Do proteins learn to evolve? The Hopfield network as a basis for the understanding of protein evolution.
Do proteins learn to evolve? The Hopfield network as a basis for the understanding of protein evolution.
复制标题
蛋白质会学习进化吗？
DOI：
10.1006/jtbi.1999.1043
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发表时间：
2000
影响因子：
2
通讯作者：
M. Dufton
中科院分区：
文献类型：
--
作者：
L. Pritchard;M. Dufton
氨基酸残基之间的相关性可以在一组对齐的蛋白质序列中观察到，并且已经彻底研究了它们的统计和进化意义和分布的分析。在本文中，我们提出了一个模型的基础上，在蛋白质序列中，有相互影响的残基对联合收割机产生一个类似于Hopfield神经网络的系统。这种网络的涌现特性，如软故障和网络结构与存储记忆之间的联系，在已知蛋白质中有密切的相似之处。该模型表明，对观察到的蛋白质特征的解释，例如通过远离活性位点的取代而减少功能，可以基于一个架构执行多种功能的蛋白质折叠（超折叠）的存在，以及对不稳定取代的结构和功能弹性可能来自其固有的网络状结构。该模型还可以为蛋白质家族的结构、功能和进化历史之间的关系提供基础，从而成为理性工程的有力工具。
Correlations between amino-acid residues can be observed in sets of aligned protein sequences, and the analysis of their statistical and evolutionary significance and distribution has been thoroughly investigated. In this paper, we present a model based on such covariations in protein sequences in which the pairs of residues that have mutual influence combine to produce a system analogous to a Hopfield neural network. The emergent properties of such a network, such as soft failure and the connection between network architecture and stored memory, have close parallels in known proteins. This model suggests that an explanation for observed characters of proteins such as the diminution of function by substitutions distant from the active site, the existence of protein folds (superfolds) that can perform several functions based on one architecture, and structural and functional resilience to destabilizing substitutions might derive from their inherent network-like structure. This model may also provide a basis for mapping the relationship between structure, function and evolutionary history of a protein family, and thus be a powerful tool for rational engineering.
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通过丙氨酸扫描诱变检查各个侧链对 BPTI 稳定性的贡献。
DOI：
10.1006/jmbi.1995.0304
发表时间：
1995
期刊：
Journal of molecular biology.
影响因子：
--
作者：
Yu,MH;Weissman,JS;Kim,PS
通讯作者：
Kim,PS
影响因子：
13.8
作者：
Baldwin, RL;Rose, GD
通讯作者：
Rose, GD
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影响因子：
--
作者：
Scott,DL;Sigler,PB
通讯作者：
Sigler,PB
影响因子：
13.8
作者：
Baldwin, RL;Rose, GD
通讯作者：
Rose, GD
影响因子：
3.3
作者：
Ziheng Yang;Sudhir Kumar;M. Nei
通讯作者：
Ziheng Yang;Sudhir Kumar;M. Nei