Computational Modeling of Small Molecule Ligand Binding Interactions and Affinities

Identification and characterization of novel candidate compounds targeting 6- and 7-transmembrane μ-opioid receptor isoforms

BACKGROUND AND PURPOSE

The μ-opioid receptor (μ receptor) is the primary target for opioid analgesics. The 7-transmembrane (TM) and 6TM μ receptor isoforms mediate inhibitory and excitatory cellular effects. Here, we developed compounds selective for 6TM- or 7TM-μ receptors to further our understanding of the pharmacodynamic properties of μ receptors.

EXPERIMENTAL APPROACH

We performed virtual screening of the ZINC Drug Now library of compounds using in silico 7TM- and 6TM-μ receptor structural models and identified potential compounds that are selective for 6TM- and/or 7TM-μ receptors. Subsequently, we characterized the most promising candidate compounds in functional in vitro studies using Be2C neuroblastoma transfected cells, behavioural in vivo pain assays using various knockout mice and in ex vivo electrophysiology studies.

KEY RESULTS

Our virtual screen identified 30 potential candidate compounds. Subsequent functional in vitro cellular assays shortlisted four compounds (#5, 10, 11 and 25) that demonstrated 6TM- or 7TM-μ receptor-dependent NO release. In in vivo pain assays these compounds also produced dose-dependent hyperalgesic responses. Studies using mice that lack specific opioid receptors further established the μ receptor-dependent nature of identified novel ligands. Ex vivo electrophysiological studies on spontaneous excitatory postsynaptic currents in isolated spinal cord slices also validated the hyperalgesic properties of the most potent 6TM- (#10) and 7TM-μ receptor (#5) ligands.

CONCLUSION AND IMPLICATIONS

Our novel compounds represent a new class of ligands for μ receptors and will serve as valuable research tools to facilitate the development of opioids with significant analgesic efficacy and fewer side-effects.

Prefusion spike protein stabilization through computational mutagenesis

A novel severe acute respiratory syndrome (SARS)‐like coronavirus (SARS‐CoV‐2) has emerged as a human pathogen, causing global pandemic and resulting in over 400 000 deaths worldwide. The surface spike protein of SARS‐CoV‐2 mediates the process of coronavirus entry into human cells by binding angiotensin‐converting enzyme 2 (ACE2). Due to the critical role in viral‐host interaction and the exposure of spike protein, it has been a focus of most vaccines' developments. However, the structural and biochemical studies of the spike protein are challenging because it is thermodynamically metastable. Here, we develop a new pipeline that automatically identifies mutants that thermodynamically stabilize the spike protein. Our pipeline integrates bioinformatics analysis of conserved residues, motion dynamics from molecular dynamics simulations, and other structural analysis to identify residues that significantly contribute to the thermodynamic stability of the spike protein. We then utilize our previously developed protein design tool, Eris, to predict thermodynamically stabilizing mutations in proteins. We validate the ability of our pipeline to identify protein stabilization mutants through known prefusion spike protein mutants. We finally utilize the pipeline to identify new prefusion spike protein stabilization mutants.

Adverse drug reactions triggered by the common HLA-B*57:01 variant: a molecular docking study

BACKGROUND

Human leukocyte antigen (HLA) surface proteins are directly involved in idiosyncratic adverse drug reactions. Herein, we present a structure-based analysis of the common HLA-B*57:01 variant known to be responsible for several HLA-linked adverse effects such as the abacavir hypersensitivity syndrome.

METHODS

First, we analyzed three X-ray crystal structures involving the HLA-B*57:01 protein variant, the anti-HIV drug abacavir, and different co-binding peptides present in the antigen-binding cleft. We superimposed the three complexes and showed that abacavir had no significant conformational variation whatever the co-binding peptide. Second, we self-docked abacavir in the HLA-B*57:01 antigen binding cleft with and without peptide using Glide. Third, we docked a small test set of 13 drugs with known ADRs and suspected HLA associations.

RESULTS

In the presence of an endogenous co-binding peptide, we found a significant stabilization (~2 kcal/mol) of the docking scores and identified several modified abacavir-peptide interactions indicating that the peptide does play a role in stabilizing the HLA-abacavir complex. Next, our model was used to dock a test set of 13 drugs at HLA-B*57:01 and measured their predicted binding affinities. Drug-specific interactions were observed at the antigen-binding cleft and we were able to discriminate the compounds with known HLA-B*57:01 liability from inactives.

CONCLUSIONS

Overall, our study highlights the relevance of molecular docking for evaluating and analyzing complex HLA-drug interactions. This is particularly important for virtual drug screening over thousands of HLA variants as other experimental techniques (e.g., in vitro HTS) and computational approaches (e.g., molecular dynamics) are more time consuming and expensive to conduct. As the attention for drugs' HLA liability is on the rise, we believe this work participates in encouraging the use of molecular modeling for reliably studying and predicting HLA-drug interactions. Graphical abstract.