Biology subjects

Anand, A. A.

Publications and source records attributed to Anand, A. A..

7 recordsLinked to original sources

bioRxiv2026

Microbial Signal Recognition & Neuronal Mimicry (SRNM) axis in IBD

Inflammatory bowel disease (IBD), encompassing Crohns disease (CD) and ulcerative colitis (UC), is a chronic inflammatory disorder whose pathogenesis involves intricate host-microbiome interactions. Molecular mimicry (the structural or functional resemblance between microbial and host proteins) represents a plausible mechanism by which gut microbiota may trigger or perpetuate autoimmune responses. Here we present a comprehensive, multi-layered molecular mimicry in silico pipeline (MMIP) analysis of 39 baseline shotgun metagenomic samples from Human Microbiome Project 2 (HMP2/IBDMDB). Using DIAMOND-based homology search against the SwissProt followed by UniProt Retrieve/ID Mapping (URIM), we characterized microbial protein functional space through three complementary frameworks: (i) normalized GO term frequency comparison across diagnostic groups, (ii) protein family (PFAM) domain enrichment analysis, and (iii) sequence-level mimicry analysis identifying microbial proteins with direct homology to human proteins. Taxonomic profiling using MetaPhlAn3 pre-computed abundance profiles provided further biological context. CD microbiomes exhibited greater enrichment of immune-relevant biological processes and a higher per-sample sequence-level mimicry rate than UC. CD and UC also showed distinct pathobiont profiles, with CD enriched for oral-origin taxa including Haemophilus parainfluenzae and UC enriched for Fusobacterium nucleatum and Prevotella species. Notably, healthy gut microbiome maintains coordinated mimicry of host neuronal, signal-recognition-particle-associated, and antimicrobial peptide machinery, a repertoire dismantled in IBD and replaced by disease-subtype-specific signatures, alongside a candidate mimicry link between CD-enriched bacteria and NOD2. These results represent the first metagenome-wide characterization of molecular mimicry across IBD subtypes using shotgun metagenomic data, offering new mechanistic insight into how microbial dysbiosis may contribute to immune dysregulation in IBD.

bioinformatics↗

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↗

bioRxiv2020

An ultra-high affinity synthetic nanobody blocks SARS-CoV-2 infection by locking Spike into an inactive conformation

Without an effective prophylactic solution, infections from SARS-CoV-2 continue to rise worldwide with devastating health and economic costs. SARS-CoV-2 gains entry into host cells via an interaction between its Spike protein and the host cell receptor angiotensin converting enzyme 2 (ACE2). Disruption of this interaction confers potent neutralization of viral entry, providing an avenue for vaccine design and for therapeutic antibodies. Here, we develop single-domain antibodies (nanobodies) that potently disrupt the interaction between the SARS-CoV-2 Spike and ACE2. By screening a yeast surface-displayed library of synthetic nanobody sequences, we identified a panel of nanobodies that bind to multiple epitopes on Spike and block ACE2 interaction via two distinct mechanisms. Cryogenic electron microscopy (cryo-EM) revealed that one exceptionally stable nanobody, Nb6, binds Spike in a fully inactive conformation with its receptor binding domains (RBDs) locked into their inaccessible down-state, incapable of binding ACE2. Affinity maturation and structure-guided design of multivalency yielded a trivalent nanobody, mNb6-tri, with femtomolar affinity for SARS-CoV-2 Spike and picomolar neutralization of SARS-CoV-2 infection. mNb6-tri retains stability and function after aerosolization, lyophilization, and heat treatment. These properties may enable aerosol-mediated delivery of this potent neutralizer directly to the airway epithelia, promising to yield a widely deployable, patient-friendly prophylactic and/or early infection therapeutic agent to stem the worst pandemic in a century.

biochemistry↗

bioRxiv2018

Structural basis of eIF2B-catalyzed GDP exchange and phosphoregulation by the integrated stress response

The integrated stress response (ISR) tunes the rate of protein synthesis. Control is exerted by phosphorylation of the general translation initiation factor eIF2. eIF2 is a GTPase, that becomes activated by eIF2B, a two-fold symmetric and heterodecameric complex that functions as eIF2s dedicated nucleotide exchange factor. Phosphorylation converts eIF2 from a substrate into an inhibitor of eIF2B. We report cryoEM structures of eIF2 bound to eIF2B in the dephosphorylated state. The structures reveal that the eIF2B decamer is a static platform upon which one or two flexible eIF2 trimers bind and align with eIF2Bs bipartite catalytic centers to catalyze guanine nucleotide exchange. Phosphorylation refolds eIF2, allowing it to contact eIF2B at a different interface and, we surmise, thereby sequesters it into a non-productive complex.\n\nOne Sentence SummaryStructures of translation factors eIF2 and eIF2B reveal the mechanism of nucleotide exchange and its phosphoregulation during stress.

biochemistry↗

bioRxiv2017

Structure of the nucleotide exchange factor eIF2B reveals mechanism of memory-enhancing molecule

Regulation by the integrated stress response (ISR) converges on the phosphorylation of translation initiation factor eIF2 in response to a variety of stresses. Phosphorylation converts eIF2 from substrate to competitive inhibitor of its dedicated guanine nucleotide exchange factor, eIF2B, inhibiting translation. ISRIB, a drug-like eIF2B activator, reverses the effects of eIF2 phosphorylation and, remarkably, in rodents enhances cognition and corrects cognitive deficits after brain injury. To determine its mechanism of action, we solved an atomic-resolution structure of ISRIB bound in a deep cleft within decameric human eIF2B by electron cryo-microscopy. Structural and biochemical analyses revealed that formation of fully active, decameric eIF2B holoenzyme depended on the assembly of two identical tetrameric subcomplexes, and that ISRIB promoted this step by cross-bridging a central symmetry interface. Regulation of eIF2B assembly emerges as a rheostat for eIF2B activity that tunes translation during the ISR and that can be further modulated by ISRIB.

biochemistry↗