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Graphics processing unit-based alignment of protein interaction networks.

Jiang Xie1, Zhonghua Zhou2, Jin Ma2

  • 1School of Computer Engineering and Science, Shanghai University, Shanghai, People's Republic of China. jiangx@shu.edu.cn.

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This study introduces a faster protein-protein interaction network alignment method, HGA-2N, using GPU acceleration. It significantly reduces computation time for large networks, aiding in conserved and potential PPI discovery and phylogenetic tree reconstruction.

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Area of Science:

  • Bioinformatics
  • Computational Biology
  • Systems Biology

Background:

  • Understanding human protein-protein interactions (PPIs) is crucial for deciphering biological functions, often studied through model organisms.
  • Aligning protein interaction networks (PINs) is computationally intensive due to the subgraph isomorphism problem, hindering analysis of large-scale networks.
  • Sequential computing methods are insufficient for efficiently aligning extensive biological networks.

Purpose of the Study:

  • To develop an accelerated algorithm for protein interaction network alignment.
  • To leverage parallel computing, specifically graphics processing unit (GPU) acceleration, to overcome computational limitations.
  • To enhance the discovery of conserved and potential PPIs and reconstruct phylogenetic trees.

Main Methods:

  • Proposed a novel Hungarian-Greedy Algorithm with 2-nearest neighbours (HGA-2N).
  • Implemented GPU acceleration for the HGA-2N algorithm, optimizing parallel patterns, computing, and storage modes.
  • Validated the method using large-scale network alignment, PPI prediction, and phylogenetic tree reconstruction for herpes viruses.

Main Results:

  • HGA-2N achieves alignments comparable to the standard Hungarian-Greedy Algorithm (HGA) but with significantly reduced computation time.
  • GPU-accelerated HGA-2N demonstrates a superior CPU-to-GPU time ratio compared to HGA for large networks.
  • Predicted potential PPIs using HGA-2N showed a 42.8% validation rate against the Human Protein Reference Database based on Gene Ontology terms.
  • A new phylogenetic tree reconstruction method using the aligned networks yielded results consistent with established methods for herpes viruses.

Conclusions:

  • GPU-accelerated HGA-2N is an efficient approach for aligning large-scale protein interaction networks.
  • The method facilitates the identification of conserved and novel PPIs, contributing to a deeper understanding of biological networks.
  • The proposed technique offers a robust tool for phylogenetic analysis, demonstrating its versatility in computational biology.