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Enhancing SILCS-MC via GPU Acceleration and Ligand Conformational Optimization with Genetic and Parallel Tempering

Mingtian Zhao1, Wenbo Yu1, Alexander D MacKerell1

  • 1Computer Aided Drug Design Center, Department of Pharmaceutical Sciences, University of Maryland, School of Pharmacy, 20 Penn Street, Baltimore, Maryland 21201, United States.

The Journal of Physical Chemistry. B
|July 20, 2024
PubMed
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Enhanced computational methods improve drug design. Graphical Processing Units (GPUs) accelerate Site Identification by Ligand Competitive Saturation (SILCS) calculations by over 100-fold, while genetic algorithms and parallel tempering offer minor precision gains in ligand binding site prediction.

Area of Science:

  • Computational chemistry and molecular modeling
  • Computer-aided drug design (CADD)
  • Biophysics and structural biology

Background:

  • Accurate estimation of ligand-protein binding is crucial for drug discovery and optimization.
  • The Site Identification by Ligand Competitive Saturation (SILCS) method uses 3D free-energy functional group affinity maps (FragMaps) for ligand docking.
  • Existing SILCS-Monte Carlo (MC) methods require enhancement for efficiency and sampling breadth.

Purpose of the Study:

  • To improve the efficiency and sampling capabilities of the SILCS-MC methodology.
  • To leverage parallel computing (GPUs) and advanced sampling algorithms (GA, PT) for enhanced ligand-protein binding predictions.
  • To assess the impact of these improvements on computational speed and prediction accuracy.

Main Methods:

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  • Implementation of the SILCS-MC methodology on Graphics Processing Units (GPUs) for parallel computation.
  • Integration of a genetic algorithm (GA) with MC for evolutionary ligand sampling.
  • Investigation of parallel tempering (PT) to further enhance sampling efficiency.

Main Results:

  • GPU implementation of SILCS-MC accelerates calculations by over two orders of magnitude (>100x).
  • Genetic algorithms and parallel tempering show minor improvements in the precision of docked ligand orientations and binding free energies compared to standard Markov-chain MC.
  • Significant speed enhancements are achieved via GPU acceleration, with modest gains in docking precision from GA and PT.

Conclusions:

  • The GPU-accelerated SILCS-MC methodology dramatically enhances computational efficiency for drug design.
  • While GA and PT offer slight precision improvements, the primary benefit lies in the substantial speed-up provided by GPUs.
  • This optimized approach facilitates more rapid screening and optimization of potential drug candidates.