CPORT: A Consensus Interface Predictor and Its Performance in Prediction-Driven Docking with HADDOCK

By S. D. de Vries, Alexandre M. J. J. Bonvin. PLoS ONE, 2011

308 citations recorded by the source corpus.

Background Macromolecular complexes are the molecular machines of the cell. Knowledge at the atomic level is essential to understand and influence their function. However, their number is huge and a significant fraction is extremely difficult to study using classical structural methods such as NMR and X-ray crystallography. Therefore, the importance of large-scale computational approaches in structural biology is evident. This study combines two of these computational approaches, interface prediction and docking, to obtain atomic-level structures of protein-protein complexes, starting from their unbound components. Methodology/Principal Findings Here we combine six interface prediction web servers into a consensus method called CPORT (Consensus Prediction Of interface Residues in Transient complexes). We show that CPORT gives more stable and reliable predictions than each of the individual predictors on its own. A protocol was developed to integrate CPORT predictions into our data-driven docking program HADDOCK. 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. Prediction-driven docking was performed on a large and diverse set of protein-protein complexes in a blind manner. 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. Finally, the original interface predictions could be further improved by interface post-prediction (contact analysis of the docking solutions). Conclusions/Significance The current study shows that blind, prediction-driven docking using CPORT and HADDOCK is competitive with ab initio docking methods. This is encouraging since prediction-driven docking represents the absolute bottom line for data-driven docking: any additional biological knowledge will greatly improve the results obtained by prediction-driven docking alone. Finally, the fact that original interface predictions could be further improved by interface post-prediction suggests that prediction-driven docking has not yet been pushed to the limit. A web server for CPORT is freely available at http://haddock.chem.uu.nl/services/CPORT.

Subfields: Computational Theory and Mathematics, Molecular Biology

View Paper: https://www.semanticscholar.org/paper/a25e8c26a930347ffb4e164744b356140ce263c9

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In Computational Theory and Mathematics

  1. João P. G. L. M. Rodrigues
  2. M. V. van Dijk
  3. Dima Kozakov
  4. Ezgi Karaca
  5. Panagiotis L. Kastritis
  6. Adrien S. J. Melquiond
  7. Mikael E. Trellet
  8. Sjoerd Jacob de Vries

In Molecular Biology

  1. Alexandre M. J. J. Bonvin
  2. M. V. van Dijk
  3. João P. G. L. M. Rodrigues
  4. Dima Kozakov
  5. Kathryn A. Porter
  6. Sandor Vajda
  7. Ezgi Karaca
  8. Panagiotis L. Kastritis

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