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PHACT:错义突变容忍度的系统发育感知计算。

PHACT: Phylogeny-Aware Computing of Tolerance for Missense Mutations.

机构信息

Faculty of Engineering and Natural Sciences, Sabanci University, Istanbul 34956, Turkey.

出版信息

Mol Biol Evol. 2022 Jun 2;39(6). doi: 10.1093/molbev/msac114.

Abstract

Evolutionary conservation is a fundamental resource for predicting the substitutability of amino acids and the loss of function in proteins. The use of multiple sequence alignment alone-without considering the evolutionary relationships among sequences-results in the redundant counting of evolutionarily related alteration events, as if they were independent. Here, we propose a new method, PHACT, that predicts the pathogenicity of missense mutations directly from the phylogenetic tree of proteins. PHACT travels through the nodes of the phylogenetic tree and evaluates the deleteriousness of a substitution based on the probability differences of ancestral amino acids between neighboring nodes in the tree. Moreover, PHACT assigns weights to each node in the tree based on their distance to the query organism. For each potential amino acid substitution, the algorithm generates a score that is used to calculate the effect of substitution on protein function. To analyze the predictive performance of PHACT, we performed various experiments over the subsets of two datasets that include 3,023 proteins and 61,662 variants in total. The experiments demonstrated that our method outperformed the widely used pathogenicity prediction tools (i.e., SIFT and PolyPhen-2) and achieved a better predictive performance than other conventional statistical approaches presented in dbNSFP. The PHACT source code is available at https://github.com/CompGenomeLab/PHACT.

摘要

进化保守性是预测氨基酸替代和蛋白质功能丧失的基本资源。仅使用多序列比对-而不考虑序列之间的进化关系-会导致冗余地计算进化相关的改变事件,就好像它们是独立的一样。在这里,我们提出了一种新的方法 PHACT,它可以直接从蛋白质的系统发育树预测错义突变的致病性。PHACT 在系统发育树的节点之间移动,并根据树中相邻节点之间祖先氨基酸的概率差异来评估替代的有害性。此外,PHACT 根据它们与查询生物体的距离为树中的每个节点分配权重。对于每个潜在的氨基酸替代,该算法生成一个分数,用于计算替代对蛋白质功能的影响。为了分析 PHACT 的预测性能,我们对包含总共 3023 种蛋白质和 61662 种变体的两个数据集的子集进行了各种实验。实验表明,我们的方法优于广泛使用的致病性预测工具(即 SIFT 和 PolyPhen-2),并且比 dbNSFP 中提出的其他传统统计方法具有更好的预测性能。PHACT 的源代码可在 https://github.com/CompGenomeLab/PHACT 上获得。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/a84e/9178230/d87eb8c223bb/msac114f1.jpg

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