Successful discrimination of protein interactions

We present results from the prediction of protein complexes associated with the first Critical Assessment of PRediction of Interactions (CAPRI) experiment. Our algorithm, SmoothDock, comprises four steps: (1) we perform rigid body docking using the program DOT, keeping the top 20,000 structures as r...

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Published inProteins, structure, function, and bioinformatics Vol. 52; no. 1; pp. 92 - 97
Main Authors Camacho, Carlos J., Gatchell, David W.
Format Journal Article
LanguageEnglish
Published Hoboken Wiley Subscription Services, Inc., A Wiley Company 01.07.2003
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Abstract We present results from the prediction of protein complexes associated with the first Critical Assessment of PRediction of Interactions (CAPRI) experiment. Our algorithm, SmoothDock, comprises four steps: (1) we perform rigid body docking using the program DOT, keeping the top 20,000 structures as ranked by surface complementarity; (2) we rerank these structures according to a free energy estimate that includes both desolvation and electrostatics and retain the top 2000 complexes; (3) we cluster the filtered complexes using a pairwise root‐mean‐square deviation (RMSD) criterion; (4) the 25 largest clusters are subject to a smooth docking discrimination algorithm where van der Waals forces are taken into account. We predicted targets 1, 6, and 7 with RMSDs of 9.5, 2.4, and 2.6 Å, respectively. More importantly, from the perspective of biological applications, our approach consistently ranked the correct model first (i.e., with highest confidence). For target 5 we identified the binding region but not the correct orientation. Although we were able to find reasonable clusters for all targets, low‐affinity complexes (Kd < nM) were harder to discriminate. For four of seven targets, the top models predicted by our automated procedure were among the best communitywide predictions. Proteins 2003;52:92–97. © 2003 Wiley‐Liss, Inc.
AbstractList We present results from the prediction of protein complexes associated with the first Critical Assessment of PRediction of Interactions (CAPRI) experiment. Our algorithm, SmoothDock, comprises four steps: (1) we perform rigid body docking using the program DOT, keeping the top 20,000 structures as ranked by surface complementarity; (2) we rerank these structures according to a free energy estimate that includes both desolvation and electrostatics and retain the top 2000 complexes; (3) we cluster the filtered complexes using a pairwise root-mean-square deviation (RMSD) criterion; (4) the 25 largest clusters are subject to a smooth docking discrimination algorithm where van der Waals forces are taken into account. We predicted targets 1, 6, and 7 with RMSDs of 9.5, 2.4, and 2.6 A, respectively. More importantly, from the perspective of biological applications, our approach consistently ranked the correct model first (i.e., with highest confidence). For target 5 we identified the binding region but not the correct orientation. Although we were able to find reasonable clusters for all targets, low-affinity complexes (K(d) < nM) were harder to discriminate. For four of seven targets, the top models predicted by our automated procedure were among the best communitywide predictions.
We present results from the prediction of protein complexes associated with the first Critical Assessment of PRediction of Interactions (CAPRI) experiment. Our algorithm, SmoothDock, comprises four steps: (1) we perform rigid body docking using the program DOT, keeping the top 20,000 structures as ranked by surface complementarity; (2) we rerank these structures according to a free energy estimate that includes both desolvation and electrostatics and retain the top 2000 complexes; (3) we cluster the filtered complexes using a pairwise root‐mean‐square deviation (RMSD) criterion; (4) the 25 largest clusters are subject to a smooth docking discrimination algorithm where van der Waals forces are taken into account. We predicted targets 1, 6, and 7 with RMSDs of 9.5, 2.4, and 2.6 Å, respectively. More importantly, from the perspective of biological applications, our approach consistently ranked the correct model first (i.e., with highest confidence). For target 5 we identified the binding region but not the correct orientation. Although we were able to find reasonable clusters for all targets, low‐affinity complexes (Kd < nM) were harder to discriminate. For four of seven targets, the top models predicted by our automated procedure were among the best communitywide predictions. Proteins 2003;52:92–97. © 2003 Wiley‐Liss, Inc.
