Therefore, interface prediction is a field that is rapidly developing. While older methods could only be tested on a case-by-case basis or on a small set of similar complexes, large-scale statistical analysis and validation on non-redundant benchmarks has become the norm. Genomic and structural genomic initiatives, combined with advances in computer technology, have allowed protein interfaces to be analyzed and predicted today in a far more systematic way than what was possible in the past. Interface prediction is based on the extraction and combination of distinguishing features from protein sequences and structures. Docking takes this one step further by predicting the three-dimensional structure of a protein complex, starting from the free, unbound structures of its constituents. Interface prediction aims, by computational means, to identify the residues on the protein surface that interact with another protein or biomolecule. This study combines two of these computational approaches, interface prediction and docking. Therefore, the importance of large-scale computational approaches in structural biology is evident. Since complexes are often weak, dynamic and/or very large, a significant fraction of these will be extremely difficult to study using any experimental method. However, the number of expected macromolecular complexes will exceed the number of proteins in a proteome by at least one order of magnitude. In recent years, tens of thousands of single protein structures have been solved using these methods, as well as an increasing number of complexes. The classical methods to obtain atomic-resolution structures are X-ray crystallography and Nuclear Magnetic Resonance (NMR). An atomic-resolution structure is also an important first step in rational drug design and other efforts to influence the function of macromolecular complexes, which is of high medical relevance. In order to fully understand how the various units work together to fulfill their tasks, structural knowledge at the atomic level is required. Macromolecular complexes are the molecular machines of the cell. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.Ĭompeting interests: The authors have declared that no competing interests exist. ![]() The work was carried out under the HPC-EUROPA2 project (project number: 228398) with the support of the European Commission Capacities Area - Research Infrastructures Initiative. 032220 and FP7 e-Infrastructure grant, contract no. This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.įunding: This work was supported by the Netherlands Organization for Scientific Research (VICI grant #700.56.442 to AMJJB) and by the European Community (Integrated Project SPINE2-COMPLEX contract no. Received: NovemAccepted: FebruPublished: March 25, 2011Ĭopyright: © 2011 de Vries, Bonvin. PLoS ONE 6(3):Įditor: Narcis Fernandez-Fuentes, Leeds Institute of Molecular Medicine, United Kingdom Finally, the original interface predictions could be further improved by interface post-prediction (contact analysis of the docking solutions).Ĭitation: de Vries SJ, Bonvin AMJJ (2011) CPORT: A Consensus Interface Predictor and Its Performance in Prediction-Driven Docking with HADDOCK. Our results indicate that the performance of the HADDOCK-CPORT combination is competitive with ZDOCK-ZRANK, a state-of-the-art ab initio docking/scoring combination. Prediction-driven docking was performed on a large and diverse set of protein-protein complexes in a blind manner. For cases where experimental information is limited, this prediction-driven docking protocol presents an alternative to ab initio docking, the docking of complexes without the use of any information. A protocol was developed to integrate CPORT predictions into our data-driven docking program HADDOCK. We show that CPORT gives more stable and reliable predictions than each of the individual predictors on its own. ![]() Here we combine six interface prediction web servers into a consensus method called CPORT (Consensus Prediction Of interface Residues in Transient complexes).
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