_hunter.py) defines ORFs solely through promoter and terminator predictions and applies a strict length cutoff (33-150 nt), which is inherently biased. Furthermore, the possibility that the AMPs reported in the cited literature occur naturally is reasonable. Still, the study in the manuscript appears to have mined short sequences that lack transcriptional and translational potential but exhibit moderate antibacterial activity, which may not reflect biological relevance. If these peptides do not correspond to naturally occurring or biologically active entities, their antimicrobial potency may not genuinely support the significance of this study.\n2. The authors' own data, showing most contigs are longer than 1 kb, provides a direct opportunity to validate the fragmented origin of these ORFs. Specifically for the AMPs listed in Figure 2a (e.g., AMP12, AMP29, AMP30), did the authors attempt to annotate the larger protein sequences encoded on these same assembled contigs?\n3. The study's central contribution needs to be clarified. The authors should decide whether the primary focus is on demonstrating the mining capabilities of the AMPLiT tool itself, or on revealing the AMP potential of ancient gut microbiomes and S. copri. This focus should be clearly reflected in targeted modifications to the abstract and main text better to highlight the study's core novelty and findings.", "reviewer_2": "While I thank the authors for their efforts, I feel that some points were not adequately addressed in the rebuttal. Perhaps this is due to my lack of clarity, so I will attempt to clarify my concerns and be more specific.\nThe authors answer to my concern #2 about using efficiency as a justification seems to misdirect. They claim that their tool is better because it uses more features, but that is not about efficiency. The sentence on L100 still claims \"AMPLiT identified putative AMPs within one hour, an efficiency infeasible for previous large pipelines on standard hardware.\" This is simply not true and the references given do not support it.\nWith respect to whether their ORF detection is correct (my previous concern #5), I again feel that the authors are answering a different point than the one I was making. Indeed, encrypted peptides can sometimes be found in larger proteins (and be active). Often active peptides can be \"trimmed\" down to an active core. It could have been interesting to run 6-frame translation on the input, but this is not what their code is doing. To expand on the example given previously, the following sequence codes for AMP10.387_945:\n>GMSC10.SMORF.002_433_233_034\nATGCTCCTCGCTGGCGATGAGTTCCGCCCCCTCCTCAAGAAGGAAGGCCTGCTGACCCGCGACCCCAGAATGAAGGAGCGCAAGAAGTACGGCTTGAAAGCAGCCCGTCGCGCACCCCAGTTCTCCAAGAGATAA\nTheir code extracts two ORFs from this sequence:\nATGAGTTCCGCCCCCTCCTCAAGAAGGAAGGCCTGCTGA\nand\nATGAAGGAGCGCAAGAAGTACGGCTTGAAAGCAGCCCGTCGCGCACCCCAGTTCTCCAAGAGATAA\nThe second one appears to be the shorter fragment that was discussed earlier, that matches the AMP10.387_945 sequence from AMPSphere. As a side-note: this behaviour (outputting embedded ORFs) explains the lower molecular weights observed in AMPLiT predictions compared to other databases, as many peptides will be truncated due to the presence of out-of-frame start codons (cf. the statements on L104+).\nHowever, consider the variation below with a single base change:\n>GMSC10.SMORF.002_433_233_034_variant\nATGCTCCTCGCTGGCGATGAGTTCCGCCCCCTCCTCAAGAAGGAAGGCCTGCTGACCCGCGACCCCAGAATGAAGGAGCGCAAGAAGTACGGCTTGAAAGCAGCCCGTCGCGCACCCCAGTTCTCCAAGAGATGA\nThis changes the penultimate base from A to G, which should be a silent mutation as TAA and TGA are both STOP in the standard table. This sequence codes for the exact same peptide, but now their code fails to extract the second one because the string ATG in the last four bases triggers the start of a new ORF even though it is out of frame!\nThe 3rd point in their answer is very interesting, but a complete speculation from their data.\nOverall, I still feel that the authors have not adequately addressed my concerns. The authors may wish to simply acknowledge that their code inadvertently misses some encrypted peptides due to the way ORFs are extracted, and that this could be improved in future versions.", "reviewer_3": "The authors have mostly addressed my prior comments."