Building Macromolecular Assemblies by Information-driven Docking
2010; Elsevier BV; Volume: 9; Issue: 8 Linguagem: Inglês
10.1074/mcp.m000051-mcp201
ISSN1535-9484
AutoresEzgi Karaca, Adrien S. J. Melquiond, Sjoerd J. de Vries, Panagiotis L. Kastritis, Alexandre M. J. J. Bonvin,
Tópico(s)Protein Structure and Dynamics
ResumoOver the last years, large scale proteomics studies have generated a wealth of information of biomolecular complexes. Adding the structural dimension to the resulting interactomes represents a major challenge that classical structural experimental methods alone will have difficulties to confront. To meet this challenge, complementary modeling techniques such as docking are thus needed. Among the current docking methods, HADDOCK (High Ambiguity-Driven DOCKing) distinguishes itself from others by the use of experimental and/or bioinformatics data to drive the modeling process and has shown a strong performance in the critical assessment of prediction of interactions (CAPRI), a blind experiment for the prediction of interactions. Although most docking programs are limited to binary complexes, HADDOCK can deal with multiple molecules (up to six), a capability that will be required to build large macromolecular assemblies. We present here a novel web interface of HADDOCK that allows the user to dock up to six biomolecules simultaneously. This interface allows the inclusion of a large variety of both experimental and/or bioinformatics data and supports several types of cyclic and dihedral symmetries in the docking of multibody assemblies. The server was tested on a benchmark of six cases, containing five symmetric homo-oligomeric protein complexes and one symmetric protein-DNA complex. Our results reveal that, in the presence of either bioinformatics and/or experimental data, HADDOCK shows an excellent performance: in all cases, HADDOCK was able to generate good to high quality solutions and ranked them at the top, demonstrating its ability to model symmetric multicomponent assemblies. Docking methods can thus play an important role in adding the structural dimension to interactomes. However, although the current docking methodologies were successful for a vast range of cases, considering the variety and complexity of macromolecular assemblies, inclusion of some kind of experimental information (e.g. from mass spectrometry, nuclear magnetic resonance, cryoelectron microscopy, etc.) will remain highly desirable to obtain reliable results. Over the last years, large scale proteomics studies have generated a wealth of information of biomolecular complexes. Adding the structural dimension to the resulting interactomes represents a major challenge that classical structural experimental methods alone will have difficulties to confront. To meet this challenge, complementary modeling techniques such as docking are thus needed. Among the current docking methods, HADDOCK (High Ambiguity-Driven DOCKing) distinguishes itself from others by the use of experimental and/or bioinformatics data to drive the modeling process and has shown a strong performance in the critical assessment of prediction of interactions (CAPRI), a blind experiment for the prediction of interactions. Although most docking programs are limited to binary complexes, HADDOCK can deal with multiple molecules (up to six), a capability that will be required to build large macromolecular assemblies. We present here a novel web interface of HADDOCK that allows the user to dock up to six biomolecules simultaneously. This interface allows the inclusion of a large variety of both experimental and/or bioinformatics data and supports several types of cyclic and dihedral symmetries in the docking of multibody assemblies. The server was tested on a benchmark of six