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Molecules
Volume 26
Issue 9
10.3390/molecules26092487
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Article

iT4SE-EP: Accurate Identification of Bacterial Type IV Secreted Effectors by Exploring Evolutionary Features from Two PSI-BLAST Profiles

by
Haitao Han
,
Chenchen Ding
,
Xin Cheng
,
Xiuzhi Sang
and
Taigang Liu
*
College of Information Technology, Shanghai Ocean University, Shanghai 201306, China
*
Author to whom correspondence should be addressed.
Academic Editors: Sabina Podlewska and Giosuè Costa
Molecules 2021, 26(9), 2487; https://doi.org/10.3390/molecules26092487
Received: 17 March 2021 / Revised: 16 April 2021 / Accepted: 20 April 2021 / Published: 24 April 2021
(This article belongs to the Special Issue The Machine Learning Applications in the Discovery of New Bioactive Molecules)
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Abstract

Many gram-negative bacteria use type IV secretion systems to deliver effector molecules to a wide range of target cells. These substrate proteins, which are called type IV secreted effectors (T4SE), manipulate host cell processes during infection, often resulting in severe diseases or even death of the host. Therefore, identification of putative T4SEs has become a very active research topic in bioinformatics due to its vital roles in understanding host-pathogen interactions. PSI-BLAST profiles have been experimentally validated to provide important and discriminatory evolutionary information for various protein classification tasks. In the present study, an accurate computational predictor termed iT4SE-EP was developed for identifying T4SEs by extracting evolutionary features from the position-specific scoring matrix and the position-specific frequency matrix profiles. First, four types of encoding strategies were designed to transform protein sequences into fixed-length feature vectors based on the two profiles. Then, the feature selection technique based on the random forest algorithm was utilized to reduce redundant or irrelevant features without much loss of information. Finally, the optimal features were input into a support vector machine classifier to carry out the prediction of T4SEs. Our experimental results demonstrated that iT4SE-EP outperformed most of existing methods based on the independent dataset test. View Full-Text
Keywords: type IV secreted effectors; support vector machine; random forest; position-specific scoring matrix; position-specific frequency matrix type IV secreted effectors; support vector machine; random forest; position-specific scoring matrix; position-specific frequency matrix
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MDPI and ACS Style

Han, H.; Ding, C.; Cheng, X.; Sang, X.; Liu, T. iT4SE-EP: Accurate Identification of Bacterial Type IV Secreted Effectors by Exploring Evolutionary Features from Two PSI-BLAST Profiles. Molecules 2021, 26, 2487. https://doi.org/10.3390/molecules26092487

AMA Style

Han H, Ding C, Cheng X, Sang X, Liu T. iT4SE-EP: Accurate Identification of Bacterial Type IV Secreted Effectors by Exploring Evolutionary Features from Two PSI-BLAST Profiles. Molecules. 2021; 26(9):2487. https://doi.org/10.3390/molecules26092487

Chicago/Turabian Style

Han, Haitao, Chenchen Ding, Xin Cheng, Xiuzhi Sang, and Taigang Liu. 2021. "iT4SE-EP: Accurate Identification of Bacterial Type IV Secreted Effectors by Exploring Evolutionary Features from Two PSI-BLAST Profiles" Molecules 26, no. 9: 2487. https://doi.org/10.3390/molecules26092487

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