Journal
PLOS COMPUTATIONAL BIOLOGY
Volume 13, Issue 7, Pages -Publisher
PUBLIC LIBRARY SCIENCE
DOI: 10.1371/journal.pcbi.1005690
Keywords
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Funding
- National Science Foundation [IIS-1302285, DMS-1721024]
- Michigan State University
- Div Of Information & Intelligent Systems
- Direct For Computer & Info Scie & Enginr [1302285] Funding Source: National Science Foundation
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Although deep learning approaches have had tremendous success in image, video and audio processing, computer vision, and speech recognition, their applications to three-dimensional (3D) biomolecular structural data sets have been hindered by the geometric and biological complexity. To address this problem we introduce the element-specific persistent homology (ESPH) method. ESPH represents 3D complex geometry by one-dimensional (1D) topological invariants and retains important biological information via a multichannel image-like representation. This representation reveals hidden structure-function relationships in biomolecules. We further integrate ESPH and deep convolutional neural networks to construct a multichannel topological neural network (TopologyNet) for the predictions of protein-ligand binding affinities and protein stability changes upon mutation. To overcome the deep learning limitations from small and noisy training sets, we propose a multi-task multichannel topological convolutional neural network (MM-TCNN). We demonstrate that TopologyNet outperforms the latest methods in the prediction of protein-ligand binding affinities, mutation induced globular protein folding free energy changes, and mutation induced membrane protein folding free energy changes. Availability: weilab.math.msu.edu/TDL/
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