}, "author_response": "POINT-BY-POINT REPLY TO EDITORS AND REVIEWERS\nReviewer #1:\nRemarks to the Author:\n1. The existing biological mechanisms of encrypted peptides generation (post-translational cleavage; genomic mutations) cited in the authors' references are inconsistent with their computational approach. The custom script (ORF_hunter.py) defines ORFs solely through promoter and terminator predictions and applies a strict length cutoff (33-150 nt), which is inherently biased. Furthermore, the possibility that the AMPs reported in the cited literature occur naturally is reasonable. Still, the study in the manuscript appears to have mined short sequences that lack transcriptional and translational potential but exhibit moderate antibacterial activity, which may not reflect biological relevance. If these peptides do not correspond to naturally occurring or biologically active entities, their antimicrobial potency may not genuinely support the significance of this study.\nThank you so much for your critical comments and valuable suggestions, which are highly valuable for not only our current work but also for the advances in the whole field.\nSince the proteomic data for ancient gut microbiomes are not available, the conventional approaches [1]-[2] for identifying encrypted peptides do not apply to the ancient metagenome used in this study (Lines 174-183). Therefore, we chose to conduct the mentioned metagenome-based ORF identification as an initial filter to screen for potential candidates, and then we employed systematic biological assays to confirm the antimicrobial activity. We acknowledge that our method has some limitations, and it may not fully capture the complexity of certain encrypted peptides. We have discussed this limitation in the revised manuscript (Lines 94-95, lines 177-183), and we believe that further studies using multi-omics data may help fill this gap.\nBesides, the mentioned selection of length cutoff (33-150 nt) was based on properties of most known AMPs in public dataset [3]-[4] and was employed to narrow down the scope of AMP candidates with considerations of downstream chemical synthesis applicability and translational potentials. It may miss certain AMP candidates beyond the scope, and we have highlighted this in the revised manuscript (Lines 177-183, lines 209-212).\nWe agree with the reviewer that these mined short sequences may lack transcriptional and translational potential. To mitigate potential biases and enhance biological relevance, we implemented strict downstream filtering (Lines 94-97, manuscript Fig.1g), including prevalences across samples (occurred in at least 5 out of 6 ancient metagenomic samples), genomic context investigation, consistent conservation, and in vivo/in vitro experimental validations, co-supporting the potential biological significance of these AMPs. Additionally, we conducted Frequency of optimal codons (FOP) and Codon Adaptation Index (CAI) Analysis [5]-[7] to evaluate the codon usage compatibility. Among these S.copri-derived AMP ORFs, only limited ORFs exhibit low FOP (<0.6) and CAI (<0.6) indexes, indicating that the codon usage among most of these ORFs resembles the patterns of the expressed host genes (annotated functional genes from S. copri genome NCBI Ref Seq: GCF_025151535.1) and implies their potential for transcription and translation. These results further supported the transcriptional and translational potential of our identified AMPs, but studies using multi-omics are needed to further confirm this. We have acknowledged this limitation in the revised manuscript (Lines 180-183).\nThank you so much for prompting a deeper consideration of our work’s scope and implications. We explicitly acknowledged that more efforts (e.g. multi-omics approaches) are needed to further reveal the natural occurrence of these peptides in the future. Accordingly, these aspects mentioned by the reviewer have also been discussed in the manuscript to improve the clarity (Lines 174-191). We strongly believe that these opinions by the reviewer is very important not only for our study, but also for the advances in the relevant field.\nReferences:\n[1] Molecular de-extinction of ancient antimicrobial peptides enabled by machine learning, Cell Host & Microbe, 31, 8, 1260-1274.e6\n[2] Deep-learning-enabled antibiotic discovery through molecular de-extinction. Nature Biomedical Engineering 8, 854-871\n[3] The multifaceted nature of antimicrobial peptides: current synthetic chemistry approaches and future directions. Chemical Society Reviews, 50, 7820-7880\n[4] Computational Methods and Tools in Antimicrobial Peptide Research. Journal of Chemical Information and Modeling, 61, 3172-3196\n[5] The codon adaptation index-a measure of directional synonymous codon usage bias, and its potential applications. Nucleic Acids Research, 15 (3): 1281–1295.