Biology subjects

Bhargava, Y.

Publications and source records attributed to Bhargava, Y..

6 recordsLinked to original sources

bioRxiv2026

Retrieval of binding sites across the AlphaFold human proteome using protein language model representations

Protein language models (PLMs) provide powerful representations of protein sequence, but their utility for proteome-scale binding-site retrieval remains unclear. Here, we present PocketScope, a training-free framework that represents cavity-lining residues using frozen ESM-C 600M embeddings and retrieves related binding sites through exhaustive late-interaction MaxSim, without pooling or approximate nearest-neighbor search. PocketScope identified 153,805 cavities across 37,682 proteins in the AlphaFold human proteome and recovered documented drug off-targets across a curated set of pharmacological pairs. On the ProSPECCTs benchmark, PocketScope ranks 1st of 23 methods by mean rank across the ten collections. PocketScope provides a practical framework for proteome-scale off-target prediction. PocketScope is open source and also freely available as a web server at https://www.bhargavaresearch.org/pocketscope.

bioinformatics↗

bioRxiv2026

NDST1 as a substrate-reduction target in Mucopolysaccharidosis type IIIC: virtual screening, microsecond molecular dynamics, and peptide design

Mucopolysaccharidosis IIIC (Sanfilippo syndrome type C) is a rare lysosomal storage disorder caused by loss-of-function mutations in HGSNAT, which encodes an enzyme involved in heparan sulfate (HS) degradation, leading to impaired HS catabolism, lysosomal accumulation, and progressive neurodegeneration. Because enzyme replacement therapies have limited penetration across the blood-brain barrier, substrate-reduction therapy represents an alternative therapeutic strategy. Here, N-deacetylase/N-sulfotransferase 1 (NDST1), a key enzyme responsible for HS biosynthesis, was investigated as a potential substrate-reduction target. A structure-based computational pipeline was used to identify and evaluate inhibitors targeting the NDST1 sulfotransferase domain. Approximately 4.1 million drug-like compounds and FDA-approved drugs were screened by molecular docking, followed by pharmacokinetic filtering, molecular dynamics simulations, and MM/PBSA binding free energy calculations. In parallel, peptide binders targeting the same site were generated using diffusion-based protein design and evaluated using molecular dynamics and MM/GBSA analysis. Four chemically distinct small-molecule scaffolds and three peptide candidates were identified as stable binders to the NDST1 active site. The lead small-molecule candidate exhibited a predicted binding free energy of -13.36 {+/-} 5.87 kcal mol-1. These provide a focused set of candidates for further investigation and support the feasibility of targeting NDST1 as a substrate-reduction strategy for MPS IIIC.

biophysics↗

bioRxiv2026

In Silico Targeting of Calpain-5 in Autosomal Dominant Neovascular Inflammatory Vitreoretinopathy (ADNIV) with Peptides and Natural Products

Autosomal dominant neovascular inflammatory vitreoretinopathy (ADNIV) is a rare retinal disease caused by gain-of-function mutations in the non-classical calcium-activated cysteine protease calpain-5 (CAPN5). These mutations lower the calcium threshold for catalytic-triad alignment with downstream effects including excessive proteolysis and retinal degeneration, making CAPN5 a therapeutic target. Clinical studies showed that knockout of calpain-5 resulted in no negative side effects, supporting therapy through inhibition. We mapped the druggable pockets of CAPN5 with a 500 ns phenol cosolvent molecular dynamics (MD) simulation. Occupancy analysis resolved five pockets, against which 448,314 COCONUT natural products were screened with Uni-Dock (2,241,570 docked combinations). In parallel, BoltzGen was used to design peptide binders against multiple candidate regions, from which three were selected: the PC1-PC2 subdomain interface, the PC2 regulatory loop (PC2L1) and the catalytic region. The top three designs were co-folded with Boltz-2 at high interface confidence (ipTM 0.91-0.95). The top three peptides and four small molecules were then simulated against wild-type CAPN5 and the four canonical ADNIV variants R243L, L244P, K250N and R289W, each condition in independent triplicate, giving 105 production simulations of 100 ns. Scoring by MM-PBSA revealed favorable peptide interface energies, the most favorable being the largest of the three designs ({Delta}TOTAL -58.6 {+/-} 5.6 kcal/mol for a 23-residue peptide against wild type), while the small-molecule panel returned -8.7 to -23.8 kcal/mol. A total of 15.8 {micro}s of cosolvent, filtering, and production MD prioritizes the catalytic cleft and an adjacent groove for experimental testing and provides candidate peptide and small-molecule binders for evaluating CAPN5 inhibition in ADNIV.

biophysics↗

bioRxiv2026

Dynamics of the α2-chimaerin-Rac1 interface in Duane retraction syndrome and computational design of candidate probes

