Biology subjects

Kalyaanamoorthy, S.

Publications and source records attributed to Kalyaanamoorthy, S..

9 recordsLinked to original sources

bioRxiv2026

Computational Modeling of Functionalized Graphene Quantum Dots Binding to Tau Fibrils in Alzheimers Disease

The aggregation of Tau protein into straight filaments (SFs) and paired helical filaments (PHFs) is central to Alzheimers disease (AD) pathology and a key target for therapeutic inhibition. Graphene quantum dots (GQDs) are biocompatible nanomaterials that have shown promise in inhibiting amyloidogenic protein aggregation across related neurological pathologies. The effect of GQD functionalization on interactions with Tau aggregates (TAs) is poorly understood, though recent evidence suggests that anionic GQDs are effective TA inhibitors. In this study, we survey how GQD functionalization influences binding to SFs and PHFs to guide future development of therapeutic GQDs. We identify binding sites in SFs and PHFs, dock our GQD library to these sites, and perform molecular dynamics simulations on promising complexes, totaling 28 {micro}s of sampling. We discover that anionic GQDs preferentially bind to the positively charged SF large protofilament interface, whereas in PHFs, anionic GQDs have a modest binding preference for the C-shaped curve region. Binding of GQDs at the C-shaped curve in both TAs induces distinct protofilament conformational dynamics resembling a pinching motion to capture the GQD. Together, these binding modes may represent early intermediates of the TA disaggregation mechanism. We find that functional groups capable of possessing a negative charge (e.g., COO-, O-, and S-) produce impressive binding affinities. We propose that enriching these functionalizations during GQD synthesis and preparation, particularly sulfur as it is less studied, may yield more potent TA inhibitors and generalize to other amyloid pathologies with positively charged fibril cores. O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=108 SRC="FIGDIR/small/741532v1_ufig1.gif" ALT="Figure 1"> View larger version (43K): org.highwire.dtl.DTLVardef@d5d794org.highwire.dtl.DTLVardef@13c59e1org.highwire.dtl.DTLVardef@130c369org.highwire.dtl.DTLVardef@6d3385_HPS_FORMAT_FIGEXP M_FIG C_FIG

biochemistry↗

bioRxiv2026

Surface electrostatic networks control hydrophobic core remodeling in a pH-dependent switching protein

Communication between protein surfaces and their buried cores is central to protein structure and function, yet this phenomenon remains challenging to predict and control at high resolution. Changes in the protonation of surface ionizable residues communicate with the hydrophobic core, for example, in diverse pH-dependent protein functions. Hisactophilin, a histidine-rich actin- and membrane-binding protein, provides a general model for exploring such communication as it exhibits a finely tuned pH-regulated myristoyl-switching function. Upon reversible proton binding, the myristoyl group shifts between being sequestered in the hydrophobic core and more solvent accessible. In the current study we utilize experimental and computational approaches we uncover how binding of [~]1.5 net protons alters electrostatic interactions involving ionizable residues distributed across much of the protein surface. These changes are transmitted to the hydrophobic core through dynamic communities of ionizable and hydrophobic residues which substantially rearrange upon switching. The effects of mutating individual ionizable residues are weaker than those of core hydrophobic residues, and only combined mutation of multiple ionizable residues caused substantial functional change. Together, these results reveal how communication between surface ionizable residues and the hydrophobic core is mediated by extensive interaction networks that reorganize in response to changes in protonation. These results may provide general insights for understanding protein cooperativity and the coupling of surface and core residues in protein function, disease, evolution, engineering, and design. Significance StatementHow changes on the protein surface, such as proton binding to ionizable amino acids, are communicated to the protein core to regulate protein stability and function remains ill-defined. Synthesis of experimental and computational analyses resolves the distributed networks of surface ionizable residue interactions coupled to the hydrophobic core that control pH-dependent myristoyl switching in hisactophilin. Small changes in protonation that create and alleviate local electrostatic repulsion give rise to protein-wide changes in fluctuating surface-core interactions. This distributed electrostatics-core coupling mechanism may help explain the often underrecognized and long-range impacts of ionizable residues in proteins and provide a framework for interpreting the effects of mutations in fundamental and applied protein science.

biophysics↗

bioRxiv2026

Robust Multi-Mutant Protein Stability Prediction from a Fine-Tuned Evolutionary Scale Model

