MetaPSICOV: combining coevolution methods for accurate prediction of contacts and long range hydrogen bonding in proteins.
MetaPSICOV: combining coevolution methods for accurate prediction of contacts and long range hydrogen bonding in proteins.
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DOI：
10.1093/bioinformatics/btu791
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发表时间：
2015-04-01
期刊：
影响因子：
--
通讯作者：
Tetchner S
中科院分区：
文献类型：
--
作者：
Jones DT;Singh T;Kosciolek T;Tetchner S
动机：最近统计技术的发展推断残基对之间的直接进化耦合，使得基于协变的接触预测成为精确的蛋白质3D建模的可行方法，除了所需的序列之外没有其他信息。为了扩展接触预测的有用性，我们设计了一个新的元预测器（MetaPSICOV），它结合了三种不同的方法来推断来自多个序列对齐的协变信号，考虑了广泛的其他序列衍生特征，以及独特的一系列描述输入多序列对齐的局部和全局质量的指标。最后，我们使用一个两阶段预测器，其中第二阶段过滤第一阶段的输出。这个两阶段预测器还被评估其准确预测氢键的远程网络的能力，包括正确分配供体和受体残基。结果：使用原始的150个蛋白质家族的PSICOV基准集，MetaPSICOV在top-L预测远程接触的平均精度为0.54，比PSICOV高约60%，比CCMpred高约40%。在使用FRAGFOLD进行从头蛋白质结构预测时，与PSICOV相比，MetaPSICOV能够提高模型的tm得分，中位数为0.05。最后，对于远程氢键的预测，MetaPSICOV- hb对于顶部l /10氢键的精度达到0.69，而基线MetaPSICOV的精度仅为0.26。可用性和实现：MetaPSICOV是一个免费的web服务器，网址是http://bioinf.cs.ucl.ac.uk/MetaPSICOV。原始数据（预测联系人列表和3D模型）和源代码可以从http://bioinf.cs.ucl.ac.uk/downloads/MetaPSICOV下载。补充信息：补充数据可在Bioinformatics在线获取。
Motivation: Recent developments of statistical techniques to infer direct evolutionary couplings between residue pairs have rendered covariation-based contact prediction a viable means for accurate 3D modelling of proteins, with no information other than the sequence required. To extend the usefulness of contact prediction, we have designed a new meta-predictor (MetaPSICOV) which combines three distinct approaches for inferring covariation signals from multiple sequence alignments, considers a broad range of other sequence-derived features and, uniquely, a range of metrics which describe both the local and global quality of the input multiple sequence alignment. Finally, we use a two-stage predictor, where the second stage filters the output of the first stage. This two-stage predictor is additionally evaluated on its ability to accurately predict the long range network of hydrogen bonds, including correctly assigning the donor and acceptor residues. Results: Using the original PSICOV benchmark set of 150 protein families, MetaPSICOV achieves a mean precision of 0.54 for top-L predicted long range contacts—around 60% higher than PSICOV, and around 40% better than CCMpred. In de novo protein structure prediction using FRAGFOLD, MetaPSICOV is able to improve the TM-scores of models by a median of 0.05 compared with PSICOV. Lastly, for predicting long range hydrogen bonding, MetaPSICOV-HB achieves a precision of 0.69 for the top-L/10 hydrogen bonds compared with just 0.26 for the baseline MetaPSICOV. Availability and implementation: MetaPSICOV is available as a freely available web server at http://bioinf.cs.ucl.ac.uk/MetaPSICOV. Raw data (predicted contact lists and 3D models) and source code can be downloaded from http://bioinf.cs.ucl.ac.uk/downloads/MetaPSICOV. Contact: d.t.jones@ucl.ac.uk Supplementary information: Supplementary data are available at Bioinformatics online.
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影响因子：
7.7
作者：
Ovchinnikov S;Kamisetty H;Baker D
通讯作者：
Baker D
影响因子：
64.5
作者：
Hopf TA;Colwell LJ;Sheridan R;Rost B;Sander C;Marks DS
通讯作者：
Marks DS
DOI：
10.1093/bioinformatics/btu458
发表时间：
2014-09-01
期刊：
Bioinformatics (Oxford, England)
影响因子：
--
作者：
Michel M;Hayat S;Skwark MJ;Sander C;Marks DS;Elofsson A
通讯作者：
Elofsson A
影响因子：
5.8
作者：
Dunn, S. D.;Wahl, L. M.;Gloor, G. B.
通讯作者：
Gloor, G. B.
影响因子：
2.4
作者：
Konopka, Bogumil M.;Ciombor, Marika;Kotulska, Malgorzata
通讯作者：
Kotulska, Malgorzata