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基于结构的致病性关系标识符,用于预测单个误解变异的影响,并发现高阶癌症易感性突变群
Boshen Wang1, Xue Lei1, Wei Tian1
1Center for Bioinformatics and Quantitative Biology, Richard and Loan Hill, Department of Biomedical Engineering, University of Illinois at Chicago, W103 Suite, 820 S Wood St, 60612 IL, USA.
Briefings in bioinformatics
|June 18, 2023
概括
我们介绍了基于结构的致病性关系标识符 (SPRI),这是一个计算工具,可以准确预测突变对蛋白质结构和功能的影响. SPRI识别致病突变并发现新的致病突变集群.
科学领域:
- 计算生物学是一种计算生物学.
- 基因组学就是基因组学.
- 结构生物学是结构生物学.
背景情况:
- 误解突变是遗传变异的主要来源,有助于孟德尔病和癌症.
- 准确预测突变致病性对于遗传诊断和治疗开发至关重要.
- 现有的方法往往难以捕捉突变效应的复杂结构决定因素.
研究的目的:
- 开发和验证一个新的计算工具,基于结构的致病性关系标识符 (SPRI),用于评估误解突变致病性.
- 为了能够识别与孟德尔病和癌症驱动因素相关的有害突变.
- 发现高阶致病性突变集群 (patHOS) 和候选癌症驱动基因/突变.
主要方法:
- SPRI利用蛋白质结构信息来提取与致病性相关的特性.
- 该工具的评估是与孟德尔病相关的生殖系突变和癌症的体质突变.
- 将SPRI的性能与现有的突变预测方法进行了比较.
- 证明了SPRI利用AlphaFold2预测结构的能力.
主要成果:
- SPRI准确地识别出生殖线和体源的有害误解突变.
- 与现有方法相比,该工具在预测突变致病性方面表现优越.
- SPRI成功地发现了空间组织的致病性更高阶突变空间 (patHOS),包括低复发的突变.
- SPRI促进了候选癌症驱动基因和突变的发现.
结论:
- SPRI是一种强大的计算工具,用于使用蛋白质结构评估突变致病性.
- 该工具有助于识别致病突变,发现新的致病突变模式.
- SPRI使用预测结构的能力,如AlphaFold2的结构,扩大了它对大规模分析的适用性,包括全蛋白质和突变分析.
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