Mut-Map：癌症相关突变的结构映射与分析的全面计算流程

Mut-Map: Comprehensive Computational Pipeline for Structural Mapping and Analysis of Cancer-Associated Mutations

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BRIEFINGS IN BIOINFORMATICS

影响因子:7.7

分区:生物学2区 / 数学与计算生物学1区生化研究方法2区

发表日期:2024 Sep 23

作者: Ali F Alsulami

DOI: 10.1093/bib/bbae514

摘要

理解遗传突变对蛋白质结构的功能影响对于推进癌症研究和靶向治疗具有重要意义。主要挑战在于准确映射这些突变到蛋白质结构上并分析其对蛋白功能的影响。为此，Mut-Map（https://genemutation.org/）开发了一个全面的计算流程，集成了来自癌症体细胞突变目录（COSMIC）的突变数据、蛋白质结构数据库（PDB）和AlphaFold模型。该流程以一个UniProt ID起始，先映射对应的PDB结构，再重新编号残基，评估无序比例，然后叠加突变数据，根据结构背景分类突变，并利用MolStar等先进工具进行可视化。此方法能详细分析突变如何通过影响DNA接口、配体结合位点和二聚体相互作用等关键区域，破坏蛋白质功能。以TP53基因为例——一种常见的肿瘤抑制基因，突变多发生在DNA结合界面——验证了该流程的有效性。Mut-Map为揭示癌症相关突变的结构机制提供了有力工具，有助于开发更具针对性的治疗策略，深化对蛋白质结构与功能关系的理解。

Abstract

Understanding the functional impact of genetic mutations on protein structures is essential for advancing cancer research and developing targeted therapies. The main challenge lies in accurately mapping these mutations to protein structures and analysing their effects on protein function. To address this, Mut-Map (https://genemutation.org/) is a comprehensive computational pipeline designed to integrate mutation data from the Catalogue Of Somatic Mutations In Cancer database with protein structural data from the Protein Data Bank and AlphaFold models. The pipeline begins by taking a UniProt ID and proceeds through mapping corresponding Protein Data Bank structures, renumbering residues, and assessing disorder percentages. It then overlays mutation data, categorizes mutations based on structural context, and visualizes them using advanced tools like MolStar. This approach allows for a detailed analysis of how mutations may disrupt protein function by affecting key regions such as DNA interfaces, ligand-binding sites, and dimer interactions. To validate the pipeline, a case study on the TP53 gene, a critical tumour suppressor often mutated in cancers, was conducted. The analysis highlighted the most frequent mutations occurring at the DNA-binding interface, providing insights into their potential role in cancer progression. Mut-Map offers a powerful resource for elucidating the structural implications of cancer-associated mutations, paving the way for more targeted therapeutic strategies and advancing our understanding of protein structure-function relationships.