Combining Ramachandran plot and molecular dynamics simulation for structural-based variant classification: Using TP53 variants as model

Benjamin Tam; Siddharth Sinha; San Ming Wang

doi:10.1016/j.csbj.2020.11.041

Combining Ramachandran plot and molecular dynamics simulation for structural-based variant classification: Using TP53 variants as model

Comput Struct Biotechnol J. 2020 Dec 2:18:4033-4039. doi: 10.1016/j.csbj.2020.11.041. eCollection 2020.

Authors

Benjamin Tam¹, Siddharth Sinha¹, San Ming Wang¹

Affiliation

¹ Cancer Centre and Institute of Translational Medicine, Faculty of Health Sciences, University of Macau, Macau.

Abstract

The wide application of new DNA sequencing technologies is generating vast quantities of genetic variation data at unprecedented speed. Developing methodologies to decode the pathogenicity of the variants is imperatively demanding. We hypothesized that as deleterious variants may function through disturbing structural stability of their affected proteins, information from structural change caused by genetic variants can be used to identify the variants with deleterious effects. In order to measure the structural change for proteins with large size, we designed a method named RP-MDS composed of Ramachandran plot (RP) and Molecular Dynamics Simulation (MDS). Ramachandran plot captures the variant-caused secondary structural change, whereas MDS provides a quantitative measure for the variant-caused globular structural change. We tested the method using variants in TP53 DNA binding domain of 219 residues as the model. In total, RP-MDS identified 23 of 38 (60.5%) TP53 known Pathogenic variants and 17 of 42 (41%) TP53 VUS that caused significant changes of P53 structure. Our study demonstrates that RP-MDS method provides a powerful protein structure-based tool to screen deleterious genetic variants affecting large-size proteins.

Keywords: Molecular Dynamic Simulation; Pathogenic; Protein structure; Ramachandran plot; TP53; Variant of Uncertain Significance.