Multidomain protein structure prediction using information about residues interacting on multimeric protein interfaces.

conformations reranking interaction residue pair multidomain protein protein tertiary structure prediction rigid-body docking

Journal

Biophysics and physicobiology
ISSN: 2189-4779
Titre abrégé: Biophys Physicobiol
Pays: Japan
ID NLM: 101675089

Informations de publication

Date de publication:
2020
Historique:
received: 13 11 2019
accepted: 12 12 2019
entrez: 9 6 2020
pubmed: 9 6 2020
medline: 9 6 2020
Statut: epublish

Résumé

Protein functions can be predicted based on their three-dimensional structures. However, many multidomain proteins have unstable structures, making it difficult to determine the whole structure in biological experiments. Additionally, multidomain proteins are often decomposed and identified based on their domains, with the structure of each domain often found in public databases. Recent studies have advanced structure prediction methods of multidomain proteins through computational analysis. In existing methods, proteins that serve as templates are used for three-dimensional structure prediction. However, when no protein template is available, the accuracy of the prediction is decreased. This study was conducted to predict the structures of multidomain proteins without the need for whole structure templates. We improved structure prediction methods by performing rigid-body docking from the structure of each domain and reranking a structure closer to the correct structure to have a higher value. In the proposed method, the score for the domain-domain interaction obtained without a structural template of the multidomain protein and score for the three-dimensional structure obtained during docking calculation were newly incorporated into the score function. We successfully predicted the structures of 50 of 55 multidomain proteins examined in the test dataset. Interaction residue pair information of the protein-protein complex interface contributes to domain reorganizations even when a structural template for a multidomain protein cannot be obtained. This approach may be useful for predicting the structures of multidomain proteins with important biochemical functions.

Identifiants

pubmed: 32509489
doi: 10.2142/biophysico.BSJ-2019050
pii: JST.JSTAGE/biophysico/BSJ-2019050
pmc: PMC7246089
doi:

Types de publication

Journal Article

Langues

eng

Pagination

2-13

Informations de copyright

2020 THE BIOPHYSICAL SOCIETY OF JAPAN.

Références

J Mol Biol. 2001 Jul 6;310(2):311-25
pubmed: 11428892
Proc Natl Acad Sci U S A. 2016 Oct 18;113(42):11841-11846
pubmed: 27698144
Nat Rev Mol Cell Biol. 2005 Mar;6(3):197-208
pubmed: 15738986
Proteins. 2005 Aug 1;60(2):176-80
pubmed: 15981248
Nat Protoc. 2010 Apr;5(4):725-38
pubmed: 20360767
Nucleic Acids Res. 2000 Jan 1;28(1):235-42
pubmed: 10592235
Proteins. 2003 Jul 1;52(1):80-7
pubmed: 12784371
J Mol Biol. 1997 Apr 4;267(3):707-26
pubmed: 9126848
Curr Opin Struct Biol. 2004 Apr;14(2):208-16
pubmed: 15093836
Protein Pept Lett. 2014;21(8):766-78
pubmed: 23855673
J Mol Biol. 2005 Apr 22;348(1):231-43
pubmed: 15808866
Protein Sci. 2007 Feb;16(2):165-75
pubmed: 17189483
J Mol Biol. 1995 Apr 7;247(4):536-40
pubmed: 7723011
J Mol Biol. 1999 Jan 29;285(4):1735-47
pubmed: 9917408
Protein Sci. 2005 Sep;14(9):2350-60
pubmed: 16081657
Science. 1973 Jul 20;181(4096):223-30
pubmed: 4124164
BMC Bioinformatics. 2008 Oct 16;9:441
pubmed: 18925951
Proteins. 2007 Jun 1;67(4):1078-86
pubmed: 17373710
Nucleic Acids Res. 2002 Jan 1;30(1):281-3
pubmed: 11752315
J Mol Biol. 2003 Oct 3;332(5):989-98
pubmed: 14499603
Curr Opin Struct Biol. 2002 Apr;12(2):176-81
pubmed: 11959494
Bioinformatics. 2014 Nov 15;30(22):3281-3
pubmed: 25100686
Biopolymers. 1983 Dec;22(12):2577-637
pubmed: 6667333
Proc Natl Acad Sci U S A. 2012 Jun 12;109(24):9438-41
pubmed: 22645367
Nat Protoc. 2012 Jul 19;7(8):1511-22
pubmed: 22814390
Bioinformatics. 2015 Jul 1;31(13):2098-105
pubmed: 25701568

Auteurs

Shumpei Matsuno (S)

Department of Computer Science, School of Computing, Tokyo Institute of Technology, Meguro-ku, Tokyo 152-8550, Japan.
AIST-TokyoTech Real World Big-Data Computation Open Innovation Laboratory (RWBC-OIL), National Institute of Advanced Industrial Science and Technology, Tsukuba, Ibaraki 305-8560, Japan.

Masahito Ohue (M)

Department of Computer Science, School of Computing, Tokyo Institute of Technology, Meguro-ku, Tokyo 152-8550, Japan.
Middle-Molecule IT-based Drug Discovery Laboratory (MIDL), Tokyo Institute of Technology, Kawasaki, Kanagawa 210-0821, Japan.

Yutaka Akiyama (Y)

Department of Computer Science, School of Computing, Tokyo Institute of Technology, Meguro-ku, Tokyo 152-8550, Japan.
Middle-Molecule IT-based Drug Discovery Laboratory (MIDL), Tokyo Institute of Technology, Kawasaki, Kanagawa 210-0821, Japan.

Classifications MeSH