Protein tertiary structure modeling driven by deep learning and contact distance prediction in CASP13
Protein tertiary structure modeling driven by deep learning and contact distance prediction in CASP13
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DOI：
10.1002/prot.25697
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发表时间：
2019-12-01
影响因子：
2.9
通讯作者：
Cheng, Jianlin
中科院分区：
文献类型：
--
作者：
Hou, Jie;Wu, Tianqi;Cheng, Jianlin
自2014年CASP 11实验以来，预测残基-残基距离关系（如接触）已成为推进蛋白质结构预测的关键方向，而深度学习自2012年CASP 10实验首次亮相以来，彻底改变了接触和距离分布预测技术。在2018年CASP 13实验期间，我们增强了MULTICOM蛋白质结构预测系统，其中包括三个主要组件：基于深度卷积神经网络的接触距离预测，距离驱动的无模板（从头算）建模，以及通过深度学习和接触预测实现的蛋白质模型排名。我们的实验表明，接触距离预测和深度学习方法是MULTICOM在CASP 13的无模板和基于模板的结构建模中在所有98个预测器中排名第三的关键原因。深度卷积神经网络可以利用协进化分数等成对残差-残差特征中的全局信息，大幅改善接触距离预测，这在正确折叠一些自由建模和基于硬模板的建模目标方面发挥了决定性作用。深度学习还成功地整合了一维结构特征、二维接触信息和三维结构质量分数，以改善蛋白质模型质量评估，其中接触预测首次被证明可以持续提高蛋白质模型的排名。MULTICOM系统的成功表明，深度学习驱动的蛋白质接触距离预测和模型选择是解决蛋白质结构预测问题的关键。然而，在准确预测蛋白质的接触距离时，仍然存在挑战，有几个同源序列，折叠蛋白质从嘈杂的接触距离，和排名模型的硬目标。
Predicting residue-residue distance relationships (eg, contacts) has become the key direction to advance protein structure prediction since 2014 CASP11 experiment, while deep learning has revolutionized the technology for contact and distance distribution prediction since its debut in 2012 CASP10 experiment. During 2018 CASP13 experiment, we enhanced our MULTICOM protein structure prediction system with three major components: contact distance prediction based on deep convolutional neural networks, distance-driven template-free (ab initio) modeling, and protein model ranking empowered by deep learning and contact prediction. Our experiment demonstrates that contact distance prediction and deep learning methods are the key reasons that MULTICOM was ranked 3rd out of all 98 predictors in both template-free and template-based structure modeling in CASP13. Deep convolutional neural network can utilize global information in pairwise residue-residue features such as coevolution scores to substantially improve contact distance prediction, which played a decisive role in correctly folding some free modeling and hard template-based modeling targets. Deep learning also successfully integrated one-dimensional structural features, two-dimensional contact information, and three-dimensional structural quality scores to improve protein model quality assessment, where the contact prediction was demonstrated to consistently enhance ranking of protein models for the first time. The success of MULTICOM system clearly shows that protein contact distance prediction and model selection driven by deep learning holds the key of solving protein structure prediction problem. However, there are still challenges in accurately predicting protein contact distance when there are few homologous sequences, folding proteins from noisy contact distances, and ranking models of hard targets.