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PubMed2026

PMGen: from peptide-MHC structure prediction to peptide generation.

MOTIVATION: Accurate structural modeling of peptide-major histocompatibility complex (pMHC) complexes is essential for structure-driven immunotherapy design, yet current prediction tools suffer from narrow class coverage, restricted peptide lengths, insufficient accuracy, and a lack of built-in structure-aware peptide sampling. Consequently, most mimotope and altered peptide ligand designs rely solely on sequence substitution, leaving spatial and biophysical insights from pMHC structures largely unexploited. RESULTS: We introduce peptide-MHC generator (PMGen), an integrated framework for structure prediction and structure-guided design of variable-length peptides across MHC Class I and II. PMGen enforces anchor constraints within AlphaFold2 through two complementary strategies, initial guess and template engineering, achieving state-of-the-art structural fidelity without model fine-tuning. On a comprehensive benchmark, PMGen outperforms all existing methods, yielding median peptide-core Cα RMSDs of 0.62 Å for MHC-I and 0.33 Å for MHC-II. We show that PMGen can recover incorrectly predicted anchor positions and that AlphaFold pLDDT scores enable sequence-independent binding-core identification. Applied to a published neoantigen/wild-type pair, PMGen accurately captures mutation-induced conformational changes. Beyond structure prediction, we show that ProteinMPNN sampling on PMGen-predicted backbones yields higher affinity peptides while preserving the parental 3D conformation. Using PMGen to generate 63 817 high-confidence pMHC structures as training data, we further improve ProteinMPNN's peptide sequence recovery from 0.14 to 0.64 on a test set of 85 unseen MHC-I alleles, highlighting the value of accurate predicted structures for downstream machine learning tasks. AVAILABILITY AND IMPLEMENTATION: PMGen is freely available at https://github.com/soedinglab/PMGen, with an interactive Colab notebook at https://colab.research.google.com/github/soedinglab/PMGen/blob/master/colab.ipynb.

Peptides↗

PubMed2026

A mechanism-guided framework for prioritizing membrane-interaction anti-Vibrio peptides from peptidomics data.

A mechanism-guided framework for prioritizing membrane-interaction antimicrobial peptide candidates from proteomics-derived peptide mixtures is presented. The framework integrates conservative machine-learning-based antimicrobial peptide (AMP) screening with a literature-derived membrane-interaction plausibility (MAP) assessment and a data-driven membrane-interaction ranking function (AIPx), followed by structural visualization for interpretability. MAP encodes physicochemical characteristics commonly associated with peptide-membrane interaction and provides a graded plausibility assessment. Building upon this physicochemically interpretable framework, AIPx ranks peptides using feature weights calibrated from experimentally characterized anti-Vibrio peptides, where minimum inhibitory concentration (MIC) values are used as a coarse-grained ranking reference rather than a direct prediction target. In a peptidomics-based peptide fractionation study targeting Vibrio spp., AIPx exhibited a consistent relationship with experimentally observed antibacterial activity. Distributional analysis revealed that peptide fractions exhibiting high anti-Vibrio activity are characterized by enrichment of high-ranking peptides rather than by AMP abundance alone. By structuring AMP identification and prioritization as sequential stages, the MAP + AIPx framework enables interpretable and experimentally actionable candidate selection by reducing biologically implausible candidates. The framework facilitates species-oriented prioritization of AMP candidates, addressing a key challenge in antimicrobial peptide discovery where activity may depend on target-specific membrane characteristics. Moreover, the approach is extensible through species-specific calibration and supports interpretable, mechanism-informed prioritization in antimicrobial peptide discovery.

Proteomics↗

PubMed2026

Gastrointestinal digestion governs insect protein hydrolysis and predicted bioactive peptide release: Species-dependent implications for functional food applications.

