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Precise parallel volumetric comparison of molecular surfaces and electrostatic isopotentials.

Georgi D Georgiev1, Kevin F Dodd1, Brian Y Chen1

  • 1Department of Computer Science and Engineering, Lehigh University, 113 Research Drive, Bethlehem, PA USA.

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Summary

pClay is a novel algorithm for precise molecular surface comparisons, improving protein binding specificity analysis. Its parallel processing enhances accuracy, identifying steric influences overlooked by previous methods.

Keywords:
Molecular representationsSolid modelingSpecificity annotation

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Area of Science:

  • Computational biology
  • Structural bioinformatics
  • Algorithm design

Background:

  • Protein binding specificity relies on subtle geometric and electrostatic features.
  • Existing algorithms lack the precision and efficiency for detailed analysis of these subtle features.

Purpose of the Study:

  • Introduce pClay, the first algorithm for parallel and arbitrarily precise comparisons of molecular surfaces and electrostatic isopotentials.
  • Demonstrate pClay's ability to yield more precise structural inferences than existing methods.

Main Methods:

  • Developed pClay, an algorithm utilizing parallelism for high-precision geometric solid comparisons.
  • Applied pClay to analyze molecular surfaces and electrostatic isopotentials.
  • Evaluated pClay's performance on workstation CPUs and a 61-core Xeon Phi.

Main Results:

  • pClay enables practical, arbitrarily high-precision comparisons of molecular surfaces and electrostatic isopotentials.
  • Models trained with pClay data identify 100% of steric influences on specificity, compared to 47% with existing methods.
  • pClay demonstrates significant parallel performance gains on multi-core processors.

Conclusions:

  • pClay offers superior precision in geometric comparisons, leading to more accurate structural inferences.
  • The algorithm enhances the training data for statistical models of protein binding.
  • pClay has potential applications in explaining binding mechanisms and designing protein binding preferences.