Literature DB >> 32043137

EP3: an ensemble predictor that accurately identifies type III secreted effectors.

Jing Li, Leyi Wei, Fei Guo, Quan Zou.   

Abstract

Type III secretion systems (T3SS) can be found in many pathogenic bacteria, such as Dysentery bacillus, Salmonella typhimurium, Vibrio cholera and pathogenic Escherichia coli. The routes of infection of these bacteria include the T3SS transferring a large number of type III secreted effectors (T3SE) into host cells, thereby blocking or adjusting the communication channels of the host cells. Therefore, the accurate identification of T3SEs is the precondition for the further study of pathogenic bacteria. In this article, a new T3SEs ensemble predictor was developed, which can accurately distinguish T3SEs from any unknown protein. In the course of the experiment, methods and models are strictly trained and tested. Compared with other methods, EP3 demonstrates better performance, including the absence of overfitting, strong robustness and powerful predictive ability. EP3 (an ensemble predictor that accurately identifies T3SEs) is designed to simplify the user's (especially nonprofessional users) access to T3SEs for further investigation, which will have a significant impact on understanding the progression of pathogenic bacterial infections. Based on the integrated model that we proposed, a web server had been established to distinguish T3SEs from non-T3SEs, where have EP3_1 and EP3_2. The users can choose the model according to the species of the samples to be tested. Our related tools and data can be accessed through the link http://lab.malab.cn/∼lijing/EP3.html.
© The Author(s) 2020. Published by Oxford University Press. All rights reserved. For Permissions, please email: journals.permissions@oup.com.

Entities:  

Keywords:  Smith–Waterman algorithm; label propagation; type III secreted effectors

Year:  2021        PMID: 32043137     DOI: 10.1093/bib/bbaa008

Source DB:  PubMed          Journal:  Brief Bioinform        ISSN: 1467-5463            Impact factor:   11.622


  5 in total

1.  DeepT3_4: A Hybrid Deep Neural Network Model for the Distinction Between Bacterial Type III and IV Secreted Effectors.

Authors:  Lezheng Yu; Fengjuan Liu; Yizhou Li; Jiesi Luo; Runyu Jing
Journal:  Front Microbiol       Date:  2021-01-21       Impact factor: 5.640

2.  iT3SE-PX: Identification of Bacterial Type III Secreted Effectors Using PSSM Profiles and XGBoost Feature Selection.

Authors:  Chenchen Ding; Haitao Han; Qianyue Li; Xiaoxia Yang; Taigang Liu
Journal:  Comput Math Methods Med       Date:  2021-01-06       Impact factor: 2.238

3.  Comparative genome analysis of plant ascomycete fungal pathogens with different lifestyles reveals distinctive virulence strategies.

Authors:  Yansu Wang; Jie Wu; Jiacheng Yan; Ming Guo; Lei Xu; Liping Hou; Quan Zou
Journal:  BMC Genomics       Date:  2022-01-07       Impact factor: 3.969

4.  iAIPs: Identifying Anti-Inflammatory Peptides Using Random Forest.

Authors:  Dongxu Zhao; Zhixia Teng; Yanjuan Li; Dong Chen
Journal:  Front Genet       Date:  2021-11-30       Impact factor: 4.599

5.  i4mC-EL: Identifying DNA N4-Methylcytosine Sites in the Mouse Genome Using Ensemble Learning.

Authors:  Yanjuan Li; Zhengnan Zhao; Zhixia Teng
Journal:  Biomed Res Int       Date:  2021-05-29       Impact factor: 3.411

  5 in total

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