This study investigates the digestion of insect proteins and the release of predicted bioactive peptides during human gastrointestinal digestion. Using the Infogest in vitro model, mealworm, cricket, and black soldier fly larvae (BSFL) proteins were digested and analyzed through discovery proteomics and bioinformatics to identify predicted bioactive peptides. Sequential windowed acquisition of all theoretical fragment ion mass spectra (SWATH-MS) quantified insect proteins including predicted bioactive peptide precursor proteins, the precursors of predicted bioactive peptides. Results indicated that gastrointestinal digestion strongly influences peptide release, with the gastric phase exhibiting a richer predicted bioactive peptide profile than the small intestinal phase. Many predicted bioactive peptides were rapidly hydrolysed under small intestine conditions, which may lead to reduced stability or diminished activity in vivo, potentially explaining why certain peptides show strong bioactivity in vitro but limited effects in vivo. Additionally, predicted bioactive peptide release varied by insect species, influenced by genetic factors and peptide abundance. These findings highlight the importance of species selection and consideration of proteolytic digestion patterns in optimizing insect-derived bioactive peptides for functional foods and nutraceutical applications.

Animals↗

PubMed2026

On-filter fractionation by empFASP improves identification of membrane peptides in proteomic experiments.

Membrane proteins remain among the most analytically challenging targets in bottom-up proteomics due to their limited solubility and low abundance of protease-accessible sites within transmembrane domains. In addition, hydrophobic peptides are frequently lost during detergent removal and the on-filter processing steps. Here, we present empFASP, a straightforward on-filter-fractionation-based modification of the enhanced filter-aided sample preparation (eFASP) workflow that enhances recovery of membrane-embedded peptides otherwise lost during digestion and cleanup. The method combines controlled on-filter inversion with sequential ethyl acetate extraction at defined pH values, enabling recovery of peptide material retained on the filter and redistributed into detergent micelles. Compared with SP3 and SP4 in HEK293T lysates, empFASP increased unique hydrophobic peptide identifications by up to 48% and increased the proportion of detected transmembrane peptides. Application to mouse mitochondrial membranes and phosphatidylethanolamine-deficient and PE-containing Escherichia coli membranes showed that the additional fractions of empFASP contribute complementary recovery of hydrophobic and membrane-associated peptides, with the strongest gains observed at the peptide level. Because empFASP requires no specialized reagents or instrumentation, it can be readily implemented in standard proteomics workflows to improve coverage of membrane-embedded regions. SIGNIFICANCE: The empFASP (enhanced membrane peptide) workflow offers a practical solution to one of the persistent limitations in membrane proteomics-the underrepresentation of hydrophobic and transmembrane peptides in standard digests. By integrating simple pH-controlled extractions into an on-filter format, empFASP recovers peptides otherwise lost through adsorption or detergent micelle retention, substantially improving coverage of the membrane proteome. This method expands the analytical reach of bottom-up proteomics without requiring specialized instrumentation, making it immediately applicable for studies of membrane topology, protein-lipid interactions, and the structural consequences of altered membrane composition.

Proteomics↗

PubMed2025

Accurate prediction of toxicity peptide and its function using multi-view tensor learning and latent semantic learning framework.

MOTIVATION: Therapeutic peptide is an important ingredient in the treatment of various diseases and drug discovery. The toxicity of peptides is one of the major challenges in peptide drug therapy. With the abundance of therapeutic peptides generated in the post-genomics era, it is a challenge to promptly identify toxicity peptides using computational methods. Although several efforts have been made, few algorithms are designed to identify whether a query peptide exhibits toxicity. Considering the varied levels of biological activities, the toxicity peptides should be further classified into multi-functional peptides. RESULTS: This study introduces a two-level predictor, ToxPre-2L, developed using the multi-view tensor learning and latent semantic learning framework. The proposed method utilized multi-label learning with feature induced labels to avoid the redundancy of information from each view. Then the multi-view tensor learning was employed to establish the latent semantic information among different views, while low-rank constraint learning was leveraged to exploit the correlation information among multi-labels. Finally, we constructed an updated toxicity peptide benchmark dataset to assess the effectiveness of the proposed method. Experimental results demonstrated that ToxPre-2L achieves a better performance than alternative computational methods in the prediction of toxicity peptides and their multi-functional types. AVAILABILITY AND IMPLEMENTATION: The source code and data of ToxPre-2L can be accessed at http://bliulab.net/ToxPre-2L.

Peptides↗

PubMed2026

PepGen: conditional generation of peptides for MHC binding.