cases, containing five symmetric homo-oligomeric protein complexes and one symmetric protein-DNA complex. Our results reveal that, in the presence of either bioinformatics and/or experimental data, HADDOCK shows an excellent performance: in all cases, HADDOCK was able to generate good to high quality solutions and ranked them at the top, demonstrating its ability to model symmetric multicomponent assemblies. Docking methods can thus play an important role in adding the structural dimension to interactomes. However, although the current docking methodologies were successful for a vast range of cases, considering the variety and complexity of macromolecular assemblies, inclusion of some kind of experimental information (e.g. from mass spectrometry, nuclear magnetic resonance, cryoelectron microscopy, etc.) will remain highly desirable to obtain reliable results. Proteins are the wheels and millstones of the complex machinery that underlies human life. Catalyzing a huge diversity of chemical processes, proteins work in close association with other biomolecules: nucleic acids, sugars, lipids, and other proteins. This huge network of protein interactions enables the cell to respond quickly to changes in the environment, such as temperature, oxygen, or nutrient concentration. However, to fully understand this network, insights at the atomic level are needed. In the wake of the elucidation of the human genome (1.Lander E.S. Linton L.M. Birren B. Nusbaum C. Zody M.C. Baldwin J. Devon K. Dewar K. Doyle M. FitzHugh W. Funke R. Gage D. Harris K. Heaford A. Howland J. Kann L. Lehoczky J. LeVine R. McEwan P. McKernan K. Meldrim J. Mesirov J.P. Miranda C. Morris W. Naylor J. Raymond C. Rosetti M. Santos R. Sheridan A. Sougnez C. Stange-Thomann N. Stojanovic N. Subramanian A. Wyman D. Rogers J. Sulston J. Ainscough R. Beck S. Bentley D. Burton J. Clee C. Carter N. Coulson A. Deadman R. Deloukas P. Dunham A. Dunham I. Durbin R. French L. Grafham D. Gregory S. Hubbard T. Humphray S. Hunt A. Jones M. Lloyd C. McMurray A. Matthews L. Mercer S. Milne S. Mullikin J.C. Mungall A. Plumb R. Ross M. Shownkeen R. Sims S. Waterston R.H. Wilson R.K. Hillier L.W. McPherson J.D. Marra M.A. Mardis E.R. Fulton L.A. Chinwalla A.T. Pepin K.H. Gish W.R. Chissoe S.L. Wendl M.C. Delehaunty K.D. Miner T.L. Delehaunty A. Kramer J.B. Cook L.L. Fulton R.S. Johnson D.L. Minx P.J. Clifton S.W. Hawkins T. Branscomb E. Predki P. Richardson P. Wenning S. Slezak T. Doggett N. Cheng J.F. Olsen A. Lucas S. Elkin C. Uberbacher E. Frazier M. Gibbs R.A. Muzny D.M. Scherer S.E. Bouck J.B. Sodergren E.J. Worley K.C. Rives C.M. Gorrell J.H. Metzker M.L. Naylor S.L. Kucherlapati R.S. Nelson D.L. Weinstock G.M. Sakaki Y. Fujiyama A. Hattori M. Yada T. Toyoda A. Itoh T. Kawagoe C. Watanabe H. Totoki Y. Taylor T. Weissenbach J. Heilig R. Saurin W. Artiguenave F. Brottier P. Bruls T. Pelletier E. Robert C. Wincker P. Smith D.R. Doucette-Stamm L. Rubenfield M. Weinstock K. Lee H.M. Dubois J. Rosenthal A. Platzer M. Nyakatura G. Taudien S. Rump A. Yang H. Yu J. Wang J. Huang G. Gu J. Hood L. Rowen L. Madan A. Qin S. Davis R.W. Federspiel N.A. Abola A.P. Proctor M.J. Myers R.M. Schmutz J. Dickson M. Grimwood J. Cox D.R. Olson M.V. Kaul R. Raymond C. Shimizu N. Kawasaki K. Minoshima S. Evans G.A. Athanasiou M. Schultz R. Roe B.A. Chen F. Pan H. Ramser J. Lehrach H. Reinhardt R. McCombie W.R. de la Bastide M. Dedhia N. Blöcker H. Hornischer K. Nordsiek G. Agarwala R. Aravind L. Bailey J.A. Bateman A. Batzoglou S. Birney E. Bork P. Brown D.G. Burge C.B. Cerutti L. Chen H.C. Church D. Clamp M. Copley R.R. Doerks T. Eddy S.R. Eichler E.E. Furey T.S. Galagan J. Gilbert J.G. Harmon C. Hayashizaki Y. Haussler D. Hermjakob H. Hokamp K. Jang W. Johnson L.S. Jones T.A. Kasif S. Kaspryzk A. Kennedy S. Kent W.J. Kitts P. Koonin E.V. Korf I. Kulp D. Lancet D. Lowe T.M. McLysaght A. Mikkelsen T. Moran J.V. Mulder N. Pollara V.J. Ponting C.P. Schuler G. Schultz J. Slater G. Smit A.F. Stupka E. Szustakowski J. Thierry-Mieg D. Thierry-Mieg J. Wagner L. Wallis J. Wheeler R. Williams A. Wolf Y.I. Wolfe K.H. Yang S.P. Yeh R.F. Collins F. Guyer M.S. Peterson J. Felsenfeld A. Wetterstrand K.A. Patrinos A. Morgan M.J. de Jong P. Catanese J.J. Osoegawa K. Shizuya H. Choi S. Chen Y.J. Szustakowki J. Initial sequencing and analysis of the human genome.Nature. 