Identification of clathrin proteins by incorporating hyperparameter optimization in deep learning and PSSM profiles. (August 2019)

• Record Type: Journal Article
• Authors: Le, Nguyen Quoc Khanh; Huynh, Tuan-Tu; Yapp, Edward Kien Yee; Yeh, Hui-Yuan
• Is Part Of: Computer methods and programs in biomedicine. Volume 177(2019)
• Page Start: 81
• Page End: 88
• Publication Date: 2019-08
• Subjects: Clathrin coated pits -- Convolutional neural network -- Vesicular transport -- Molecular function -- Adaptor protein complex -- Position specific scoring matrix; Medicine -- Computer programs -- Periodicals; Biology -- Computer programs -- Periodicals; Computers -- Periodicals; Medicine -- Periodicals; Médecine -- Logiciels -- Périodiques; Biologie -- Logiciels -- Périodiques; Biology -- Computer programs; Medicine -- Computer programs; Periodicals; Electronic journals; 610.28
• DOI: 10.1016/j.cmpb.2019.05.016
• Languages: English
• ISSNs: 0169-2607

Abstract

Highlights: A deep learning technique for identifying molecular functions of clathrin with high performance. The proposed idea is to transform the position-specific scoring matrices to 2D images and feed into 2D convolutional neural networks. Compared with the other state-of-the-art techniques, our method had a significant improvement in all of the measurement metrics. A powerful model to help biologists discover the new sequences that belong to clathrin. A basis for further research that can improve the performance of protein function prediction using deep neural networks. Abstract: 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 × 20 matrix as an image of 20 × 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 athttps://www.github.com/khanhlee/deep-clathrin/ .