Recently, high-throughput experimental techniques have propelled improvements in deep learning-based prediction of mutation effects on protein stability. However, leading stability predictors still struggle to predict the combined effect of multiple mutations and prefer mutations that negatively impact other properties, including expressibility. To mitigate these limitations, we apply Low-Rank Adaptation (LoRA) to specialize ESM3 for stability prediction by fine-tuning on the Megascale protease susceptibility dataset, developing a novel dual-perspective inference mechanism to provide explicit mutant context information. ESM-Mutant Stability Ranker (ESM-MSR) significantly exceeds all contemporary methods tested on the prioritization of stabilizing mutations ({Delta}NDCG@96 [≥] +0.12), double mutant ranking ({Delta}{rho}avg [≥]+0.068) and direct epistasis ranking ({Delta}{rho}avg [≥] +0.164) within the Megascale test set. Further, it generalizes effectively to heterogeneous thermostability benchmarks, consistently matching or exceeding current approaches across our comprehensive suite. Finally, a single parameter{sigma} enables tunable control of the models compromise between stability and more general sequence fitness, leading to state-of-the-art performance in the Human Domainome 1 benchmark ({rho}avg = 0.573) at{sigma} = 0.5, demonstrating the broad applicability of ESM-MSR as a protein engineering tool.

bioinformatics↗

bioRxiv2025

Investigation of the HLA locus in autopsy-confirmed progressive supranuclear palsy

ObjectivesProgressive supranuclear palsy (PSP) is a neurodegenerative disease showing pathological tau accumulation in subcortical neurons and glial cells. The human leukocyte antigen (HLA) locus on chromosome 6 is a polymorphic region with complex linkage patterns that has been implicated in several autoimmune and neurological disorders. The HLA locus has not been systematically examined in PSP. It is unclear whether tau and HLA can interact to induce an autoimmune disease mechanism. MethodsWe evaluated an autopsy confirmed PSP cohort (n=44) and compared allele/haplotype frequencies to those of the reference group of a local deceased Canadian donor pool. We performed HLA/Tau peptide binding prediction and modelling of HLA Class II and Tau Peptide interactions. FindingsOdds ratio was 2.94 (95% CI 1.01 to 8.55; p=0.047) for DQB1*06:01 allele, and 2.59 (95% CI 1.39 to 4.83; p=0.0025) for the narcolepsy associated haplotype (DRB1*15:01DQB1*06:02). One patient with 4 repeat tau PSP type pathology was a carrier of the IgLON5-associated haplotype (DRB1*10:01DQB1*05:01). HLA/Tau peptide binding prediction and modelling of HLA Class II/Tau Peptide interactions revealed strong binding tau peptides but not the PSP protofilament fold for alleles DQA1*01:02DQB1*06:02 and DQA1*01:03DQB1*06:01. ConclusionOur study suggests that epitopes within the tau peptide may bind to HLA alleles that are found in a subset of PSP patients supporting the notion of an autoimmune pathophysiological component. These findings have implications for subtyping and stratifying patients for therapies, including those targeting immune modulation.

immunology↗

bioRxiv2024

P4ward: an automated modelling platformfor Protac ternary complexes

Proteolysis Targeting Chimeras (Protacs) are a new class of drugs which promote degradation of a protein of interest (POI) by hijacking the Ubiquitin-Proteasome system. Struc tural knowledge of an E3 ligase: Protac:POI ternary complex is required for Protac rational design, and computational modelling of such heteromeric complex structures is nontrivial. To date, few programs have been developed to address this challenge, however, there remains a need for readily accessible tools that can significantly improve ternary complex modelling accuracy. Particularly, programs that can also support the screening phase of Protac discovery, where speed and the ability to test multiple Protacs is essential to advance the field of Protac therapeutics. To bridge these gaps, we present P4ward, a free and fully automated Protac ternary complex modelling pipeline. P4ward achieves a hit-rate of 76.5% with an average rank of 7.26, and substantially reduces the rank of the near-native pose by 73-98% compared to earlier programs. We believe that P4ward could be a user-friendly, fast, and effective tool for gaining atomistic insights necessary for Protac modelling and optimization.

bioinformatics↗

bioRxiv2023

Zero-Shot Transfer of Protein Sequence Likelihood Models to Thermostability Prediction

