UMMS Affiliation

Program in Bioinformatics and Integrative Biology

Date

9-2011

Document Type

Article

Subjects

*Algorithms; Computational Biology; Databases, Protein; *Models, Molecular; *Peptide Library; Proteins; Time Factors

Disciplines

Bioinformatics | Life Sciences | Medicine and Health Sciences

Abstract

Computational prediction of the 3D structures of molecular interactions is a challenging area, often requiring significant computational resources to produce structural predictions with atomic-level accuracy. This can be particularly burdensome when modeling large sets of interactions, macromolecular assemblies, or interactions between flexible proteins. We previously developed a protein docking program, ZDOCK, which uses a fast Fourier transform to perform a 3D search of the spatial degrees of freedom between two molecules. By utilizing a pairwise statistical potential in the ZDOCK scoring function, there were notable gains in docking accuracy over previous versions, but this improvement in accuracy came at a substantial computational cost. In this study, we incorporated a recently developed 3D convolution library into ZDOCK, and additionally modified ZDOCK to dynamically orient the input proteins for more efficient convolution. These modifications resulted in an average of over 8.5-fold improvement in running time when tested on 176 cases in a newly released protein docking benchmark, as well as substantially less memory usage, with no loss in docking accuracy. We also applied these improvements to a previous version of ZDOCK that uses a simpler non-pairwise atomic potential, yielding an average speed improvement of over 5-fold on the docking benchmark, while maintaining predictive success. This permits the utilization of ZDOCK for more intensive tasks such as docking flexible molecules and modeling of interactomes, and can be run more readily by those with limited computational resources.

Comments

Citation: Pierce BG, Hourai Y, Weng Z (2011) Accelerating Protein Docking in ZDOCK Using an Advanced 3D Convolution Library. PLoS ONE 6(9): e24657. doi:10.1371/journal.pone.0024657. Link to article on publisher's site

Copyright: © 2011 Pierce et al. This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.

Related Resources

Link to Article in PubMed

PubMed ID

21949741

 
 

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