We present results from the prediction of protein complexes associated with the first Critical Assessment of PRediction of Interactions (CAPRI) experiment. Our algorithm, SmoothDock, comprises four steps: (1) we perform rigid body docking using the program DOT, keeping the top 20,000 structures as ranked by surface complementarity; (2) we rerank these structures according to a free energy estimate that includes both desolvation and electrostatics and retain the top 2000 complexes; (3) we cluster the filtered complexes using a pairwise root-mean-square deviation (RMSD) criterion; (4) the 25 largest clusters are subject to a smooth docking discrimination algorithm where van der Waals forces are taken into account. We predicted targets 1, 6, and 7 with RMSDs of 9.5, 2.4, and 2.6 A, respectively. More importantly, from the perspective of biological applications, our approach consistently ranked the correct model first (i.e., with highest confidence). For target 5 we identified the binding region but not the correct orientation. Although we were able to find reasonable clusters for all targets, low-affinity complexes (K(d) < nM) were harder to discriminate. For four of seven targets, the top models predicted by our automated procedure were among the best communitywide predictions.We present results from the prediction of protein complexes associated with the first Critical Assessment of PRediction of Interactions (CAPRI) experiment. Our algorithm, SmoothDock, comprises four steps: (1) we perform rigid body docking using the program DOT, keeping the top 20,000 structures as ranked by surface complementarity; (2) we rerank these structures according to a free energy estimate that includes both desolvation and electrostatics and retain the top 2000 complexes; (3) we cluster the filtered complexes using a pairwise root-mean-square deviation (RMSD) criterion; (4) the 25 largest clusters are subject to a smooth docking discrimination algorithm where van der Waals forces are taken into account. We predicted targets 1, 6, and 7 with RMSDs of 9.5, 2.4, and 2.6 A, respectively. More importantly, from the perspective of biological applications, our approach consistently ranked the correct model first (i.e., with highest confidence). For target 5 we identified the binding region but not the correct orientation. Although we were able to find reasonable clusters for all targets, low-affinity complexes (K(d) < nM) were harder to discriminate. For four of seven targets, the top models predicted by our automated procedure were among the best communitywide predictions.
We present results from the prediction of protein complexes associated with the first Critical Assessment of PRediction of Interactions (CAPRI) experiment. Our algorithm, SmoothDock, comprises four steps: (1) we perform rigid body docking using the program DOT, keeping the top 20,000 structures as ranked by surface complementarity; (2) we rerank these structures according to a free energy estimate that includes both desolvation and electrostatics and retain the top 2000 complexes; (3) we cluster the filtered complexes using a pairwise root‐mean‐square deviation (RMSD) criterion; (4) the 25 largest clusters are subject to a smooth docking discrimination algorithm where van der Waals forces are taken into account. We predicted targets 1, 6, and 7 with RMSDs of 9.5, 2.4, and 2.6 Å, respectively. More importantly, from the perspective of biological applications, our approach consistently ranked the correct model first (i.e., with highest confidence). For target 5 we identified the binding region but not the correct orientation. Although we were able to find reasonable clusters for all targets, low‐affinity complexes (K d < nM) were harder to discriminate. For four of seven targets, the top models predicted by our automated procedure were among the best communitywide predictions. Proteins 2003;52:92–97. © 2003 Wiley‐Liss, Inc.
Author Gatchell, David W.
Camacho, Carlos J.
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References_xml – reference: Fernández-Recio J, Totrov M, Abagyan R. Soft protein-protein docking in internal coordinates. Protein Sci 2002; 11: 280-291.
– reference: Fieulaine S, Morera S, Poncet S, Mijakovic I, Galinier A, Janin J, Deutscher J, Nessler S. X-ray structure of a bifunctional protein kinase in complex with its protein substrate HPr. Proc Natl Acad Sci USA 2002; 99: 13437-13441.
– reference: Li H, Llera A, Tsuchiya D, Ysern X, Schlievert PM, Karjalainen K, Mariuzza RA. Three-dimensional structure of the complex between a T cell receptor beta chain and the superantigen staphylococcal enterotoxin B. Immunity 1998; 9: 807-816.
– reference: Smith GR, Sternberg MJE. Prediction of protein-protein interactions by docking methods. Curr Opin Struct Biol 2002; 12: 28-35.
– reference: Camacho CJ, Vajda S. Protein-protein association kinetics and protein docking. Curr Opin Struct Biol 2002; 12: 36-40.
