Dataset Information

TopologyNet: Topology based deep convolutional and multi-task neural networks for biomolecular property predictions.

ABSTRACT: 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/.

SUBMITTER: Cang Z

PROVIDER: S-EPMC5549771 | biostudies-literature | 2017 Jul

REPOSITORIES: biostudies-literature

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TopologyNet: Topology based deep convolutional and multi-task neural networks for biomolecular property predictions.

Cang Zixuan Z Wei Guo-Wei GW

PLoS computational biology 20170727 7

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 informat ...[more]

PMID: 28749969

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Dataset Information

TopologyNet: Topology based deep convolutional and multi-task neural networks for biomolecular property predictions.

Publications

TopologyNet: Topology based deep convolutional and multi-task neural networks for biomolecular property predictions.

Similar Datasets

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