Inferring Domain Annotated Protein-Protein Interactions through 3D-Domain Interologs

• Main Author: 陳永強
• Other Authors: Jinn-Moon Yang
• Format: Others
• Language: zh-TW
• Published: 2006
• Online Access: http://ndltd.ncl.edu.tw/handle/35393223786695873530

碩士 === 國立交通大學 === 生物資訊研究所 === 94 === The interaction between proteins is one of the most important features to most biological processes. In the postgenomic era, the ability to identify protein-protein interactions on a genomic scale is very important to determine networks of protein interactions. To predict protein interactions large-scalely, Lu et al. presented “interologs mapping”, — predicting protein-protein interactions from one organism to another by using computational comparative genomics. However, behind protein interactions there are protein domains interacting physically with one another to perform the specific functions. According to the increasing number of solved structures involving protein complexes, it is ripe to test putative interactions on complexes of known 3D structures. In this study, we proposed a new concept “3D-domain interologs mapping” to inferred domain-annotated protein interactions. The 3D-domain interologs mapping is defined as “Domain a (in chain A) interacts with domain b (in chain B) in a 3D complex, their inferring protein pair A' (containing domain a) and B' (containing domain b) in the same species would be likely to interact with each other if both protein pairs (A' and A as well as proteins B and B') are homologous ” The key novelties of our method are fast genome-scale prediction across hundreds of organisms and construction of a pair Position Specific Scoring Matrix (pairPSSM). This matrix is able to provide statistical significance of residue pairs at various contact positions by evolutionary profiles, leading to a more sensitive scoring system. Our method successfully distinguishes the true protein complexes and unreasonable protein pairs with about 90% accuracy. We also evaluate our method in yeast proteome and get about 10% improvements than previous methods. The mean correlation of the gene expression profiles of our predictions is significantly higher than that for non-interacting protein pairs in S. cerevisiae. Finally, our method applies to seven organisms commonly used in molecular research, including Homo sapiens, Mus musculus, Rattus norvegicus, Caenorhabditis elegans, Drosophila melanogaster, Saccharomyces cerevisiae and Escherichia coli. In these seven organisms, our method predicts ~450,000 new interactions in which the interacting domains and residues are automatically modeled. In conclusion, this study suggests that 3D-domain interologs mapping and pairPSSM are useful methods for predicting protein-protein interactions and detailed analyzing networks of protein interactions.