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

Area of Science:

• Computational Biology
• Structural Bioinformatics
• Pharmacogenomics

Background:

• Molecular simulations offer crucial insights into protein structure, dynamics, and the functional impact of sequence variations.
• Applications span drug screening, novel therapy development, and personalized treatment planning.
• Predicting the effects of genetic variations on protein function is vital for precision medicine.

Purpose of the Study:

• To introduce the SNP2SIM workflow for generating reproducible molecular dynamics and docking simulations.
• To facilitate functional variant analysis of protein targets for small molecule therapies.
• To enable large-scale computational mutagenesis and analysis of variant-specific drug interactions.

Main Methods:

• The SNP2SIM workflow utilizes Python, NAMD, VMD, and AutoDock Vina.
• It generates mutant protein structures and configuration files for molecular dynamics simulations.
• Trajectories are clustered to identify variant scaffolds for small molecule docking simulations.

Main Results:

• SNP2SIM generates variant scaffolds from wildtype protein structures through a three-module process.
• It simulates solvated protein variant structures and clusters trajectories based on ligand-binding residues.
• Variant-induced changes in drug binding are predicted by docking small molecule libraries to unique structural conformations.

Conclusions:

• SNP2SIM provides a platform for molecular simulation-based functional analysis of sequence variations in drug targets.
• The workflow simplifies the simulation of variant-specific drug interactions and enables large-scale computational mutagenesis.
• It facilitates parallelization of computationally intensive simulations and comparison of simulation parameters.