2001; 409: 860-921Crossref PubMed Scopus (17824) Google Scholar, 2.Venter J.C. Adams M.D. Myers E.W. Li P.W. Mural R.J. Sutton G.G. Smith H.O. Yandell M. Evans C.A. Holt R.A. Gocayne J.D. Amanatides P. Ballew R.M. Huson D.H. Wortman J.R. Zhang Q. Kodira C.D. Zheng X.H. Chen L. Skupski M. Subramanian G. Thomas P.D. Zhang J. Gabor Miklos G.L. Nelson C. Broder S. Clark A.G. Nadeau J. McKusick V.A. Zinder N. Levine A.J. Roberts R.J. Simon M. Slayman C. Hunkapiller M. Bolanos R. Delcher A. Dew I. Fasulo D. Flanigan M. Florea L. Halpern A. Hannenhalli S. Kravitz S. Levy S. Mobarry C. Reinert K. Remington K. Abu-Threideh J. Beasley E. Biddick K. Bonazzi V. Brandon R. Cargill M. Chandramouliswaran I. Charlab R. Chaturvedi K. Deng Z. Di Francesco V. Dunn P. Eilbeck K. Evangelista C. Gabrielian A.E. Gan W. Ge W. Gong F. Gu Z. Guan P. Heiman T.J. Higgins M.E. Ji R.R. Ke Z. Ketchum K.A. Lai Z. Lei Y. Li Z. Li J. Liang Y. Lin X. Lu F. Merkulov G.V. Milshina N. Moore H.M. Naik A.K. Narayan V.A. Neelam B. Nusskern D. Rusch D.B. Salzberg S. Shao W. Shue B. Sun J. Wang Z. Wang A. Wang X. Wang J. Wei M. Wides R. Xiao C. Yan C. Yao A. Ye J. Zhan M. Zhang W. Zhang H. Zhao Q. Zheng L. Zhong F. Zhong W. Zhu S. Zhao S. Gilbert D. Baumhueter S. Spier G. Carter C. Cravchik A. Woodage T. Ali F. An H. Awe A. Baldwin D. Baden H. Barnstead M. Barrow I. Beeson K. Busam D. Carver A. Center A. Cheng M.L. Curry L. Danaher S. Davenport L. Desilets R. Dietz S. Dodson K. Doup L. Ferriera S. Garg N. Gluecksmann A. Hart B. Haynes J. Haynes C. Heiner C. Hladun S. Hostin D. Houck J. Howland T. Ibegwam C. Johnson J. Kalush F. Kline L. Koduru S. Love A. Mann F. May D. McCawley S. McIntosh T. McMullen I. Moy M. Moy L. Murphy B. Nelson K. Pfannkoch C. Pratts E. Puri V. Qureshi H. Reardon M. Rodriguez R. Rogers Y.H. Romblad D. Ruhfel B. Scott R. Sitter C. Smallwood M. Stewart E. Strong R. Suh E. Thomas R. Tint N.N. Tse S. Vech C. Wang G. Wetter J. Williams S. Williams M. Windsor S. Winn-Deen E. Wolfe K. Zaveri J. Zaveri K. Abril J.F. Guigó R. Campbell M.J. Sjolander K.V. Karlak B. Kejariwal A. Mi H. Lazareva B. Hatton T. Narechania A. Diemer K. Muruganujan A. Guo N. Sato S. Bafna V. Istrail S. Lippert R. Schwartz R. Walenz B. Yooseph S. Allen D. Basu A. Baxendale J. Blick L. Caminha M. Carnes-Stine J. Caulk P. Chiang Y.H. Coyne M. Dahlke C. Mays A. Dombroski M. Donnelly M. Ely D. Esparham S. Fosler C. Gire H. Glanowski S. Glasser K. Glodek A. Gorokhov M. Graham K. Gropman B. Harris M. Heil J. Henderson S. Hoover J. Jennings D. Jordan C. Jordan J. Kasha J. Kagan L. Kraft C. Levitsky A. Lewis M. Liu X. Lopez J. Ma D. Majoros W. McDaniel J. Murphy S. Newman M. Nguyen T. Nguyen N. Nodell M. Pan S. Peck J. Peterson M. Rowe W. Sanders R. Scott J. Simpson M. Smith T. Sprague A. Stockwell T. Turner R. Venter E. Wang M. Wen M. Wu D. Wu M. Xia A. Zandieh A. Zhu X. The sequence of the human genome.Science. 