Topological deep learning based deep mutational scanning.

Journal: Computers in biology and medicine
Published Date: Jul 17, 2023

Abstract

High-throughput deep mutational scanning (DMS) experiments have significantly impacted protein engineering, drug discovery, immunology, cancer biology, and evolutionary biology by enabling the systematic understanding of protein functions. However, the mutational space associated with proteins is astronomically large, making it overwhelming for current experimental capabilities. Therefore, alternative methods for DMS are imperative. We propose a topological deep learning (TDL) paradigm to facilitate in silico DMS. We utilize a new topological data analysis (TDA) technique based on the persistent spectral theory, also known as persistent Laplacian, to capture both topological invariants and the homotopic shape evolution of data. To validate our TDL-DMS model, we use SARS-CoV-2 datasets and show excellent accuracy and reliability for binding interface mutations. This finding is significant for SARS-CoV-2 variant forecasting and designing effective antibodies and vaccines. Our proposed model is expected to have a significant impact on drug discovery, vaccine design, precision medicine, and protein engineering.

Authors

  • Jiahui Chen
    Molecular Analytics and Photonics (MAP) Lab, Program of Polymer and Color Chemistry, Department of Textile Engineering, Chemistry and Science, North Carolina State University, 1020 Main Campus Drive, Raleigh, NC, 27606, USA.
  • Daniel R Woldring
    Department of Chemical Engineering, Michigan State University, East Lansing, MI 48824, USA.
  • Faqing Huang
    Department of Chemistry and Biochemistry, University of Southern Mississippi, Hattiesburg, MS 39406, USA.
  • Xuefei Huang
    Department of Chemistry, Michigan State University, MI 48824, USA; Department of Biomedical Engineering, Michigan State University, East Lansing, MI 48824, USA; The Institute for Quantitative Health Science and Engineering, Michigan State University, East Lansing, MI 48824, USA.
  • Guo-Wei Wei
    Department of Mathematics, Department of Electrical and Computer Engineering, Department of Biochemistry and Molecular Biology, Michigan State University, MI 48824, USA.

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