Biology subjects

Shekhar, M.

Publications and source records attributed to Shekhar, M..

15 recordsLinked to original sources

bioRxiv2026

Improving Molecular Docking Pose Recovery through Targeted Side-Chain Monte Carlo Sampling of an Apo Structure

Receptor flexibility complicates molecular docking when only an unbound (apo) protein structure is available. Local side-chain rearrangements can alter binding-pocket accessibility and ligand complementarity, motivating approaches that sample these motions efficiently. Here, we evaluate a targeted Markov Chain Monte Carlo (MCMC) protocol that combines local and larger side-chain torsional proposals with periodic molecular dynamics (MD) relaxation under harmonic backbone restraints. Receptor ensembles generated from an apo structure were evaluated against 859 experimental complexes from the OpenBind 2A protease benchmark and compared with the apo receptor and ensembles generated by restrained and unrestrained MD. With regular-space representative selection, MCMC recovered a ligand pose within 2 [A] of the experimental pose for 74.4% of complexes, compared with 51.0% for apo and 59.5% for restrained MD. Median minimum ligand heavy-atom RMSD decreased from 1.96 [A] for apo and 1.82 [A] for restrained MD to 1.62 [A] for MCMC. The largest gains occurred for complexes with greater side-chain displacement relative to pocket backbone displacement, consistent with the motions targeted by the protocol. MCMC representatives retained pocket geometries within the experimentally observed range, whereas unrestrained-MD representatives exhibited reduced pocket free volume and poor recovery. These findings support targeted side-chain sampling as a practical approach to generating apo-derived docking ensembles. The reported improvements measure best-case pose coverage across generated poses and receptors; their extension to prospective pose ranking and virtual screening remains to be established.

bioinformatics↗

bioRxiv2026

Model Validation Protocols for Machine Learning in Small Molecule Drug Discovery

Machine learning (ML) models for molecular property prediction are increasingly deployed in drug discovery, yet their adoption in real-world scenarios requires an understanding of the conditions in which a model succeeds or fails. While standardized benchmarks are powerful instruments to measure and unlock progress in ML research, they should not be blindly treated as the end goal. Especially static and retrospective benchmarks, in which no true unknown test set is employed, limit our ability to robustly validate a model's performance. Building on the collective expertise of a cross-industry consortium, we present a model validation framework consisting of five recommendations that would enable the community to move beyond aggregate metrics toward understanding where and why molecular property prediction models fail. We connect evaluation choices to real-world applications and case studies encountered in pharmaceutical research. The framework proposes splitting strategies that mimic realistic distribution shifts and expose common failure modes. We apply the recommended framework to a recently released dataset of absorption, distribution, metabolism, and excretion (ADME) properties. Across two complementary model algorithms, our case studies reveal four distinct failure modes (extrapolation, interpolation, representation, and evaluation), showing that model errors arise not only from distribution shift but also from limitations in molecular representations. Our results show that commonly used evaluation protocols can significantly overestimate performance and may not detect important model failure modes. All software and data are released via https://github.com/srijitseal/polaris.

bioinformatics↗

bioRxiv2026

Early-Enrichment Hit Discovery via Reversible-Work c(t) Estimation in Metadynamics (CTMD)

Virtual screening for small-molecule binders is often limited by false positives from approximate scoring functions and rigid-receptor assumptions. These can be addressed downstream through accurate but expensive free energy calculations. At the same time, recent artificial-intelligence-based co-folding methods have been proposed that claim to achieve accuracy of free energy methods at much lower cost, but these have not yet delivered consistent improvements in early enrichment and can be confounded by memorization. Here we address this gap by introducing c(t)-based metadynamics (CTMD), a physics-based, high-throughput hit-triaging protocol tailored for early enrichment. CTMD uses the nonequilibrium reversible-work estimator c(t) introduced by Tiwary and Parrinello (Journal of Physical Chemistry B, 2015 119 736), computed from a small number of short, independent well-tempered metadynamics trajectories, to rank binding stability without requiring converged binding free energies. Across diverse targets and chemotypes, CTMD provides robust early enrichment while remaining fast, transferable with minimal parameter tuning, and resistant to memorization-driven artifacts--underscoring both an immediately deployable physics-based alternative for screening. For these systems we show how co-folding, particularly Boltz-2, achieves enrichment directly proportional to similairty with training set, and more worrying, even in the presence of signficant modifications to active site. Given its simplicity of implementation, CTMD should thus be an "embarassingly" open-source, early enrichment method available for use by the broad pharma and academic community that sits right between approximate but fast docking or AI based co-folding methods, and more expensive but accurate free energy calculations, expected to lead to saving significant financial and human capital in drug discovery campaigns.

