Suppr超能文献

用于天然结构识别和蛋白质模型质量评估的多体粗粒度势。

Multibody coarse-grained potentials for native structure recognition and quality assessment of protein models.

机构信息

Faculty of Chemistry, University of Warsaw, Warsaw, Poland.

出版信息

Proteins. 2011 Jun;79(6):1923-9. doi: 10.1002/prot.23015. Epub 2011 Apr 19.

Abstract

Multibody potentials have been of much interest recently because they take into account three dimensional interactions related to residue packing and capture the cooperativity of these interactions in protein structures. Our goal was to combine long range multibody potentials and short range potentials to improve recognition of native structure among misfolded decoys. We optimized the weights for four-body nonsequential, four-body sequential, and short range potentials to obtain optimal model ranking results for threading and have compared these data against results obtained with other potentials (26 different coarse-grained potentials from the Potentials 'R'Us web server have been used). Our optimized multibody potentials outperform all other contact potentials in the recognition of the native structure among decoys, both for models from homology template-based modeling and from template-free modeling in CASP8 decoy sets. We have compared the results obtained for this optimized coarse-grained potentials, where each residue is represented by a single point, with results obtained by using the DFIRE potential, which takes into account atomic level information of proteins. We found that for all proteins larger than 80 amino acids our optimized coarse-grained potentials yield results comparable to those obtained with the atomic DFIRE potential.

摘要

多体势最近引起了广泛关注,因为它们考虑了与残基堆积有关的三维相互作用,并捕捉了蛋白质结构中这些相互作用的协同性。我们的目标是结合长程多体势和短程势,以提高错误折叠诱饵中天然结构的识别能力。我们优化了非顺序四体、顺序四体和短程势的权重,以获得最佳的排序结果,并将这些数据与其他势(来自 Potentials 'R'Us 网络服务器的 26 种不同的粗粒化势)的结果进行了比较。在识别同源模板建模和无模板建模的 CASP8 诱饵集中的天然结构时,我们优化的多体势在识别天然结构方面优于所有其他接触势。我们比较了这种优化的粗粒化势(其中每个残基用一个点表示)和 DFIRE 势(考虑蛋白质的原子水平信息)的结果。我们发现,对于所有大于 80 个氨基酸的蛋白质,我们优化的粗粒化势的结果与原子 DFIRE 势的结果相当。

相似文献

10
Protein structure refinement by optimization.通过优化进行蛋白质结构细化。
Proteins. 2015 Sep;83(9):1616-24. doi: 10.1002/prot.24846. Epub 2015 Jul 21.

引用本文的文献

1
Protein Docking Model Evaluation by Graph Neural Networks.基于图神经网络的蛋白质对接模型评估
Front Mol Biosci. 2021 May 25;8:647915. doi: 10.3389/fmolb.2021.647915. eCollection 2021.
5
Knowledge-based entropies improve the identification of native protein structures.基于知识的熵改进了天然蛋白质结构的识别。
Proc Natl Acad Sci U S A. 2017 Mar 14;114(11):2928-2933. doi: 10.1073/pnas.1613331114. Epub 2017 Mar 6.
10
Redundancy-weighting for better inference of protein structural features.用于更好地推断蛋白质结构特征的冗余加权
Bioinformatics. 2014 Aug 15;30(16):2295-301. doi: 10.1093/bioinformatics/btu242. Epub 2014 Apr 25.

本文引用的文献

4
Backbone flexibility in computational protein design.计算蛋白质设计中的骨架灵活性。
Curr Opin Biotechnol. 2009 Aug;20(4):420-8. doi: 10.1016/j.copbio.2009.07.006. Epub 2009 Aug 24.
5
A guide to template based structure prediction.基于模板的结构预测指南。
Curr Protein Pept Sci. 2009 Jun;10(3):270-85. doi: 10.2174/138920309788452182.
6
Quality assessment of protein structure models.蛋白质结构模型的质量评估。
Curr Protein Pept Sci. 2009 Jun;10(3):216-28. doi: 10.2174/138920309788452173.
8
Convergence and combination of methods in protein-protein docking.蛋白质-蛋白质对接中方法的融合与结合
Curr Opin Struct Biol. 2009 Apr;19(2):164-70. doi: 10.1016/j.sbi.2009.02.008. Epub 2009 Mar 25.
10
Enzyme (re)design: lessons from natural evolution and computation.酶(重新)设计:来自自然进化与计算的经验教训。
Curr Opin Chem Biol. 2009 Feb;13(1):10-8. doi: 10.1016/j.cbpa.2009.01.014. Epub 2009 Feb 23.

文献AI研究员

20分钟写一篇综述,助力文献阅读效率提升50倍。

立即体验

用中文搜PubMed

大模型驱动的PubMed中文搜索引擎

马上搜索

文档翻译

学术文献翻译模型,支持多种主流文档格式。

立即体验