MOTIVATION: Peptide-MHC II binding drives adaptive immunity, yet discovery of novel binder peptides remains challenging due to open binding grooves of MHC-II that accommodate variable-length peptides. While discriminative models perform well, they are unfeasible for generation via enumeration due to vast peptide space (2013≈8×1016 for peptides of length 13 amino acids). Generative AI approaches could accelerate binder design to enable vaccines targeted to particular MHC-II alleles or optimize other peptide chemical properties. RESULTS: We introduce PepGen, the first protein language model for MHC II peptide generation building on Generalized Language Modeling. PepGen conditions on alleles, arbitrary partial peptides including putative TCR-interacting motifs, and continuous binding affinity. Across multiple benchmarks including infilling and de novo generation, PepGen outperformed frequency sampling, Gibbs clustering, and autoregressive baselines. Adjusted log-probabilities enable good classification performance. Experimental validation confirmed that the SARS-CoV-2 peptide TEGALNTPKDHIGTR binding the HLA-DQA101:03-DQB106:03 allele can be redesigned to bind the HLA-DQA101:02-DQB105:02 allele. PepGen generated three putative TCR-motif-preserving binders gaining up to 70% of original MFI. Overall, PepGen provides scalable, motif-constrained MHC II peptide redesign and de novo generation, validated through thorough benchmarks and functional assays. AVAILABILITY AND IMPLEMENTATION: Code and Data are available at https://github.com/DaniTheOrange/PepGen.

Peptides↗

PubMed2024

NovoBoard: A Comprehensive Framework for Evaluating the False Discovery Rate and Accuracy of De Novo Peptide Sequencing.

De novo peptide sequencing is one of the most fundamental research areas in mass spectrometry-based proteomics. Many methods have often been evaluated using a couple of simple metrics that do not fully reflect their overall performance. Moreover, there has not been an established method to estimate the false discovery rate (FDR) of de novo peptide-spectrum matches. Here we propose NovoBoard, a comprehensive framework to evaluate the performance of de novo peptide-sequencing methods. The framework consists of diverse benchmark datasets (including tryptic, nontryptic, immunopeptidomics, and different species) and a standard set of accuracy metrics to evaluate the fragment ions, amino acids, and peptides of the de novo results. More importantly, a new approach is designed to evaluate de novo peptide-sequencing methods on target-decoy spectra and to estimate and validate their FDRs. Our FDR estimation provides valuable information to assess the reliability of new peptides identified by de novo sequencing tools, especially when no ground-truth information is available to evaluate their accuracy. The FDR estimation can also be used to evaluate the capability of de novo peptide sequencing tools to distinguish between de novo peptide-spectrum matches and random matches. Our results thoroughly reveal the strengths and weaknesses of different de novo peptide-sequencing methods and how their performances depend on specific applications and the types of data.

Peptides↗

PubMed2026

Immunopeptidomics-driven MHC class II peptide-binding motif discovery for 2 common canine DR alleles.

Despite the central role of major histocompatibility complex (MHC) class II in adaptive immunity, peptide-binding motifs have yet to be characterized for any canine MHC class II allele. Here, we report the first immunopeptidomics-derived binding motifs for DLA-DRB1*015:01 (DLA-DR15) and DLA-DRB1*012:01 (DLA-DR12), 2 alleles overrepresented in breeds predisposed to immune-mediated diseases. Because dogs co-express DLA-DR and DLA-DQ, the MHC class II Ab clone YKIX334.2 was validated to be DLA-DR-specific, enabling allele-selective immunoaffinity purification of DLA-DR molecules from homozygous DLA-DR15 and DLA-DR12 donor spleens. Mass spectrometry and GibbsCluster motif deconvolution of 838 DLA-DR15-associated and 644 DLA-DR12-associated peptides eluted from their respective peptide-binding grooves revealed distinct allele-specific binding motifs, with characterization of anchor residue preferences, peptide-length distributions, cross-species comparisons with human and murine MHC class II motifs, and source protein composition of the eluted self-peptidome. To evaluate the translational utility of these motifs, recombinant DLA-DR15 and DLA-DR12 molecules were used to screen rabies virus glycoprotein and nucleoprotein peptide libraries via fluorescence-based peptide competition assays, identifying high-affinity candidate binders for both alleles. Spearman rank correlation between immunopeptidomics-derived position-specific scoring matrix scores and peptide competition assay rankings demonstrated modest associations, consistent with these approaches capturing complementary dimensions of peptide-MHC class II interaction. Ultimately, these findings establish what we believe is the first allele-specific peptide-binding motif framework for canine MHC class II, providing a foundation for DLA-allele-informed CD4+ T-cell epitope discovery studies and Ag-specific immune response characterization in the dog.

