Improving protein flexibility predictions by combining statistical sampling with a mean-field virtual Pebble Game

  • Authors:
  • Luis C. González;Dennis R. Livesay;Donald J. Jacobs

  • Affiliations:
  • UNC Charlotte;UNC Charlotte;UNC Charlotte

  • Venue:
  • Proceedings of the 2nd ACM Conference on Bioinformatics, Computational Biology and Biomedicine
  • Year:
  • 2011

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Abstract

The Pebble Game (PG) algorithm is an efficient method to characterize protein flexibility. The PG calculates several mechanical graph rigidity quantities based on a network of chemical interactions. Because noncovalent chemical interactions continually break and reform, a distinct PG calculation is necessary for each network topology. As such, equilibrium properties are calculated by averaging over an ensemble of input networks, each with an accompanying PG characterization. We have recently developed a new mean field approach, called the Virtual Pebble Game (VPG), that speeds up calculation times by eliminating sampling. Here, we test a hybrid VPG-x algorithm that combines the advantages of both by applying the mean field approximation to some interactions while sampling the rest. Across several different quantitative and qualitative comparisons, the VPG-x algorithm consistently outperforms the strictly mean-field VPG.