Dynamical important residue network (DIRN): network inference via conformational change

Li，Quan1; Luo，Ray2,3,4,5,6; Chen，Hai Feng1

2019-11-01

10.1093/bioinformatics/btz298

BIOINFORMATICS   影响因子和分区

1367-4803

1367-4811

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.

SCI

英语

其他

National Institutes of Health/NIGMS[GM093040] ; National Institutes of Health/NIGMS[GM079383]

Biochemistry & Molecular Biology ; Biotechnology & Applied Microbiology ; Computer Science ; Mathematical & Computational Biology ; Mathematics

Biochemical Research Methods ; Biotechnology & Applied Microbiology ; Computer Science, Interdisciplinary Applications ; Mathematical & Computational Biology ; Statistics & Probability

WOS:000501728500016

OXFORD UNIV PRESS

BIOLOGY & BIOCHEMISTRY

2-s2.0-85070456491

Scopus

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,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,China

Li，Quan,Luo，Ray,Chen，Hai Feng. Dynamical important residue network (DIRN): network inference via conformational change[J]. BIOINFORMATICS,2019,35(22):4664-4670.

Li，Quan,Luo，Ray,&Chen，Hai Feng.(2019).Dynamical important residue network (DIRN): network inference via conformational change.BIOINFORMATICS,35(22),4664-4670.

Li，Quan,et al."Dynamical important residue network (DIRN): network inference via conformational change".BIOINFORMATICS 35.22(2019):4664-4670.