2001; 291: 1304-1351Crossref PubMed Scopus (10636) Google Scholar), many structural genomics projects are solving the structures of what is now becoming a considerable fraction of the human proteome (3.Xie L. Bourne P.E. Functional coverage of the human genome by existing structures, structural genomics targets, and homology models.PLoS Comput. Biol. 2005; 1: e31Crossref PubMed Google Scholar). These projects are now moving to the next level, which is solving the atomic resolution structures of protein complexes. However, this is a challenge that is considerably greater than obtaining the structures of single proteins. First of all, a protein can take part in 10 interactions on average; thus, the number of complexes is expected to be at least an order of magnitude larger than the proteome, and their composition can even vary over time. Second, associations between subunits in protein complexes are often weak and reversible, which make purification and crystallization difficult. Finally, there are some very well studied classes of interactions, such as enzyme-inhibitor, antibody-antigen, and GTPase-GAP (GTPase-activating protein) interactions, but these classes represent binary interactions between proteins. In contrast, many of the most important functions in the cell are carried out by large, dynamic molecular assemblies, such as the ribosome, the proteasome, the spliceosome, RNA polymerases, and the nuclear pore complex (4.Alber F. Förster F. Korkin D. Topf M. Sali A. Integrating diverse data for structure determination of macromolecular assemblies.Annu. Rev. Biochem. 2008; 77: 443-477Crossref PubMed Scopus (182) Google Scholar, 5.Mueller M. Jenni S. Ban N. Strategies for crystallization and structure determination of very large macromolecular assemblies.Curr. Opin. Struct. Biol. 2007; 17: 572-579Crossref PubMed Scopus (31) Google Scholar). For such assemblies, high resolution methods such as x-ray crystallography and NMR spectroscopy often provide atomic level information at the level of individual subunits or subcomplexes, but they typically encounter difficulties at the level of the full complex. Fortunately, low resolution information about protein complexes can often be obtained. Affinity purification (6.Collins S.R. Kemmeren P. Zhao X.C. Greenblatt J.F. Spencer F. Holstege F.C. Weissman J.S. Krogan N.J. Toward a comprehensive atlas of the physical interactome of Saccharomyces cerevisiae.Mol. Cell. Proteomics. 2007; 6: 439-450Abstract Full Text Full Text PDF PubMed Scopus (643) Google Scholar, 7.Krogan N.J. Cagney G. Yu H. Zhong G. Guo X. Ignatchenko A. Li J. Pu S. Datta N. Tikuisis A.P. Punna T. Peregrín-Alvarez J.M. Shales M. Zhang X. Davey M. Robinson M.D. Paccanaro A. Bray J.E. Sheung A. Beattie B. Richards D.P. Canadien V. Lalev A. Mena F. Wong P. Starostine A. Canete M.M. Vlasblom J. Wu S. Orsi C. Collins S.R. Chandran S. Haw R. Rilstone J.J. Gandi K. Thompson N.J. Musso G. St Onge P. Ghanny S. Lam M.H. Butland G. Altaf-Ul A.M. Kanaya S. Shilatifard A. O'Shea E. Weissman J.S. Ingles C.J. Hughes T.R. Parkinson J. Gerstein M. Wodak S.J. Emili A. Greenblatt J.F. Global landscape of protein complexes in the yeast Saccharomyces cerevisiae.Nature. 2006; 440: 637-643Crossref PubMed Scopus (2338) Google Scholar) followed by mass spectrometry is a high throughput technique to study the composition of a complex. However, dissociation inside the mass spectrometer can be a problem for transient or unstable complexes in which case chemical cross-linking can help. Once the composition of the complex is known, there is a variety of experimental techniques available to obtain structural information on the complex. The most detailed information can be gathered by using data obtained from various NMR experiments, for example chemical shift perturbations (8.van Dijk A.D. Kaptein R. Boelens R. Bonvin A.M. Combining NMR relaxation with chemical shift perturbation data to drive protein-protein docking.J. Biomol. NMR. 2006; 34: 237-244Crossref PubMed Scopus (36) Google Scholar) or residual dipolar couplings (9.van Dijk A.D. Fushman D. Bonvin A.M. Various strategies of using residual dipolar couplings in NMR-driven protein docking: application to Lys48-linked di-ubiquitin and validation against 15N-relaxation data.Proteins. 