Type
ArticleKAUST Department
KAUST Catalysis Center (KCC)Chemical Science Program
Physical Science and Engineering (PSE) Division
Date
2021-12-10Permanent link to this record
http://hdl.handle.net/10754/669803
Metadata
Show full item recordAbstract
Herein, we present the results of a machine learning approach we developed to single out correct 3D docking models of protein-protein complexes obtained by popular docking software. To this aim, we generated 3 × 104 docking models for each of the 230 complexes in the protein-protein benchmark, version 5 (BM5), using three different docking programs (HADDOCK, FTDock and ZDOCK), for a cumulative set of ≈ 7 × 106 docking models. Three different machine-learning approaches (Random Forest, Supported Vector Machine and Perceptron) were used to train classifiers with 158 different scoring functions (features). The Random Forest algorithm outperformed the other two algorithms and was selected for further optimization. Using a features selection algorithm, and optimizing the random forest hyperparameters, allowed us to train and validate a random forest classifier, named CoDES (COnservation Driven Expert System). Testing of CoDES on independent datasets, as well as results of its comparative performance with machine-learning methods recently developed in the field for the scoring of docking decoys, confirm its state-of-the-art ability to discriminate correct from incorrect decoys both in terms of global parameters and in terms of decoys ranked at the top positions.Citation
Barradas-Bautista, D., Cao, Z., Vangone, A., Oliva, R., & Cavallo, L. (2021). A Random Forest Classifier for Protein-Protein Docking Models. Bioinformatics Advances. doi:10.1093/bioadv/vbab042Sponsors
The IRaPPA dataset was a courtesy of the methods authors Iain H. Moal and Juan Fernandez-Recio. LC thanks the Supercomputing Laboratory at the King Abdullah University of Science and Technology (KAUST) for technical support and access to the Shaheen facilities. DBB was supported by funding from the AI Initiative at KAUST.Publisher
Oxford University Press (OUP)Journal
Bioinformatics AdvancesAdditional Links
https://academic.oup.com/bioinformaticsadvances/advance-article/doi/10.1093/bioadv/vbab042/6459166Relations
Is Supplemented By:- [Dataset]
Barradas-Bautista, D., Oliva, R., & Cavallo, L. (2020). A protein-protein docking decoys set from three different rigid body methods (one) [Data set]. Zenodo. https://doi.org/10.5281/ZENODO.4012018. DOI: 10.5281/zenodo.4012018 Handle: 10754/674130
ae974a485f413a2113503eed53cd6c53
10.1093/bioadv/vbab042
Scopus Count
Except where otherwise noted, this item's license is described as This is a pre-copyedited, author-produced PDF of an article accepted for publication in Bioinformatics Advances following peer review. The version of record is available online at: https://academic.oup.com/bioinformaticsadvances/advance-article/doi/10.1093/bioadv/vbab042/6459166.