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折叠的序列熵和氨基酸替换的绝对速率。

Sequence entropy of folding and the absolute rate of amino acid substitutions.

机构信息

Division of Infection and Immunity, University College London, London, WC1E 6BT, UK.

Department of Biochemistry and Molecular Genetics, University of Colorado School of Medicine, Aurora, CO, 80045, USA.

出版信息

Nat Ecol Evol. 2017 Dec;1(12):1923-1930. doi: 10.1038/s41559-017-0338-9. Epub 2017 Oct 23.

DOI:10.1038/s41559-017-0338-9
PMID:29062121
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC5701738/
Abstract

Adequate representations of protein evolution should consider how the acceptance of mutations depends on the sequence context in which they arise. However, epistatic interactions among sites in a protein result in hererogeneities in the substitution rate, both temporal and spatial, that are beyond the capabilities of current models. Here we use parallels between amino acid substitutions and chemical reaction kinetics to develop an improved theory of protein evolution. We constructed a mechanistic framework for modelling amino acid substitution rates that uses the formalisms of statistical mechanics, with principles of population genetics underlying the analysis. Theoretical analyses and computer simulations of proteins under purifying selection for thermodynamic stability show that substitution rates and the stabilization of resident amino acids (the 'evolutionary Stokes shift') can be predicted from biophysics and the effect of sequence entropy alone. Furthermore, we demonstrate that substitutions predominantly occur when epistatic interactions result in near neutrality; substitution rates are determined by how often epistasis results in such nearly neutral conditions. This theory provides a general framework for modelling protein sequence change under purifying selection, potentially explains patterns of convergence and mutation rates in real proteins that are incompatible with previous models, and provides a better null model for the detection of adaptive changes.

摘要

充分体现蛋白质进化的特征应考虑到突变的接受程度取决于其出现的序列背景。然而,蛋白质中各位点的上位性相互作用导致了替代率的时空异质性,这超出了当前模型的能力范围。在这里,我们使用氨基酸替换与化学反应动力学之间的相似性来开发改进的蛋白质进化理论。我们构建了一个用于模拟氨基酸替代率的机械框架,该框架使用统计力学的形式主义,分析的基础是群体遗传学的原则。对热力学稳定性进行净化选择的蛋白质的理论分析和计算机模拟表明,替代率和驻留氨基酸的稳定性(“进化斯托克斯位移”)可以仅通过生物物理学和序列熵的影响来预测。此外,我们证明,当上位性相互作用导致近中性时,替代主要发生;替代率取决于上位性导致这种几乎中性条件的频率。该理论为在净化选择下模拟蛋白质序列变化提供了一个通用框架,可能解释了与先前模型不兼容的真实蛋白质中趋同和突变率的模式,并为检测适应性变化提供了更好的零模型。

相似文献

1
Sequence entropy of folding and the absolute rate of amino acid substitutions.折叠的序列熵和氨基酸替换的绝对速率。
Nat Ecol Evol. 2017 Dec;1(12):1923-1930. doi: 10.1038/s41559-017-0338-9. Epub 2017 Oct 23.
2
The tangled bank of amino acids.错综复杂的氨基酸库
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Are residues in a protein folding nucleus evolutionarily conserved?蛋白质折叠核心中的残基在进化上保守吗?
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A protein evolution model with independent sites that reproduces site-specific amino acid distributions from the Protein Data Bank.一种具有独立位点的蛋白质进化模型,可从蛋白质数据库中重现位点特异性氨基酸分布。
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Epistasis as the primary factor in molecular evolution.上位性是分子进化的主要因素。
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Selection on protein structure, interaction, and sequence.对蛋白质结构、相互作用和序列的选择。
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Sibe: a computation tool to apply protein sequence statistics to predict folding and design in silico.希贝:一种计算工具,用于应用蛋白质序列统计信息来预测折叠和设计计算。
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本文引用的文献

