Predicting protein complex geometries with a neural network

Proteins
Myong-Ho ChaeErnst-Walter Knapp

Abstract

A major challenge of the protein docking problem is to define scoring functions that can distinguish near-native protein complex geometries from a large number of non-native geometries (decoys) generated with noncomplexed protein structures (unbound docking). In this study, we have constructed a neural network that employs the information from atom-pair distance distributions of a large number of decoys to predict protein complex geometries. We found that docking prediction can be significantly improved using two different types of polar hydrogen atoms. To train the neural network, 2000 near-native decoys of even distance distribution were used for each of the 185 considered protein complexes. The neural network normalizes the information from different protein complexes using an additional protein complex identity input neuron for each complex. The parameters of the neural network were determined such that they mimic a scoring funnel in the neighborhood of the native complex structure. The neural network approach avoids the reference state problem, which occurs in deriving knowledge-based energy functions for scoring. We show that a distance-dependent atom pair potential performs much better than a simple atom-pair contact pot...Continue Reading

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Citations

Oct 2, 2013·BMC Bioinformatics·Iain H MoalJuan Fernández-Recio
Nov 17, 2015·Journal of Chemical Information and Modeling·Sergei GrudininGeorgy Cheremovskiy
Jun 17, 2016·Bioinformatics·Sankar Basu, Björn Wallner
Sep 18, 2021·Chemical Biology & Drug Design·Mohammed BuleMohammad Abdollahi

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