biophysics↗

bioRxiv2025

Mechanisms of HSV-1 helicase-primase inhibition and replication fork complex assembly

Herpesviruses are widespread double-stranded DNA viruses that establish lifelong latency and cause various diseases. Although DNA polymerase-targeting antivirals are effective, increasing drug resistance underscores the need for alternatives. Helicase-primase inhibitors (HPIs) are promising antivirals, but their mechanisms of action are poorly defined. Furthermore, how the helicase-primase (H/P) complex and DNA polymerase coordinate genome replication is not well understood for herpesviruses. Here, we report cryo-EM structures of the herpes simplex virus (HSV) H/P complex bound to HPIs, showing that these lock the helicase-primase complex in an inactive conformation. Single-molecule assays reveal that HPIs cause helicase-primase complexes to pause in unwinding activity on DNA. The structure of an HPI-bound replication fork complex, comprising the H/P complex (UL5, UL52, and UL8) and polymerase holoenzyme (UL30 and UL42), reveals a previously uncharacterized interface bridging these complexes. These findings provide a structural framework for understanding herpesvirus replisome assembly and advancing inhibitor development.

microbiology↗

bioRxiv2025

Phosphorylation-inducing chimera rewires oncogenic kinase to trigger apoptosis

The active sites electric field is integral to enzymatic catalysis (e.g., substrate recognition) and nature employs charge-altering post-translational modifications (e.g., phosphorylation) to perturb this electric field and regulate enzymes. A chromosomal translocation converts Abelson kinase (ABL) to BCR-ABL, whose hyperactivity drives several cancers. Here, we developed a small molecule, BRD8833, that induces BCR-ABL phosphorylation, which perturbs its active sites electric field with loss of hyperactivity. Unlike "occupancy-driven" inhibitors that require stoichiometric concentrations, BRD8833 operates through an event-driven, substoichiometric mechanism by inducing proximity between two BCR-ABL molecules to trigger the inhibitory phosphorylation and selective apoptosis of BCR-ABL-dependent cancer cells. Furthermore, BRD8833 is effective against other oncogenic ABL fusions or clinically observed resistance mutations, including those to occupancy-driven drugs with the same binding site as BRD8833, suggesting differences in their resistance mechanisms. These studies lay the foundation for electric-field and "event-driven" modalities to control hyperactive enzymes with orthogonal resistance mechanisms to occupancy-driven drugs. GRAPHICAL ABSTRACT (TOC) O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=197 SRC="FIGDIR/small/659082v2_ufig1.gif" ALT="Figure 1"> View larger version (48K): org.highwire.dtl.DTLVardef@1607a40org.highwire.dtl.DTLVardef@fa10deorg.highwire.dtl.DTLVardef@1698232org.highwire.dtl.DTLVardef@1e2e0f1_HPS_FORMAT_FIGEXP M_FIG C_FIG

synthetic biology↗

bioRxiv2024

Human-like dissociations between confidence and accuracy in convolutional neural networks

