Coupling Molecular Dynamics and Deep Learning to Mine Protein Conformational Space
34 Pages Posted: 18 Jan 2019 Sneak Peek Status: PublishedMore...
Flexibility is often a key determinant of protein function. In order to elucidate the link between their molecular structure and role in an organism, computational techniques such as molecular dynamics can be leveraged to characterize their conformational space. Extensive sampling is however required to obtain reliable results, useful to rationalize experimental data or predict outcomes before experiments are carried out. We demonstrate that a generative neural network trained on protein structures produced by molecular simulation can be used to obtain new, plausible conformations complementing and extending pre-existing ones. To demonstrate this, we show that a trained neural network can be exploited in a protein-protein docking scenario to predict large conformational changes upon binding. Overall, this work shows that neural networks can be used as an exploratory tool for the study of molecular conformational space.
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