Integration of deep learning with Ramachandran plot molecular dynamics simulation for genetic variant classification
Published in iScience, 2023
Recommended citation: Tam, B., Qin, Z., ..., Lei, C.L. (2023). "Integration of deep learning with Ramachandran plot molecular dynamics simulation for genetic variant classification." iScience, 26, 106122. https://doi.org/10.1016/j.isci.2023.106122
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/.