Protein sequence likelihood models (PSLMs) are an emerging class of self-supervised deep learning algorithms which learn distributions over amino acid identities in structural and evolutionary contexts. Recently, PSLMs have demonstrated impressive performance in predicting the relative fitness of variant sequences without any task-specific training. In this work, we comprehensively analyze the capacity of six PSLMs to predict experimental measurements of thermostability for variants of hundreds of heterogeneous proteins. We assess performance of PSLMs relative to state-of-the-art supervised models, highlight relative strengths and weaknesses, and examine the complementarity between these models. We focus our analyses on stability engineering applications, assessing which methods and combinations of methods can most consistently identify and prioritize mutations for experimental validation. Our results indicate that structure-based PSLMs have competitive performance with the best existing supervised methods and can augment the predictions of supervised methods by integrating insights from their disparate training objectives.

bioinformatics↗

bioRxiv2022

The H163A Mutation Unravels an Oxidized Conformation of the SARS-CoV-2 Main Protease and Opens a New Avenue for Anti-Viral Therapeutic Design

The main protease of SARS-CoV-2 (Mpro) is an important target for developing COVID-19 therapeutics. Recent work has highlighted Mpros susceptibility to undergo redox-associated conformational changes in response to cellular and immune-system-induced oxidation. Despite structural evidence indicating large-scale rearrangements upon oxidation, the mechanisms of conformational change and its functional consequences are poorly understood. Here, we present the crystal structure of a new Mpro point mutant (H163A) that shows an oxidized conformation with the catalytic cysteine in a disulfide bond. We hypothesize that Mpro adopts this conformation under oxidative stress to protect against over-oxidation. Our metadynamics simulations illustrated a potential mechanism by which H163 modulates this transition and suggest that this equilibrium exists in the wild-type enzyme. We show that other point mutations can also significantly shift the equilibrium towards this state by altering conformational free energies. New therapeutic strategies against SARS-CoV-2 can be explored by understanding how H163 modulates this equilibrium.

biochemistry↗

bioRxiv2020

Molecular dynamics and in silico mutagenesis on the reversible inhibitor-bound SARS-CoV-2 Main Protease complexes reveal the role of lateral pocket in enhancing the ligand affinity

The 2019 novel coronavirus pandemic caused by SARS-CoV-2 remains a serious health threat to humans and a number of countries are already in the middle of the second wave of infection. There is an urgent need to develop therapeutics against this deadly virus. Recent scientific evidences have suggested that the main protease (Mpro) enzyme in SARS-CoV-2 can be an ideal drug target due to its crucial role in the viral replication and transcription processes. Therefore, there are ongoing research efforts to identify drug candidates against SARS-CoV-2 Mpro that resulted in hundreds of X-ray crystal structures of ligand bound Mpro complexes in the protein data bank (PDB) that describe structural details of different chemotypes of fragments binding within different sites in Mpro. In this work, we perform rigorous molecular dynamics (MD) simulation of 62 reversible ligand-Mpro complexes in the PDB to gain mechanistic insights about their interactions at atomic level. Using a total of ~2.25 s long MD trajectories, we identified and characterized different pockets and their conformational dynamics in the apo Mpro structure. Later, using the published PDB structures, we analyzed the dynamic interactions and binding affinity of small ligands within those pockets. Our results identified the key residues that stabilize the ligands in the catalytic sites and other pockets in Mpro. Our analyses unraveled the role of a lateral pocket in the catalytic site in Mpro that is critical for enhancing the ligand binding to the enzyme. We also highlighted the important contribution from HIS163 in this lateral pocket towards ligand binding and affinity against Mpro through computational mutation analyses. Further, we revealed the effects of explicit water molecules and Mpro dimerization in the ligand association with the target. Thus, comprehensive molecular level insights gained from this work can be useful to identify or design potent small molecule inhibitors against SARS-CoV-2 Mpro.

biophysics↗

bioRxiv2020

A minimum reporting standard for multiple sequence alignments

Multiple sequence alignments (MSAs) play a pivotal role in studies of molecular sequence data, but nobody has developed a minimum reporting standard (MRS) to quantify the completeness of MSAs in terms of completely-specified nucleotides or amino acids. We present an MRS that relies on four simple completeness metrics. The metrics are implemented in AliStat, a program developed to support the MRS. A survey of published MSAs illustrates the benefits and unprecedented transparency offered by the MRS.

bioinformatics↗