Strengths and weaknesses of data-driven docking in critical assessment of prediction of interactions.
The recent CAPRI rounds have introduced new docking challenges in the form of protein-RNA complexes, multiple alternative interfaces, and an unprecedented number of targets for which homology modeling was required. We present here the performance of HADDOCK and its web server in the CAPRI experiment and discuss the strengths and weaknesses of data-driven docking. HADDOCK was successful for 6 out of 9 complexes (6 out of 11 targets) and accurately predicted the individual interfaces for two more complexes. The HADDOCK server, which is the first allowing the simultaneous docking of generic multi-body complexes, was successful in 4 out of 7 complexes for which it participated. In the scoring experiment, we predicted the highest number of targets of any group. The main weakness of data-driven docking revealed from these last CAPRI results is its vulnerability for incorrect experimental data related to the interface or the stoichiometry of the complex. At the same time, the use of experimental and/or predicted information is also the strength of our approach as evidenced for those targets for which accurate experimental information was available (e.g., the 10 three-stars predictions for T40!). Even when the models show a wrong orientation, the individual interfaces are generally well predicted with an average coverage of 60% ± 26% over all targets. This makes data-driven docking particularly valuable in a biological context to guide experimental studies like, for example, targeted mutagenesis.
de Vries
Sjoerd J
SJ
NMR Research Group, Bijvoet Center for Biomolecular Research, Utrecht University, 3584 CH Utrecht, The Netherlands.
Melquiond
Adrien S J
AS
Kastritis
Panagiotis L
PL
Karaca
Ezgi
E
Bordogna
Annalisa
A
van Dijk
Marc
M
Rodrigues
João P G L M
JP
Bonvin
Alexandre M J J
AM
D019295:Computational Biology / Q000379:methods*; D030562:Databases, Protein; D029968:Escherichia coli Proteins / Q000737:chemistry / Q000378:metabolism; D008956:Models, Chemical; D008958:Models, Molecular; D015233:Models, Statistical; D011485:Protein Binding; D011487:Protein Conformation; D025941:Protein Interaction Mapping / Q000379:methods; D012313:RNA / Q000737:chemistry* / Q000378:metabolism; D016601:RNA-Binding Proteins / Q000737:chemistry* / Q000378:metabolism; D012984:Software
D029968:Escherichia coli Proteins; D016601:RNA-Binding Proteins; D012313:RNA