• 文献检索
  • 文档翻译
  • 深度研究
  • 学术资讯
  • Suppr Zotero 插件Zotero 插件
  • 邀请有礼
  • 套餐&价格
  • 历史记录
应用&插件
Suppr Zotero 插件Zotero 插件浏览器插件Mac 客户端Windows 客户端微信小程序
定价
高级版会员购买积分包购买API积分包
服务
文献检索文档翻译深度研究API 文档MCP 服务
关于我们
关于 Suppr公司介绍联系我们用户协议隐私条款
关注我们

Suppr 超能文献

核心技术专利:CN118964589B侵权必究
粤ICP备2023148730 号-1Suppr @ 2026

文献检索

告别复杂PubMed语法,用中文像聊天一样搜索,搜遍4000万医学文献。AI智能推荐,让科研检索更轻松。

立即免费搜索

文件翻译

保留排版,准确专业,支持PDF/Word/PPT等文件格式,支持 12+语言互译。

免费翻译文档

深度研究

AI帮你快速写综述,25分钟生成高质量综述,智能提取关键信息,辅助科研写作。

立即免费体验

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.

DOI:10.1093/molbev/msac114
PMID:35639618
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC9178230/
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/9178230/12ae1502ddd4/msac114f7.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/a84e/9178230/d87eb8c223bb/msac114f1.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/a84e/9178230/bb21c2fc3ee5/msac114f2.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/a84e/9178230/8fecdd30ac73/msac114f3.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/a84e/9178230/2b401eaa7db3/msac114f4.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/a84e/9178230/9c46f8089e9c/msac114f5.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/a84e/9178230/df11447e772f/msac114f6.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/a84e/9178230/12ae1502ddd4/msac114f7.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/a84e/9178230/d87eb8c223bb/msac114f1.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/a84e/9178230/bb21c2fc3ee5/msac114f2.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/a84e/9178230/8fecdd30ac73/msac114f3.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/a84e/9178230/2b401eaa7db3/msac114f4.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/a84e/9178230/9c46f8089e9c/msac114f5.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/a84e/9178230/df11447e772f/msac114f6.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/a84e/9178230/12ae1502ddd4/msac114f7.jpg

相似文献

1
PHACT: Phylogeny-Aware Computing of Tolerance for Missense Mutations.PHACT:错义突变容忍度的系统发育感知计算。
Mol Biol Evol. 2022 Jun 2;39(6). doi: 10.1093/molbev/msac114.
2
PHACTboost: A Phylogeny-Aware Pathogenicity Predictor for Missense Mutations via Boosting.PHACTboost:一种基于提升算法的基于系统发育的错义突变致病性预测器。
Mol Biol Evol. 2024 Jul 3;41(7). doi: 10.1093/molbev/msae136.
3
On the quality of tree-based protein classification.论基于树的蛋白质分类的质量。
Bioinformatics. 2005 May 1;21(9):1876-90. doi: 10.1093/bioinformatics/bti244. Epub 2005 Jan 12.
4
Topiary: Pruning the manual labor from ancestral sequence reconstruction.树篱:从祖先序列重建中削减人工劳动。
Protein Sci. 2023 Feb;32(2):e4551. doi: 10.1002/pro.4551.
5
Predicting functional effect of human missense mutations using PolyPhen-2.使用PolyPhen-2预测人类错义突变的功能效应。
Curr Protoc Hum Genet. 2013 Jan;Chapter 7:Unit7.20. doi: 10.1002/0471142905.hg0720s76.
6
Using SIFT and PolyPhen to predict loss-of-function and gain-of-function mutations.使用SIFT和PolyPhen预测功能丧失和功能获得性突变。
Genet Test Mol Biomarkers. 2010 Aug;14(4):533-7. doi: 10.1089/gtmb.2010.0036.
7
Bayesian coestimation of phylogeny and sequence alignment.系统发育与序列比对的贝叶斯联合估计
BMC Bioinformatics. 2005 Apr 1;6:83. doi: 10.1186/1471-2105-6-83.
8
CoDP: predicting the impact of unclassified genetic variants in MSH6 by the combination of different properties of the protein.CoDP:通过组合 MSH6 蛋白的不同性质来预测未分类遗传变异的影响。
J Biomed Sci. 2013 Apr 28;20(1):25. doi: 10.1186/1423-0127-20-25.
9
OMAmer: tree-driven and alignment-free protein assignment to subfamilies outperforms closest sequence approaches.OMAmer:基于树的、无需比对的蛋白质亚家族分配方法优于最接近序列的方法。
Bioinformatics. 2021 Sep 29;37(18):2866-2873. doi: 10.1093/bioinformatics/btab219.
10
Computational approaches for predicting the biological effect of p53 missense mutations: a comparison of three sequence analysis based methods.预测p53错义突变生物学效应的计算方法:三种基于序列分析的方法比较
Nucleic Acids Res. 2006 Mar 6;34(5):1317-25. doi: 10.1093/nar/gkj518. Print 2006.

