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蛋白质结构预测关键评估第10轮(CASP10)中残基-残基接触预测的评估

Evaluation of residue-residue contact prediction in CASP10.

作者信息

Monastyrskyy Bohdan, D'Andrea Daniel, Fidelis Krzysztof, Tramontano Anna, Kryshtafovych Andriy

机构信息

Genome Center, University of California, Davis, California, 95616.

出版信息

Proteins. 2014 Feb;82 Suppl 2(0 2):138-53. doi: 10.1002/prot.24340. Epub 2013 Aug 31.

DOI:10.1002/prot.24340
PMID:23760879
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC3823628/
Abstract

We present the results of the assessment of the intramolecular residue-residue contact predictions from 26 prediction groups participating in the 10th round of the CASP experiment. The most recently developed direct coupling analysis methods did not take part in the experiment likely because they require a very deep sequence alignment not available for any of the 114 CASP10 targets. The performance of contact prediction methods was evaluated with the measures used in previous CASPs (i.e., prediction accuracy and the difference between the distribution of the predicted contacts and that of all pairs of residues in the target protein), as well as new measures, such as the Matthews correlation coefficient, the area under the precision-recall curve and the ranks of the first correctly and incorrectly predicted contact. We also evaluated the ability to detect interdomain contacts and tested whether the difficulty of predicting contacts depends upon the protein length and the depth of the family sequence alignment. The analyses were carried out on the target domains for which structural homologs did not exist or were difficult to identify. The evaluation was performed for all types of contacts (short, medium, and long-range), with emphasis placed on long-range contacts, i.e. those involving residues separated by at least 24 residues along the sequence. The assessment suggests that the best CASP10 contact prediction methods perform at approximately the same level, and comparably to those participating in CASP9.

摘要

我们展示了对参与第10轮CASP实验的26个预测小组所做的分子内残基-残基接触预测的评估结果。最新开发的直接耦合分析方法没有参与该实验,可能是因为它们需要非常深度的序列比对,而这对于114个CASP10目标中的任何一个都不可用。接触预测方法的性能通过先前CASP中使用的指标(即预测准确性以及预测接触的分布与目标蛋白中所有残基对的分布之间的差异)以及新的指标进行评估,如新的指标,如马修斯相关系数、精确召回率曲线下的面积以及首次正确和错误预测接触的排名。我们还评估了检测结构域间接触的能力,并测试了预测接触的难度是否取决于蛋白质长度和家族序列比对的深度。分析是在不存在结构同源物或难以识别结构同源物的目标结构域上进行的。对所有类型的接触(短程、中程和长程)进行了评估,重点是长程接触,即那些沿着序列至少相隔24个残基的残基之间的接触。评估表明,最好的CASP10接触预测方法的表现大致处于同一水平,与参与CASP9的方法相当。

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本文引用的文献

1
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Proteins. 2014 Feb;82 Suppl 2(0 2):14-25. doi: 10.1002/prot.24434. Epub 2013 Nov 22.
2
CASP prediction center infrastructure and evaluation measures in CASP10 and CASP ROLL.CASP10和CASP ROLL中的CASP预测中心基础设施及评估措施。
Proteins. 2014 Feb;82 Suppl 2(0 2):7-13. doi: 10.1002/prot.24399. Epub 2013 Oct 18.
3
Emerging methods in protein co-evolution.蛋白质共进化的新兴方法。
利用新的多尺度网络和同源模板改进蛋白质结构预测。
Adv Sci (Weinh). 2021 Dec;8(24):e2102592. doi: 10.1002/advs.202102592. Epub 2021 Oct 31.
4
Assessing the accuracy of contact and distance predictions in CASP14.评估 CASP14 中接触和距离预测的准确性。
Proteins. 2021 Dec;89(12):1888-1900. doi: 10.1002/prot.26248. Epub 2021 Oct 3.
5
Folding non-homologous proteins by coupling deep-learning contact maps with I-TASSER assembly simulations.通过将深度学习接触图与 I-TASSER 组装模拟相结合来折叠非同源蛋白质。
Cell Rep Methods. 2021 Jul 26;1(3). doi: 10.1016/j.crmeth.2021.100014. Epub 2021 Jun 21.
6
Deep Learning in Protein Structural Modeling and Design.蛋白质结构建模与设计中的深度学习
Patterns (N Y). 2020 Nov 12;1(9):100142. doi: 10.1016/j.patter.2020.100142. eCollection 2020 Dec 11.
7
Chasing coevolutionary signals in intrinsically disordered proteins complexes.追踪无序蛋白质复合物中的共进化信号。
Sci Rep. 2020 Oct 21;10(1):17962. doi: 10.1038/s41598-020-74791-6.
8
Evaluating the significance of contact maps in low-homology protein modeling using contact-assisted threading.使用接触辅助对接评估低同源性蛋白质建模中接触图的意义。
Sci Rep. 2020 Feb 19;10(1):2908. doi: 10.1038/s41598-020-59834-2.
9
StructureDistiller: Structural relevance scoring identifies the most informative entries of a contact map.结构蒸馏器:结构相关性评分可识别接触图中最具信息量的条目。
Sci Rep. 2019 Dec 6;9(1):18517. doi: 10.1038/s41598-019-55047-4.
10
Protein structure prediction assisted with sparse NMR data in CASP13.利用稀疏 NMR 数据进行蛋白质结构预测在 CASP13 中。
Proteins. 2019 Dec;87(12):1315-1332. doi: 10.1002/prot.25837.
Nat Rev Genet. 2013 Apr;14(4):249-61. doi: 10.1038/nrg3414. Epub 2013 Mar 5.
4
Protein structure prediction from sequence variation.从序列变异预测蛋白质结构。
Nat Biotechnol. 2012 Nov;30(11):1072-80. doi: 10.1038/nbt.2419.
5
Deep architectures for protein contact map prediction.用于蛋白质接触图预测的深度架构。
Bioinformatics. 2012 Oct 1;28(19):2449-57. doi: 10.1093/bioinformatics/bts475. Epub 2012 Jul 30.
6
Genomics-aided structure prediction.基于基因组学的结构预测。
Proc Natl Acad Sci U S A. 2012 Jun 26;109(26):10340-5. doi: 10.1073/pnas.1207864109. Epub 2012 Jun 12.
7
Protein 3D structure computed from evolutionary sequence variation.基于进化序列变异计算的蛋白质 3D 结构。
PLoS One. 2011;6(12):e28766. doi: 10.1371/journal.pone.0028766. Epub 2011 Dec 7.
8
Direct-coupling analysis of residue coevolution captures native contacts across many protein families.残基共进化的直接耦联分析捕获了许多蛋白质家族中的天然接触。
Proc Natl Acad Sci U S A. 2011 Dec 6;108(49):E1293-301. doi: 10.1073/pnas.1111471108. Epub 2011 Nov 21.
9
PSICOV: precise structural contact prediction using sparse inverse covariance estimation on large multiple sequence alignments.PSICOV:使用基于稀疏逆协方差估计的大型多重序列比对进行精确结构接触预测。
Bioinformatics. 2012 Jan 15;28(2):184-90. doi: 10.1093/bioinformatics/btr638. Epub 2011 Nov 17.
10
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