– reference: Sundberg EJ, Hongmin L, Liera AS, McCormick JK, Tormo J, Schlievert PM, Karjalainen K, Mariuzza RA. Structures of two streptococcal superantigens bound to TCR β chains reveal diversity in the architecture of T cell signaling complexes. Structure 2002; 10: 687-699.
– reference: Camacho CJ, Gatchell DW, Kimura SR, Vajda S. Scoring docked conformations generated by rigid-body protein-protein docking. Proteins 2000; 40: 525-537.
– reference: Camacho CJ, Vajda S. Protein docking along smooth association pathways. Proc Natl Acad Sci USA 2001; 98: 10636-10641.
– reference: Berman HM, Westbrook J, Feng Z, Gilliland G, Bhat TN, Weissig H, Shindyalov IN, Bourne PE. The Protein Data Bank. Nucleic Acids Res 2000; 28: 235-242.
– reference: Desmyter A, Spinelli S, Payan F, Lauwereys M, Wyns L, Muyldermans S, Cambillau C. Three camelid VHH domains in complex with porcine pancreatic α-amylase. Inhibition and versatility of binding topology. J Biol Chem 2002; 277: 23645-23650.
– reference: Brooks BR, Bruccoleri RE, Olafson BD, States DJ, Swaminathan S, Karplus M. CHARMM: a program for macromolecular energy, minimization, and dynamics calculations. J Comput Chem 1983; 4: 187-217.
– reference: Janin J. Welcome to CAPRI: a critical assessment of predicted interactions. Proteins 2002; 47: 257.
– reference: Zhang C, Vasmatzis G, Cornette JL. Determination of atomic desolvation energies from the structures of crystallized proteins. J Mol Biol 1997; 267: 707-726.
– reference: Barbey-Martin C, Gigant B, Bizebard T, Calder LJ, Wharton SA, Skehel JJ, Knossow M. An antibody that prevents the hemagglutinin low pH fusgenic transition. Virology 2002; 294: 70-74.
– reference: Schaefer, M. Karplus, M. A comprehensive analytical treatment of continuum electrostatics. J Phys Chem 1996; 100: 1578-1599.
– reference: Bruccoleri RE. Application of systematic conformational search to protein modeling. Mol Simulat 1993; 10: 151-174.
– reference: Katchalski-Katzir E, Shariv I, Eisenstein M, Friesem A, Aflalo C, Vakser I. Molecular surface recognition: determinination of geometric fit between proteins and their ligands by correlation techniques. Proc Natl Acad Sci USA 1992; 89: 2195-2199.
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Snippet We present results from the prediction of protein complexes associated with the first Critical Assessment of PRediction of Interactions (CAPRI) experiment. Our...
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SubjectTerms Algorithms
alpha-Amylases - chemistry
alpha-Amylases - metabolism
Animals
Antibodies - chemistry
Antibodies - immunology
Antigens, Viral
Bacterial Proteins - chemistry
Bacterial Proteins - metabolism
Binding Sites
CAPRI
Capsid Proteins - chemistry
Capsid Proteins - immunology
clusters
decoys
desolvation
docking
Exotoxins - chemistry
Exotoxins - metabolism
Hemagglutinin Glycoproteins, Influenza Virus - chemistry
Hemagglutinin Glycoproteins, Influenza Virus - immunology
Macromolecular Substances
Membrane Proteins - chemistry
Membrane Proteins - metabolism
Models, Molecular
Phosphoenolpyruvate Sugar Phosphotransferase System - chemistry
Phosphoenolpyruvate Sugar Phosphotransferase System - metabolism
Protein Interaction Mapping
Protein-Serine-Threonine Kinases - chemistry
Protein-Serine-Threonine Kinases - metabolism
Proteins - chemistry
Proteins - metabolism
Receptors, Antigen, T-Cell, alpha-beta - chemistry
Receptors, Antigen, T-Cell, alpha-beta - metabolism
SmoothDock
Title Successful discrimination of protein interactions
URI https://api.istex.fr/ark:/67375/WNG-5570KJFH-L/fulltext.pdf
https://onlinelibrary.wiley.com/doi/abs/10.1002%2Fprot.10394
https://www.ncbi.nlm.nih.gov/pubmed/12784373
https://www.proquest.com/docview/73381548
Volume 52
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