\n[6] Revisiting the codon adaptation index from a whole-genome perspective: analyzing the relationship between gene expression and codon occurrence in yeast using a variety of models\". Nucleic Acids Research, 31 (8): 2242–2251.\n[7] The Codon Statistics Database: A Database of Codon Usage Bias. Molecular Biology and Evolution, 39, 8, msac157.\n2. The authors' own data, showing most contigs are longer than 1 kb, provides a direct opportunity to validate the fragmented origin of these ORFs. Specifically for the AMPs listed in Figure 2, did the authors attempt to annotate the larger protein sequences encoded on these same assembled contigs?\nThank you so much for the valuable comments, and we sincerely agree with your opinions that the size of these ancient contigs provides feasibility to trace the origins of these ORFs. For the AMPs listed in Figure 2a, we have confirmed the phylogenetic origins of these ancient contigs in S. copri (left panel below). And, we further annotated the larger protein sequences encoded by these ancient contigs, with genomic information consistent with the results shown in manuscript Figure 2a (right panel below). We have added this into the revised manuscript (Lines 112-114; Figure S2).\nWe sincerely believe that these valuable suggestions from reviewer are important for improving the clarity of our current manuscript, and we hope our efforts can alleviate the concerns from reviewer.\n3. The study's central contribution needs to be clarified. The authors should decide whether the primary focus is on demonstrating the mining capabilities of the AMPLiT tool itself, or on revealing the AMP potential of ancient gut microbiomes and S. copri. This focus should be clearly reflected in targeted modifications to the abstract and main text better to highlight the study's core novelty and findings.\nWe sincerely agree with your valuable opinions that clarifying the central contribution of our study is important. Based on your kind suggestions, the abstract and main text body have been restructured to focus more on the biological significance of using ancient metagenomic samples for novel AMPs discovery (Lines 36-46, lines 174-195). We have moved the description of AMPLiT to the methods part (Lines 237-246). We believe that these targeted modifications suggested by the reviewer have significantly sharpened the manuscript's quality.\nReviewer #2:\nRemarks to the Author:\n1. While I thank the authors for their efforts, I feel that some points were not adequately addressed in the rebuttal. Perhaps this is due to my lack of clarity, so I will attempt to clarify my concerns and be more specific. The authors answer to my concern #2 about using efficiency as a justification seems to misdirect. They claim that their tool is better because it uses more features, but that is not about efficiency. The sentence on L100 still claims \"AMPLiT identified putative AMPs within one hour, an efficiency infeasible for previous large pipelines on standard hardware.\" This is simply not true and the references given do not support it.\nWe deeply thank the reviewer for this critical correction and for holding us to a high standard of accuracy. We sincerely apologize for our inaccurate claim regarding computational efficiency. We have deleted it and revised the text (Lines 87-88) to state: “AMPLiT deployable on standard hardware was used to identify putative AMP candidates.” We have also deleted unsuitable references in the revised manuscript.\nWe sincerely acknowledge that tools like MACREL are computationally rapid in the AMP prediction. Our intended point, which we didn’t articulate clearly in previous response letter, was to position AMPLiT's primary strength not as a fast predictor, but as a tool that offers a practical balance between predictive performance and computational burdens, especially in comparison to recently proposed AMP model demands extensive GPU resources and training time [1] (e.g. they claimed that 11 days of training costs are required for AI model training step). We have discussed this in the revised manuscript (Lines 80-83, lines 243-246).\nLastly, we feel grateful for the reviewer's re-clarification and kind suggestions. We are grateful for the reviewer's rigorous feedback, which has been essential in improving not only our current study.\nReferences:\n[1] Discovery of antimicrobial peptides with notable antibacterial potency by an LLM-based foundation model. Science Advances, 11, eads893.