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2
Homology modeling, molecular docking, and molecular dynamics simulations elucidated α-fetoprotein binding modes.同源建模、分子对接和分子动力学模拟阐明了甲胎蛋白的结合模式。
BMC Bioinformatics. 2013;14 Suppl 14(Suppl 14):S6. doi: 10.1186/1471-2105-14-S14-S6. Epub 2013 Oct 9.
3
What has high-throughput sequencing ever done for us?高通量测序为我们带来了什么?
Nat Rev Microbiol. 2013 Oct;11(10):664-5. doi: 10.1038/nrmicro3112. Epub 2013 Aug 27.
4
Rapid similarity search of proteins using alignments of domain arrangements.利用结构域排列的比对进行蛋白质的快速相似性搜索。
Bioinformatics. 2014 Jan 15;30(2):274-81. doi: 10.1093/bioinformatics/btt379. Epub 2013 Jul 4.
5
Protein threading using context-specific alignment potential.使用上下文特定对齐势的蛋白质穿线。
Bioinformatics. 2013 Jul 1;29(13):i257-65. doi: 10.1093/bioinformatics/btt210.
6
A glimpse into past, present, and future DNA sequencing.一瞥过去、现在和未来的 DNA 测序。
Mol Genet Metab. 2013 Sep-Oct;110(1-2):3-24. doi: 10.1016/j.ymgme.2013.04.024. Epub 2013 May 11.
7
The utility of artificially evolved sequences in protein threading and fold recognition.人工进化序列在蛋白质结构预测和折叠识别中的应用。
J Theor Biol. 2013 Jul 7;328:77-88. doi: 10.1016/j.jtbi.2013.03.018. Epub 2013 Mar 27.
8
Amino acid distribution rules predict protein fold.氨基酸分布规律预测蛋白质折叠。
Biochem Soc Trans. 2013 Apr;41(2):616-9. doi: 10.1042/BST20120308.
9
Probing the protein space for extending the detection of weak homology folds.探测蛋白质空间以扩展对弱同源折叠的检测。
J Theor Biol. 2013 Mar 7;320:152-8. doi: 10.1016/j.jtbi.2012.12.005. Epub 2012 Dec 19.
10
eThread: a highly optimized machine learning-based approach to meta-threading and the modeling of protein tertiary structures.eThread:一种高度优化的基于机器学习的元线程和蛋白质三级结构建模方法。
PLoS One. 2012;7(11):e50200. doi: 10.1371/journal.pone.0050200. Epub 2012 Nov 21.

从局部结构到全局框架:蛋白质折叠的识别

From local structure to a global framework: recognition of protein folds.

作者信息

Joseph Agnel Praveen, de Brevern Alexandre G

机构信息

Science and Technology Facilities Council, Rutherford Appleton Laboratory, Harwell Oxford, , Didcot OX11 0QX, UK.

出版信息

J R Soc Interface. 2014 Apr 16;11(95):20131147. doi: 10.1098/rsif.2013.1147. Print 2014 Jun 6.

DOI:10.1098/rsif.2013.1147
PMID:24740960
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC4006237/
Abstract

Protein folding has been a major area of research for many years. Nonetheless, the mechanisms leading to the formation of an active biological fold are still not fully apprehended. The huge amount of available sequence and structural information provides hints to identify the putative fold for a given sequence. Indeed, protein structures prefer a limited number of local backbone conformations, some being characterized by preferences for certain amino acids. These preferences largely depend on the local structural environment. The prediction of local backbone conformations has become an important factor to correctly identifying the global protein fold. Here, we review the developments in the field of local structure prediction and especially their implication in protein fold recognition.

摘要

多年来,蛋白质折叠一直是一个主要的研究领域。尽管如此,导致活性生物折叠形成的机制仍未被完全理解。大量可用的序列和结构信息为识别给定序列的假定折叠提供了线索。实际上,蛋白质结构倾向于有限数量的局部主链构象,其中一些以对某些氨基酸的偏好为特征。这些偏好很大程度上取决于局部结构环境。局部主链构象的预测已成为正确识别全局蛋白质折叠的一个重要因素。在这里,我们综述了局部结构预测领域的进展,特别是它们在蛋白质折叠识别中的意义。