Name
Title | ||
|---|---|---|
Accurate prediction of docked protein structure similarity using neural networks and restricted Boltzmann machines |
Abstract | ||
|---|---|---|
One of the major challenges for protein-protein docking is to accurately discriminate native-like structures from false-positives. While there is an agreement on the existence of a relationship between various favorable intermolecular interactions (e.g., Van der Waals, electrostatic, desolvation forces, etc.) and the similarity of a conformation to its native structure, the exact nature of this relationship is not clear. Different docking algorithms often formulate this relationship as a weighted sum of selected terms and calibrate their weights against a training set to evaluate and rank candidate complexes. Despite improvement in the predictive abilities of recent docking methods, even state-of-the-art methods often fail to predict the binding of many complexes and still output a large number of false positive complexes. We propose a novel machine learning approach that not only ranks candidate structures relative to each other, but also predicts how similar each candidate is to the native conformation. We trained a two-layer neural network, a deep neural network and a network of Restricted Boltzmann Machines against extensive datasets of unbound complexes. We tested these methods with a set of candidate structures. Our method is able to predict the RMSDs of unbound docked complexes with a very small, often |
Year | DOI | Venue |
|---|---|---|
2015 | 10.1109/BIBM.2015.7359866 | IEEE International Conference on Bioinformatics and Biomedicine |
Keywords | Field | DocType |
Protein docking and refinement, scoring functions, RMSD prediction, machine learning, neural networks | Boltzmann machine,Searching the conformational space for docking,Computer science,Docking (dog),van der Waals force,Artificial intelligence,Bioinformatics,Artificial neural network,Intermolecular force,Machine learning,Protein structure,Scoring functions for docking | Conference |
ISSN | Citations | PageRank |
2156-1125 | 0 | 0.34 |
References | Authors | |
10 | 3 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Roshanak Farhoodi | 1 | 17 | 2.77 |
| Bahar Akbal-Delibas | 2 | 70 | 7.95 |
| Nurit Haspel | 3 | 60 | 14.11 |