Improving prediction of secondary structure, local backbone angles, and solvent accessible surface area of proteins by iterative deep learning.
Improving prediction of secondary structure, local backbone angles, and solvent accessible surface area of proteins by iterative deep learning.
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通过迭代深度学习改进蛋白质二级结构、局部主链角度和溶剂可及表面积的预测
DOI：
10.1038/srep11476
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发表时间：
2015-06-22
影响因子：
4.6
通讯作者：
Zhou Y
中科院分区：
文献类型：
--
作者：
Heffernan R;Paliwal K;Lyons J;Dehzangi A;Sharma A;Wang J;Sattar A;Yang Y;Zhou Y
从序列中直接预测蛋白质结构是一个具有挑战性的问题。一种有效的方法是将其分解为独立的子问题。这些子问题，如蛋白质二级结构的预测，可以独立解决。在之前的研究中，我们发现迭代使用预测的二级结构和骨干扭转角可以进一步改善二级结构和扭转角的预测。在这项研究中，我们扩展了迭代特征，包括溶剂可达表面积和基于Cα原子的主角和二面体。利用深度学习神经网络进行3次迭代，对1199个蛋白质的二级结构预测精度达到82%，预测与实际溶剂可及表面积的相关系数为0.76，主链φ角和ψ角的平均绝对误差分别为19°和30°，c α基θ角和τ角的平均绝对误差分别为8°和32°。对于72个casp11目标，该方法的准确性略低，但远高于目前最先进技术的模型结构。这表明在模型评估和排序中使用这些预测属性可能是有益的。
Direct prediction of protein structure from sequence is a challenging problem. An effective approach is to break it up into independent sub-problems. These sub-problems such as prediction of protein secondary structure can then be solved independently. In a previous study, we found that an iterative use of predicted secondary structure and backbone torsion angles can further improve secondary structure and torsion angle prediction. In this study, we expand the iterative features to include solvent accessible surface area and backbone angles and dihedrals based on Cα atoms. By using a deep learning neural network in three iterations, we achieved 82% accuracy for secondary structure prediction, 0.76 for the correlation coefficient between predicted and actual solvent accessible surface area, 19° and 30° for mean absolute errors of backbone φ and ψ angles, respectively and 8° and 32° for mean absolute errors of Cα-based θ and τ angles, respectively, for an independent test dataset of 1199 proteins. The accuracy of the method is slightly lower for 72 CASP 11 targets but much higher than those of model structures from current state-of-the-art techniques. This suggests the potentially beneficial use of these predicted properties for model assessment and ranking.
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通讯作者：
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