Animals↗

PubMed2026

Bioactive peptides for meat quality and preservation: Integrating peptidomics and computational screening.

Bioactive peptides generated from meat proteins, fermented meat products, and slaughter by-products have attracted increasing attention as functional molecules for improving meat quality and preservation. In meat systems, peptides can be produced through endogenous postmortem proteolysis, microbial fermentation, gastrointestinal digestion, or controlled enzymatic hydrolysis of underutilized animal by-products. These peptides are closely associated with key meat science endpoints, including postmortem tenderization, oxidative stability, color retention, flavor development, microbial inhibition, and the valorization of processing by-products. However, although high-resolution peptidomics has greatly expanded the identification of meat-derived peptide sequences, their translation into practical meat applications remains limited by matrix interactions, processing stability, sensory constraints, safety concerns, and insufficient validation in real meat systems. This review synthesizes recent advances in meat-related peptidomics and computational screening, including sequence-based prediction, machine learning, molecular docking, molecular dynamics, stability assessment, and safety-oriented filtering. Particular attention is given to how these approaches can prioritize peptides with antioxidant, antimicrobial, flavor-modulating, and preservation-related functions under meat-specific technological constraints. By integrating peptide generation pathways, mass spectrometry-based identification, in silico prioritization, and meat quality endpoints, this review proposes a stage-gated framework for translating meat-derived bioactive peptides from discovery to application. Future research should strengthen matrix-specific validation, standardized peptidomic reporting, and safety assessment to support the use of bioactive peptides in meat quality improvement, clean-label preservation, and circular utilization of meat industry by-products.

Animals↗

PubMed2024

Multiple urinary peptides are associated with hypertension: a link to molecular pathophysiology.

OBJECTIVES: Hypertension is a common condition worldwide; however, its underlying mechanisms remain largely unknown. This study aimed to identify urinary peptides associated with hypertension to further explore the relevant molecular pathophysiology. METHODS: Peptidome data from 2876 individuals without end-organ damage were retrieved from the Human Urinary Proteome Database, belonging to general population (discovery) or type 2 diabetic (validation) cohorts. Participants were divided based on systolic blood pressure (SBP) and diastolic BP (DBP) into hypertensive (SBP ≥140 mmHg and/or DBP ≥90 mmHg) and normotensive (SBP <120 mmHg and DBP <80 mmHg, without antihypertensive treatment) groups. Differences in peptide abundance between the two groups were confirmed using an external cohort ( n  = 420) of participants without end-organ damage, matched for age, BMI, eGFR, sex, and the presence of diabetes. Furthermore, the association of the peptides with BP as a continuous variable was investigated. The findings were compared with peptide biomarkers of chronic diseases and bioinformatic analyses were conducted to highlight the underlying molecular mechanisms. RESULTS: Between hypertensive and normotensive individuals, 96 (mostly COL1A1 and COL3A1) peptides were found to be significantly different in both the discovery (adjusted) and validation (nominal significance) cohorts, with consistent regulation. Of these, 83 were consistently regulated in the matched cohort. A weak, yet significant, association between their abundance and standardized BP was also observed. CONCLUSION: Hypertension is associated with an altered urinary peptide profile with evident differential regulation of collagen-derived peptides. Peptides related to vascular calcification and sodium regulation were also affected. Whether these modifications reflect the pathophysiology of hypertension and/or early subclinical organ damage requires further investigation.

Humans↗

PubMed2026

From sequence space to ecological function: microbiome-derived antimicrobial peptides as community effectors and therapeutic leads.

