COPCOP: A Novel Algorithm and Parallel Optimization Framework for Co-Evolutionary Domain Detection

Xiaoyu Zhang, Xiangke Liao, Hao Zhu, Kenli Li, Benyun SHI, Shaoliang Peng

Research output: Contribution to journalArticlepeer-review

Abstract

Co-evolution exists ubiquitously in biological systems. At the molecular level, interacting proteins, such as ligands and their receptors and components in protein complexes, co-evolve to maintain their structural and functional interactions. Many proteins contain multiple functional domains interacting with different partners, making co-evolution of interacting domains occur more prominently. Multiple methods have been developed to predict interacting proteins or domains within proteins by detecting their co-variation. This strategy neglects the fact that interacting domains can be highly co-conserved due to their functional interactions. Here we report a novel algorithm COPCOP to detect signals of both co-positive selection (co-variation) and co-purifying selection (co-conservation). Results show that our algorithm performs well and outperforms the popular co-variation analysis program CAPS. We also design and implement a multi-level parallel acceleration strategy for COPCOP based on Tianhe-2 CPU-MIC heterogeneous supercomputer system to meet the need of large-scale co-evolutionary domain detection.

Original languageEnglish
JournalIEEE/ACM Transactions on Computational Biology and Bioinformatics
DOIs
Publication statusAccepted/In press - 2018

Scopus Subject Areas

  • Biotechnology
  • Genetics
  • Applied Mathematics

User-Defined Keywords

  • Amino acids
  • Bioinformatics
  • coevolution
  • collaborated parallel
  • evolution
  • Evolution (biology)
  • Genetics
  • Microsoft Windows
  • positive selection
  • Proteins
  • purifying selection
  • Supercomputers
  • Tianhe-2

Fingerprint

Dive into the research topics of 'COPCOP: A Novel Algorithm and Parallel Optimization Framework for Co-Evolutionary Domain Detection'. Together they form a unique fingerprint.

Cite this