De novo structure prediction of globular proteins aided by sequence variation-derived contacts.
De novo structure prediction of globular proteins aided by sequence variation-derived contacts.
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DOI：
10.1371/journal.pone.0092197
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发表时间：
2014
期刊：
影响因子：
3.7
通讯作者：
Jones DT
中科院分区：
文献类型：
--
作者：
Kosciolek T;Jones DT
高精度残基-残基蛋白质内接触预测方法的出现使得从头结构预测的质量显著提高。在这里，我们调查相结合的潜在好处，一个完善的片段为基础的折叠算法- FRAGFOLD，与PSICOV，接触预测方法，使用稀疏逆协方差估计，以确定在多个序列比对共变网站。使用一套全面的150个不同的球状靶蛋白，长达266个氨基酸的长度，我们能够解决的有效性和一些局限性，这种方法的球状蛋白在实践中。总的来说，我们发现，使用片段组装与统计潜力和预测的接触是显着优于统计潜力或单独的接触。结果显示，在分析的数据集内，正确预测（TM评分≥0.5）接近80%，平均TM评分为0.54。不成功的建模情况下出现的构象抽样问题，或接触预测精度不足。然而，观察到满意的预测的长距离接触的分数上的最终模型的质量的强烈依赖性。这不仅突出了这些接触对确定蛋白质折叠的重要性，而且（与其他集合衍生的质量相结合）为正确模型的选择和所选模型的全局质量提供了强有力的指导。建议的质量评估评分函数达到0.93的准确率和0.77召回正确的褶皱在我们的诱饵数据集的歧视。这些研究结果表明，该方法非常适合于盲预测的各种球状蛋白的未知的三维结构，提供了足够的同源序列可用于构建一个大的和准确的多序列比对的初始接触预测步骤。
The advent of high accuracy residue-residue intra-protein contact prediction methods enabled a significant boost in the quality of de novo structure predictions. Here, we investigate the potential benefits of combining a well-established fragment-based folding algorithm – FRAGFOLD, with PSICOV, a contact prediction method which uses sparse inverse covariance estimation to identify co-varying sites in multiple sequence alignments. Using a comprehensive set of 150 diverse globular target proteins, up to 266 amino acids in length, we are able to address the effectiveness and some limitations of such approaches to globular proteins in practice. Overall we find that using fragment assembly with both statistical potentials and predicted contacts is significantly better than either statistical potentials or contacts alone. Results show up to nearly 80% of correct predictions (TM-score ≥0.5) within analysed dataset and a mean TM-score of 0.54. Unsuccessful modelling cases emerged either from conformational sampling problems, or insufficient contact prediction accuracy. Nevertheless, a strong dependency of the quality of final models on the fraction of satisfied predicted long-range contacts was observed. This not only highlights the importance of these contacts on determining the protein fold, but also (combined with other ensemble-derived qualities) provides a powerful guide as to the choice of correct models and the global quality of the selected model. A proposed quality assessment scoring function achieves 0.93 precision and 0.77 recall for the discrimination of correct folds on our dataset of decoys. These findings suggest the approach is well-suited for blind predictions on a variety of globular proteins of unknown 3D structure, provided that enough homologous sequences are available to construct a large and accurate multiple sequence alignment for the initial contact prediction step.
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影响因子：
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作者：
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