Name
Title | ||
|---|---|---|
Accurate refinement of docked protein complexes using evolutionary information and deep learning. |
Abstract | ||
|---|---|---|
One of the major challenges for protein docking methods is to accurately discriminate native-like structures from false positives. Docking methods are often inaccurate and the results have to be refined and re-ranked to obtain native-like complexes and remove outliers. In a previous work, we introduced AccuRefiner, a machine learning based tool for refining protein-protein complexes. Given a docked complex, the refinement tool produces a small set of refined versions of the input complex, with lower root-mean-square-deviation (RMSD) of atomic positions with respect to the native structure. The method employs a unique ranking tool that accurately predicts the RMSD of docked complexes with respect to the native structure. In this work, we use a deep learning network with a similar set of features and five layers. We show that a properly trained deep learning network can accurately predict the RMSD of a docked complex with 1.40 angstrom error margin on average, by approximating the complex relationship between a wide set of scoring function terms and the RMSD of a docked structure. The network was trained on 35000 unbound docking complexes generated by RosettaDock. We tested our method on 25 different putative docked complexes produced also by RosettaDock for five proteins that were not included in the training data. The results demonstrate that the high accuracy of the ranking tool enables AccuRefiner to consistently choose the refinement candidates with lower RMSD values compared to the coarsely docked input structures. |
Year | DOI | Venue |
|---|---|---|
2016 | 10.1142/S0219720016420026 | JOURNAL OF BIOINFORMATICS AND COMPUTATIONAL BIOLOGY |
Keywords | Field | DocType |
Protein docking,ranking and scoring functions,deep learning neural networks | Ranking,Docking (dog),Computer science,Molecular Docking Simulation,Outlier,Macromolecular docking,Artificial intelligence,Deep learning,Bioinformatics,Small set,Machine learning,False positive paradox | Journal |
Volume | Issue | ISSN |
14 | SP3 | 0219-7200 |
Citations | PageRank | References |
6 | 0.50 | 4 |
Authors | ||
4 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Bahar Akbal-Delibas | 1 | 70 | 7.95 |
| Roshanak Farhoodi | 2 | 17 | 2.77 |
| Marc Pomplun | 3 | 215 | 31.83 |
| Nurit Haspel | 4 | 60 | 14.11 |