aPRBind: protein-RNA interface prediction by combining sequence and I-TASSER model-based structural features learned with convolutional neural networks
aPRBind: protein-RNA interface prediction by combining sequence and I-TASSER model-based structural features learned with convolutional neural networks
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DOI：
10.1093/bioinformatics/btaa747
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发表时间：
2020-08
期刊：
影响因子：
5.8
通讯作者：
Yang Liu;Weikang Gong;Yanpeng Zhao;Xueqing Deng;Shan Zhang;Chunhua Li
中科院分区：
文献类型：
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作者：
Yang Liu;Weikang Gong;Yanpeng Zhao;Xueqing Deng;Shan Zhang;Chunhua Li
动机蛋白质-RNA 相互作用在各种生物过程中发挥着关键作用。蛋白质中RNA结合残基的准确预测一直是计算生物学领域最具挑战性和最有趣的问题之一。现有的方法仍然具有相对较低的精度，特别是对于基于序列的从头算方法。结果在这项工作中，我们提出了一种方法aPRBind，一种基于卷积神经网络（CNN）的从头开始方法，用于RNA结合残基预测。 aPRBind 使用 I-TASSER 从预测结构中提取的序列特征和结构特征（特别是我们开发的残基动态信息和残基核苷酸倾向）进行训练。特征贡献分析表明序列特征最重要，其次是动态信息，并且序列和结构特征在结合位点预测中是互补的。我们的方法与基准数据集上其他同行方法的性能比较表明，aPRBind 优于一些最先进的 ab-initio 方法。此外，aPRBind可以对TM-score≥0.5的建模结构给出更好的预测，同时由于结构特征对精细的3维结构不是很敏感，aPRBind对结构模型的准确性只有边际依赖，这使得aPRBind可以应用于建模或未结合结构的RNA结合位点预测。可用性 源代码可在 https://github.com/ChunhuaLiLab/aPRbind 获取。补充信息 补充数据可在生物信息学在线获取。
MOTIVATION Protein-RNA interactions play a critical role in various biological processes. The accurate prediction of RNA-binding residues in proteins has been one of the most challenging and intriguing problems in the field of computational biology. The existing methods still have a relatively low accuracy especially for the sequence based ab-initio methods. RESULTS In this work, we propose an approach aPRBind, a convolutional neural network (CNN)-based ab-initio method for RNA-binding residue prediction. aPRBind is trained with sequence features and structural ones (particularly including residue dynamics information and residue-nucleotide propensity developed by us) that are extracted from the predicted structures by I-TASSER. The analysis of feature contributions indicates the sequence features are most important, followed by dynamics information, and the sequence and structural features are complementary in binding site prediction. The performance comparison of our method with other peer ones on benchmark dataset shows that aPRBind outperforms some state-of-the-art ab-initio methods. Additionally, aPRBind can give a better prediction for the modeled structures with TM-score ≥ 0.5, and meanwhile since the structural features are not very sensitive to the refined 3-dimensional structures, aPRBind has only a marginal dependence on the accuracy of the structure model, which allows aPRBind to be applied to the RNA-binding site prediction for the modeled or unbound structures. AVAILABILITY The source code is available at https://github.com/ChunhuaLiLab/aPRbind. SUPPLEMENTARY INFORMATION Supplementary data are available at Bioinformatics online.