The role of water and protein flexibility in the structure-based virtual screening of allosteric GPCR modulators: an mGlu5 receptor case study

Structural dynamics at the active site of the cancer-associated flavoenzyme NQO1 probed by chemical modification with PMSF

A large conformational heterogeneity of human NAD(P)H:quinone oxidoreductase 1 (NQO1), a flavoprotein associated with various human diseases, has been observed to occur in the catalytic site of the enzyme. Here, we report the X-ray structure of NQO1 with phenylmethylsulfonyl fluoride (PMSF) at 1.6 Å resolution. Activity assays confirmed that, despite being covalently bound to the Tyr128 residue at the catalytic site, PMSF did not abolish NQO1 activity. This may indicate that the PMSF molecule does not reduce the high flexibility of Tyr128, thus allowing NADH and DCPIP substrates to bind to the enzyme. Our results show that targeting Tyr128, a key residue in NQO1 function, with small covalently bound molecules could possibly not be a good drug discovery strategy to inhibit this enzyme.

Structure-Based Discovery of Negative Allosteric Modulators of the Metabotropic Glutamate Receptor 5

Recently determined structures of class C G protein-coupled receptors (GPCRs) revealed the location of allosteric binding sites and opened new opportunities for the discovery of novel modulators. In this work, molecular docking screens for allosteric modulators targeting the metabotropic glutamate receptor 5 (mGlu₅) were performed. The mGlu₅ receptor is activated by the main excitatory neurotransmitter of the nervous central system, L-glutamate, and mGlu₅ receptor activity can be allosterically modulated by negative or positive allosteric modulators. The mGlu₅ receptor is a promising target for the treatment of psychiatric and neurodegenerative diseases, and several allosteric modulators of this GPCR have been evaluated in clinical trials. Chemical libraries containing fragment- (1.6 million molecules) and lead-like (4.6 million molecules) compounds were docked to an allosteric binding site of mGlu₅ identified in X-ray crystal structures. Among the top-ranked compounds, 59 fragments and 59 lead-like compounds were selected for experimental evaluation. Of these, four fragment- and seven lead-like compounds were confirmed to bind to the allosteric site with affinities ranging from 0.43 to 8.6 μM, corresponding to a hit rate of 9%. The four compounds with the highest affinities were demonstrated to be negative allosteric modulators of mGlu₅ signaling in functional assays. The results demonstrate that virtual screens of fragment- and lead-like chemical libraries have complementary advantages and illustrate how access to high-resolution structures of GPCRs in complex with allosteric modulators can accelerate lead discovery.

Optimization of Cavity-Based Negative Images to Boost Docking Enrichment in Virtual Screening

Molecular docking is a key in silico method used routinely in modern drug discovery projects. Although docking provides high-quality ligand binding predictions, it regularly fails to separate the active compounds from the inactive ones. In negative image-based rescoring (R-NiB), the shape/electrostatic potential (ESP) of docking poses is compared to the negative image of the protein's ligand binding cavity. While R-NiB often improves the docking yield considerably, the cavity-based models do not reach their full potential without expert editing. Accordingly, a greedy search-driven methodology, brute force negative image-based optimization (BR-NiB), is presented for optimizing the models via iterative editing and benchmarking. Thorough and unbiased training, testing and stringent validation with a multitude of drug targets, and alternative docking software show that BR-NiB ensures excellent docking efficacy. BR-NiB can be considered as a new type of shape-focused pharmacophore modeling, where the optimized models contain only the most vital cavity information needed for effectively filtering docked actives from the inactive or decoy compounds. Finally, the BR-NiB code for performing the automated optimization is provided free-of-charge under MIT license via GitHub (https://github.com/jvlehtonen/brutenib) for boosting the success rates of docking-based virtual screening campaigns.

Structural Characterization of Receptor-Receptor Interactions in the Allosteric Modulation of G Protein-Coupled Receptor (GPCR) Dimers

G protein-coupled receptor (GPCR) oligomerization, while contentious, continues to attract the attention of researchers. Numerous experimental investigations have validated the presence of GPCR dimers, and the relevance of dimerization in the effectuation of physiological functions intensifies the attractiveness of this concept as a potential therapeutic target. GPCRs, as a single entity, have been the main source of scrutiny for drug design objectives for multiple diseases such as cancer, inflammation, cardiac, and respiratory diseases. The existence of dimers broadens the research scope of GPCR functions, revealing new signaling pathways that can be targeted for disease pathogenesis that have not previously been reported when GPCRs were only viewed in their monomeric form. This review will highlight several aspects of GPCR dimerization, which include a summary of the structural elucidation of the allosteric modulation of class C GPCR activation offered through recent solutions to the three-dimensional, full-length structures of metabotropic glutamate receptor and γ-aminobutyric acid B receptor as well as the role of dimerization in the modification of GPCR function and allostery. With the growing influence of computational methods in the study of GPCRs, we will also be reviewing recent computational tools that have been utilized to map protein-protein interactions (PPI).

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.

Ligand Pose Predictions for Human G Protein-Coupled Receptors: Insights from the Amber-Based Hybrid Molecular Mechanics/Coarse-Grained Approach

Human G protein-coupled receptors (hGPCRs) are the most frequent targets of Food and Drug Administration (FDA)-approved drugs. Structural bioinformatics, along with molecular simulation, can support structure-based drug design targeting hGPCRs. In this context, several years ago, we developed a hybrid molecular mechanics (MM)/coarse-grained (CG) approach to predict ligand poses in low-resolution hGPCR models. The approach was based on the GROMOS96 43A1 and PRODRG united-atom force fields for the MM part. Here, we present a new MM/CG implementation using, instead, the Amber 14SB and GAFF all-atom potentials for proteins and ligands, respectively. The new implementation outperforms the previous one, as shown by a variety of applications on models of hGPCR/ligand complexes at different resolutions, and it is also more user-friendly. Thus, it emerges as a useful tool to predict poses in low-resolution models and provides insights into ligand binding similarly to all-atom molecular dynamics, albeit at a lower computational cost.