2005; 60: 367-381Crossref PubMed Scopus (74) Google Scholar); unfortunately, NMR is limited to complexes that are fairly small in size, making its applicability in the context of large assemblies less suited. Techniques that provide information about the shape of a protein complex, such as small angle x-ray scattering (SAXS), 1The abbreviations used are:SAXSsmall angle x-ray scatteringAIRambiguous interaction restraintCAPRIcritical assessment of prediction of interactionsCNSCrystallography & NMR SystemCPORTconsensus prediction of interface residues in transient complexesEMelectron microscopyE2Aglucose-specific enzyme IIAHADDOCKhigh ambiguity-driven dockingHPrhistidine-containing phosphocarrier proteinIMion mobility separationNCSnon-crystallographic symmetryPPIprotein-protein interactionsr.m.s.d.root mean square deviationRPradical probei-r.m.s.d.interface root mean square deviationl-r.m.s.d.ligand r.m.s.d.fnatfraction of native contacts. cryoelectron tomography, and single molecule cryoelectron microscopy (cryo-EM), are more suited to characterize large complexes. Unfortunately, all of these techniques suffer from limitations in resolution that are either fundamental or caused by structural heterogeneities of the complex. small angle x-ray scattering ambiguous interaction restraint critical assessment of prediction of interactions Crystallography & NMR System consensus prediction of interface residues in transient complexes electron microscopy glucose-specific enzyme IIA high ambiguity-driven docking histidine-containing phosphocarrier protein ion mobility separation non-crystallographic symmetry protein-protein interactions root mean square deviation radical probe interface root mean square deviation ligand r.m.s.d. fraction of native contacts. A well known approach to obtain information on residues at an interface is site-directed mutagenesis (10.Cunningham B.C. Jhurani P. Ng P. Wells J.A. Receptor and antibody epitopes in human growth hormone identified by homolog-scanning mutagenesis.Science. 1989; 243: 1330-1336Crossref PubMed Scopus (264) Google Scholar). In principle, a loss of binding affinity indicates that the mutated residue mediates the interaction, although the reverse is not true. Also, one must take care of secondary effects, such as unfolding or conformational change caused by the mutation. Apart from that, very detailed information about interface residues can be obtained by extensive mutagenesis experiments, such as alanine scanning and double mutant cycles. Mass spectrometry offers the opportunity to get peptide level or residue level information about protein interfaces by accurate mass measurements of peptides from the protein complex, generated either a priori through proteolytic cleavage, or inside the mass spectrometer (MS/MS). For example, interface residues can be identified as residues that undergo slower hydrogen/deuterium exchange upon complex formation. This process can be monitored at the peptide level by mass spectrometry (or in smaller complexes, at the residue level by NMR), although this method is very sensitive to noise caused by conformational changes upon binding. In the same way, radical probe MS (RP-MS) uses differences in oxidation of residues by hydroxyl radicals generated in the mass spectrometer to identify interface residues. Finally, chemical cross-linking followed by MS can provide direct information about residue contact sites between different binding partners of the complex. Several cross-linking reagents can provide complementary information. However, it has been reported that the cross-linkers may disrupt the structure of the protein complex and that care should therefore be taken to interpret the results (11.Peters K. Richards F.M. Chemical cross-linking: reagents and problems in studies of membrane structure.Annu. Rev. Biochem. 