Protein-protein docking by simulating the process of association subject to biochemical constraints

We present a computational procedure for modeling protein–protein association and predicting the structures of protein–protein complexes. The initial sampling stage is based on an efficient Brownian dynamics algorithm that mimics the physical process of diffusional association. Relevant biochemical...

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Veröffentlicht in:	Proteins, structure, function, and bioinformatics Jg. 71; H. 4; S. 1955 - 1969
Hauptverfasser:	Motiejunas, Domantas, Gabdoulline, Razif, Wang, Ting, Feldman-Salit, Anna, Johann, Tim, Winn, Peter J., Wade, Rebecca C.
Format:	Journal Article
Sprache:	Englisch
Veröffentlicht:	Hoboken Wiley Subscription Services, Inc., A Wiley Company 01.06.2008
Schlagworte:	Algorithms Amino Acid Sequence Animals Biochemical Phenomena Biochemistry Brownian dynamics clustering Computational Biology - methods Computer Simulation Crystallography, X-Ray Databases, Factual Diffusion encounter complex flexible refinement Fourier Analysis Humans Hydrogen Bonding Models, Biological molecular dynamics Molecular Sequence Data Osmolar Concentration Protein Conformation Protein Structure, Secondary Protein Structure, Tertiary protein-protein association protein-protein docking Proteins - chemistry Proteins - metabolism Static Electricity
ISSN:	0887-3585, 1097-0134, 1097-0134
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Abstract	We present a computational procedure for modeling protein–protein association and predicting the structures of protein–protein complexes. The initial sampling stage is based on an efficient Brownian dynamics algorithm that mimics the physical process of diffusional association. Relevant biochemical data can be directly incorporated as distance constraints at this stage. The docked configurations are then grouped with a hierarchical clustering algorithm into ensembles that represent potential protein–protein encounter complexes. Flexible refinement of selected representative structures is done by molecular dynamics simulation. The protein–protein docking procedure was thoroughly tested on 10 structurally and functionally diverse protein–protein complexes. Starting from X‐ray crystal structures of the unbound proteins, in 9 out of 10 cases it yields structures of protein–protein complexes close to those determined experimentally with the percentage of correct contacts >30% and interface backbone RMSD <4 Å. Detailed examination of all the docking cases gives insights into important determinants of the performance of the computational approach in modeling protein–protein association and predicting of protein–protein complex structures. Proteins 2008. © 2008 Wiley‐Liss, Inc.
AbstractList	We present a computational procedure for modeling protein-protein association and predicting the structures of protein-protein complexes. The initial sampling stage is based on an efficient Brownian dynamics algorithm that mimics the physical process of diffusional association. Relevant biochemical data can be directly incorporated as distance constraints at this stage. The docked configurations are then grouped with a hierarchical clustering algorithm into ensembles that represent potential protein-protein encounter complexes. Flexible refinement of selected representative structures is done by molecular dynamics simulation. The protein-protein docking procedure was thoroughly tested on 10 structurally and functionally diverse protein-protein complexes. Starting from X-ray crystal structures of the unbound proteins, in 9 out of 10 cases it yields structures of protein-protein complexes close to those determined experimentally with the percentage of correct contacts >30% and interface backbone RMSD <4 A. Detailed examination of all the docking cases gives insights into important determinants of the performance of the computational approach in modeling protein-protein association and predicting of protein-protein complex structures.We present a computational procedure for modeling protein-protein association and predicting the structures of protein-protein complexes. The initial sampling stage is based on an efficient Brownian dynamics algorithm that mimics the physical process of diffusional association. Relevant biochemical data can be directly incorporated as distance constraints at this stage. The docked configurations are then grouped with a hierarchical clustering algorithm into ensembles that represent potential protein-protein encounter complexes. Flexible refinement of selected representative structures is done by molecular dynamics simulation. The protein-protein docking procedure was thoroughly tested on 10 structurally and functionally diverse protein-protein complexes. Starting from X-ray crystal structures of the unbound proteins, in 9 out of 10 cases it yields structures of protein-protein complexes close to those determined experimentally with the percentage of correct contacts >30% and interface backbone RMSD <4 A. Detailed examination of all the docking cases gives insights into important determinants of the performance of the computational approach in modeling protein-protein association and predicting of protein-protein complex structures. We present a computational procedure for modeling protein–protein association and predicting the structures of protein–protein complexes. The initial sampling stage is based on an efficient Brownian dynamics algorithm that mimics the physical process of diffusional association. Relevant biochemical data can be directly incorporated as distance constraints at this stage. The docked configurations are then grouped with a hierarchical clustering algorithm into ensembles that represent potential protein–protein encounter complexes. Flexible refinement of selected representative structures is done by molecular dynamics simulation. The protein–protein docking procedure was thoroughly tested on 10 structurally and functionally diverse protein–protein complexes. Starting from X‐ray crystal structures of the unbound proteins, in 9 out of 10 cases it yields structures of protein–protein complexes close to those determined experimentally with the percentage of correct contacts >30% and interface backbone RMSD <4 Å. Detailed examination of all the docking cases gives insights into important determinants of the performance of the computational approach in modeling protein–protein association and predicting of protein–protein complex structures. Proteins 2008. © 2008 Wiley‐Liss, Inc. We present a computational procedure for modeling protein-protein association and predicting the structures of protein-protein complexes. The initial sampling stage is based on an efficient Brownian dynamics algorithm that mimics the physical process of diffusional association. Relevant biochemical data can be directly incorporated as distance constraints at this stage. The docked configurations are then grouped with a hierarchical clustering algorithm into ensembles that represent potential protein-protein encounter complexes. Flexible refinement of selected representative structures is done by molecular dynamics simulation. The protein-protein docking procedure was thoroughly tested on 10 structurally and functionally diverse protein-protein complexes. Starting from X-ray crystal structures of the unbound proteins, in 9 out of 10 cases it yields structures of protein-protein complexes close to those determined experimentally with the percentage of correct contacts >30% and interface backbone RMSD <4 A. Detailed examination of all the docking cases gives insights into important determinants of the performance of the computational approach in modeling protein-protein association and predicting of protein-protein complex structures.
