Energetic contributions of amino acid residues and its cross‐talk to delineate ligand‐binding mechanism

Abstract

Receptor‐based QSAR approaches can enumerate the energetic contributions of amino acid residues toward ligand binding only when experimental binding affinity is associated. The structural data of protein‐ligand complexes are witnessing a tremendous growth in the Protein Data Bank deposited with a few entries on binding affinity. We present here a new approach to compute the Energetic CONTributions of Amino acid residues and its possible Cross‐Talk (ECONTACT) to study ligand binding using per‐residue energy decomposition, molecular dynamics simulations and rescoring method without the need for experimental binding affinity. This approach recognizes potential cross‐talks among amino acid residues imparting a nonadditive effect to the binding affinity with evidence of correlative motions in the dynamics simulations. The protein‐ligand interaction energies deduced from multiple structures are decomposed into per‐residue energy terms, which are employed as variables to principal component analysis and generated cross‐terms. Out of 16 cross‐talks derived from eight datasets of protein‐ligand systems, the ECONTACT approach is able to associate 10 potential cross‐talks with site‐directed mutagenesis, free energy, and dynamics simulations data strongly. We modeled these key determinants of ligand binding using joint probability density function (jPDF) to identify cross‐talks in protein structures. The top two cross‐talks identified by ECONTACT approach corroborated with the experimental findings. Furthermore, virtual screening exercise using ECONTACT models better discriminated known inhibitors from decoy molecules. This approach proposes the jPDF metric to estimate the probability of observing cross‐talks in any protein‐ligand complex. The source code and related resources to perform ECONTACT modeling is available freely at https://www.gujaratuniversity.ac.in/econtact/.

Pinpointing the potential hits for hindering interaction of SARS-CoV-2 S-protein with ACE2 from the pool of antiviral phytochemicals utilizing molecular docking and molecular dynamics (MD) simulations

“…Such a methodology utilizes molecular docking and molecular dynamics as the center strategies for the in-silico examination, and by utilizing these techniques several lead compounds are recognized to have potential to meddle with the biochemistry and life cycle of SARS-CoV-2 as of late. There are a few spaces of life sciences where the approaches of docking and MD reproductions have been of an incredible use [ [51] , [52] , [53] ]. Comparable examinations utilizing docking and MD reproductions are likewise performed for accessing the potency of hydroxychloroquine with various targets of SARS-CoV-2 [ 54 ].…”

Development of cardiotoxicity model using ligand-centric and receptor-centric descriptors

“…The simulation cell was placed by inspecting the centroid of the bound ligand and cell boundaries were defined in each direction by 8 Å distance. 25 We chose the YASARA Vina docking module for virtual screening in which Vina applies a stochastic-based dock pose search and implements the YASARA scoring function to score the docked pose in energy units (kcal/mol). According to YASARA conventions, positive energy score indicates high affinity in direct contrast to other known scoring functions where low negative energy values represent best binding pose.…”

“…22,24 We have proposed a toxicity prediction model in this article for the prediction of probable cardiotoxic drugs using a multi-parametric approach combining drug status, calculated physico-chemical and ADMET properties (ligand-centric descriptors) and docking scores obtained from protein target interactions (receptor-centric descriptors). 20,25 All these properties were then used to decipher the discriminant function using Linear Discriminant Analysis (LDA) for distinguishing cardiotoxic and non-toxic compounds. Fisher Discriminant Analysis (FDA) classified the compound data in the form of two classes based on independent and dependent variables.…”

Identification of potential inhibitors of coronavirus hemagglutinin-esterase using molecular docking, molecular dynamics simulation and binding free energy calculation

“…To identify natural product-based drug leads, 1574 natural compounds were retrieved from the NPACT database [44]. These structure files were prepared using YASARA Structure (academic license) clean module such as removing crystallographic waters, adding polar hydrogens and assigning charges to titratable amino acids [45] followed by atom typing using Amber03 force field, and geometry optimization using the steepest gradient approach (100 iterations) [46,47].…”