Deep architectures for protein contact map prediction
Deep architectures for protein contact map prediction
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DOI：
10.1093/bioinformatics/bts475
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发表时间：
2012-10-01
期刊：
影响因子：
5.8
通讯作者：
Baldi, Pierre
中科院分区：
文献类型：
--
作者：
Di Lena, Pietro;Nagata, Ken;Baldi, Pierre
动机：残基-残基接触预测对于蛋白质结构预测和其他应用非常重要。然而，目前的接触预测方法对远距离接触的准确率往往仅略高于20%，达不到从头算结构预测的要求。结果：本文提出了一种基于三步递增分辨率的接触地图预测的机器学习方法。首先，我们使用2D递归神经网络来预测二级结构元素之间的粗略接触和方向。其次，我们使用基于能量的方法来对齐二级结构元素，并预测在接触α-螺旋或链时残基之间的接触概率。第三，我们使用深度神经网络架构来组织和逐步完善联系人的预测，整合空间和时间上的信息。我们在一大组非冗余蛋白质上训练该架构，并在一大组非同源结构域以及最近两个CASP8和CASP9实验中用于接触预测的蛋白质结构域上进行测试。对于远距离接触，新的CMAPPro预报器的准确率接近30%，比现有方法有了显著提高。
Motivation: Residue-residue contact prediction is important for protein structure prediction and other applications. However, the accuracy of current contact predictors often barely exceeds 20% on long-range contacts, falling short of the level required for ab initio structure prediction.Results: Here, we develop a novel machine learning approach for contact map prediction using three steps of increasing resolution. First, we use 2D recursive neural networks to predict coarse contacts and orientations between secondary structure elements. Second, we use an energy-based method to align secondary structure elements and predict contact probabilities between residues in contacting alpha-helices or strands. Third, we use a deep neural network architecture to organize and progressively refine the prediction of contacts, integrating information over both space and time. We train the architecture on a large set of non-redundant proteins and test it on a large set of non-homologous domains, as well as on the set of protein domains used for contact prediction in the two most recent CASP8 and CASP9 experiments. For long-range contacts, the accuracy of the new CMAPpro predictor is close to 30%, a significant increase over existing approaches.