Biology subjects

Kokh, D. B.

Publications and source records attributed to Kokh, D. B..

5 recordsLinked to original sources

bioRxiv2026

On the determinants of residence times and dissociation mechanisms of complexes of interleukin-13 with its low and high affinity receptors

Interleukin-13 (IL-13) is an immunomodulatory cell signaling cytokine that has been implicated in neurodegenerative disease and chronic inflammation. IL-13 binds to its low and high affinity receptors, IL-13 receptor 1 (IL-13R1) and IL-13 receptor 2 (IL-13R2), respectively, with residence times that vary accordingly. As the binding kinetics of the cytokine-receptor complexes influence cellular responses, we employed the molecular dynamics (MD) simulation-based{tau} -random acceleration molecular dynamics method ({tau}RAMD) to compute relative residence times for wild-type (WT) IL-13 and 19 IL-13 mutants to the two receptors. Comparison with experimental kinetic data shows that the{tau} RAMD computations capture the trends in residence times. Analysis of simulated dissociation trajectories of the cytokine-receptor complexes reveals two distinct dissociation pathways of IL-13 from each of the receptors. This study thus pinpoints key determinants of the interaction of IL-13 with its receptors which could be targeted for therapeutic design. Statement of SignificanceCytokines are regulatory proteins that bind to cell surface receptors and thereby send signals to the cellular interior. Interleukin-13 (IL-13) is a cytokine that has a low and a high affinity receptor. It has important physiological roles, and its deregulation is involved in diseases such as atopic dermatitis and asthma. We employed a molecular dynamics simulation-based method to compute the effects of changes in the sequence of IL-13 on the lifetimes of complexes of IL-13 and its receptors. Comparison with experiments supports the validity of the computational approach and analysis of the simulations reveals two distinct ways in which IL-13 dissociates from each receptor. These results thus provide a map for targeting IL-13 - receptor interactions for the design of therapeutics.

biophysics↗

bioRxiv2024

Computational screening of the effects of mutations on protein-protein off-rates and dissociation mechanisms by {tau}RAMD

The dissociation rate, or its reciprocal, the residence time ({tau}), is a crucial parameter for understanding the duration and biological impact of biomolecular interactions. Accurate prediction of {tau} is essential for understanding protein-protein interactions (PPIs) and identifying potential drug targets or modulators for tackling diseases. Conventional molecular dynamics simulation techniques are inherently constrained by their limited timescales, making it challenging to estimate residence times, which typically range from minutes to hours. Building upon its successful application in protein-small molecule systems, {tau}-Random Acceleration Molecular Dynamics ({tau}RAMD) is here investigated for estimating dissociation rates of protein-protein complexes. {tau}RAMD enables the observation of unbinding events on the nanosecond timescale, facilitating rapid and efficient computation of relative residence times. We tested this methodology for three protein-protein complexes and their extensive mutant datasets, achieving good agreement between computed and experimental data. By combining {tau}RAMD with MD-IFP (Interaction Fingerprint) analysis, dissociation mechanisms were characterized and their sensitivity to mutations investigated, enabling the identification of molecular hotspots for selective modulation of dissociation kinetics. In conclusion, our findings underscore the versatility of {tau}RAMD as a simple and computationally efficient approach for computing relative protein-protein dissociation rates and investigating dissociation mechanisms, thereby aiding the design of PPI modulators.

bioinformatics↗

bioRxiv2021

G-Protein Coupled Receptor-Ligand Dissociation Rates and Mechanisms from {tau}RAMD Simulations

There is a growing appreciation of the importance of drug-target binding kinetics for lead optimization. For G protein-coupled receptors (GPCRs), which mediate signaling over a wide range of timescales, the drug dissociation rate is often a better predictor of in vivo efficacy than binding affinity, although it is more challenging to compute. Here, we assess the ability of the {tau}-Random Acceleration Molecular Dynamics ({tau}RAMD) approach to reproduce relative residence times and reveal dissociation mechanisms and the effects of allosteric modulation for two important membrane-embedded drug targets: the {beta}2-adrenergic receptor and the muscarinic acetylcholine receptor M2. The dissociation mechanisms observed in the relatively short RAMD simulations (in which molecular dynamics (MD) simulations are performed using an additional force with an adaptively assigned random orientation applied to the ligand) are in general agreement with much more computationally intensive conventional MD and metadynamics simulations. Remarkably, although decreasing the magnitude of the random force generally reduces the number of egress routes observed, the ranking of ligands by dissociation rate is hardly affected and agrees well with experiment. The simulations also reproduce changes in residence time due to allosteric modulation and reveal associated changes in ligand dissociation pathways. TABLE OF CONTENTS GRAPHIC O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=100 SRC="FIGDIR/small/449151v2_ufig1.gif" ALT="Figure 1"> View larger version (43K): org.highwire.dtl.DTLVardef@743dcdorg.highwire.dtl.DTLVardef@1857381org.highwire.dtl.DTLVardef@62359aorg.highwire.dtl.DTLVardef@1ad38a3_HPS_FORMAT_FIGEXP M_FIG C_FIG

biophysics↗

bioRxiv2020

A blueprint for high affinity SARS-CoV-2 Mpro inhibitors from activity-based compound library screening guided by analysis of protein dynamics

The SARS-CoV-2 coronavirus outbreak continues to spread at a rapid rate worldwide. The main protease (Mpro) is an attractive target for anti-COVID-19 agents. Unfortunately, unexpected difficulties have been encountered in the design of specific inhibitors. Here, by analyzing an ensemble of ~30,000 SARS-CoV-2 Mpro conformations from crystallographic studies and molecular simulations, we show that small structural variations in the binding site dramatically impact ligand binding properties. Hence, traditional druggability indices fail to adequately discriminate between highly and poorly druggable conformations of the binding site. By performing ~200 virtual screenings of compound libraries on selected protein structures, we redefine the proteins druggability as the consensus chemical space arising from the multiple conformations of the binding site formed upon ligand binding. This procedure revealed a unique SARS-CoV-2 Mpro blueprint that led to a definition of a specific structure-based pharmacophore. The latter explains the poor transferability of potent SARS-CoV Mpro inhibitors to SARS-CoV-2 Mpro, despite the identical sequences of the active sites. Importantly, application of the pharmacophore predicted novel high affinity inhibitors of SARS-CoV-2 Mpro, that were validated by in vitro assays performed here and by a newly solved X-ray crystal structure. These results provide a strong basis for effective rational drug design campaigns against SARS-CoV-2 Mpro and a new computational approach to screen protein targets with malleable binding sites.

biophysics↗

bioRxiv2019

Druggability Assessment in TRAPP using Machine Learning Approaches

Accurate protein druggability predictions are important for the selection of drug targets in the early stages of drug discovery. Due to the flexible nature of proteins, the druggability of a binding pocket may vary due to conformational changes. We have therefore developed two statistical models, a logistic regression model (TRAPP-LR) and a convolutional neural network model (TRAPP-CNN), for predicting druggability and how it varies with changes in the spatial and physicochemical properties of a binding pocket. These models are integrated into TRAPP (TRAnsient Pockets in Proteins), a tool for the analysis of binding pocket variations along a protein motion trajectory. The models, which were trained on publicly available and self-augmented data sets, show equivalent or superior performance to existing methods on test sets of protein crystal structures, and have sufficient sensitivity to identify potentially druggable protein conformations in trajectories from molecular dynamics simulations. Visualization of the evidence for the decisions of the models in TRAPP facilitates identification of the factors affecting the druggability of protein binding pockets.

bioinformatics↗