引用本文的文献

1
PHACE: Phylogeny-Aware Detection of Molecular Coevolution.PHACE:分子协同进化的系统发育感知检测
Mol Biol Evol. 2025 Jul 1;42(7). doi: 10.1093/molbev/msaf150.
2
Variant effect predictor correlation with functional assays is reflective of clinical classification performance.变异效应预测器与功能测定的相关性反映了临床分类性能。
Genome Biol. 2025 Apr 22;26(1):104. doi: 10.1186/s13059-025-03575-w.
3
Evolutionary history of calcium-sensing receptors unveils hyper/hypocalcemia-causing mutations.钙敏感受体的进化历史揭示了引起高/低钙血症的突变。

本文引用的文献

1
MVP predicts the pathogenicity of missense variants by deep learning.MVP 通过深度学习预测错义变异的致病性。
Nat Commun. 2021 Jan 21;12(1):510. doi: 10.1038/s41467-020-20847-0.
2
dbNSFP v4: a comprehensive database of transcript-specific functional predictions and annotations for human nonsynonymous and splice-site SNVs.dbNSFP v4:一个全面的人类非同义突变和剪接位点 SNVs 转录体特异性功能预测和注释数据库。
Genome Med. 2020 Dec 2;12(1):103. doi: 10.1186/s13073-020-00803-9.
3
UniProt: the universal protein knowledgebase in 2021.UniProt:2021 年的通用蛋白质知识库。
PLoS Comput Biol. 2024 Nov 12;20(11):e1012591. doi: 10.1371/journal.pcbi.1012591. eCollection 2024 Nov.
4
Variant Impact Predictor database (VIPdb), version 2: trends from three decades of genetic variant impact predictors.变异影响预测器数据库(VIPdb),版本 2:三十年来遗传变异影响预测器的趋势。
Hum Genomics. 2024 Aug 28;18(1):90. doi: 10.1186/s40246-024-00663-z.
5
Assessing predictions on fitness effects of missense variants in HMBS in CAGI6.评估CAGI6中对HMBS错义变体适应性效应的预测。
Hum Genet. 2025 Mar;144(2-3):173-189. doi: 10.1007/s00439-024-02680-3. Epub 2024 Aug 7.
6
PHACTboost: A Phylogeny-Aware Pathogenicity Predictor for Missense Mutations via Boosting.PHACTboost:一种基于提升算法的基于系统发育的错义突变致病性预测器。
Mol Biol Evol. 2024 Jul 3;41(7). doi: 10.1093/molbev/msae136.
Nucleic Acids Res. 2021 Jan 8;49(D1):D480-D489. doi: 10.1093/nar/gkaa1100.
4
The mutational constraint spectrum quantified from variation in 141,456 humans.从 141456 名人类个体的变异中量化的突变约束谱。
Nature. 2020 May;581(7809):434-443. doi: 10.1038/s41586-020-2308-7. Epub 2020 May 27.
5
LIST-S2: taxonomy based sorting of deleterious missense mutations across species.列表 S2:基于分类学的跨物种有害错义突变排序。
Nucleic Acids Res. 2020 Jul 2;48(W1):W154-W161. doi: 10.1093/nar/gkaa288.
6
The advantages of the Matthews correlation coefficient (MCC) over F1 score and accuracy in binary classification evaluation.马修斯相关系数(MCC)在二分类评估中优于 F1 得分和准确率的优势。
BMC Genomics. 2020 Jan 2;21(1):6. doi: 10.1186/s12864-019-6413-7.
7
GEMME: A Simple and Fast Global Epistatic Model Predicting Mutational Effects.GEMME:一种简单快速的预测突变效应的全局上位性模型。
Mol Biol Evol. 2019 Nov 1;36(11):2604-2619. doi: 10.1093/molbev/msz179.
8
RAxML-NG: a fast, scalable and user-friendly tool for maximum likelihood phylogenetic inference.RAxML-NG:用于最大似然系统发育推断的快速、可扩展和用户友好的工具。
Bioinformatics. 2019 Nov 1;35(21):4453-4455. doi: 10.1093/bioinformatics/btz305.
9
CADD: predicting the deleteriousness of variants throughout the human genome.CADD:预测整个人类基因组中变异的有害性。
Nucleic Acids Res. 2019 Jan 8;47(D1):D886-D894. doi: 10.1093/nar/gky1016.
10
Deep generative models of genetic variation capture the effects of mutations.深度生成模型捕获遗传变异的突变效应。
Nat Methods. 2018 Oct;15(10):816-822. doi: 10.1038/s41592-018-0138-4. Epub 2018 Sep 24.