A Machine Learning Protocol for Predicting Protein Infrared Spectra.

Journal: Journal of the American Chemical Society
Published Date: Nov 11, 2020

Abstract

Infrared (IR) absorption provides important chemical fingerprints of biomolecules. Protein secondary structure determination from IR spectra is tedious since its theoretical interpretation requires repeated expensive quantum-mechanical calculations in a fluctuating environment. Herein we present a novel machine learning protocol that uses a few key structural descriptors to rapidly predict amide I IR spectra of various proteins and agrees well with experiment. Its transferability enabled us to distinguish protein secondary structures, probe atomic structure variations with temperature, and monitor protein folding. This approach offers a cost-effective tool to model the relationship between protein spectra and their biological/chemical properties.

Authors

  • Sheng Ye
    Hefei National Laboratory for Physical Science at the Microscale, Department of Chemical Physics, University of Science and Technology of China, Hefei, Anhui 230026, China.
  • Kai Zhong
    Hefei National Laboratory for Physical Sciences at the Microscale, CAS Center for Excellence in Nanoscience, School of Chemistry and Materials Science, University of Science and Technology of China, Hefei, Anhui 230026, People's Republic of China.
  • Jinxiao Zhang
    Hefei National Laboratory for Physical Science at the Microscale, Department of Chemical Physics, University of Science and Technology of China, Hefei, Anhui 230026, China.
  • Wei Hu
    State Key Laboratory for Novel Software Technology, Nanjing University, Nanjing, China.
  • Jonathan D Hirst
    School of Chemistry, University of Nottingham, Nottingham, NG7 2RD, United Kingdom.
  • Guozhen Zhang
    Hefei National Laboratory for Physical Sciences at the Microscale, CAS Center for Excellence in Nanoscience, School of Chemistry and Materials Science, University of Science and Technology of China, Hefei, Anhui 230026, People's Republic of China.
  • Shaul Mukamel
    Departments of Chemistry, and Physics & Astronomy, University of California, Irvine, California 92697, United States.
  • Jun Jiang
    Key Laboratory of Precision and Intelligent Chemistry, School of Chemistry and Materials Science, University of Science and Technology of China, China.

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