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 language | English |
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Number of pages | 10 |
Journal | IEEE/ACM Transactions on Computational Biology and Bioinformatics |
DOIs | |
Publication status | E-pub ahead of print - 30 Oct 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