Author	Gabdoulline, Razif Wang, Ting Winn, Peter J. Feldman-Salit, Anna Johann, Tim Wade, Rebecca C. Motiejunas, Domantas
Author_xml	– sequence: 1 givenname: Domantas surname: Motiejunas fullname: Motiejunas, Domantas email: domantas.motiejunas@eml-r.villa-bosch.de organization: EML Research gGmbH, Schloss-Wolfsbrunnenweg 33, 69118 Heidelberg, Germany – sequence: 2 givenname: Razif surname: Gabdoulline fullname: Gabdoulline, Razif organization: EML Research gGmbH, Schloss-Wolfsbrunnenweg 33, 69118 Heidelberg, Germany – sequence: 3 givenname: Ting surname: Wang fullname: Wang, Ting organization: EML Research gGmbH, Schloss-Wolfsbrunnenweg 33, 69118 Heidelberg, Germany – sequence: 4 givenname: Anna surname: Feldman-Salit fullname: Feldman-Salit, Anna organization: EML Research gGmbH, Schloss-Wolfsbrunnenweg 33, 69118 Heidelberg, Germany – sequence: 5 givenname: Tim surname: Johann fullname: Johann, Tim organization: EML Research gGmbH, Schloss-Wolfsbrunnenweg 33, 69118 Heidelberg, Germany – sequence: 6 givenname: Peter J. surname: Winn fullname: Winn, Peter J. organization: EML Research gGmbH, Schloss-Wolfsbrunnenweg 33, 69118 Heidelberg, Germany – sequence: 7 givenname: Rebecca C. surname: Wade fullname: Wade, Rebecca C. email: rebecca.wade@eml-r.villa-bosch.de organization: EML Research gGmbH, Schloss-Wolfsbrunnenweg 33, 69118 Heidelberg, Germany
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Cites_doi	10.1002/prot.1070 10.1110/ps.0217002 10.1093/bioinformatics/bti322 10.1016/j.jmb.2005.01.058 10.1002/(SICI)1097-0134(20000601)39:4<372::AID-PROT100>3.0.CO;2-Q 10.1073/pnas.89.6.2195 10.1002/(SICI)1097-0134(199612)26:4<363::AID-PROT1>3.0.CO;2-D 10.1006/jmbi.1997.1203 10.1093/nar/gkh039 10.1016/0021-9991(77)90098-5 10.1002/prot.21321 10.1016/S0969-2126(00)00528-1 10.1016/0010-4655(95)00043-F 10.1110/ps.041222905 10.1002/prot.20562 10.1016/S0959-440X(02)00287-7 10.1021/ja026939x 10.1021/bi000079k 10.1002/prot.10248 10.1006/meth.1998.0588 10.1002/prot.20272 10.1021/jp953109f 10.1002/jcc.10329 10.1002/(SICI)1099-1352(199907/08)12:4<226::AID-JMR462>3.0.CO;2-P 10.1093/nar/gki481 10.1093/nar/28.1.235 10.1021/ja9739906 10.1021/jm049314d 10.1016/S0959-440X(02)00311-1 10.1016/j.tibtech.2004.01.006 10.1038/nsb0698-422 10.1002/prot.21391 10.1093/protein/12.8.639 10.1002/jcc.20035 10.1073/pnas.90.24.11613 10.1006/jmbi.2000.4404 10.1006/jmbi.2000.4154 10.1093/proeng/gzg021 10.1002/prot.20551 10.1016/0263-7855(90)80070-V 10.1002/jcc.20290 10.1021/ct0501607 10.1038/77929 10.1016/S0065-3233(03)66002-X 10.1038/nbt1018 10.1021/ja9621760
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Snippet	We present a computational procedure for modeling protein–protein association and predicting the structures of protein–protein complexes. The initial sampling... We present a computational procedure for modeling protein-protein association and predicting the structures of protein-protein complexes. The initial sampling...
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SubjectTerms	Algorithms Amino Acid Sequence Animals Biochemical Phenomena Biochemistry Brownian dynamics clustering Computational Biology - methods Computer Simulation Crystallography, X-Ray Databases, Factual Diffusion encounter complex flexible refinement Fourier Analysis Humans Hydrogen Bonding Models, Biological molecular dynamics Molecular Sequence Data Osmolar Concentration Protein Conformation Protein Structure, Secondary Protein Structure, Tertiary protein-protein association protein-protein docking Proteins - chemistry Proteins - metabolism Static Electricity
Title	Protein-protein docking by simulating the process of association subject to biochemical constraints
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