Identification of clathrin proteins by incorporating hyperparameter optimization in deep learning and PSSM profiles
Identification of clathrin proteins by incorporating hyperparameter optimization in deep learning and PSSM profiles
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DOI：
10.1016/j.cmpb.2019.05.016
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发表时间：
2019-08-01
影响因子：
6.1
通讯作者：
Yeh, Hui-Yuan
中科院分区：
文献类型：
--
作者：
Nguyen Quoc Khanh Le;Tuan-Tu Huynh;Yeh, Hui-Yuan
背景和目标：网格蛋白是一种衔接蛋白，其作为囊泡-包被复合物的主要成分，并且对于膜切割以从质膜分配内陷囊泡是重要的。网格蛋白的功能丧失与许多人类疾病有关，即，因此，建立一个精确的模型来识别其功能是理解人类疾病和设计药物靶点的关键一步。方法：我们提出了一个深度学习模型，使用二维卷积神经网络（CNN）和位置特异性评分矩阵（PSSM）配置文件从高通量序列中识别网格蛋白。传统上，2D CNN将图像作为输入，因此我们将具有20 x 20矩阵的PSSM配置文件视为20 x 20像素的图像。然后将输入PSSM配置文件连接到我们的2D CNN，我们在其中设置了各种参数以提高模型的性能。基于10倍交叉验证结果，采用超参数优化过程为我们的数据集找到最佳模型。结果：在独立数据集上，该模型识别网格蛋白的灵敏度为92.2%，特异度为91.2%，准确度为91.8%，MCC为0.83。与最先进的传统神经网络相比，我们的方法在所有典型的测量metrics.Conclusions：在整个拟议的研究中，我们提供了一个有效的工具，调查网格蛋白和我们的成果可以促进深度学习在生物医学研究中的使用。我们还在https://www.github.com/khanhlee/deep-clathrin/免费提供源代码和数据集。(C)2019 Elsevier B. V.版权所有。
Background and Objectives: Clathrin is an adaptor protein that serves as the principal element of the vesicle-coating complex and is important for the membrane cleavage to dispense the invaginated vesicle from the plasma membrane. The functional loss of clathrins has been tied to a lot of human diseases, i.e., neurodegenerative disorders, cancer, Alzheimer's diseases, and so on. Therefore, creating a precise model to identify its functions is a crucial step towards understanding human diseases and designing drug targets.Methods: We present a deep learning model using a two-dimensional convolutional neural network (CNN) and position-specific scoring matrix (PSSM) profiles to identify clathrin proteins from high throughput sequences. Traditionally, the 2D CNNs take images as an input so we treated the PSSM profile with a 20 x 20 matrix as an image of 20 x 20 pixels. The input PSSM profile was then connected to our 2D CNN in which we set a variety of parameters to improve the performance of the model. Based on the 10-fold cross-validation results, hyper-parameter optimization process was employed to find the best model for our dataset. Finally, an independent dataset was used to assess the predictive ability of the current model.Results: Our model could identify clathrin proteins with sensitivity of 92.2%, specificity of 91.2%, accuracy of 91.8%, and MCC of 0.83 in the independent dataset. Compared to state-of-the-art traditional neural networks, our method achieved a significant improvement in all typical measurement metrics.Conclusions: Throughout the proposed study, we provide an effective tool for investigating clathrin proteins and our achievement could promote the use of deep learning in biomedical research. We also provide source codes and dataset freely at https://www.github.com/khanhlee/deep-clathrin/. (C) 2019 Elsevier B.V. All rights reserved.