Quan Li1, Ray Luo2,3,4,5,6, Hai-Feng Chen1. 1. State Key Laboratory of Microbial Metabolism, Department of Bioinformatics and Biostatistics, National Experimental Teaching Center for Life Sciences and Biotechnology, School of Life Sciences and Biotechnology, Shanghai Jiao Tong University, Shanghai 200240, China. 2. Department of Molecular Biology and Biochemistry, University of California, Irvine, CA 92697, USA. 3. Department of Chemical and Biomolecular Engineering, University of California, Irvine, CA 92697, USA. 4. Department of Materials Science and Engineering, University of California, Irvine, CA 92697, USA. 5. Department of Biomedical Engineering, University of California, Irvine, CA 92697, USA. 6. Department of Chemistry, Southern University of Science and Technology, Shenzhen 518055, Guangdong, China.
Abstract
MOTIVATION: Protein residue interaction network has emerged as a useful strategy to understand the complex relationship between protein structures and functions and how functions are regulated. In a residue interaction network, every residue is used to define a network node, adding noises in network post-analysis and increasing computational burden. In addition, dynamical information is often necessary in deciphering biological functions. RESULTS: We developed a robust and efficient protein residue interaction network method, termed dynamical important residue network, by combining both structural and dynamical information. A major departure from previous approaches is our attempt to identify important residues most important for functional regulation before a network is constructed, leading to a much simpler network with the important residues as its nodes. The important residues are identified by monitoring structural data from ensemble molecular dynamics simulations of proteins in different functional states. Our tests show that the new method performs well with overall higher sensitivity than existing approaches in identifying important residues and interactions in tested proteins, so it can be used in studies of protein functions to provide useful hypotheses in identifying key residues and interactions. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.
MOTIVATION: Protein residue interaction network has emerged as a useful strategy to understand the complex relationship between protein structures and functions and how functions are regulated. In a residue interaction network, every residue is used to define a network node, adding noises in network post-analysis and increasing computational burden. In addition, dynamical information is often necessary in deciphering biological functions. RESULTS: We developed a robust and efficient protein residue interaction network method, termed dynamical important residue network, by combining both structural and dynamical information. A major departure from previous approaches is our attempt to identify important residues most important for functional regulation before a network is constructed, leading to a much simpler network with the important residues as its nodes. The important residues are identified by monitoring structural data from ensemble molecular dynamics simulations of proteins in different functional states. Our tests show that the new method performs well with overall higher sensitivity than existing approaches in identifying important residues and interactions in tested proteins, so it can be used in studies of protein functions to provide useful hypotheses in identifying key residues and interactions. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.
Authors: Hugh P Morgan; Francis J O'Reilly; Martin A Wear; J Robert O'Neill; Linda A Fothergill-Gilmore; Ted Hupp; Malcolm D Walkinshaw Journal: Proc Natl Acad Sci U S A Date: 2013-03-25 Impact factor: 11.205