Determining Optimal Features for Predicting Type IV Secretion System Effector Proteins for Coxiella burnetii

Abstract

Type IV secretion systems (T4SS) are constructed from multiple protein complexes that exist in some types of bacterial pathogens and are responsible for delivering type IV effector proteins into host cells. Effectors target eukaryotic cells and try to manipulate host cell processes and the immune system of the host. Some work has been done to validate effectors experimentally, and recently a few scoring and machine learning-based methods have been developed to predict effectors from whole genome sequences. However, different types of features have been suggested to be effective. In this work, we gathered the features proposed in pre-vious reports and calculated their values for a dataset of effectors and non-effectors of Coxiella burnetii. Then we ranked the features based on their importance in classifying effectors and non-effectors to determine the set of optimal features. Finally, a Support Vector Machine model was developed to test the optimal features by comparing them to a set of features proposed in a previous study. The outcome of the comparison supports the effectiveness of our optimal features.