Abstract
Type IV secretion systems (T4SS) are used by a number of bacterial pathogens to attack the host cell. The complex protein structure of the T4SS is used to directly translocate effector proteins into host cells, often causing fatal diseases in humans and animals. Identification of effector proteins is the first step in understanding how they function to cause virulence and pathogenicity. Accurate prediction of effector proteins via a machine learning approach can assist in the process of their identification. The main goal of this study is to predict a set of candidate effectors for the tick-borne pathogen Anaplasma phagocytophilum, the causative agent of anaplasmosis in humans. To our knowledge, we present the first computational study for effector prediction with a focus on A. phagocytophilum. In a previous study, we systematically selected a set of optimal features from more than 1,000 possible protein characteristics for predicting T4SS effector candidates. This was followed by a study of the features using the proteome of Legionella pneumophila strain Philadelphia deduced from its complete genome. In this manuscript we introduce the OPT4e software package for Optimal-features Predictor for T4SS Effector proteins. An earlier version of OPT4e was verified using cross-validation tests, accuracy tests, and comparison with previous results for L. pneumophila. We use OPT4e to predict candidate effectors from the proteomes of A. phagocytophilum strains HZ and HGE-1 and predict 48 and 46 candidates, respectively, with 16 and 18 deemed most probable as effectors. These latter include the three known validated effectors for A. phagocytophilum.
Highlights
Anaplasma phagocytophilum is a tick-borne zoonotic Gram-negative pathogen that causes Human Granulocytic Anaplasmosis (HGA)
OPT4e is based on predictions using a specific machine learning algorithm while taking advantage of two additional algorithms in order to present the results in three different groups of more-likely, possible, and less-likely candidate effectors
The main goal of this study was predicting a set of candidate effectors for A. phagocytophilum using a new package called OPT4e which we developed for this purpose
Summary
Anaplasma phagocytophilum is a tick-borne zoonotic Gram-negative pathogen that causes Human Granulocytic Anaplasmosis (HGA). Incidence of this potentially fatal disease is rising in the United States, with the number of cases increasing from 348 in 2000 to 5,762 in 2017 and incidence rates increasing from 1.4 cases per million people in 2000 to 17.9 cases per million in 2017. The number of cases in the United States increased 39% from 2016 to 2017 alone (CDC, 2019). HGA is the third most common vector-borne infection in the United States (Dumler et al, 2005; Dumler, 2012; Bakken and Dumler, 2015; Sinclair et al, 2015; CDC, 2019)
Sign-in/Register to view highlights & summary Sign-in/Register to access full text options 
Free) 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a call
