DeepConv-DTI: Prediction of drug-target interactions via deep learning with convolution on protein sequences
DeepConv-DTI: Prediction of drug-target interactions via deep learning with convolution on protein sequences
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DOI：
10.1371/journal.pcbi.1007129
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发表时间：
2019-06-01
影响因子：
4.3
通讯作者：
Nam, Hojung
中科院分区：
文献类型：
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作者：
Lee, Ingo;Keum, Jongsoo;Nam, Hojung
药物-靶标相互作用（DTI）的鉴定在药物发现中起着关键作用。体外和体内实验的高成本和劳动密集型性质突出了基于计算机的DTI预测方法的重要性。在几个计算模型中，传统的蛋白质描述符已被证明没有足够的信息来预测准确的DTI。因此，在这项研究中，我们提出了一个基于深度学习的DTI预测模型，捕获参与DTI的蛋白质的局部残基模式。当我们在原始蛋白质序列上使用卷积神经网络（CNN）时，我们对各种长度的氨基酸序列进行卷积，以捕获广义蛋白质类的局部残基模式。我们使用大规模DTI信息训练我们的模型，并使用在训练阶段看不到的独立数据集来证明所提出的模型的性能。因此，我们的模型比以前的基于蛋白质的模型表现得更好。此外，我们的模型在大规模预测DTI方面的表现优于最近开发的深度学习模型。通过检查合并的卷积结果，我们证实了我们的模型可以检测DTI的蛋白质结合位点。总之，我们的预测模型用于检测目标蛋白质的局部残基模式，成功地丰富了蛋白质序列的蛋白质特征，产生了比以前的方法更好的预测结果。我们的代码可在https://github.com/GIST-CSBL/DeepConv-DTI.Author上获得。摘要药物通过与靶蛋白相互作用来激活或抑制靶蛋白的生物过程。因此，DTI的鉴定是药物发现的关键步骤。然而，通过生物测定鉴定候选药物是非常耗时和成本消耗的，这引入了对用于鉴定DTI的计算预测方法的需要。在这项工作中，我们构建了一种新的DTI预测模型，使用基于CNN的深度学习方法提取靶蛋白序列的局部残基模式。因此，检测到的蛋白质序列的局部特征比用于DTI预测的其他蛋白质描述符和用于预测PubChem独立测试数据集的先前模型表现得更好。也就是说，我们用CNN捕获局部残基模式的方法成功地从原始序列中丰富了蛋白质特征。
Identification of drug-target interactions (DTIs) plays a key role in drug discovery. The high cost and labor-intensive nature of in vitro and in vivo experiments have highlighted the importance of in silico-based DTI prediction approaches. In several computational models, conventional protein descriptors have been shown to not be sufficiently informative to predict accurate DTIs. Thus, in this study, we propose a deep learning based DTI prediction model capturing local residue patterns of proteins participating in DTIs. When we employ a convolutional neural network (CNN) on raw protein sequences, we perform convolution on various lengths of amino acids subsequences to capture local residue patterns of generalized protein classes. We train our model with large-scale DTI information and demonstrate the performance of the proposed model using an independent dataset that is not seen during the training phase. As a result, our model performs better than previous protein descriptor-based models. Also, our model performs better than the recently developed deep learning models for massive prediction of DTIs. By examining pooled convolution results, we confirmed that our model can detect binding sites of proteins for DTIs. In conclusion, our prediction model for detecting local residue patterns of target proteins successfully enriches the protein features of a raw protein sequence, yielding better prediction results than previous approaches. Our code is available at https://github.com/GIST-CSBL/DeepConv-DTI.Author summary Drugs work by interacting with target proteins to activate or inhibit a target's biological process. Therefore, identification of DTIs is a crucial step in drug discovery. However, identifying drug candidates via biological assays is very time and cost consuming, which introduces the need for a computational prediction approach for the identification of DTIs. In this work, we constructed a novel DTI prediction model to extract local residue patterns of target protein sequences using a CNN-based deep learning approach. As a result, the detected local features of protein sequences perform better than other protein descriptors for DTI prediction and previous models for predicting PubChem independent test datasets. That is, our approach of capturing local residue patterns with CNN successfully enriches protein features from a raw sequence.