Antimicrobial peptide research has long centred on host defence molecules, yet microbiomes themselves encode a diverse and increasingly important repertoire of peptide-based antimicrobials. These microbiome-derived antimicrobial peptides include bacteriocins, ribosomally synthesised and post-translationally modified peptides, cryptic short open reading frame-encoded peptides, embedded antimicrobial regions within larger proteins, and selected peptide antibiotics recovered from human, animal, plant and environmental microbiomes. Recent advances in genome mining, metagenomics, and machine learning have greatly expanded the scale of discovery, moving the field from a handful of landmark exemplars to large candidate catalogues spanning the global microbiome. In the clearest cases, these molecules are not only anti-infective leads but ecological effectors: they mediate microbial competition, enforce colonisation resistance, and influence community structure within densely occupied niches. The present review synthesises the field across discovery classes, microbiome sources, ecological roles, and translational bottlenecks, emphasizing a central limitation of the field: candidate catalogues are expanding at extraordinary scale, while evidence for native expression, producer assignment, ecological function, and in vivo relevance remains limited for the vast majority of predicted molecules. Progress will depend on workflows that connect sequence level prediction to biological context through expression support, producer assignment, community level validation, and perturbation-based approaches that distinguish ecological association from causal function. Microbiome-derived antimicrobial peptides are best understood not only as promising therapeutic leads, but also as molecular mediators of microbial social life whose ecological origins are central to their interpretation and future application.

Microbiota↗

PubMed2026

Antibacterial mechanisms and pathogen-dependent protective effects of the golden pompano LEAP2-derived peptide TroLEAP2-21.

Antimicrobial peptides (AMPs) are essential components of the innate immune system, with liver-expressed antimicrobial peptide 2 (LEAP2) playing a pivotal role in fish immunity. This study investigated the antimicrobial activity and mechanisms of TroLEAP2-21, a 21-amino-acid short peptide from golden pompano (Trachinotus ovatus), against Gram-positive (Lactococcus garvieae, Staphylococcus epidermidis) and Gram-negative (Vibrio alginolyticus, Vibrio harveyi) bacteria. The predicted three-dimensional structure and helical wheel projection of TroLEAP2-21 suggested typical AMP-like physicochemical features. troleap2 expression in the liver and intestine of T. ovatus was significantly upregulated post L. garvieae or V. harveyi infection, suggesting its potential involvement in antibacterial defense. In vitro, TroLEAP2-21 exhibited antibacterial activity against the tested bacterial strains, with membrane disruption, increased membrane permeability, cytoplasmic leakage, and membrane depolarization observed after peptide treatment. Gel retardation assays further indicated species-dependent association of TroLEAP2-21 with bacterial genomic DNA. In vivo, under the tested intraperitoneal injection conditions, TroLEAP2-21 was associated with improved survival and reduced tissue damage in V. harveyi-infected T. ovatus, whereas no significant survival benefit was observed against L. garvieae. Transcriptomic analysis at 48 h post-infection showed transcriptional changes in immune-related DEGs (rsad2, mx1/mx2, il-8) and enrichment of TLR and Jak-STAT signaling pathways at the transcriptional level in peptide-treated fish. FISH showed the tissue localization of tnf-α and nf-κb transcripts and revealed treatment-associated changes in fluorescence signals, and qRT-PCR of eight immune genes supported transcriptomic results in tissues at 48 h post-infection. Collectively, these findings characterize TroLEAP2-21 as a short LEAP2-derived peptide with antibacterial and immunomodulatory activities. Its comparative advantages over other LEAP2-related peptides and its practical application potential remain to be further investigated.

AMPs↗

PubMed2024

Molecular basis for antibody recognition of multiple drug-peptide/MHC complexes.

The HapImmuneTM platform exploits covalent inhibitors as haptens for creating major histocompatibility complex (MHC)-presented tumor-specific neoantigens by design, combining targeted therapies with immunotherapy for the treatment of drug-resistant cancers. A HapImmune antibody, R023, recognizes multiple sotorasib-conjugated KRAS(G12C) peptides presented by different human leukocyte antigens (HLAs). This high specificity to sotorasib, coupled with broad HLA-binding capability, enables such antibodies, when reformatted as T cell engagers, to potently and selectively kill sotorasib-resistant KRAS(G12C) cancer cells expressing different HLAs upon sotorasib treatment. The loosening of HLA restriction could increase the patient population that can benefit from this therapeutic approach. To understand the molecular basis for its unconventional binding capability, we used single-particle cryogenic electron microscopy to determine the structures of R023 bound to multiple sotorasib-peptide conjugates presented by different HLAs. R023 forms a pocket for sotorasib between the VH and VL domains, binds HLAs in an unconventional, angled way, with VL making most contacts with them, and makes few contacts with the peptide moieties. This binding mode enables the antibody to accommodate different hapten-peptide conjugates and to adjust its conformation to different HLAs presenting hapten-peptides. Deep mutational scanning validated the structures and revealed distinct levels of mutation tolerance by sotorasib- and HLA-binding residues. Together, our structural information and sequence landscape analysis reveal key features for achieving MHC-restricted recognition of multiple hapten-peptide antigens, which will inform the development of next-generation therapeutic antibodies.

