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Large-Scale Functional Group Symmetry-Adapted Perturbation Theory on Graphical Processing Units.

Robert M Parrish1,2, Keiran C Thompson1,2, Todd J Martínez1,2

  • 1Department of Chemistry and the PULSE Institute , Stanford University , Stanford , California 94305 , United States.

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|January 19, 2018
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We present a scalable functional group Symmetry-Adapted Perturbation Theory (F-SAPT) method for analyzing intermolecular interactions in large systems. This approach makes F-SAPT computationally feasible for complex ligand-protein interactions, aiding drug design.

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Area of Science:

  • Computational Chemistry
  • Molecular Interactions
  • Biophysics

Background:

  • Symmetry-Adapted Perturbation Theory (SAPT) is crucial for understanding intermolecular forces.
  • Functional group SAPT (F-SAPT) provides detailed insights into non-covalent interactions.
  • Previous SAPT methods were computationally prohibitive for large systems like ligand-protein complexes.

Purpose of the Study:

  • To develop a large-scale, computationally tractable implementation of F-SAPT.
  • To enable detailed analysis of non-covalent interactions in large molecular systems.
  • To facilitate insights for optimizing ligand-protein binding through chemical modifications.

Main Methods:

  • A variant of F-SAPT was developed using pragmatic computational choices.
  • Ab initio dispersion calculations were replaced with empirical dispersion corrections.
  • Basis sets avoiding augmented functions were used for efficient integral screening.
  • Coulomb and exchange matrix builds were optimized for graphical processing units (GPUs).

Main Results:

  • The new F-SAPT formulation is applicable to systems exceeding 3000 atoms and 25,000 basis functions.
  • The method is optimized for systems with monomers of significantly different sizes.
  • Demonstrated F-SAPT analysis on the indinavir @ HIV-II protease complex (1HSG).

Conclusions:

  • The developed large-scale F-SAPT method significantly expands the applicability of SAPT analysis.
  • This computational advancement allows for detailed interaction analysis in previously intractable systems.
  • Enables more informed strategies for drug discovery and optimization of ligand-protein interactions.