Integration of deep learning with Ramachandran plot molecular dynamics simulation for genetic variant classification

Benjamin Tam; Zixin Qin; Bojin Zhao; San Ming Wang; Chon Lok Lei

doi:10.1016/j.isci.2023.106122

Integration of deep learning with Ramachandran plot molecular dynamics simulation for genetic variant classification

iScience. 2023 Feb 2;26(3):106122. doi: 10.1016/j.isci.2023.106122. eCollection 2023 Mar 17.

Authors

Benjamin Tam^{1

2

3}, Zixin Qin^{1

2

3}, Bojin Zhao^{1

2

3}, San Ming Wang^{1

2

3}, Chon Lok Lei^{1

2

3}

Affiliations

¹ Ministry of Education Frontiers Science Center for Precision Oncology, Faculty of Health Sciences, University of Macau, Macau SAR, China.
² Cancer Centre, Faculty of Health Sciences, University of Macau, Macau SAR, China.
³ Institute of Translational Medicine, Faculty of Health Sciences, University of Macau, Macau SAR, China.

Abstract

Functional classification of genetic variants is a key for their clinical applications in patient care. However, abundant variant data generated by the next-generation DNA sequencing technologies limit the use of experimental methods for their classification. Here, we developed a protein structure and deep learning (DL)-based system for genetic variant classification, DL-RP-MDS, which comprises two principles: 1) Extracting protein structural and thermodynamics information using the Ramachandran plot-molecular dynamics simulation (RP-MDS) method, 2) combining those data with an unsupervised learning model of auto-encoder and a neural network classifier to identify the statistical significance patterns of the structural changes. We observed that DL-RP-MDS provided higher specificity than over 20 widely used in silico methods in classifying the variants of three DNA damage repair genes: TP53, MLH1, and MSH2. DL-RP-MDS offers a powerful platform for high-throughput genetic variant classification. The software and online application are available at https://genemutation.fhs.um.edu.mo/DL-RP-MDS/.

Keywords: Biological sciences; Genetics; Systems biology.