Improving protein docking with binding site prediction
Protein-protein and protein-ligand interactions are fundamental as many proteins mediate their biological function through these interactions. Many important applications follow directly from the identification of residues in the interfaces between protein-protein and protein-ligand interactions, su...
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2008
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ndltd-DRESDEN-oai-qucosa.de-bsz-14-ds-1216305428189-099512013-01-07T19:48:50Z Improving protein docking with binding site prediction Huang, Bingding Protein docking protein binding site prediction BDOCK prtotein docking Vorhersage von Proteinbindungsstellen BDOCK LIGSITEcsc metaPPI ddc:570 rvk:WD 5100 Protein-protein and protein-ligand interactions are fundamental as many proteins mediate their biological function through these interactions. Many important applications follow directly from the identification of residues in the interfaces between protein-protein and protein-ligand interactions, such as drug design, protein mimetic engineering, elucidation of molecular pathways, and understanding of disease mechanisms. The identification of interface residues can also guide the docking process to build the structural model of protein-protein complexes. This dissertation focuses on developing computational approaches for protein-ligand and protein-protein binding site prediction and applying these predictions to improve protein-protein docking. First, we develop an automated approach LIGSITEcs to predict protein-ligand binding site, based on the notion of surface-solvent-surface events and the degree of conservation of the involved surface residues. We compare our algorithm to four other approaches, LIGSITE, CAST, PASS, and SURFNET, and evaluate all on a dataset of 48 unbound/bound structures and 210 bound-structures. LIGSITEcs performs slightly better than the other tools and achieves a success rate of 71% and 75%, respectively. Second, for protein-protein binding site, we develop metaPPI, a meta server for interface prediction. MetaPPI combines results from a number of tools, such as PPI_Pred, PPISP, PINUP, Promate, and SPPIDER, which predict enzyme-inhibitor interfaces with success rates of 23% to 55% and other interfaces with 10% to 28% on a benchmark dataset of 62 complexes. After refinement, metaPPI significantly improves prediction success rates to 70% for enzyme-inhibitor and 44% for other interfaces. Third, for protein-protein docking, we develop a FFT-based docking algorithm and system BDOCK, which includes specific scoring functions for specific types of complexes. BDOCK uses family-based residue interface propensities as a scoring function and obtains improvement factors of 4-30 for enzyme-inhibitor and 4-11 for antibody-antigen complexes in two specific SCOP families. Furthermore, the degrees of buriedness of surface residues are integrated into BDOCK, which improves the shape discriminator for enzyme-inhibitor complexes. The predicted interfaces from metaPPI are integrated as well, either during docking or after docking. The evaluation results show that reliable interface predictions improve the discrimination between near-native solutions and false positive. Finally, we propose an implicit method to deal with the flexibility of proteins by softening the surface, to improve docking for non enzyme-inhibitor complexes. Saechsische Landesbibliothek- Staats- und Universitaetsbibliothek Dresden Technische Universität Dresden, Informatik Prof. Dr. Michael Schroeder Prof. Dr. Michael Schroeder Prof. Dr. Volkhard Helms Prof. Dr. Pedro Barahona 2008-07-17 doc-type:doctoralThesis application/pdf http://nbn-resolving.de/urn:nbn:de:bsz:14-ds-1216305428189-09951 urn:nbn:de:bsz:14-ds-1216305428189-09951 PPN285409484 http://www.qucosa.de/fileadmin/data/qucosa/documents/560/1216305428189-0995.pdf eng |
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NDLTD |
language |
English |
format |
Doctoral Thesis |
sources |
NDLTD |
topic |
Protein docking protein binding site prediction BDOCK prtotein docking Vorhersage von Proteinbindungsstellen BDOCK LIGSITEcsc metaPPI ddc:570 rvk:WD 5100 |
spellingShingle |
Protein docking protein binding site prediction BDOCK prtotein docking Vorhersage von Proteinbindungsstellen BDOCK LIGSITEcsc metaPPI ddc:570 rvk:WD 5100 Huang, Bingding Improving protein docking with binding site prediction |
description |
Protein-protein and protein-ligand interactions are fundamental as many proteins mediate their biological function through these interactions. Many important applications follow directly from the identification of residues in the interfaces between protein-protein and protein-ligand interactions, such as drug design, protein mimetic engineering, elucidation of molecular pathways, and understanding of disease mechanisms. The identification of interface residues can also guide the docking process to build the structural model of protein-protein complexes. This dissertation focuses on developing computational approaches for protein-ligand and protein-protein binding site prediction and applying these predictions to improve protein-protein docking. First, we develop an automated approach LIGSITEcs to predict protein-ligand binding site, based on the notion of surface-solvent-surface events and the degree of conservation of the involved surface residues. We compare our algorithm to four other approaches, LIGSITE, CAST, PASS, and SURFNET, and evaluate all on a dataset of 48 unbound/bound structures and 210 bound-structures. LIGSITEcs performs slightly better than the other tools and achieves a success rate of 71% and 75%, respectively. Second, for protein-protein binding site, we develop metaPPI, a meta server for interface prediction. MetaPPI combines results from a number of tools, such as PPI_Pred, PPISP, PINUP, Promate, and SPPIDER, which predict enzyme-inhibitor interfaces with success rates of 23% to 55% and other interfaces with 10% to 28% on a benchmark dataset of 62 complexes. After refinement, metaPPI significantly improves prediction success rates to 70% for enzyme-inhibitor and 44% for other interfaces. Third, for protein-protein docking, we develop a FFT-based docking algorithm and system BDOCK, which includes specific scoring functions for specific types of complexes. BDOCK uses family-based residue interface propensities as a scoring function and obtains improvement factors of 4-30 for enzyme-inhibitor and 4-11 for antibody-antigen complexes in two specific SCOP families. Furthermore, the degrees of buriedness of surface residues are integrated into BDOCK, which improves the shape discriminator for enzyme-inhibitor complexes. The predicted interfaces from metaPPI are integrated as well, either during docking or after docking. The evaluation results show that reliable interface predictions improve the discrimination between near-native solutions and false positive. Finally, we propose an implicit method to deal with the flexibility of proteins by softening the surface, to improve docking for non enzyme-inhibitor complexes. |
author2 |
Technische Universität Dresden, Informatik |
author_facet |
Technische Universität Dresden, Informatik Huang, Bingding |
author |
Huang, Bingding |
author_sort |
Huang, Bingding |
title |
Improving protein docking with binding site prediction |
title_short |
Improving protein docking with binding site prediction |
title_full |
Improving protein docking with binding site prediction |
title_fullStr |
Improving protein docking with binding site prediction |
title_full_unstemmed |
Improving protein docking with binding site prediction |
title_sort |
improving protein docking with binding site prediction |
publisher |
Saechsische Landesbibliothek- Staats- und Universitaetsbibliothek Dresden |
publishDate |
2008 |
url |
http://nbn-resolving.de/urn:nbn:de:bsz:14-ds-1216305428189-09951 http://nbn-resolving.de/urn:nbn:de:bsz:14-ds-1216305428189-09951 http://www.qucosa.de/fileadmin/data/qucosa/documents/560/1216305428189-0995.pdf |
work_keys_str_mv |
AT huangbingding improvingproteindockingwithbindingsiteprediction |
_version_ |
1716470564178100224 |