了解基因突变对蛋白质结构的功能影响对于推进癌症研究和开发靶向疗法至关重要。主要挑战在于准确地将这些突变映射到蛋白质结构并分析它们对蛋白质功能的影响。为了解决这个问题，Mut-Map (https://genemutation.org/) 是一个综合计算管道，旨在将癌症体细胞突变目录数据库中的突变数据与蛋白质数据库和 AlphaFold 模型中的蛋白质结构数据整合起来。该流程首先获取 UniProt ID，然后绘制相应的蛋白质数据库结构、对残基重新编号以及评估无序百分比。然后它会叠加突变数据，根据结构背景对突变进行分类，并使用 MolStar 等高级工具将它们可视化。这种方法可以详细分析突变如何通过影响 DNA 界面、配体结合位点和二聚体相互作用等关键区域来破坏蛋白质功能。为了验证该产品线，对 TP53 基因（一种在癌症中经常发生突变的关键肿瘤抑制基因）进行了案例研究。该分析强调了 DNA 结合界面上最常见的突变，从而深入了解它们在癌症进展中的潜在作用。 Mut-Map 为阐明癌症相关突变的结构影响提供了强大的资源，为更有针对性的治疗策略铺平了道路，并增进了我们对蛋白质结构与功能关系的理解。© 作者 2024。由牛津大学出版社出版。

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.© The Author(s) 2024. Published by Oxford University Press.