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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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 |
work_keys_str_mv |
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