Prior research has shown that manipulating stimulus energy by changing both stimulus contrast and variability results in confidence-accuracy dissociations in humans. Specifically, even when performance is matched, higher stimulus energy leads to higher confidence. The most common explanation for this effect is the positive evidence heuristic where confidence neglects evidence that disconfirms the choice. However, an alternative explanation is the signal-and-variance-increase hypothesis, according to which these dissociations arise from low-level changes in the separation and variance of perceptual representations. Because artificial neural networks lack built-in confidence heuristics, they can serve as a test for the necessity of confidence heuristics in explaining confidence-accuracy dissociations. Therefore, we tested whether confidence-accuracy dissociations induced by stimulus energy manipulations emerge naturally in convolutional neural networks (CNNs). We found that, across three different energy manipulations, CNNs produced confidence-accuracy dissociations similar to those found in humans. This effect was present for a range of CNN architectures from shallow 4-layer networks to very deep ones, such as VGG-19 and ResNet -50 pretrained on ImageNet. Further, we traced back the reason for the confidence-accuracy dissociations in all CNNs to the same signal-and-variance increase that has been proposed for humans: higher stimulus energy increased the separation and variance of the CNNs internal representations leading to higher confidence even for matched accuracy. These findings cast doubt on the necessity of the positive evidence heuristic to explain human confidence and establish CNNs as promising models for adjudicating between low-level, stimulus-driven and high-level, cognitive explanations of human behavior.

animal behavior and cognition↗

bioRxiv2022

High-resolution structures map the metal import pathway in an Nramp transporter

Transporters of the Nramp (Natural resistance-associated macrophage protein) family import divalent transition metal ions into cells of most organisms. By supporting metal homeostasis, Nramps prevent disorders related to metal insufficiency or overload. Previous studies revealed that Nramps take on a LeuT fold and identified the metal-binding site. We present high- resolution structures of Deinococcus radiodurans Nramp in three stable conformations of the transport cycle revealing that global conformational changes are supported by distinct coordination geometries of its physiological substrate, Mn2+, across conformations and conserved networks of polar residues lining the inner and outer gates. A Cd2+-bound structure highlights differences in coordination geometry for Mn2+ and Cd2+. Measurements of metal binding using isothermal titration calorimetry indicate that the thermodynamic landscape for binding and transporting physiological metals like Mn2+ is different and more robust to perturbation than for transporting the toxic Cd2+ metal.

biochemistry↗

bioRxiv2021

Revealing a hidden intermediate of rotatory catalysis with X-ray crystallography and Molecular simulations

The mechanism of rotatory catalysis in ATP-hydrolyzing molecular motors remain an unresolved puzzle in biological energy transfer. Notwith standing the wealth of available biochemical and structural information inferred from years of experiments, knowledge on how the coupling between the chemical and mechanical steps within motors enforces directional rotatory movements remains fragmentary. Even more contentious is to pinpoint the rate-limiting step of a multi-step rotation process. Here, using Vacuolar or V1-type hexameric ATPase as an exemplary rotational motor, we present a model of the complete 4-step conformational cycle involved in rotatory catalysis. First, using X-ray crystallography a new intermediate or dwell is identified, which enables the release of an inorganic phosphate (or Pi) after ATP hydrolysis. Using molecular dynamics simulations, this new dwell is placed in a sequence with three other crystal structures to derive a putative cyclic rotation path. Free-energy simulations are employed to estimate the rate of the hexameric protein transformations, and delineate allosteric effects that allow new reactant ATP entry only after hydrolysis product exit. An analysis of transfer entropy brings to light how the sidechain-level interactions transcend into larger-scale reorganizations, highlighting the role of the ubiquitous arginine-finger residues in coupling chemical and mechanical information. Inspection of all known rates encompassing the 4-step rotation mechanism implicates overcoming of the ADP interactions with V1-ATPase to be the rate-limiting step of motor action.

biophysics↗

bioRxiv2021

Protein flexibility and dissociation pathway differentiation can explain onset of resistance mutations in kinases