Humans↗

PubMed2025

Relative quantification of proteins and post-translational modifications in proteomic experiments with shared peptides: a weight-based approach.

MOTIVATION: Bottom-up mass spectrometry-based proteomics studies changes in protein abundance and structure across conditions. Since the currency of these experiments are peptides, i.e. subsets of protein sequences that carry the quantitative information, conclusions at a different level must be computationally inferred. The inference is particularly challenging in situations where the peptides are shared by multiple proteins or post-translational modifications. While many approaches infer the underlying abundances from unique peptides, there is a need to distinguish the quantitative patterns when peptides are shared. RESULTS: We propose a statistical approach for estimating protein abundances, as well as site occupancies of post-translational modifications, based on quantitative information from shared peptides. The approach treats the quantitative patterns of shared peptides as convex combinations of abundances of individual proteins or modification sites, and estimates the abundance of each source in a sample together with the weights of the combination. In simulation-based evaluations, the proposed approach improved the precision of estimated fold changes between conditions. We further demonstrated the practical utility of the approach in experiments with diverse biological objectives, ranging from protein degradation and thermal proteome stability, to changes in protein post-translational modifications. AVAILABILITY AND IMPLEMENTATION: The approach is implemented in an open-source R package MSstatsWeightedSummary. The package is currently available at https://github.com/Vitek-Lab/MSstatsWeightedSummary (doi: 10.5281/zenodo.14662989). Code required to reproduce the results presented in this article can be found in a repository https://github.com/mstaniak/MWS_reproduction (doi: 10.5281/zenodo.14656053).

Protein Processing, Post-Translational↗

PubMed2026

Discovery and characterization of multifunctional bioactive peptides from Alaska Pollock (Gadus chalcogrammus) milt: hybrid in silico, in vitro, and proteomic approaches.

The growing demand for multifunctional bioactive peptides has sparked interest in underutilized marine by-products as sustainable bioresources. This study explored Alaska Pollock (Gadus chalcogrammus) milt protein as a novel source of peptides with anti-inflammatory, anti-hypertensive, and anti-diabetic effects. Protein composition was analyzed via LC-MS, followed by in silico digestion and bioactivity prediction. Molecular docking identified peptides targeting DPP-IV, α-glucosidase, ACE, GLP-1 receptor, COX-2, MuRF1, and the 20S proteasome. Among the candidates, a promising peptide (CLPPH) was synthesized and validated in vitro, demonstrating inhibitory effects on nitric oxide production, DPP-IV, ACE, and α-glucosidase. These results highlight CLPPH's potential as a multifunctional bioactive peptide and support the valorization of Alaska Pollock milt as a sustainable source for functional foods and nutraceutical applications.

Animals↗

PubMed2025

Cancer-testis antigen ACRBP: Cytotoxic response to its HLA-A2 restricted peptide and immune features in ovarian cancer.

While our prior study identified the HLA-A *0201-restricted ACRBP epitope peptide and demonstrated its capacity to generate cytotoxic T lymphocytes (CTLs) in vitro, the clinical relevance of the peptide-induced T cell reactivity in ovarian cancer (OC) patients and the in vivo anti-tumor efficacy of these CTLs remain unexplored. In this study, dendritic cells were sensitized with ACRBP peptide (ALLVLCYSI) and co-cultured with autologous CD8+T cells to induce the production of specific cytotoxic T lymphocytes (Pep-CTLs). The anti-tumor effects of Pep-CTLs were evaluated in SCID mice bearing human ovarian cancer (OC) OVCAR-3 cells. Concurrently, we co-cultured ALLVLCYSI peptide with peripheral blood mononuclear cells (PBMCs) from OC patients (HLA-A2+, ACRBP+) and assessed the number of specific T cells using ELISPOT assays. The immunological impact of the ACRBP peptide against human OC was validated through both in vitro and in vivo experiments. These findings establish a preclinical foundationfor developing ACRBP peptide-based vaccines in OC immunotherapy. To further elucidate ACRBP's role in OC treatment, the study analyzed single-cell RNA sequencing data from 8 OC patients and bulk RNA sequencing data from the Cancer Genome Atlas Project (TCGA) comprising 308 ovarian cancer cases. This analysis aimed to explore the heterogeneity among ACRBP-expressing tumor cell populations and to investigate the correlation between ACRBP expression and immune molecule expression (including MHC and chemokines) alongside chemotherapy response. These insights furnish a theoretical framework supporting the future application of ACRBP in tumor immunotherapy and strategies to prevent immune escape.

