High Throughput Meta-analysis of Antimicrobial Peptides for Characterizing Class Specific Therapeutic Candidates: An In Silico Approach.

Abstract

The increasing incidence of antimicrobial resistance is becoming a serious concern worldwide and requires newer drugs. Recent evidence has shown growing interest in peptide-based therapeutics. Here, we performed a meta-analysis of nearly 867,000 predicted antimicrobial peptides and assessed their antibacterial (ABPs), antifungal (AFPs), and antiviral (AVPs) activity. We created high-quality, class-specific datasets and performed several computational analyses. Composition analysis revealed enrichment of aliphatic (V, A, I, and L) and positively charged (K and R) amino acids in ABPs: aliphatic (G, I), basic (K and R), and aromatic amino acids (F) in AFPs and sulfur containing (M) and aliphatic amino acids (V, I, and L) in AVPs. We observed significant differences in the molecular weight, charge, isoelectric point, and instability index of the peptides among three classes. We observed AFPs possessing the highest molecular weight and ABPs showing the highest charge and isoelectric point, whereas instability index was found to be comparable among the three classes. Motif analysis shows enrichment of unique motifs such as "VRVR" and "AKKPA" in ABPs, "DFFAI" and "FFAI" in AFPs, and "VVV" and "IM" in AVPs. We further developed seven distinct machine learning models to predict peptide activity where ExtraTree model achieved the highest AUROC of 0.98 in classifying ABPs and non-ABPs, 0.99 for classifying AFPs and non-AFPs, and 0.99 for classifying AVPs and non-AVPs on an independent dataset. To assist scientific community, we have provided the dataset and models at our GitHub page ( https://github.com/agrawalpiyush-srm/AMP_MetaAnalysis ). Subsequent filtering of peptides based on moonlighting properties (toxicity, allergenicity, cell-penetrating ability, half-life, and secondary structure) yielded a list of peptides that exhibit substantial therapeutic potential. We further selected the top ten peptides in each class, predicted their 3D structures using ColabFold embedded in ChimeraX1.8 software and performed molecular docking analysis with a pathogenic protein selected from an organism in each class using HDOCK webserver. Docking studies demonstrated strong interaction between peptides and the proteins. Lastly, we proposed list of peptides with high therapeutic potential in each class.