Email updates

Keep up to date with the latest news and content from BMC Research Notes and BioMed Central.

Open Access Technical Note

Accelerating large-scale protein structure alignments with graphics processing units

Bin Pang1, Nan Zhao1, Michela Becchi2, Dmitry Korkin13 and Chi-Ren Shyu13*

Author Affiliations

1 Informatics Institute, University of Missouri, Columbia, MO, USA

2 Department of Electrical and Computer Engineering, University of, Columbia, MO, USA

3 Department of Computer Science, University of Missouri, Columbia, 65211, MO, USA

For all author emails, please log on.

BMC Research Notes 2012, 5:116  doi:10.1186/1756-0500-5-116

Published: 22 February 2012

Abstract

Background

Large-scale protein structure alignment, an indispensable tool to structural bioinformatics, poses a tremendous challenge on computational resources. To ensure structure alignment accuracy and efficiency, efforts have been made to parallelize traditional alignment algorithms in grid environments. However, these solutions are costly and of limited accessibility. Others trade alignment quality for speedup by using high-level characteristics of structure fragments for structure comparisons.

Findings

We present ppsAlign, a

    p
arallel
    p
rotein
    s
tructure
    Align
ment framework designed and optimized to exploit the parallelism of Graphics Processing Units (GPUs). As a general-purpose GPU platform, ppsAlign could take many concurrent methods, such as TM-align and Fr-TM-align, into the parallelized algorithm design. We evaluated ppsAlign on an NVIDIA Tesla C2050 GPU card, and compared it with existing software solutions running on an AMD dual-core CPU. We observed a 36-fold speedup over TM-align, a 65-fold speedup over Fr-TM-align, and a 40-fold speedup over MAMMOTH.

Conclusions

ppsAlign is a high-performance protein structure alignment tool designed to tackle the computational complexity issues from protein structural data. The solution presented in this paper allows large-scale structure comparisons to be performed using massive parallel computing power of GPU.