MDFit: automated molecular simulations workflow enables high throughput assessment of ligands-protein dynamics

• Published in: Journal of computer-aided molecular design Vol. 38; no. 1; p. 24
• Main Authors: Brueckner, Alexander C., Shields, Benjamin, Kirubakaran, Palani, Suponya, Alexander, Panda, Manoranjan, Posy, Shana L., Johnson, Stephen, Lakkaraju, Sirish Kaushik
• Format: Journal Article
• Language: English
• Published: Cham Springer International Publishing 01-12-2024; Springer Nature B.V
• Subjects: Animal Anatomy; Automation; Binding; Binding Sites; Chemical activity; Chemical fingerprinting; Chemistry; Chemistry and Materials Science; Computer Applications in Chemistry; Drug Design; Dynamic structural analysis; Energy methods; Free energy; Histology; Humans; Libraries; Ligands; Machine Learning; Molecular docking; Molecular Docking Simulation; Molecular dynamics; Molecular Dynamics Simulation; Molecular structure; Morphology; Peptides; Physical Chemistry; Protein Binding; Protein Conformation; Proteins; Proteins - chemistry; Proteins - metabolism; Rigid structures; Simulation; Software; Workflow; pocket dynamics; automated workflows; simulation fingerprints; MD; molecular dynamics; machine learning; ML
• ISSN: 0920-654X; 1573-4951; 1573-4951
• DOI: 10.1007/s10822-024-00564-2

Molecular dynamics (MD) simulation is a powerful tool for characterizing ligand–protein conformational dynamics and offers significant advantages over docking and other rigid structure-based computational methods. However, setting up, running, and analyzing MD simulations continues to be a multi-step process making it cumbersome to assess a library of ligands in a protein binding pocket using MD. We present an automated workflow that streamlines setting up, running, and analyzing Desmond MD simulations for protein–ligand complexes using machine learning (ML) models. The workflow takes a library of pre-docked ligands and a prepared protein structure as input, sets up and runs MD with each protein–ligand complex, and generates simulation fingerprints for each ligand. Simulation fingerprints (SimFP) capture protein–ligand compatibility, including stability of different ligand-pocket interactions and other useful metrics that enable easy rank-ordering of the ligand library for pocket optimization. SimFPs from a ligand library are used to build & deploy ML models that predict binding assay outcomes and automatically infer important interactions. Unlike relative free-energy methods that are constrained to assess ligands with high chemical similarity, ML models based on SimFPs can accommodate diverse ligand sets. We present two case studies on how SimFP helps delineate structure–activity relationship (SAR) trends and explain potency differences across matched-molecular pairs of (1) cyclic peptides targeting PD-L1 and (2) small molecule inhibitors targeting CDK9.