1977; 46: 523-551Crossref PubMed Scopus (445) Google Scholar). There is a need for computational approaches to translate this low resolution information into atomic resolution models that can provide functional and mechanistic insights. One of the most promising approaches is docking, the prediction of the structure of a complex starting from the free, unbound structures of its constituents. In recent years, docking methods have made much progress in the blind prediction of the structure of protein complexes as seen in the recent rounds of the critical assessment of prediction of interactions (CAPRI) experiment (12.Janin J. Henrick K. Moult J. Eyck L.T. Sternberg M.J. Vajda S. Vakser I. Wodak S.J. CAPRI: a Critical Assessment of PRedicted Interactions.Proteins. 2003; 52: 2-9Crossref PubMed Scopus (512) Google Scholar, 13.Méndez R. Leplae R. De Maria L. Wodak S.J. Assessment of blind predictions of protein-protein interactions: current status of docking methods.Proteins. 2003; 52: 51-67Crossref PubMed Scopus (330) Google Scholar). Most docking methods are ab initio, which means that experimental data are not required. However, it is possible in several ab initio methods to use experimentally determined interface residues in the docking: in MolFit (14.Katchalski-Katzir E. Shariv I. Eisenstein M. Friesem A.A. Aflalo C. Vakser I.A. Molecular surface recognition: determination of geometric fit between proteins and their ligands by correlation techniques.Proc. Natl. Acad. Sci. U.S.A. 1992; 89: 2195-2199Crossref PubMed Scopus (863) Google Scholar, 15.Ben-Zeev E. Eisenstein M. Weighted geometric docking: incorporating external information in the rotation-translation scan.Proteins. 2003; 52: 24-27Crossref PubMed Scopus (49) Google Scholar) and ATTRACT (16.Zacharias M. Protein-protein docking with a reduced protein model accounting for side-chain flexibility.Protein Sci. 2003; 12: 1271-1282Crossref PubMed Scopus (273) Google Scholar, 17.Zacharias M. Rapid protein-ligand docking using soft modes from molecular dynamics simulations to account for protein deformability: binding of FK506 to FKBP.Proteins. 2004; 54: 759-767Crossref PubMed Scopus (84) Google Scholar), it is possible to up-weight the interaction scores of interface residues; in ZDOCK (18.Chen R. Li L. Weng Z. ZDOCK: an initial-stage protein-docking algorithm.Proteins. 2003; 52: 80-87Crossref PubMed Scopus (1093) Google Scholar, 19.Pierce B. Weng Z. ZRANK: reranking protein docking predictions with an optimized energy function.Proteins. 2007; 67: 1078-1086Crossref PubMed Scopus (358) Google Scholar), it is possible to block non-interface residues; and in PatchDock (20.Schneidman-Duhovny D. Inbar Y. Nussinov R. Wolfson H.J. PatchDock and SymmDock: servers for rigid and symmetric docking.Nucleic Acids Res. 2005; 33: W363-W367Crossref PubMed Scopus (2188) Google Scholar, 21.Schneidman-Duhovny D. Inbar Y. Polak V. Shatsky M. Halperin I. Benyamini H. Barzilai A. Dror O. Haspel N. Nussinov R. Wolfson H.J. Taking geometry to its edge: fast unbound rigid (and hinge-bent) docking.Proteins. 2003; 52: 107-112Crossref PubMed Scopus (218) Google Scholar), ZDOCK, pyDock (22.Grosdidier S. Pons C. Solernou A. Fernández-Recio J. Prediction and scoring of docking poses with pyDock.Proteins. 2007; 69: 852-858Crossref PubMed Scopus (38) Google Scholar, 23.Cheng T.M. Blundell T.L. Fernandez-Recio J. pyDock: electrostatics and desolvation for effective scoring of rigid-body protein-protein docking.Proteins. 