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

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/. [Display omitted] •Classifying genetic variants in ClinVar database by using RP-MDS and deep learning•DL-RP-MDS achieved the highest specificity compared to over 20 in silico methods•Demonstrated with variant classification for DNA damage repair genes: TP53, MLH1, and MSH2•Online platform available at https://genemutation.fhs.um.edu.mo/DL-RP-MDS/ Biological sciences; Genetics; Systems biology.