Suppr超能文献

电路拓扑结构可预测错义突变的致病性。

Circuit topology predicts pathogenicity of missense mutations.

机构信息

Medical Systems Biophysics and Bioengineering, Leiden Academic Centre for Drug Research, Faculty of Science, Leiden University, Leiden, The Netherlands.

Department of Computational Medicine and Bioinformatics, University of Michigan, Ann Arbor, Michigan, USA.

出版信息

Proteins. 2022 Sep;90(9):1634-1644. doi: 10.1002/prot.26342. Epub 2022 Apr 23.

Abstract

The contact topology of a protein determines important aspects of the folding process. The topological measure of contact order has been shown to be predictive of the rate of folding. Circuit topology is emerging as another fundamental descriptor of biomolecular structure, with predicted effects on the folding rate. We analyze the residue-based circuit topological environments of 21 K mutations labeled as pathogenic or benign. Multiple statistical lines of reasoning support the conclusion that the number of contacts in two specific circuit topological arrangements, namely inverse parallel and cross relations, with contacts involving the mutated residue have discriminatory value in determining the pathogenicity of human variants. We investigate how results vary with residue type and according to whether the gene is essential. We further explore the relationship to a number of structural features and find that circuit topology provides nonredundant information on protein structures and pathogenicity of mutations. Results may have implications for the polymer physics of protein folding and suggest that "local" topological information, including residue-based circuit topology and residue contact order, could be useful in improving state-of-the-art machine learning algorithms for pathogenicity prediction.

摘要

蛋白质的接触拓扑结构决定了折叠过程的重要方面。接触顺序的拓扑度量已被证明可以预测折叠速率。电路拓扑结构作为生物分子结构的另一个基本描述符正在出现,预计会对折叠速率产生影响。我们分析了 21000 个突变的基于残基的电路拓扑环境,这些突变被标记为致病性或良性。多种统计推理支持这样的结论,即涉及突变残基的两种特定电路拓扑排列(即反向平行和交叉关系)中接触的数量,在确定人类变体的致病性方面具有区分价值。我们研究了结果如何随残基类型和基因是否必需而变化。我们进一步探讨了与许多结构特征的关系,并发现电路拓扑结构提供了关于蛋白质结构和突变致病性的非冗余信息。结果可能对蛋白质折叠的聚合物物理有影响,并表明“局部”拓扑信息,包括基于残基的电路拓扑结构和残基接触顺序,可以用于改进用于致病性预测的最先进的机器学习算法。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/39d5/9543832/8022653a819d/PROT-90-1634-g004.jpg

文献AI研究员

20分钟写一篇综述,助力文献阅读效率提升50倍。

立即体验

用中文搜PubMed

大模型驱动的PubMed中文搜索引擎

马上搜索

文档翻译

学术文献翻译模型,支持多种主流文档格式。

立即体验