Finding correct protein–protein docking models using ProQDock

By Sankar Basu, Björn Wallner. 2016

48 citations recorded by the source corpus.

Motivation: Protein–protein interactions are a key in virtually all biological processes. For a detailed understanding of the biological processes, the structure of the protein complex is essential. Given the current experimental techniques for structure determination, the vast majority of all protein complexes will never be solved by experimental techniques. In lack of experimental data, computational docking methods can be used to predict the structure of the protein complex. A common strategy is to generate many alternative docking solutions (atomic models) and then use a scoring function to select the best. The success of the computational docking technique is, to a large degree, dependent on the ability of the scoring function to accurately rank and score the many alternative docking models. Results: Here, we present ProQDock, a scoring function that predicts the absolute quality of docking model measured by a novel protein docking quality score (DockQ). ProQDock uses support vector machines trained to predict the quality of protein docking models using features that can be calculated from the docking model itself. By combining different types of features describing both the protein–protein interface and the overall physical chemistry, it was possible to improve the correlation with DockQ from 0.25 for the best individual feature (electrostatic complementarity) to 0.49 for the final version of ProQDock. ProQDock performed better than the state-of-the-art methods ZRANK and ZRANK2 in terms of correlations, ranking and finding correct models on an independent test set. Finally, we also demonstrate that it is possible to combine ProQDock with ZRANK and ZRANK2 to improve performance even further. Availability and implementation: http://bioinfo.ifm.liu.se/ProQDock Contact: bjornw@ifm.liu.se Supplementary information: Supplementary data are available at Bioinformatics online.

Subfields: Computational Theory and Mathematics, Molecular Biology

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Citations in the corpus

DockQ: A Quality Measure for Protein-Protein Docking Models (2016, methodology): “The potential use of DockQ as a target function in the design of a docking scoring function by training support vector regression machines to predict quality of docking models has already been demonstrated in a separate study [16].”
Improving peptide-protein docking with AlphaFold-Multimer using forced sampling (2021, methodology): “Apart from the factors that can be calculated from AlphaFold input data, such as protein lengths and MSA depths, an updated version of ProQDock (Basu and Wallner, 2016b) was used to calculate the shape complementarity (Sc) (Lawrence and Colman, 1993) and electrostatic complementarity (EC) (McCoy et ”