A study and benchmark of DNcon: a method for protein residue-residue contact prediction using deep networks.
A study and benchmark of DNcon: a method for protein residue-residue contact prediction using deep networks.
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DOI：
10.1186/1471-2105-14-s14-s12
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发表时间：
2013
影响因子：
3
通讯作者：
Cheng J
中科院分区：
文献类型：
--
作者：
Eickholt J;Cheng J
近年来，预测的蛋白质残基-残基接触的使用和重要性已经相当大地增长，其应用已被证明是例如药物设计、蛋白质三级结构预测和模型质量评估。尽管如此，报告的准确度在25-35%的范围内顽固地仍然是基于序列的硬目标远距离接触预测的标准。这是在代表社区的长期努力，以提高残留物-残留物接触预测的性能。需要对当前残留物-残留物接触预测的质量和使用的评价指标以及对当前方法的分析进行深入研究，以刺激接触预测及其应用的进一步发展。这样的研究将更好地解释目前的方法产生的残留物-残留物接触预测的质量和性质，并因此导致更好地使用这种接触信息。我们评估了几个基于序列的残基-残基接触预测因子，它们参与了第十次蛋白质结构预测关键评估（CASP）实验。评估使用标准评估技术（例如官方CASP评估员使用的技术）以及两个新的评估指标（即，簇精度和簇计数）。深入分析显示，虽然生成的大多数残留物-残留物接触预测在残留物水平上不准确，但当允许低于残留物水平精度时，存在相当强的接触信号。我们的残基-残基接触预测器DNcon在大小为2的邻域中进行评估时，表现特别好，对于前L/10长距离接触，其准确度达到66%。与其他方法相比，DNcon的残留物-残留物接触面积的覆盖范围也更大。我们还分析了DNcon的底层架构和用于分类的功能。我们的新的评估指标表明，目前的残留物-残留物接触预测确实包含一个强大的接触信号，并且比标准评估指标显示的质量更好。我们的方法，DNcon，是一个强大的，国家的最先进的残差序列为基础的接触预测和优秀的一些评估方案。它作为一个网络服务在http://iris.rnet.missouri.edu/dncon/上提供。
In recent years, the use and importance of predicted protein residue-residue contacts has grown considerably with demonstrated applications such as drug design, protein tertiary structure prediction and model quality assessment. Nevertheless, reported accuracies in the range of 25-35% stubbornly remain the norm for sequence based, long range contact predictions on hard targets. This is in spite of a prolonged effort on behalf of the community to improve the performance of residue-residue contact prediction. A thorough study of the quality of current residue-residue contact predictions and the evaluation metrics used as well as an analysis of current methods is needed to stimulate further advancement in contact prediction and its application. Such a study will better explain the quality and nature of residue-residue contact predictions generated by current methods and as a result lead to better use of this contact information. We evaluated several sequence based residue-residue contact predictors that participated in the tenth Critical Assessment of protein Structure Prediction (CASP) experiment. The evaluation was performed using standard assessment techniques such as those used by the official CASP assessors as well as two novel evaluation metrics (i.e., cluster accuracy and cluster count). An in-depth analysis revealed that while most residue-residue contact predictions generated are not accurate at the residue level, there is quite a strong contact signal present when allowing for less than residue level precision. Our residue-residue contact predictor, DNcon, performed particularly well achieving an accuracy of 66% for the top L/10 long range contacts when evaluated in a neighbourhood of size 2. The coverage of residue-residue contact areas was also greater with DNcon when compared to other methods. We also provide an analysis of DNcon with respect to its underlying architecture and features used for classification. Our novel evaluation metrics demonstrate that current residue-residue contact predictions do contain a strong contact signal and are of better quality than standard evaluation metrics indicate. Our method, DNcon, is a robust, state-of-the-art residue-residue sequence based contact predictor and excelled under a number of evaluation schemes. It is available as a web service at http://iris.rnet.missouri.edu/dncon/.