Humans↗

PubMed2025

Unveiling novel antimicrobial peptides from the ruminant gastrointestinal microbiomes: A deep learning-driven approach yields an anti-MRSA candidate.

INTRODUCTION: Antimicrobial peptides (AMPs) present a promising avenue to combat the growing threat of antibiotic resistance. The ruminant gastrointestinal microbiome serves as a unique ecosystem that offers untapped potential for AMP discovery. OBJECTIVES: The aims of this study are to develop an effective methodology for the identification of novel AMPs from ruminant gastrointestinal microbiomes, followed by evaluating their antimicrobial efficacy and elucidating the mechanisms underlying their activity. METHODS: We developed a deep learning-based model to identify AMP candidates from a dataset comprising 120 metagenomes and 10,373 metagenome-assembled genomes derived from the ruminant gastrointestinal tract. Both in vivo and in vitro experiments were performed to examine and validate the antimicrobial activities of the AMP candidates that were selected through bioinformatic analysis and subsequently synthesized chemically. Additionally, molecular dynamics simulations were conducted to explore the action mechanism of the most potent AMP candidate. RESULTS: The deep learning model identified 27,192 potential secretory AMP candidates. Following bioinformatic analysis, 39 candidates were synthesized and tested. Remarkably, all synthesized peptides demonstrated antimicrobial activity against Staphylococcus aureus, with 79.5% showing effectiveness against multiple pathogens. Notably, Peptide 4, which exhibited the highest antimicrobial activity against methicillin-resistant Staphylococcus aureus (MRSA), confirmed this effect in a mouse model with wound infection, exhibiting a low propensity for resistance development and minimal cytotoxicity and hemolysis towards mammalian cells. Molecular dynamics simulations provided insights into the mechanism of Peptide 4, primarily its ability to disrupt bacterial cell membranes, leading to cell death. CONCLUSION: This study highlights the power of combining deep learning with microbiome research to uncover novel therapeutic candidates, paving the way for the development of next-generation antimicrobials like Peptide 4 to combat the growing threat of MRSA would infections. It also underscores the value of utilizing ruminant microbial resources.

Animals↗

PubMed2025

Improved Detection of Differentially Abundant Proteins through FDR-Control of Peptide-Identity-Propagation.

The goal of proteomics is to identify and quantify peptides and proteins within a biological sample. Almost all algorithms for the identification of peptides in LC-MS/MS data employ two steps: peptide/spectrum matching and peptide-identity-propagation (PIP), also known as match-between-runs. PIP can routinely account for up to 40% of all results, with that proportion rising as high as 75% in single-cell proteomics. Unlike peptide identities derived through peptide/spectrum matches, for which error estimation has been strictly enforced for decades, peptide identities derived through PIP have not historically been subject to statistical evaluation. As an indispensable component of label-free quantification, PIP needs a statistically rigorous method for estimating its false-discovery rate (FDR). We present a method for FDR control of PIP, called PIP-ECHO, and devise a rigorous protocol for evaluating FDR control of any PIP method. Using three different benchmark data sets, we evaluate PIP-ECHO alongside the PIP procedures implemented by FlashLFQ, IonQuant, and MaxQuant. These analyses show that only PIP-ECHO can accurately control the FDR of PIP at 1% across all data sets. When analyzing a spike-in data set, PIP-ECHO increases both the accuracy and sensitivity of differential expression analysis, yielding substantially more differentially abundant proteins than either MaxQuant or IonQuant.

Proteomics↗