Aggregating Structural Features for Computational Analysis of Mutations in BRCT Domains of BRCA1 Protein
Understanding the mechanistic interpretability of mutation effects in a protein can help predict the clinical implications of the genetic variants. Hence, computational variant effect predictions that involve protein structural features of the protein mutations might be suitable in this case. In this work, we focus on BRCT domains of BRCA1 gene that is widely studied in breast cancer studies. We retrieved 88 selected missense variants found in BRCT domains annotated in both ClinVar and gnomAD databases. To computationally characterize the pathogenic property of the mutations we used two types of features extracted from protein structures: a change in free Gibbs energy and a set of features derived from molecular dynamics simulations of each mutant. Using a dimensional reduction and Gaussian mixture model (GMM)-based clustering we demonstrate that the variants are segregated into two regions that may correspond to their pathogenic status. This method can be a potential computational pipeline for providing the preliminary mechanistic interpretation of mutation effects in terms of their thermodynamic and structural features.
Authors:
Alam Ahmad Hidayat, Rudi Nirwantono, Mahmud Isnan, Joko Pebrianto Trinugroho, and Bens Pardamean
2023 International Conference on Informatics, Multimedia, Cyber and Information Systems, ICIMCIS 2023