2007; 68: 503-515Crossref PubMed Scopus (235) Google Scholar), and several other methods, it is possible to filter the docking results based on experimental information. Next to purely ab initio approaches, there are also methods that make use of different types experimental information, for example PROXIMO (24.Gerega S.K. Downard K.M. PROXIMO–a new docking algorithm to model protein complexes using data from radical probe mass spectrometry (RP-MS).Bioinformatics. 2006; 22: 1702-1709Crossref PubMed Scopus (30) Google Scholar), based on RP-MS data, and MultiFit (25.Lasker K. Topf M. Sali A. Wolfson H.J. Inferential optimization for simultaneous fitting of multiple components into a cryoEM map of their assembly.J. Mol. Biol. 2009; 388: 180-194Crossref PubMed Scopus (99) Google Scholar), a hybrid fitting/docking approach based on electron microscopy data. A method that distinguishes itself from the variety of above mentioned docking approaches is HADDOCK (26.de Vries S.J. van Dijk A.D. Krzeminski M. van Dijk M. Thureau A. Hsu V. Wassenaar T. Bonvin A.M. HADDOCK versus HADDOCK: new features and performance of HADDOCK2.0 on the CAPRI targets.Proteins. 2007; 69: 726-733Crossref PubMed Scopus (487) Google Scholar, 27.De Vries S.J. Van Dijk M. Bonvin A.M. The HADDOCK web server for data-driven biomolecular docking.Nat. Protoc. 2010; 5: 883-897Crossref PubMed Scopus (978) Google Scholar, 28.Dominguez C. Boelens R. Bonvin A.M.J.J. HADDOCK: a protein-protein docking approach based on biochemical or biophysical information.J. Am. Chem. Soc. 2003; 125: 1731-1737Crossref PubMed Scopus (2189) Google Scholar). In HADDOCK, the docking can be driven by a variety of experimental data using information about interface, contacts, and relative orientations inside a complex simultaneously. Originally developed for NMR data, HADDOCK is able to deal with a large variety of experimental data as shown in Table I. Interface residues are defined as "active residues" that are believed to participate in the formation of the interface, and "passive residues" are those that are possibly at the interface; other kinds of data can be entered directly. (See the original HADDOCK studies (26.de Vries S.J. van Dijk A.D. Krzeminski M. van Dijk M. Thureau A. Hsu V. Wassenaar T. Bonvin A.M. HADDOCK versus HADDOCK: new features and performance of HADDOCK2.0 on the CAPRI targets.Proteins. 2007; 69: 726-733Crossref PubMed Scopus (487) Google Scholar, 27.De Vries S.J. Van Dijk M. Bonvin A.M. The HADDOCK web server for data-driven biomolecular docking.Nat. Protoc. 2010; 5: 883-897Crossref PubMed Scopus (978) Google Scholar, 28.Dominguez C. Boelens R. Bonvin A.M.J.J. HADDOCK: a protein-protein docking approach based on biochemical or biophysical information.J. Am. Chem. Soc. 2003; 125: 1731-1737Crossref PubMed Scopus (2189) Google Scholar) and "Materials and Methods" for more details.) HADDOCK has performed very well in translating these data into structures and structural models. More than 60 Protein Data Bank structures calculated using HADDOCK have been deposited to date as experimental structures in the Protein Data Bank (29.Berman H.M. Westbrook J. Feng Z. Gilliland G. Bhat T.N. Weissig H. Shindyalov I.N. Bourne P.E. The Protein Data Bank.Nucleic Acids Res. 2000; 28: 235-242Crossref PubMed Scopus (27555) Google Scholar). Moreover, HADDOCK has shown a strong performance in CAPRI. Finally, HADDOCK is a general purpose program that can integrate many kinds of data, but even with a single source of data it is able to perform as well as more specialized programs: for example, HADDOCK was able to closely reproduce the NMR-calculated E2A-HPr complex using only chemical shift perturbation data. For the ribonuclease S-protein-peptide complex (Protein Data Bank code 1J80 (30.Ratnaparkhi G.S. Varadarajan R. Osmolytes stabilize