1
Extensively Parameterized Mutation-Selection Models Reliably Capture Site-Specific Selective Constraint.广泛参数化的突变选择模型可靠地捕捉位点特异性选择约束。
Mol Biol Evol. 2016 Nov;33(11):2990-3002. doi: 10.1093/molbev/msw171. Epub 2016 Aug 10.
2
Local fitness landscape of the green fluorescent protein.绿色荧光蛋白的局部适应度景观
Nature. 2016 May 19;533(7603):397-401. doi: 10.1038/nature17995. Epub 2016 May 11.
3
The tangled bank of amino acids.错综复杂的氨基酸库
基于近似贝叶斯计算的蛋白质进化中依赖于位置的结构约束替代模型的选择。
Bioinformatics. 2024 Mar 4;40(3). doi: 10.1093/bioinformatics/btae096.
4
Main Factors Shaping Amino Acid Usage Across Evolution.影响氨基酸在进化过程中使用的主要因素。
J Mol Evol. 2023 Aug;91(4):382-390. doi: 10.1007/s00239-023-10120-5. Epub 2023 Jun 1.
5
Genes and sites under adaptation at the phylogenetic scale also exhibit adaptation at the population-genetic scale.在系统发育尺度上发生适应的基因和位点也表现出种群遗传尺度上的适应。
Proc Natl Acad Sci U S A. 2023 Mar 14;120(11):e2214977120. doi: 10.1073/pnas.2214977120. Epub 2023 Mar 10.
6
Using machine learning to predict the effects and consequences of mutations in proteins.利用机器学习预测蛋白质突变的影响和后果。
Curr Opin Struct Biol. 2023 Feb;78:102518. doi: 10.1016/j.sbi.2022.102518. Epub 2023 Jan 3.
7
Arrow of Time, Entropy, and Protein Folding: Holistic View on Biochirality.时间之箭、熵和蛋白质折叠:生物手性的整体观。
Int J Mol Sci. 2022 Mar 28;23(7):3687. doi: 10.3390/ijms23073687.
8
Evolution of Amino Acid Propensities under Stability-Mediated Epistasis.稳定性介导的上位性作用下的氨基酸倾向性进化。
Mol Biol Evol. 2022 Mar 2;39(3). doi: 10.1093/molbev/msac030.
9
Evolutionary coupling range varies widely among enzymes depending on selection pressure.进化偶联范围在不同酶之间差异很大,这取决于选择压力。
Biophys J. 2021 Oct 19;120(20):4320-4324. doi: 10.1016/j.bpj.2021.08.042. Epub 2021 Sep 2.
10
Shifts in amino acid preferences as proteins evolve: A synthesis of experimental and theoretical work.蛋白质进化过程中氨基酸偏好性的转变:实验与理论工作的综合。
Protein Sci. 2021 Oct;30(10):2009-2028. doi: 10.1002/pro.4161. Epub 2021 Aug 12.
Protein Sci. 2016 Jul;25(7):1354-62. doi: 10.1002/pro.2930. Epub 2016 May 12.
4
Contingency and entrenchment in protein evolution under purifying selection.纯化选择下蛋白质进化中的偶然性与固定性
Proc Natl Acad Sci U S A. 2015 Jun 23;112(25):E3226-35. doi: 10.1073/pnas.1412933112. Epub 2015 Jun 8.
5
A coarse-grained biophysical model of sequence evolution and the population size dependence of the speciation rate.序列进化的粗粒度生物物理模型及物种形成速率对种群大小的依赖性。
J Theor Biol. 2015 Aug 7;378:56-64. doi: 10.1016/j.jtbi.2015.04.027. Epub 2015 Apr 30.
6
Nonadaptive Amino Acid Convergence Rates Decrease over Time.非适应性氨基酸趋同率随时间下降。
Mol Biol Evol. 2015 Jun;32(6):1373-81. doi: 10.1093/molbev/msv041. Epub 2015 Mar 3.
7
A model of substitution trajectories in sequence space and long-term protein evolution.序列空间中的替代轨迹模型与长期蛋白质进化
Mol Biol Evol. 2015 Feb;32(2):542-54. doi: 10.1093/molbev/msu318. Epub 2014 Nov 17.
8
Epistatically interacting substitutions are enriched during adaptive protein evolution.上位性相互作用的替换在适应性蛋白质进化过程中富集。
PLoS Genet. 2014 May 8;10(5):e1004328. doi: 10.1371/journal.pgen.1004328. eCollection 2014 May.
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Strong evidence for protein epistasis, weak evidence against it.有强有力的证据支持蛋白质上位性,而反对它的证据则很薄弱。
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10
A penalized-likelihood method to estimate the distribution of selection coefficients from phylogenetic data.一种从系统发育数据估计选择系数分布的惩罚似然方法。
Genetics. 2014 May;197(1):257-71. doi: 10.1534/genetics.114.162263. Epub 2014 Feb 14.