T4SS Effector Protein Prediction with Deep Learning

• Main Authors: Koray Açıcı, Tunç Aşuroğlu, Çağatay Berke Erdaş, Hasan Oğul
• Format: Article
• Language: English
• Published: MDPI AG 2019-03-01
• Series: Data
• Subjects: T4SS; bacterial effectors; deep learning; convolutional neural network; classification; protein to image conversion
• Online Access: https://www.mdpi.com/2306-5729/4/1/45

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.