Accurate Machine Learning Prediction of Protein Circular Dichroism Spectra with Embedded Density Descriptors

Luyuan Zhao; Jinxiao Zhang; Yaolong Zhang; Sheng Ye; Guozhen Zhang; Xin Chen; Bin Jiang; Jun Jiang

doi:10.1021/jacsau.1c00449

Accurate Machine Learning Prediction of Protein Circular Dichroism Spectra with Embedded Density Descriptors

JACS Au. 2021 Nov 25;1(12):2377-2384. doi: 10.1021/jacsau.1c00449. eCollection 2021 Dec 27.

Authors

Luyuan Zhao¹, Jinxiao Zhang², Yaolong Zhang¹, Sheng Ye³, Guozhen Zhang¹, Xin Chen⁴, Bin Jiang¹, Jun Jiang¹

Affiliations

¹ Hefei National Laboratory for Physical Sciences at the Microscale, Collaborative Innovation Center of Chemistry for Energy Materials, School of Chemistry and Materials Science, University of Science and Technology of China, Hefei, Anhui 230026, P. R. China.
² Guangxi Key Laboratory of Electrochemical and Magneto-chemical Functional Materials, College of Chemistry and Bioengineering, Guilin University of Technology, Guilin 541006, P. R. China.
³ School of Artificial Intelligence, Anhui University, Hefei, Anhui 230601, P. R. China.
⁴ Gusu Laboratory of Materials, Suzhou, Jiangsu 215123, P. R. China.

Abstract

A data-driven approach to simulate circular dichroism (CD) spectra is appealing for fast protein secondary structure determination, yet the challenge of predicting electric and magnetic transition dipole moments poses a substantial barrier for the goal. To address this problem, we designed a new machine learning (ML) protocol in which ordinary pure geometry-based descriptors are replaced with alternative embedded density descriptors and electric and magnetic transition dipole moments are successfully predicted with an accuracy comparable to first-principle calculation. The ML model is able to not only simulate protein CD spectra nearly 4 orders of magnitude faster than conventional first-principle simulation but also obtain CD spectra in good agreement with experiments. Finally, we predicted a series of CD spectra of the Trp-cage protein associated with continuous changes of protein configuration along its folding path, showing the potential of our ML model for supporting real-time CD spectroscopy study of protein dynamics.