ribonuclease S by stabilizing its fragments S protein and S peptide to compact folding-competent states.J. Biol. Chem. 2001; 276: 28789-28798Abstract Full Text Full Text PDF PubMed Scopus (68) Google Scholar)) for which RP-MS data are available, PROXIMO was able to closely reproduce the crystal structure (root mean square deviation (r.m.s.d.) of the top scoring model from the reference crystal structure is 1.26 Å); using the same data, HADDOCK could get even closer with an r.m.s.d. of only 0.68 Å from the crystal structure (results not shown).Table IVarious experimental data that can be incorporated into HADDOCKExperimental dataHADDOCK representationMutagenesis dataActive and passive residuesHydrogen/deuterium exchange dataActive and passive residuesBioinformatics interface predictionsActive and passive residuesMass spectrometry dataCross-linking dataCustom CNS restraintsRadical probe mass spectrometryActive and passive residuesLimited proteolysis mass spectrometryActive and passive residues or directly as an MTMDAT-generated HADDOCK parameter fileNMR dataChemical shift perturbation dataActive and passive residuesCross-saturation experimentsActive and passive residuesResidual dipolar couplingsDirectlyDiffusion anisotropy restraintsDirectlyNOEsaNuclear Overhauser effects. as custom CNS restraintsCustom CNS restraintsDihedral anglesDirectlyHydrogen bondsDirectlyParamagnetic restraintsUnder developmentShape dataSAXSUnder developmentEMUnder developmenta Nuclear Overhauser effects. Open table in a new tab Most docking methods are designed to deal with just two molecules, making their application limited with regard to large macromolecular assemblies. In most programs, multicomponent complexes can be assembled by adding each component one at a time, whereas simultaneous docking of the whole complex is typically not possible. Recently five ab initio docking programs (MolFit (31.Berchanski A. Eisenstein M. Construction of molecular assemblies via docking: modeling of tetramers with D2 symmetry.Proteins. 2003; 53: 817-829Crossref PubMed Scopus (38) Google Scholar, 32.Berchanski A. Segal D. Eisenstein M. Modeling oligomers with Cn or Dn symmetry: application to CAPRI target 10.Proteins. 2005; 60: 202-206Crossref PubMed Scopus (30) Google Scholar), ClusPro (33.Comeau S.R. Camacho C.J. Predicting oligomeric assemblies: N-mers a primer.J. Struct. Biol. 2005; 150: 233-244Crossref PubMed Scopus (40) Google Scholar), Rosetta (34.André I. Bradley P. Wang C. Baker D. Prediction of the structure of symmetrical protein assemblies.Proc. Natl. Acad. Sci. U.S.A. 2007; 104: 17656-17661Crossref PubMed Scopus (145) Google Scholar), M-ZDOCK (35.Pierce B. Tong W. Weng Z. M-ZDOCK: a grid-based approach for C-n symmetric multimer docking.Bioinformatics. 2005; 21: 1472-1478Crossref PubMed Scopus (134) Google Scholar), and SymmDock (36.Schneidman-Duhovny D. Inbar Y. Nussinov R. Wolfson H.J. Geometry-based flexible and symmetric protein docking.Proteins. 2005; 60: 224-231Crossref PubMed Scopus (177) Google Scholar)) gave birth to specific versions for the prediction of the symmetric multimers. Among these programs, MolFit, ClusPro, and Rosetta perform a rotational/translational search about the proper symmetry axes. These programs can deal with different types of cyclic and dihedral symmetries. Different than the other two, Rosetta is able to assemble complexes having helical and icosahedral symmetries. M-ZDOCK and SymmDock are suited for the prediction of macromolecules with cyclic symmetries. However, the ability to deal with arbitrary large molecular assemblies is currently rare. CombDock (37.Inbar Y. Benyamini H. Nussinov R. Wolfson H.J. Prediction of multimolecular assemblies by multiple docking.
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