A two-stage approach for improved prediction of residue contact maps.
A two-stage approach for improved prediction of residue contact maps.
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一种两阶段的方法，用于改善残留接触图的预测。
DOI：
10.1186/1471-2105-7-180
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发表时间：
2006-03-30
影响因子：
3
通讯作者：
Pollastri, Gianluca
中科院分区：
文献类型：
--
作者：
Vullo, Alessandro;Walsh, Ian;Pollastri, Gianluca
蛋白质的拓扑表示，如残基接触图是一个重要的中间步骤，从头预测的蛋白质结构。虽然在过去的几年中已经出现了改进，但从一级序列准确预测残基接触图的问题在很大程度上仍然没有解决。其中的原因是不平衡的性质的问题（接触的例子比非接触少得多），捕捉地图中的远程相互作用的艰巨挑战，映射一维输入序列到二维输出地图的内在困难。为了缓解这些问题，实现改进的接触图预测，在本文中，我们分为两个阶段的任务：预测的一个地图的主特征向量（PE）从主序列;接触图的重建从PE和主序列。从一级序列预测PE在于将向量映射到向量。这个任务比直接将向量映射到二维矩阵要简单，因为问题的大小大大减少了，需要学习的相互作用的尺度长度也大大减少了。我们开发的体系结构组成的合奏的两层双向递归神经网络分类的组件的PE在2，3和4类蛋白质的一级序列，预测的二级结构，和疏水性相互作用尺度。我们的预测，测试的非冗余集的2171蛋白质，实现高达72.6%，16%以上的基线统计预测的分类性能。我们设计了一个系统，用于从预测的PE预测接触图。我们的研究结果表明，通过PE预测地图会产生相当大的收益，特别是对于长距离接触，这对于精确的蛋白质3D重建特别重要。当选择顶部长度/5接触时，对于12和24的最小接触间隔，最终预测器在327个目标的非冗余集合上的准确度分别为35.4%和19.8%。在11个CASP 6新折叠靶标上，我们实现了相似的准确度（36.5%和19.7%）。这与CASP 6的最佳自动预测器相比是有利的。我们最终的接触图预测系统达到了最先进的性能，并可能提供有价值的约束条件，改善从头预测蛋白质结构。一套结构特征的预测因子，包括PE和基于PE的接触图，可在。
Protein topology representations such as residue contact maps are an important intermediate step towards ab initio prediction of protein structure. Although improvements have occurred over the last years, the problem of accurately predicting residue contact maps from primary sequences is still largely unsolved. Among the reasons for this are the unbalanced nature of the problem (with far fewer examples of contacts than non-contacts), the formidable challenge of capturing long-range interactions in the maps, the intrinsic difficulty of mapping one-dimensional input sequences into two-dimensional output maps. In order to alleviate these problems and achieve improved contact map predictions, in this paper we split the task into two stages: the prediction of a map's principal eigenvector (PE) from the primary sequence; the reconstruction of the contact map from the PE and primary sequence. Predicting the PE from the primary sequence consists in mapping a vector into a vector. This task is less complex than mapping vectors directly into two-dimensional matrices since the size of the problem is drastically reduced and so is the scale length of interactions that need to be learned. We develop architectures composed of ensembles of two-layered bidirectional recurrent neural networks to classify the components of the PE in 2, 3 and 4 classes from protein primary sequence, predicted secondary structure, and hydrophobicity interaction scales. Our predictor, tested on a non redundant set of 2171 proteins, achieves classification performances of up to 72.6%, 16% above a base-line statistical predictor. We design a system for the prediction of contact maps from the predicted PE. Our results show that predicting maps through the PE yields sizeable gains especially for long-range contacts which are particularly critical for accurate protein 3D reconstruction. The final predictor's accuracy on a non-redundant set of 327 targets is 35.4% and 19.8% for minimum contact separations of 12 and 24, respectively, when the top length/5 contacts are selected. On the 11 CASP6 Novel Fold targets we achieve similar accuracies (36.5% and 19.7%). This favourably compares with the best automated predictors at CASP6. Our final system for contact map prediction achieves state-of-the-art performances, and may provide valuable constraints for improved ab initio prediction of protein structures. A suite of predictors of structural features, including the PE, and PE-based contact maps, is available at .
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影响因子：
5.8
作者：
Pollastri, G;McLysaght, A
通讯作者：
McLysaght, A
影响因子：
2.9
作者：
Pollastri, G;Baldi, P;Casadio, R
通讯作者：
Casadio, R
DOI：
10.1002/prot.10499
发表时间：
2003-11-01
期刊：
PROTEINS-STRUCTURE FUNCTION AND GENETICS
影响因子：
--
作者：
Li, W;Zhang, Y;Skolnick, J
通讯作者：
Skolnick, J
影响因子：
--
作者：
Frasconi, P;Gori, M;Sperduti, A
通讯作者：
Sperduti, A
影响因子：
5.6
作者：
Huang, ES;Samudrala, R;Ponder, JW
通讯作者：
Ponder, JW