Machine learning recognition of protein secondary structures based on two-dimensional spectroscopic descriptors.

Journal: Proceedings of the National Academy of Sciences of the United States of America
Published Date: Apr 27, 2022

Abstract

Protein secondary structure discrimination is crucial for understanding their biological function. It is not generally possible to invert spectroscopic data to yield the structure. We present a machine learning protocol which uses two-dimensional UV (2DUV) spectra as pattern recognition descriptors, aiming at automated protein secondary structure determination from spectroscopic features. Accurate secondary structure recognition is obtained for homologous (97%) and nonhomologous (91%) protein segments, randomly selected from simulated model datasets. The advantage of 2DUV descriptors over one-dimensional linear absorption and circular dichroism spectra lies in the cross-peak information that reflects interactions between local regions of the protein. Thanks to their ultrafast (∼200 fs) nature, 2DUV measurements can be used in the future to probe conformational variations in the course of protein dynamics.

Authors

  • Hao Ren
    Department of Rheumatology, Nanfang Hospital, Southern Medical University, Guangzhou, Guangdong, China. Electronic address: renhao67@aliyun.com.
  • Qian Zhang
    The Neonatal Intensive Care Unit, Peking Union Medical College Hospital, Peking, China.
  • Zhengjie Wang
    School of Materials Science and Engineering, China University of Petroleum (East China), Qingdao 266580, Shandong, China.
  • 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.
  • Hongzhang Liu
    School of Materials Science and Engineering, China University of Petroleum (East China), Qingdao 266580, Shandong, China.
  • Wenyue Guo
    School of Materials Science and Engineering, China University of Petroleum (East China), Qingdao 266580, Shandong, 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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