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

24 January 2024, Version 1
This content is a preprint and has not undergone peer review at the time of posting.


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 using MD. We present an automated workflow that streamlines setting up, running, and analyzing Desmond MD simulations. The workflow takes a library of pre-docked ligands and a 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. SimFP from a ligand library can also be used to build machine learning (ML) models that can 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 a case study on how SimFP helps delineate structure-activity relationship (SAR) trends and explain potency differences across matched-molecular pairs of cyclic peptides targeting the PD-L1 protein.


molecular dynamics
macrocyclic peptides

Supplementary materials

Supplementary information
Supplementary Tables and Figures highlighting SimFP features and ML models performance

Supplementary weblinks


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