SNP2SIM

SNP2SIM performs molecular dynamics simulations and molecular docking to evaluate how missense protein variants alter protein conformational ensembles and small-molecule binding, informing effects on drug interactions.

Key Features:

• Modular workflow: Three independent modules generate variant-specific scaffolds from wildtype protein structures to enable analysis of missense mutations.
• Molecular dynamics (NAMD): Uses NAMD to perform molecular dynamics simulations on solvated protein variant structures.
• Trajectory clustering: Clusters simulation trajectories based on structural diversity of residues involved in ligand binding to produce one or more unique variant scaffolds capturing distinct conformational states.
• Molecular docking (AutoDock Vina): Employs AutoDock Vina to dock small-molecule ligand libraries to variant scaffolds and predict changes in binding affinity and specificity relative to wildtype.
• Scalability and parameterization (Python): Implemented in Python and parameterizable for large-scale computational mutagenesis, distributed computing infrastructures, and parallelized simulations.

Scientific Applications:

• Drug discovery and development: Predicts variant-induced changes to drug binding to aid drug screening and optimization against specific genetic variants.
• Personalized medicine: Enables prediction of patient-specific responses to small-molecule therapies based on individual missense variants.
• Structural biology and mechanism studies: Elucidates atomic-scale effects of sequence variations on protein structure, dynamics, and protein–ligand interactions.

Methodology:

Generate variant-specific scaffolds from wildtype structures; perform molecular dynamics simulations using NAMD on solvated protein variant structures; cluster trajectories by structural diversity of ligand-binding residues to produce unique variant scaffolds; perform small-molecule docking with AutoDock Vina against ligand libraries; implemented in Python and parameterizable for large-scale, distributed, and parallel execution.