Development of a Pipeline for Predicting and Modeling Damaging SNP Mutations in Human Cancer
Start Date
April 2024
Location
2nd floor - Library
Abstract
The understanding and identification of single nucleotide polymorphism (SNP) mutations within human cancers are crucial for understanding how cancer develops. This project aims to develop a pipeline to structurally model the SNP mutations and predict their potential implications in human cancer. First, utilizing COSMIC data, genomic analysis is performed with the OAKVAR platform. This analysis helped to automate the identification of SNP locations in genes of interest, and highlight mutations that could play critical roles in cancer progression through CHASM, VEST, and AlphaMissense analyses. These tools aided in discerning the structural and functional pathogenicity of mutations.Then, using PyRosetta, a software suite for protein modeling and dynamics, scripts were developed to provide insights into the ΔΔG (delta delta Gibbs free energy) of specific mutations. This analysis is crucial for understanding the destabilizing effects of mutations on protein structure, offering a window into how these changes may impact protein function. Additionally, we explored the consequences of these structural alterations on the binding affinities of small ligands through computational docking software using AutoDock Vina. The culmination of this work is a pipeline that identifies SNP mutations predicted to be damaging and establishes a method to model these mutations for further analyses.
Development of a Pipeline for Predicting and Modeling Damaging SNP Mutations in Human Cancer
2nd floor - Library
The understanding and identification of single nucleotide polymorphism (SNP) mutations within human cancers are crucial for understanding how cancer develops. This project aims to develop a pipeline to structurally model the SNP mutations and predict their potential implications in human cancer. First, utilizing COSMIC data, genomic analysis is performed with the OAKVAR platform. This analysis helped to automate the identification of SNP locations in genes of interest, and highlight mutations that could play critical roles in cancer progression through CHASM, VEST, and AlphaMissense analyses. These tools aided in discerning the structural and functional pathogenicity of mutations.Then, using PyRosetta, a software suite for protein modeling and dynamics, scripts were developed to provide insights into the ΔΔG (delta delta Gibbs free energy) of specific mutations. This analysis is crucial for understanding the destabilizing effects of mutations on protein structure, offering a window into how these changes may impact protein function. Additionally, we explored the consequences of these structural alterations on the binding affinities of small ligands through computational docking software using AutoDock Vina. The culmination of this work is a pipeline that identifies SNP mutations predicted to be damaging and establishes a method to model these mutations for further analyses.
