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易非易:高序列相似性模板的比较建模。

Easy Not Easy: Comparative Modeling with High-Sequence Identity Templates.

机构信息

Laboratory of Computational and Quantitative Biology, LCQB, UMR 7238 CNRS, IBPS, Sorbonne Université, Paris, France.

Toulouse Biotechnology Institute, TBI, Université de Toulouse, CNRS, INRA, INSA, Toulouse, France.

出版信息

Methods Mol Biol. 2023;2627:83-100. doi: 10.1007/978-1-0716-2974-1_5.

DOI:10.1007/978-1-0716-2974-1_5
PMID:36959443
Abstract

Homology modeling is the most common technique to build structural models of a target protein based on the structure of proteins with high-sequence identity and available high-resolution structures. This technique is based on the idea that protein structure shows fewer changes than sequence through evolution. While in this scenario single mutations would minimally perturb the structure, experimental evidence shows otherwise: proteins with high conformational diversity impose a limit of the paradigm of comparative modeling as the same protein sequence can adopt dissimilar three-dimensional structures. These cases present challenges for modeling; at first glance, they may seem to be easy cases, but they have a complexity that is not evident at the sequence level. In this chapter, we address the following questions: Why should we care about conformational diversity? How to consider conformational diversity when doing template-based modeling in a practical way?

摘要

同源建模是根据高序列同一性和具有高分辨率结构的蛋白质结构来构建目标蛋白质结构模型的最常用技术。该技术基于蛋白质结构在进化过程中比序列变化小的观点。虽然在这种情况下,单个突变对结构的影响最小,但实验证据表明并非如此:具有高构象多样性的蛋白质对比较建模的范例施加了限制,因为相同的蛋白质序列可以采用不同的三维结构。这些情况对建模提出了挑战;乍一看,它们似乎是简单的情况,但它们具有在序列水平上不明显的复杂性。在本章中,我们将解决以下问题:为什么我们应该关心构象多样性?在实际进行基于模板的建模时,如何考虑构象多样性?

相似文献

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Easy Not Easy: Comparative Modeling with High-Sequence Identity Templates.易非易:高序列相似性模板的比较建模。
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Specificities of Modeling of Membrane Proteins Using Multi-Template Homology Modeling.使用多模板同源建模对膜蛋白进行建模的特点。
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本文引用的文献

1
PlaToLoCo: the first web meta-server for visualization and annotation of low complexity regions in proteins.PlaToLoCo:用于可视化和注释蛋白质中低复杂度区域的第一个网络元服务器。
Nucleic Acids Res. 2020 Jul 2;48(W1):W77-W84. doi: 10.1093/nar/gkaa339.
2
Exploring Conformational Space with Thermal Fluctuations Obtained by Normal-Mode Analysis.运用正态模式分析获得的热波动探索构象空间。
J Chem Inf Model. 2020 Jun 22;60(6):3068-3080. doi: 10.1021/acs.jcim.9b01136. Epub 2020 Apr 13.
3
The SCOP database in 2020: expanded classification of representative family and superfamily domains of known protein structures.
2020 年的 SCOP 数据库:已知蛋白质结构的代表性家族和超家族域的扩展分类。
Nucleic Acids Res. 2020 Jan 8;48(D1):D376-D382. doi: 10.1093/nar/gkz1064.
4
Structural variations within proteins can be as large as variations observed across their homologues.蛋白质内的结构变化可能与同源物之间观察到的变化一样大。
Biochimie. 2019 Dec;167:162-170. doi: 10.1016/j.biochi.2019.09.013. Epub 2019 Sep 24.
5
CASP13 target classification into tertiary structure prediction categories.CASP13 目标分类到三级结构预测类别。
Proteins. 2019 Dec;87(12):1021-1036. doi: 10.1002/prot.25775. Epub 2019 Jul 24.
6
The Pfam protein families database in 2019.2019 年 Pfam 蛋白质家族数据库。
Nucleic Acids Res. 2019 Jan 8;47(D1):D427-D432. doi: 10.1093/nar/gky995.
7
Exploring Protein Conformational Diversity.探索蛋白质构象多样性。
Methods Mol Biol. 2019;1851:353-365. doi: 10.1007/978-1-4939-8736-8_20.
8
Comparative analysis of methods for evaluation of protein models against native structures.评估蛋白质模型与天然结构一致性的方法比较分析。
Bioinformatics. 2019 Mar 15;35(6):937-944. doi: 10.1093/bioinformatics/bty760.
9
How is structural divergence related to evolutionary information?结构分歧与进化信息有何关系?
Mol Phylogenet Evol. 2018 Oct;127:859-866. doi: 10.1016/j.ympev.2018.06.033. Epub 2018 Jun 25.
10
IUPred2A: context-dependent prediction of protein disorder as a function of redox state and protein binding.IUPred2A:氧化还原状态和蛋白质结合依赖性的蛋白质无序性预测的上下文相关分析。
Nucleic Acids Res. 2018 Jul 2;46(W1):W329-W337. doi: 10.1093/nar/gky384.