Biology subjects

Samanta, S. K.

Publications and source records attributed to Samanta, S. K..

3 recordsLinked to original sources

bioRxiv2025

Phylogenetic Analysis of Beta-Lactamases Reveals Distinct Evolutionary Patterns of Chromosomal and Plasmid-Encoded BLs and the Mosaic Role of VIM Linking NDM and IMP

Beta-lactamases (BLs) are a major driver of antibiotic resistance in pathogens like Acinetobacter baumannii, Enterobacteriaceae and Pseudomonas aeruginosa. This study explores the structural stability and functional divergence of metallo-beta-lactamases (MBLs) and AmpC through study of protein-protein interaction (PPI) network analysis, phylogenetics and identification of conserved domains and motifs. The study also involves analysis of co-evolutionary dynamics of BLs with related genes. PPI analysis identified NagZ as central interacting partner for BLs in P. aeruginosa and Enterobacteriaceae, suggesting its pivotal role in BL expression, and highlighting its probable role as a potential drug target. In contrast, A. baumannii exhibited such a high diversity in protein interactions, that considering a single protein as topmost interacting partner was difficult. Phylogenetic analysis revealed strong co-evolutionary trends between functionally-related proteins. A. baumannii and Enterobacteriaceae were found to be closely related in case of not just the chromosomally-encoded MBL-fold containing proteins, but also the true MBLs which are plasmid-encoded. On the other hand, similar evolutionary patterns for chromosomally-encoded BLs like AmpC and related genes was found in A. baumannii and P. aeruginosa. Analysis of PFAM domains showed that catalytic and substrate binding domains are more conserved than accessory ones. Several motifs, such as, ASNGLI were found to be conserved across all MBLs and thus carry the potential to be significant drug targets. Finally, inter-BL analysis revealed that VIM acts as an evolutionary link by acting like a mosaic between IMP and NDM as supported by intermediate GC content, positioning in phylogenetic trees and shared sequence features.

evolutionary biology↗

bioRxiv2025

AIPID: MAD-ML-Powered AIP Discovery Platform

Inflammation is a biological defense mechanism against harmful stimuli such as infection, tissue injury, or toxic agents, which, if prolonged, can lead to chronic inflammatory disorders. The limited safety and tolerability of current anti-inflammatory drugs emphasize the need for novel, selective therapeutic agents. Anti-inflammatory peptides (AIPs) have emerged as promising candidates owing to their ability to selectively target diseased cells while sparing healthy tissue. However, the identification of AIPs remains constrained by labor-intensive and expensive wet-laboratory screenings. To address this challenge, we have developed AIPID, an interactive, publicly accessible web-application for faster identification of anti-inflammatory peptides. Central to our platform is our Motif-Analysis-Driven Machine Learning (MAD-ML) model, based on the approach of representative negative dataset selection, combining iterative random sampling with motif profiling to enhance dataset diversity and model robustness. Finally, AIPID employs a Random Forest-based classifier trained on motif-filtered, biologically relevant peptide sequences, classifying inputs as AIPs or non-AIPs based on sequence-derived physiochemical descriptors. The model demonstrated excellent performance, achieving sensitivity of 95.16%, specificity of 99.98%, and F1 score of 98.14%, outperforming existing models and correctly predicting 18 of 19 experimentally validated AIPs. The AIPID platform offers an intuitive, multi-page interface for sequence-based peptide prediction, exploration of UniProt-derived AIP repositories, and access to statistical insights on peptide properties. The application is freely available at https://aipid-app-version1.streamlit.app/, providing a valuable resource for the peptide therapeutics research community.

bioinformatics↗

bioRxiv2025

How F repeats help in peptide binding with VIM-2 metallo-beta-lactamase and destabilizing the enzyme

The global surge in antimicrobial resistance is a major public health concern, largely driven by the dissemination of beta-lactamases. Among them, VIM-2 metallo-beta-lactamase (MBL) poses a significant therapeutic challenge. In this study, we explore the role of phenylalanine and lysine residues, especially their repeat patterns, in modulating peptide binding affinity and structural interactions with VIM-2. Docking, molecular dynamics simulations, free energy decomposition analysis and residue analysis were conducted on gut metagenome-derived AMPs, PolyF and a control peptide (PolyR). Binding affinity with VIM-2 decreased with declining F content, yet PolyF alone showed unexpectedly low binding. However, PolyF exhibited most negative polar solvation energy in free energy decomposition analysis. This indicates that F repeats play a role in stabilizing a VIM-2-peptide complex in presence of a solvent. PolyF peptide lacks favorable electrostatic or dynamic interactions, likely due to the absence of K residues. Interestingly, loop1 and loop2 analysis of VIM-2 revealed that F repeats interfere with the loops that play a role in functioning of substrate binding at the active site. Additionally, F repeats were found to bind to residues lying adjacent to or nearby active site residues, which points towards the fact that F helps other residues like K to bind more stably with active site residues of VIM-2. Finally, analog designing and analysis reveals that a balance of aromatic (F) and positively charged (K) residues could enhance peptide binding with MBLs like VIM-2 when having hydrophobic on N-terminal and positively charged on C-terminal.

bioinformatics↗