research-article

Determining Optimal Features for Predicting Type IV Secretion System Effector Proteins for Coxiella burnetii

Authors:

Zhila Esna Ashari,

Kelly A. Brayton,

Shira L. BroschatAuthors Info & Claims

ACM-BCB '17: Proceedings of the 8th ACM International Conference on Bioinformatics, Computational Biology,and Health Informatics

Pages 346 - 351

https://doi.org/10.1145/3107411.3107416

Published: 20 August 2017 Publication History

Abstract

Type IV secretion systems (T4SS) are constructed from multiple protein complexes that exist in some types of bacterial pathogens and are responsible for delivering type IV effector proteins into host cells. Effectors target eukaryotic cells and try to manipulate host cell processes and the immune system of the host. Some work has been done to validate effectors experimentally, and recently a few scoring and machine learning-based methods have been developed to predict effectors from whole genome sequences. However, different types of features have been suggested to be effective. In this work, we gathered the features proposed in pre-vious reports and calculated their values for a dataset of effectors and non-effectors of Coxiella burnetii. Then we ranked the features based on their importance in classifying effectors and non-effectors to determine the set of optimal features. Finally, a Support Vector Machine model was developed to test the optimal features by comparing them to a set of features proposed in a previous study. The outcome of the comparison supports the effectiveness of our optimal features.

References

[1]

Daniel E. Voth, