Prediction of T4SS Effector Proteins for Anaplasma phagocytophilum Using OPT4e, A New Software Tool

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 i...

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Main Authors: Zhila Esna Ashari, Kelly A. Brayton, Shira L. Broschat
Format: Article
Language:English
Published: Frontiers Media S.A. 2019-06-01
Series:Frontiers in Microbiology
Subjects:
Online Access:https://www.frontiersin.org/article/10.3389/fmicb.2019.01391/full
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spelling doaj-881b9e09346a4b4a903366e3b200eb5f2020-11-25T01:50:34ZengFrontiers Media S.A.Frontiers in Microbiology1664-302X2019-06-011010.3389/fmicb.2019.01391458343Prediction of T4SS Effector Proteins for Anaplasma phagocytophilum Using OPT4e, A New Software ToolZhila Esna Ashari0Kelly A. Brayton1Kelly A. Brayton2Kelly A. Brayton3Shira L. Broschat4Shira L. Broschat5Shira L. Broschat6School of Electrical Engineering and Computer Science, Washington State University, Pullman, WA, United StatesSchool of Electrical Engineering and Computer Science, Washington State University, Pullman, WA, United StatesDepartment of Veterinary Microbiology and Pathology, Washington State University, Pullman, WA, United StatesPaul G. Allen School for Global Animal Health, Washington State University, Pullman, WA, United StatesSchool of Electrical Engineering and Computer Science, Washington State University, Pullman, WA, United StatesDepartment of Veterinary Microbiology and Pathology, Washington State University, Pullman, WA, United StatesPaul G. Allen School for Global Animal Health, Washington State University, Pullman, WA, United StatesType 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.https://www.frontiersin.org/article/10.3389/fmicb.2019.01391/fullT4SS effector proteinsmachine learningAnaplasma phagocytophilumprotein predictionOPT4e software
collection DOAJ
language English
format Article
sources DOAJ
author Zhila Esna Ashari
Kelly A. Brayton
Kelly A. Brayton
Kelly A. Brayton
Shira L. Broschat
Shira L. Broschat
Shira L. Broschat
spellingShingle Zhila Esna Ashari
Kelly A. Brayton
Kelly A. Brayton
Kelly A. Brayton
Shira L. Broschat
Shira L. Broschat
Shira L. Broschat
Prediction of T4SS Effector Proteins for Anaplasma phagocytophilum Using OPT4e, A New Software Tool
Frontiers in Microbiology
T4SS effector proteins
machine learning
Anaplasma phagocytophilum
protein prediction
OPT4e software
author_facet Zhila Esna Ashari
Kelly A. Brayton
Kelly A. Brayton
Kelly A. Brayton
Shira L. Broschat
Shira L. Broschat
Shira L. Broschat
author_sort Zhila Esna Ashari
title Prediction of T4SS Effector Proteins for Anaplasma phagocytophilum Using OPT4e, A New Software Tool
title_short Prediction of T4SS Effector Proteins for Anaplasma phagocytophilum Using OPT4e, A New Software Tool
title_full Prediction of T4SS Effector Proteins for Anaplasma phagocytophilum Using OPT4e, A New Software Tool
title_fullStr Prediction of T4SS Effector Proteins for Anaplasma phagocytophilum Using OPT4e, A New Software Tool
title_full_unstemmed Prediction of T4SS Effector Proteins for Anaplasma phagocytophilum Using OPT4e, A New Software Tool
title_sort prediction of t4ss effector proteins for anaplasma phagocytophilum using opt4e, a new software tool
publisher Frontiers Media S.A.
series Frontiers in Microbiology
issn 1664-302X
publishDate 2019-06-01
description 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.
topic T4SS effector proteins
machine learning
Anaplasma phagocytophilum
protein prediction
OPT4e software
url https://www.frontiersin.org/article/10.3389/fmicb.2019.01391/full
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