Understanding how point mutations can render a ligand or a drug ineffective against a given biological target is a problem of immense fundamental and practical relevance. Often the efficacy of such resistance mutations can be explained purely on a thermo-dynamic basis wherein the mutated system displays a reduced binding affinity for the ligand. However, the more perplexing and harder to explain situation is when two protein sequences have the same binding affinity for a drug. In this work, we demonstrate how all-atom molecular dynamics simulations, specifically using recent developments grounded in statistical mechanics and information theory, can provide a detailed mechanistic rationale for such variances. We establish the dissociation mechanism for the popular anti-cancer drug Imatinib (Gleevec) against wild-type and N387S mutant of Abl kinase. We show how this single point mutation triggers a non-local response in the proteins flexibility and eventually leads to pathway differentiation during dissociation. This pathway differentiation explains why Gleevec has a long residence time in the wild-type Abl, but for the mutant, by opening up a backdoor pathway for ligand exit, an order of magnitude shorter residence time is obtained. We thus believe that this work marks an efficient and scalable approach to pinpoint the molecular determinants of resistance mutations in biomolecular receptors of pharmacological relevance that are hard to explain using a simple structural perspective and require mechanistic and kinetic insights. Significance statementRelapse in late-stage cancer patients is often correlated with the onset of drug resistance mutations. Some of these mutations are very far from the binding site and thus hard to explain from a purely structural perspective. Here we employ all-atom molecular dynamics simulations aided by ideas from information theory that can reach timescales of seconds with minimal human bias in how the sampling is enhanced. Through these we explain how a single point mutation triggers a non-local response in the protein kinases flexibility and eventually leads to pathway differentiation during dissociation, thereby significantly reducing the residence time of the drug.

biochemistry↗

bioRxiv2020

Outcomes of the 2019 EMDataResource model challenge: validation of cryo-EM models at near-atomic resolution

This paper describes outcomes of the 2019 Cryo-EM Map-based Model Metrics Challenge sponsored by EMDataResource (www.emdataresource.org). The goals of this challenge were (1) to assess the quality of models that can be produced using current modeling software, (2) to check the reproducibility of modeling results from different software developers and users, and (3) compare the performance of current metrics used for evaluation of models. The focus was on near-atomic resolution maps with an innovative twist: three of four target maps formed a resolution series (1.8 to 3.1 [A]) from the same specimen and imaging experiment. Tools developed in previous challenges were expanded for managing, visualizing and analyzing the 63 submitted coordinate models, and several novel metrics were introduced. The results permit specific recommendations to be made about validating near-atomic cryo-EM structures both in the context of individual laboratory experiments and holdings of structure data archives such as the Protein Data Bank. Our findings demonstrate the relatively high accuracy and reproducibility of cryo-EM models derived from these benchmark maps by 13 participating teams, representing both widely used and novel modeling approaches. We also evaluate the pros and cons of the commonly used metrics to assess model quality and recommend the adoption of multiple scoring parameters to provide full and objective annotation and assessment of the model, reflective of the observed density in the cryo-EM map.

biochemistry↗

bioRxiv2020

Hydrogen-deuterium exchange mass spectrometry captures distinct dynamics upon substrate and inhibitor binding to a transporter

Proton-coupled transporters use transmembrane proton gradients to power active transport of nutrients inside the cell. High-resolution structures often fail to capture the coupling between proton and ligand binding, and conformational changes associated with transport. We combine HDX-MS with mutagenesis and MD simulations to dissect the molecular mechanism of the prototypical transporter XylE. We show that protonation of a conserved aspartate triggers conformational transition from outward-facing to inward-facing state. This transition only occurs in the presence of substrate xylose, while the inhibitor glucose locks the transporter in the outward-facing state. MD simulations corroborate the experiments by showing that only the combination of protonation and xylose binding, and not glucose, sets up the transporter for conformational switch. Overall, we demonstrate the unique ability of HDX-MS to distinguish between the conformational dynamics of inhibitor and substrate binding, and show that a specific allosteric coupling between substrate binding and protonation is a key step to initiate transport.

biochemistry↗

bioRxiv2019

CryoFold: Ab-initio structure determination from electron density maps using molecular dynamics

Cryo-EM is a powerful method for determining protein structures. But it requires computational assistance. Physics-based computations have the power to give low-free-energy structures and ensembles of populations, but have been computationally limited to only small soluble proteins. Here, we introduce CryoFold. By integrating data of varying sparsity from electron density maps of 3-5 [A] resolution with coarse-grained physical knowledge of secondary and tertiary interactions, CryoFold determines ensembles of protein structures directly from sequence. We give six examples showing its broad capabilities, over proteins ranging from 72 to 2000 residues, including membrane and multi-domain proteins, and including results from two EMDB competitions. The ensembles CryoFold predicts starting from the density data of a single known protein conformation encompass multiple low-energy conformations, all of which are experimentally validated and biologically relevant.

