Paper Info
Title
AccuRMSD: a machine learning approach to predicting structure similarity of docked protein complexes
Abstract
Protein-protein docking methods aim to compute the correct bound form of two or more proteins. One of the major challenges for docking methods is to accurately discriminate native-like structures. The protein docking community agrees 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. Different docking algorithms often formulate this relationship as a weighted sum of selected terms and calibrate their weights against a specific training data to evaluate and rank candidate structures. However, the exact form of this relationship is unknown and the accuracy of such methods is impaired by the pervasiveness of false positives. Unlike the conventional scoring functions, we propose a novel machine learning approach that not only ranks the candidate structures relative to each other but also indicates how similar each candidate is to the native conformation. We trained the AccuRMSD neural network with an extensive dataset using the back-propagation learning algorithm and achieved RMSD prediction accuracy with less than 1Å error margin on 19,600 test samples.
Year
DOI
Venue
2014
10.1145/2649387.2649392
BCB
Keywords
Field
DocType
nonlinear approximation,algorithms,connectionism and neural nets,neural networks,experimentation,rmsd prediction,scoring functions,biology and genetics,protein docking and refinement,machine learning
Docking (dog),Computer science,van der Waals force,Macromolecular docking,Artificial intelligence,Bioinformatics,Artificial neural network,Intermolecular force,Margin of error,Machine learning,Scoring functions for docking,False positive paradox
Conference
Citations 
PageRank 
References 
4
0.47
4
Authors
3
Name
Order
Citations
PageRank
Bahar Akbal-Delibas1707.95
Marc Pomplun221531.83
Nurit Haspel36014.11