Reference : Docking without docking: ISEARCH--prediction of interactions using known interfaces.
Scientific journals : Article
Life sciences : Biochemistry, biophysics & molecular biology
http://hdl.handle.net/10993/472
Docking without docking: ISEARCH--prediction of interactions using known interfaces.
English
Gunther, Stefan* [> >]
May, Patrick* mailto [Zuse Institute Berlin - ZIB > Computer Science Research]
Hoppe, Andreas [> >]
Frommel, Cornelius [> >]
Preissner, Robert [> >]
* These authors have contributed equally to this work.
2007
Proteins
69
4
839-44
Yes (verified by ORBilu)
International
0887-3585
1097-0134
United States
[en] Algorithms ; Computational Biology/methods ; Computer Simulation ; Crystallography, X-Ray/methods ; Databases, Protein ; Genomics ; Ligands ; Molecular Conformation ; Protein Binding ; Protein Conformation ; Protein Interaction Mapping ; Proteins/chemistry ; Proteomics/methods ; Software
[en] The increasing number of solved protein structures provides a solid number of interfaces, if protein-protein interactions, domain-domain contacts, and contacts between biological units are taken into account. An interface library gives us the opportunity to identify surface regions on a target molecule that are similar by local structure and residue composition. If both unbound components of a possible protein complex exhibit structural similarities to a known interface, the unbound structures can be superposed onto the known interfaces. The approach is accompanied by two mathematical problems. Protein surfaces have to be quickly screened by thousands of patches, and similarity has to be evaluated by a suitable scoring scheme. The used algorithm (NeedleHaystack) identifies similar patches within minutes. Structurally related sites are recognized even if only parts of the template patches are structurally related to the interface region. A successful prediction of the protein complex depends on a suitable template of the library. However, the performed tests indicate that interaction sites are identified even if the similarity is very low. The approach complements existing ab initio methods and provides valuable results on standard benchmark sets.
http://hdl.handle.net/10993/472
10.1002/prot.21746
http://onlinelibrary.wiley.com/doi/10.1002/prot.21746/abstract
(c) 2007 Wiley-Liss, Inc.

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