SNP2SIM: a modular workflow for standardizing molecular simulation and functional analysis of protein variants

Decoding the Structural Diversity: A New Horizon in Antimicrobial Prospecting and Mechanistic Investigation

The escalating crisis of antimicrobial resistance (AMR) underscores the urgent need for novel antimicrobials. One promising strategy is the exploration of structural diversity, as diverse structures can lead to diverse biological activities and mechanisms of action. This review delves into the role of structural diversity in antimicrobial discovery, highlighting its influence on factors such as target selectivity, binding affinity, pharmacokinetic properties, and the ability to overcome resistance mechanisms. We discuss various approaches for exploring structural diversity, including combinatorial chemistry, diversity-oriented synthesis, and natural product screening, and provide an overview of the common mechanisms of action of antimicrobials. We also describe techniques for investigating these mechanisms, such as genomics, proteomics, and structural biology. Despite significant progress, several challenges remain, including the synthesis of diverse compound libraries, the identification of active compounds, the elucidation of complex mechanisms of action, the emergence of AMR, and the translation of laboratory discoveries to clinical applications. However, emerging trends and technologies, such as artificial intelligence, high-throughput screening, next-generation sequencing, and open-source drug discovery, offer new avenues to overcome these challenges. Looking ahead, we envisage an exciting future for structural diversity-oriented antimicrobial discovery, with opportunities for expanding the chemical space, harnessing the power of nature, deepening our understanding of mechanisms of action, and moving toward personalized medicine and collaborative drug discovery. As we face the continued challenge of AMR, the exploration of structural diversity will be crucial in our search for new and effective antimicrobials.

MoSDeF-GOMC: Python Software for the Creation of Scientific Workflows for the Monte Carlo Simulation Engine GOMC

MoSDeF-GOMC is a python interface for the Monte Carlo software GOMC to the Molecular Simulation Design Framework (MoSDeF) ecosystem. MoSDeF-GOMC automates the process of generating initial coordinates, assigning force field parameters, and writing coordinate (PDB), connectivity (PSF), force field parameter, and simulation control files. The software lowers entry barriers for novice users while allowing advanced users to create complex workflows that encapsulate simulation setup, execution, and data analysis in a single script. All relevant simulation parameters are encoded within the workflow, ensuring reproducible simulations. MoSDeF-GOMC's capabilities are illustrated through a number of examples, including prediction of the adsorption isotherm for CO₂ in IRMOF-1, free energies of hydration for neon and radon over a broad temperature range, and the vapor-liquid coexistence curve of a four-component surrogate for the jet fuel S-8. The MoSDeF-GOMC software is available on GitHub at https://github.com/GOMC-WSU/MoSDeF-GOMC.

Distal mutation V486M disrupts the catalytic activity of DPP4 by affecting the flap of the propeller domain

Dipeptidyl peptidase-4 (DPP4) plays a crucial role in regulating the bioactivity of glucagon-like peptide-1 (GLP-1) that enhances insulin secretion and pancreatic β-cell proliferation, making it a therapeutic target for type 2 diabetes. Although the crystal structure of DPP4 has been determined, its structure-function mechanism is largely unknown. Here, we examined the biochemical properties of sporadic human DPP4 mutations distal from its catalytic site, among which V486M ablates DPP4 dimerization and causes loss of enzymatic activity. Unbiased molecular dynamics simulations revealed that the distal V486M mutation induces a local conformational collapse in a β-propeller loop (residues 234-260, defined as the flap) and disrupts the dimerization of DPP4. The "open/closed" conformational transitions of the flap whereby capping the active site, are involved in the enzymatic activity of DPP4. Further site-directed mutagenesis guided by theoretical predictions verified the importance of the conformational dynamics of the flap for the enzymatic activity of DPP4. Therefore, the current studies that combined theoretical modeling and experimental identification, provide important insights into the biological function of DPP4 and allow for the evaluation of directed DPP4 genetic mutations before initiating clinical applications and drug development.

Machine learning-based prediction of drug and ligand binding in BCL-2 variants through molecular dynamics

Venetoclax is a BH3 (BCL-2 Homology 3) mimetic used to treat leukemia and lymphoma by inhibiting the anti-apoptotic BCL-2 protein thereby promoting apoptosis of cancerous cells. Acquired resistance to Venetoclax via specific variants in BCL-2 is a major problem for the successful treatment of cancer patients. Replica exchange molecular dynamics (REMD) simulations combined with machine learning were used to define the average structure of variants in aqueous solution to predict changes in drug and ligand binding in BCL-2 variants. The variant structures all show shifts in residue positions that occlude the binding groove, and these are the primary contributors to drug resistance. Correspondingly, we established a method that can predict the severity of a variant as measured by the inhibitory constant (Ki) of Venetoclax by measuring the structure deviations to the binding cleft. In addition, we also applied machine learning to the phi and psi angles of the amino acid backbone to the ensemble of conformations that demonstrated a generalizable method for drug resistant predictions of BCL-2 proteins that elucidates changes where detailed understanding of the structure-function relationship is less clear.

Predicting Genetic Variation Severity Using Machine Learning to Interpret Molecular Simulations

Distinct missense mutations in a specific gene have been associated with different diseases as well as differing severity of a disease. Current computational methods predict the potential pathogenicity of a missense variant but fail to differentiate between separate disease or severity phenotypes. We have developed a method to overcome this limitation by applying machine learning to features extracted from molecular dynamics simulations, creating a way to predict the effect of novel genetic variants in causing a disease, drug resistance, or another specific trait. As an example, we have applied this novel approach to variants in calmodulin associated with two distinct arrhythmias as well as two different neurodegenerative diseases caused by variants in amyloid-β peptide. The new method successfully predicts the specific disease caused by a gene variant and ranks its severity with more accuracy than existing methods. We call this method molecular dynamics phenotype prediction model.

Classification of VUS and unclassified variants in BRCA1 BRCT repeats by molecular dynamics simulation

Pathogenic mutation in BRCA1 gene is one of the most penetrant genetic predispositions towards cancer. Identification of the mutation provides important aspect in prevention and treatment of the mutation-caused cancer. Of the large quantity of genetic variants identified in human BRCA1, substantial portion is classified as Variant of Uncertain Significance (VUS) or unclassified variants due to the lack of functional evidence. In this study, we focused on the VUS and unclassified variants in BRCT repeat located at BRCA1 C-terminal. Utilizing the well-determined structure of BRCT repeats, we measured the influence of the variants on the structural conformations of BRCT repeats by using molecular dynamics simulation (MDS) consisting of RMSD (Root-mean-square-deviation), RMSF (Root-mean-square-fluctuations), Rg (Radius of gyration), SASA (Solvent accessible surface area), NH bond (hydrogen bond) and Covariance analysis. Using this approach, we analyzed 131 variants consisting of 89 VUS (Variant of Uncertain Significance) and 42 unclassified variants (unclassifiable by current methods) within BRCT repeats and were able to differentiate them into 78 Deleterious and 53 Tolerated variants. Comparing the results made by the saturation genome editing assay, multiple experimental assays, and BRCA1 reference databases shows that our approach provides high specificity, sensitivity and robust. Our study opens an avenue to classify VUS and unclassified variants in many cancer predisposition genes with known protein structure.