Combining Stochastic Deformation/Relaxation and Intermolecular Contacts Analysis for Extracting Pharmacophores from Ligand–Receptor Complexes

Abstract

We previously combined molecular dynamics (classical or simulated annealing) with ligand-receptor contacts analysis as a means to extract valid pharmacophore model(s) from single ligand-receptor complexes. However, molecular dynamics methods are computationally expensive and time-consuming. Here we describe a novel method for extracting valid pharmacophore model(s) from a single crystallographic structure within a reasonable time scale. The new method is based on ligand-receptor contacts analysis following energy relaxation of a predetermined set of randomly deformed complexes generated from the targeted crystallographic structure. Ligand-receptor contacts maintained across many deformed/relaxed structures are assumed to be critical and used to guide pharmacophore development. This methodology was implemented to develop valid pharmacophore models for PI3K-γ, RENIN, and JAK1. The resulting pharmacophore models were validated by receiver operating characteristic (ROC) analysis against inhibitors extracted from the CHEMBL database. Additionally, we implemented pharmacophores extracted from PI3K-γ to search for new inhibitors from the National Cancer Institute list of compounds. The process culminated in new PI3K-γ/mTOR inhibitory leads of low micromolar ICs.

Comprehensive Structural and Molecular Comparison of Spike Proteins of SARS-CoV-2, SARS-CoV and MERS-CoV, and Their Interactions with ACE2

“…It has been reported that the spike glycoprotein of SARS-CoV-2 is modified via homologous recombination and is a mixture of bat SARS-CoV and an unknown Beta-CoV [3,32]. Superimposition of the cryo-EM structure of the RBD of SARS-514 CoV-2 onto a previously reported SARS RBD structure revealed a root-mean-square deviation (RMSD) of 3.8 Å over 959 Cα atoms [2], which indicates highly similar 3D protein structures [63][64][65].…”

Structure-Based Discovery and Bioactivity Evaluation of Novel Aurora-A Kinase Inhibitors as Anticancer Agents via Docking-Based Comparative Intermolecular Contacts Analysis (dbCICA)

“…The fundamental use of pharmacophores models is the discovery of new chemical scaffolds demonstrating similar biological characteristics or scaffold hopping [56][57][58][59][60][61][62][63][64][65][83][84][85]. Therefore, Refined-Hypo (SB-1) pharmacophore (Figure 2) was used as a 3D query to search and screen the NCI list of compounds for new Aurora-A kinase inhibitors with novel chemotypes.…”

Discovery of new Cdc2-like kinase 4 (CLK4) inhibitors via pharmacophore exploration combined with flexible docking-based ligand/receptor contact fingerprints and machine learning

“…Distance evaluations were automatically performed employing a tailored-made Fortran-based soware. [24][25][26][47][48][49] Accordingly, each docking-scoring conguration is used to build a 2D matrix, such that each matrix is composed of row labels corresponding to docked ligands and column labels corresponding to different binding site atoms. The matrix is lled with binary code, whereby "zeros" correspond to interatomic distances that exceed the predetermined threshold (of 2.5 A) and "ones" for distances below (or equal) the predened 2.5 A threshold cutoff.…”

“…25 It proceeds by either mapping out all binding site atoms that contact a list of docked potent ligands and evade inactive ones, [24][25][26]57 or identify binding site atoms that frequently contact certain bound ligand during molecular dynamics or related simulations. [47][48][49] Machine learning (ML) is the implementation of statistical approaches for learning and predicting properties. 27,28 Supervised ML attempts to build predictive model(s) based on data collected from input and output sources.…”

“…Then, the docked poses and their corresponding protein conformations were used to generate Ligand-Receptor Contacts Figureprints (LRCFs). [24][25][26][47][48][49]57 Subsequently, LRCFs, ligand-based pharmacophores and numerous other calculatable physicochemical properties were allowed to compete within the context of genetic algorithm coupled to variety of machine learning [27][28][29][30] methods to search for optimal QSAR model(s) that can explain bioactivity variations within training compounds. Thereaer, QSAR-selected pharmacophores were validated by receiveroperating characteristic (ROC) curve analysis and were used as 3D search queries to screen the National Cancer Institute's (NCI) list of compounds for new CLK-4 inhibitors.…”