Interaction pair predictor for membrane proteins

2015 Fiscal Year Final Research Report

• Project/Area Number: 25870764
• Research Category: Grant-in-Aid for Young Scientists (B)
• Allocation Type: Multi-year Fund
• Research Field: System genome science; Life / Health / Medical informatics
• Research Institution: Tokyo Denki University
• Principal Investigator: Nemoto Wataru 東京電機大学, 理工学部, 准教授 (10455438)
• Project Period (FY): 2013-04-01 – 2016-03-31
• Keywords: 膜タンパク質 / 相互作用 / バイオインフォマティクス / 予測 / アミノ酸配列 / 立体構造 / 機械学習

Outline of Final Research Achievements

Membrane proteins are important pharmaceutical targets. Many membrane proteins exert a wide variety of molecular functions by forming their specific combinations. In addition, various membrane proteins are reportedly associated with diseases. Their oligomerization is now recognized as an important event in various biological phenomena, and many researchers are investigating this subject. We have tried to develop a support vector machine (SVM)-based method to predict interacting pairs for membrane protein oligomerization, by integrating their structure and sequence information. However, it is still difficult to develop a method to predict interacting membrane protein pairs among all membrane proteins. Hence, in this project, we focused only on G protein-coupled receptors (GPCRs), and developed a high performance method. Our method could accelerate the analyses of these interactions, and contribute to the elucidation of the global structures of the GPCR networks in membranes.

Free Research Field

立体構造インフォマティクス