\n2. Concerns regarding the codes of ORF detection used in our work. Overall, I still feel that the authors have not adequately addressed my concerns. With respect to whether their ORF detection is correct (my previous concern #5), I again feel that the authors are answering a different point than the one I was making. Indeed, encrypted peptides can sometimes be found in larger proteins (and be active). Often active peptides can be \"trimmed\" down to an active core. It could have been interesting to run 6-frame translation on the input, but this is not what their code is doing. To expand on the example given previously, the following sequence codes for AMP10.387_945:\n>GMSC10.SMORF.002_433_233_034\nATGCTCCTCGCTGGCGATGAGTTCCGCCCCCTCCTCAAGAAGGAAGGCCTGCTGACCC\nGCGACCCCAGAATGAAGGAGCGCAAGAAGTACGGCTTGAAAGCAGCCCGTCGCGCAC\nCCCAGTTCTCCAAGAGATAA\nTheir code extracts two ORFs from this sequence:\nATGAGTTCCGCCCCCTCCTCAAGAAGGAAGGCCTGCTGA\nand\nATGAAGGAGCGCAAGAAGTACGGCTTGAAAGCAGCCCGTCGCGCACCCCAGTTCTCC\nAAGAGATAA\nThe second one appears to be the shorter fragment that was discussed earlier, that matches the AMP10.387_945 sequence from AMPSphere. As a side-note: this behaviour (outputting embedded ORFs) explains the lower molecular weights observed in AMPLiT predictions compared to other databases, as many peptides will be truncated due to the presence of out-of-frame start codons (cf. the statements on L104+).\nHowever, consider the variation below with a single base change:\n>GMSC10.SMORF.002_433_233_034_variant\nATGCTCCTCGCTGGCGATGAGTTCCGCCCCCTCCTCAAGAAGGAAGGCCTGCTGACCC\nGCGACCCCAGAATGAAGGAGCGCAAGAAGTACGGCTTGAAAGCAGCCCGTCGCGCAC\nCCCAGTTCTCCAAGAGATGA\nThis changes the penultimate base from A to G, which should be a silent mutation as TAA and TGA are both STOP in the standard table. This sequence codes for the exact same peptide, but now their\ncode fails to extract the second one because the string ATG in the last four bases triggers the start of a new ORF even though it is out of frame. The authors may wish to simply acknowledge that their code inadvertently misses some encrypted peptides due to the way ORFs are extracted, and that this could be improved in future versions.\nWe sincerely appreciate the reviewer for this insightful and constructive feedback regarding our ORF detection methodology. We acknowledge that our approach, in rare edge cases, may miss some encrypted peptides. We have highlighted this limitation in the revised manuscript (Lines 174-180). Beyond this, according to the kind suggestions from the reviewer, we have prepared an updated version of the ORF detection tool (6-frame translation approach) for utilization in our or other users’ future works. The updated script now performs a comprehensive translation of the input sequence, and we have uploaded the relevant computational codes to the GitHub repository (https://github.com/ChenSizhe13893461199/AMPLiT) (“ORF_hunter_updated.py”) accordingly. And we tested the “ORF_hunter_updated.py” on the given example from reviewer below:\n>GMSC10.SMORF.002_433_233_034_variant\nATGCTCCTCGCTGGCGATGAGTTCCGCCCCCTCCTCAAGAAGGAAGGCCTGCTGACCC\nGCGACCCCAGAATGAAGGAGCGCAAGAAGTACGGCTTGAAAGCAGCCCGTCGCGCAC\nCCCAGTTCTCCAAGAGATGA\nAnd the updated “ORF_hunter_updated.py” now enables the detection of all available ORF sequences, even with the conditions of penultimate base changing from A to G (as shown below).\nWe appreciate the reviewer for highlighting this technical issue, and we will strictly follow the guidance proposed by the reviewer in our future work. The reviewer's meticulous analysis not only significantly contributes to improve our current work, but also provides a clear direction to enhance precision for our future research proposal.\n3. The 3rd point in their answer is very interesting, but a complete speculation from their data.\nWe sincerely appreciate the reviewer for this valuable comment. The reviewer's professional suggestions are beneficial to improve our current manuscript and provide insightful guidance to our analysis workflows. Based on the important comments from reviewer, we further conduct analysis to alleviate the potential concerns.\nRegarding the previously mentioned AMP1 sequence KERKKYGLKAARRAPQFSKR that seems to match a longer peptide in AMPSphere, here we strictly followed the technical suggestions from reviewer in aspects of ORF detection and analyzed the available 3 genes (as shown below) encoding the aforementioned longer peptide in AMPSphere.\n1. GMSC10.SMORF.000_305_373_387\n2. GMSC10.SMORF.001_959_964_431\n3. GMSC10.SMORF.002_433_233_034\n(https://ampsphere.big-data-biology.org/amp?accession=AMP10.387_945)\nAnalysis revealed that the aforementioned 3 genes were not found to be contained within or aligned to any regions within the same contig harboring AMP1 ORF, indicating different genetic origins between the 3 genes and AMP1 ORF (Full details of the genomic information for AMP1 on the contig are shown above).