TopologyNet: Topology based deep convolutional neural networks for biomolecular property predictions. 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 entangled geometric complexity and biological complexity. We introduce topology, i.e., element specific persistent homology (ESPH), to untangle geometric complexity and biological complexity. ESPH represents 3D complex geometry by one-dimensional (1D) topological invariants and retains crucial biological information via a multichannel image representation. It is able to reveal hidden structure-function relationships in biomolecules. We further integrate ESPH and 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 limitations to deep learning arising from small and noisy training sets, we present a multitask topological convolutional neural network (MT-TCNN). We demonstrate that the present TopologyNet architectures outperform other state-of-the-art methods in the predictions of protein-ligand binding affinities, globular protein mutation impacts, and membrane protein mutation impacts.
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References in zbMATH (referenced in 7 articles )
Showing results 1 to 7 of 7.
- Chen, Jiahui; Zhao, Rundong; Tong, Yiying; Wei, Guo-Wei: Evolutionary de Rham-Hodge method (2021)
- Wang, Rui; Zhao, Rundong; Ribando-Gros, Emily; Chen, Jiahui; Tong, Yiying; Wei, Guo-Wei: HERMES: persistent spectral graph software (2021)
- Bramer, David; Wei, Guo-Wei: Atom-specific persistent homology and its application to protein flexibility analysis (2020)
- Cang, Zixuan; Munch, Elizabeth; Wei, Guo-Wei: Evolutionary homology on coupled dynamical systems with applications to protein flexibility analysis (2020)
- Cang, Zixuan; Wei, Guo-Wei: Persistent cohomology for data with multicomponent heterogeneous information (2020)
- Som, Anirudh; Ramamurthy, Karthikeyan Natesan; Turaga, Pavan: Geometric metrics for topological representations (2020)
- Zhao, Rundong; Wang, Menglun; Chen, Jiahui; Tong, Yiying; Wei, Guo-Wei: The de Rham-Hodge analysis and modeling of biomolecules (2020)