biophysics↗

bioRxiv2018

Distinguishing the roles of dorsolateral and anterior PFC in visual metacognition

Visual metacognition depends on regions within the prefrontal cortex. Two areas in particular have been repeatedly implicated: the dorsolateral prefrontal cortex (DLPFC) and the anterior prefrontal cortex (aPFC). However, it is still unclear what the function of each of these areas is and how they differ from each other. To establish the specific roles of DLPFC and aPFC in metacognition, we employed online transcranial magnetic stimulation (TMS) to causally interfere with their functioning during confidence generation. Human subjects from both sexes performed a perceptual decision-making task and provided confidence ratings. We found a clear dissociation between the two areas: DLPFC TMS lowered confidence ratings, whereas aPFC TMS increased metacognitive ability but only for the second half of the experimental blocks. These results support a functional architecture where DLPFC reads out the strength of the sensory evidence and relays it to aPFC, which makes the confidence judgement by potentially incorporating additional, non-perceptual information. Indeed, simulations from a model that incorporates these putative DLPFC and aPFC functions reproduced our behavioral results. These findings establish DLPFC and aPFC as distinct nodes in a metacognitive network and suggest specific contributions from each of these regions to confidence generation.\n\nSignificance\n\nThe prefrontal cortex (PFC) is known to be critical for metacognition. Two of its sub-regions - dorsolateral PFC (DLPFC) and anterior PFC (aPFC) - have specifically been implicated in confidence generation. However, it is unclear if these regions have distinct functions related to the underlying metacognitive computation. Using a causal intervention with transcranial magnetic stimulation (TMS), we demonstrate that DLPFC and aPFC have dissociable contributions: targeting DLPFC decreased average confidence ratings, while targeting aPFC specifically affected metacognitive scores. Based on these results, we postulated specific functions for DLPFC and aPFC in metacognitive computation and corroborated them using a computational model that reproduced our results. Our causal results reveal the existence of a specialized modular organization in PFC for confidence generation.

animal behavior and cognition↗

bioRxiv2017

Sensory noise increases metacognitive efficiency

Visual metacognition is the ability to employ confidence ratings in order to predict the accuracy of ones decisions about visual stimuli. Despite years of research, it is still unclear how visual metacognitive efficiency can be manipulated. Here we show that a hierarchical model of confidence generation makes a counterintuitive prediction: Higher sensory noise should increase metacognitive efficiency. The reason is that sensory noise has a large negative influence on the decision (where it is the only corrupting influence) but a smaller negative influence on confidence (where it is one of two corrupting influences; the other one being metacognitive noise). To test this prediction, we used a perceptual learning paradigm to decrease the amount of sensory noise. In Experiment 1, seven days of training led to significant decrease in noise as well as a corresponding decrease in metacognitive efficiency. Experiment 2 showed the same effect in a brief 97-trial learning for each of two different tasks. Finally, in Experiment 3, we experimentally manipulated stimulus contrast to increase sensory noise and observed a corresponding increase in metacognitive efficiency. Our findings demonstrate the existence of a robust positive relationship between sensory noise and metacognitive efficiency. These results could not be captured by a standard model in which decision and confidence judgments are made based on the same underlying information. Thus, our study provides a novel way to directly manipulate metacognitive efficiency and suggests the existence of metacognitive noise that corrupts confidence but not the perceptual decision.

neuroscience↗

bioRxiv2017

Activation and desensitization mechanism of AMPA receptor-TARP complex by cryo-EM

Summary Summary Structure determination Agonists expand LBD gating... Activation by full and... Architecture of the ion... Ion-permeation profile of the... Asymmetric water/ion accessible... TARP modulation of receptor... TARP restricts LBD rearrangement... TARP modulation of AMPA... Materials and methods AUTHOR CONTRIBUTIONS AUTHOR INFORMATION References AMPA receptors mediate the majority of fast excitatory neurotransmission in the mammalian brain and transduce the binding of presynaptically released glutamate to the opening of a transmembrane cation channel. Within the postsynaptic density, however, AMPA receptors coassemble with transmembran ...

biophysics↗