\nFurther analysis also showed that the AMP1 ORF are genetically different from the 3 aforementioned genes (containing shorter ORF corresponding to AMP 1 peptide sequences), with divergent bases highlighted by yellow color (shown below). Based on these observations, we believe that our identified AMP1 is not a fragment of AMP10.387_945 but constitutes an independent AMP with a distinct genetic origin.\nWe sincerely hope our efforts can alleviate the concerns from reviewer. And we highly appreciate the reviewer for these valuable and kind comments, which have significant benefits for improving the quality of our manuscript and our future studies."}, {"round": 3, "reviewer_comments": {"reviewer_1": "Although I appreciate the authors' efforts, I believe that my comments 1-3 were not sufficiently addressed in the rebuttal. For instance, regarding comment 3, the absence of a comparison of MIC values with Vancomycin and the weak activity of the discovered AMPs do not adequately support the claim of the biological significance of using ancient metagenomic samples for novel AMP discovery.", "reviewer_2": "The authors have addressed my previous concerns.\nI think there could be more analysis on the point related to the evolution of the genes partially matching other previously reported peptides, but this does not affect the text (it is only mentioned in the rebuttal document) and I recognise it is outside the scope of the current manuscript."}, "author_response": "POINT-BY-POINT REPLY TO EDITORS AND REVIEWERS\nReviewer #1:\nRemarks to the Author:\nAlthough I appreciate the authors' efforts, I believe that my comments 1-3 were not sufficiently addressed in the rebuttal. For instance, regarding comment 3, the absence of a comparison of MIC values with Vancomycin and the weak activity of the discovered AMPs do not adequately support the claim of the biological significance of using ancient metagenomic samples for novel AMP discovery.\nWe sincerely appreciate reviewer 1 for proposing these critical comments and valuable suggestions, which are highly constructive for not only our current work but also for the advances in the whole field. We sincerely appreciate this important point and agree that the in vitro dose-response assay comparisons with clinical antibiotics such as Vancomycin are essential in our future work. To alleviate the potential concerns, we have incorporated a specific discussion of the mentioned limitations to avoid overinterpretations in the current manuscript (Lines 242-247) and highlighted the efforts that we need to improve in our future research (Lines 232-255). In this section, we clarify that the current findings primarily demonstrate the feasibility and novelty of mining ancient metagenomes, and we explicitly outline the necessary future steps for efficacy benchmarking and optimization.\nWe recognize that the antimicrobial activity observed at this stage requires further optimization for clinical translation. Nonetheless, we believe this early-stage work bridges evolutionary biology and antimicrobial discovery by illustrating how ancient gut microbiomes can serve as a novel resource for identifying unique peptide scaffolds in the fight against modern pathogens. We sincerely appreciate the reviewer 1 for proposing these insightful suggestions, which have strengthened the clarity and scope of our manuscript\nReviewer #2:\nThe authors have addressed my previous concerns. I think there could be more analysis on the point related to the evolution of the genes partially matching other previously reported peptides, but this does not affect the text (it is only mentioned in the rebuttal document) and I recognise it is outside the scope of the current manuscript.\nWe sincerely appreciate reviewer 2 for the positive feedback and constructive suggestions on our previous revisions. Regarding the comment on further analysis of gene evolution in relation to partially matching known peptides, we agree that this represents an interesting and valuable avenue for our future investigation. According to your valuable suggestions, we have taken note of the relevant aspects for our future investigations and work, where evolutionary and comparative genomic analyses could further contextualize the origin, conservation, and functional divergence of these ancient antimicrobial peptide sequences. We thank the reviewer for this thoughtful input, which is highly beneficial to our future studies in this area."}]

Decision letter