Importance of protein flexibility in ranking ERK2 Type I1/2 inhibitor affinities: a computational study

Computational study on the mechanism of small molecules inhibiting NLRP3 with ensemble docking and molecular dynamic simulations

NLRP3 (Nucleotide-binding oligomerization domain, LRR and pyrin domain-containing protein 3) is a pivotal regulator of inflammation, with strong implications in gout, neurodegenerative diseases, and various inflammatory conditions. Consequently, the exploration of NLRP3 inhibitors is of great significance for the treatment of diseases. MCC950, NP3-146, compound (3), and YQ128 are four highly bioactive NLRP3 inhibitors that show great potential; however, their mechanism of action is currently limited to targeting the ATP binding region (NACHT site) of the NLRP3 protein. To gain deeper insights into the defining features of NLRP3 inhibitors and to develop more potent inhibitors, it is imperative to elucidate the interaction mechanism between NLRP3 and these inhibitors. In this study, we employ a comprehensive computational approach to investigate the binding mechanism between NLRP3 and representative inhibitors. Utilizing the molecular mechanics/generalized Born surface area (MM/GBSA) method, we calculate the binding free energy and pinpoint the key residues involved in the binding of the four inhibitors to NLRP3. The decomposition of binding free energy by the MM/GBSA method reveals that the residues Val71, Arg195, Ile255, Phe419, Arg422, and Met505, situated around the binding pocket, play a crucial role in conferring the high bioactivity of NLRP3 inhibitors. Furthermore, pharmacophore analysis of the four NLRP3 complexes indicates that the primary interaction between the inhibitors and NLRP3 was mainly hydrophobic interaction. Our study provides a profound understanding of the interaction mechanism between NLRP3 and its inhibitors, identifies the key residues involved, and provides theoretical guidance for the design of more efficient NLRP3 inhibitors.

The latest automated docking technologies for novel drug discovery

INTRODUCTION

Molecular docking has been consolidated as one of the most important methods in the molecular modeling field. It has been recognized as a prominent tool in the study of protein-ligand complexes, to describe intermolecular interactions, to accurately predict poses of multiple ligands, to discover novel promising bioactive compounds. Molecular docking methods have evolved in terms of their accuracy and reliability; but there are pending issues to solve for improving the connection between the docking results and the experimental evidence.

AREAS COVERED

In this article, the author reviews very recent innovative molecular docking applications with special emphasis on reverse docking, treatment of protein flexibility, the use of experimental data to guide the selection of docking poses, the application of Quantum mechanics(QM) in docking, and covalent docking.

EXPERT OPINION

There are several issues being worked on in recent years that will lead to important breakthroughs in molecular docking methods in the near future These developments are related to more efficient exploration of large datasets and receptor conformations, advances in electronic description, and the use of structural information for guiding the selection of results.

Combining Molecular Dynamics and Docking Simulations to Develop Targeted Protocols for Performing Optimized Virtual Screening Campaigns on The hTRPM8 Channel

BACKGROUND

There is an increasing interest in TRPM8 ligands of medicinal interest, the rational design of which can be nowadays supported by structure-based in silico studies based on the recently resolved TRPM8 structures. Methods: The study involves the generation of a reliable hTRPM8 homology model, the reliability of which was assessed by a 1.0 μs MD simulation which was also used to generate multiple receptor conformations for the following structure-based virtual screening (VS) campaigns; docking simulations utilized different programs and involved all monomers of the selected frames; the so computed docking scores were combined by consensus approaches based on the EFO algorithm. Results: The obtained models revealed very satisfactory performances; LiGen™ provided the best results among the tested docking programs; the combination of docking results from the four monomers elicited a markedly beneficial effect on the computed consensus models. Conclusions: The generated hTRPM8 model appears to be amenable for successful structure-based VS studies; cross-talk modulating effects between interacting monomers on the binding sites can be accounted for by combining docking simulations as performed on all the monomers; this strategy can have general applicability for docking simulations involving quaternary protein structures with multiple identical binding pockets.

Design, synthesis and biological evaluation of novel amide-linked 18β-glycyrrhetinic acid derivatives as novel ALK inhibitors

A series of novel amide-linked 18β-glycyrrhetinic acid derivatives were developed by incorporating substituted piperazine amide fragments into the C30-COOH of 18β-glycyrrhetinic acid scaffold. The synthesized compounds were evaluated for their anticancer activity against Karpas299, A549, HepG2, MCF-7, and PC-3 cell lines by MTT assay. Besides, some compounds with electron-withdrawing groups on phenyl moieties exhibited noticeable antiproliferative activity. The most potent compound 4a was also found to be non-toxic to normal human hepatocytes LO2 cells. The compound 4a exhibited moderate inhibitory activity against wild-type ALK with an IC50 value of 203.56 nM and relatively weak potent activity to c-Met (IC50 > 1000 nM). Molecular docking studies were performed to explore the diversification in bonding patterns between the compound 4a and Crizotinib.

Comparison of affinity ranking using AutoDock-GPU and MM-GBSA scores for BACE-1 inhibitors in the D3R Grand Challenge 4

Molecular docking has been successfully used in computer-aided molecular design projects for the identification of ligand poses within protein binding sites. However, relying on docking scores to rank different ligands with respect to their experimental affinities might not be sufficient. It is believed that the binding scores calculated using molecular mechanics combined with the Poisson-Boltzman surface area (MM-PBSA) or generalized Born surface area (MM-GBSA) can predict binding affinities more accurately. In this perspective, we decided to take part in Stage 2 of the Drug Design Data Resource (D3R) Grand Challenge 4 (GC4) to compare the performance of a quick scoring function, AutoDock4, to that of MM-GBSA in predicting the binding affinities of a set of [Formula: see text]-Amyloid Cleaving Enzyme 1 (BACE-1) ligands. Our results show that re-scoring docking poses using MM-GBSA did not improve the correlation with experimental affinities. We further did a retrospective analysis of the results and found that our MM-GBSA protocol is sensitive to details in the protein-ligand system: (i) neutral ligands are more adapted to MM-GBSA calculations than charged ligands, (ii) predicted binding affinities depend on the initial conformation of the BACE-1 receptor, (iii) protonating the aspartyl dyad of BACE-1 correctly results in more accurate binding affinity predictions.