Duane retraction syndrome (DRS) is an eye movement disorder caused by gain-of-function mutations in CHN1, which encodes 2-chimaerin. Mutations in CHN1 cause excessive Rac1 suppression during axon guidance, but the dynamics of the 2-chimaerin-Rac1 interaction remain largely uncharacterized. We simulated the CHN1- Rac1 complex with all-atom molecular dynamics to characterize this interface. An Ile420-Arg445 hydrophobic ridge is the persistent core, while the 304-310 patch carrying the catalytic arginine finger scores lower and varies more between replicates. We screened the BioGRID partners of CHN1 with AlphaFold3 and ipSAE; only Rac1, NCK1, and NCK2 gave high-confidence predicted complexes. Rac1 and NCK1 stayed bound in every replicate, and NCK2 in two of three. The MM-GBSA interface {Delta}G for Rac1 was -20 {+/-} 10 kcal/mol. We also explored how the surface could be probed by small molecules and peptides. To identify candidate chemical probes, we sampled natural products from COCONUT, refined top hits through CReM fragment growth, and generated peptides with BoltzGen. Prioritized ligands were evaluated with molecular dynamics and MM-PBSA endpoint free-energy calculations. Across 6 {micro}s of cumulative simulation, the contact network defining the CHN1-Rac1 interface matched ligand and peptide engagement. These results define the key residues of the CHN1-Rac1 interface and identify candidate chemical probes for testing their role experimentally.

biophysics↗

bioRxiv2026

A Chromatin Biology Assessment of AlphaFold3

Biomolecular structure prediction tools such as AlphaFold have achieved remarkable success in predicting structures of single proteins and multiprotein complexes. AlphaFold3 now incorporates the capability to model complexes containing nucleic acids and chemically modified side chains. Investigators can now predict structures of proteins bound to chromatin, where interactions with nucleosomal DNA and histone post-translational modifications converge to control genome function. To evaluate its robustness in modeling chromatin complexes, we benchmarked AlphaFold3 on 115 structures containing nucleosomes whose coordinates were released by the Protein Data Bank after the training set cutoff date. We find that AlphaFold3 excels at predicting histone-driven interactions and accurately models complexes that deposit and recognize post-translational modifications. By contrast, AlphaFold3 struggles to predict structures of chromatin factors that primarily engage nucleosomal DNA, notably transcription factors and chromatin remodelers. Finally, we show that AlphaFold3 can faithfully recapitulate known post-translational modification recognition patterns, matching experimentally determined specificity profiles. This assessment of the capabilities and limitations of AF3 in chromatin structural biology provides a roadmap for its effective application to studies of chromatin regulation and PTM readout, while identifying key areas for future algorithmic refinement. SignificanceStructure prediction with AlphaFold has become an invaluable tool in experimental biology, and the accuracy of many of its predictions has been verified in structural and biochemical studies. With the recent incorporation into AlphaFold3 of nucleic acids and post-translational modifications, this prediction tool can now be applied to chromatin structural biology. Our benchmarking of AlphaFold3 reveals its strengths and weaknesses in predicting structures of proteins bound to nucleosomes, thereby providing a framework for using these models in mechanistic studies of chromatin regulation. We introduce metrics for evaluating structures of nucleosome complexes that highlight AlphaFold3s strengths in predicting protein-nucleosome interactions and post-translational modification specificity.

biophysics↗

bioRxiv2024

Accurate in silico predictions of modified RNA interactions to a prototypical RNA-binding protein with {lambda}-dynamics

RNA-binding proteins shape biology through their widespread functions in RNA biochemistry. Their function requires the recognition of specific RNA motifs for targeted binding. These RNA binding elements can be composed of both unmodified and chemically modified RNAs, of which over 170 chemical modifications have been identified in biology. Unmodified RNA sequence preferences for RNA-binding proteins have been widely studied, with numerous methods available to identify their preferred sequence motifs. However, only a few techniques can detect preferred RNA modifications, and no current method can comprehensively screen the vast array of hundreds of natural RNA modifications. Prior work demonstrated that {lambda}-dynamics is an accurate in silico method to predict RNA base binding preferences of an RNA-binding antibody. This work extends that effort by using {lambda}-dynamics to predict unmodified and modified RNA binding preferences of human Pumilio, a prototypical RNA binding protein. A library of RNA modifications was screened at eight nucleotide positions along the RNA to identify modifications predicted to affect Pumilio binding. Computed binding affinities were compared with experimental data to reveal high predictive accuracy. In silico force field accuracies were also evaluated between CHARMM and Amber RNA force fields to determine the best parameter set to use in binding calculations. This work demonstrates that {lambda}-dynamics can predict RNA interactions to a bona fide RNA-binding protein without the requirements of chemical reagents or new methods to experimentally test binding at the bench. Advancing in silico methods like {lambda}-dynamics will unlock new frontiers in understanding how RNA modifications shape RNA biochemistry.

biochemistry↗