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T4SS Effector Protein Prediction with Deep Learning

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Department of Computer Engineering, Baskent University, Bağlıca Kampüsü Fatih Sultan Mahallesi Eskişehir Yolu 18.km, Ankara 06709, Turkey
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Faculty of Computer Science, Østfold University College, P.O. Box 700, 1757 Halden, Norway
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Author to whom correspondence should be addressed.
Received: 1 February 2019 / Revised: 19 March 2019 / Accepted: 22 March 2019 / Published: 25 March 2019
Extensive research has been carried out on bacterial secretion systems, as they can pass effector proteins directly into the cytoplasm of host cells. The correct prediction of type IV protein effectors secreted by T4SS is important, since they are known to play a noteworthy role in various human pathogens. Studies on predicting T4SS effectors involve traditional machine learning algorithms. In this work we included a deep learning architecture, i.e., a Convolutional Neural Network (CNN), to predict IVA and IVB effectors. Three feature extraction methods were utilized to represent each protein as an image and these images fed the CNN as inputs in our proposed framework. Pseudo proteins were generated using ADASYN algorithm to overcome the imbalanced dataset problem. We demonstrated that our framework predicted all IVA effectors correctly. In addition, the sensitivity performance of 94.2% for IVB effector prediction exhibited our framework’s ability to discern the effectors in unidentified proteins. View Full-Text
Keywords: T4SS; bacterial effectors; deep learning; convolutional neural network; classification; protein to image conversion T4SS; bacterial effectors; deep learning; convolutional neural network; classification; protein to image conversion
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Açıcı, K.; Aşuroğlu, T.; Erdaş, Ç.B.; Oğul, H. T4SS Effector Protein Prediction with Deep Learning. Data 2019, 4, 45.

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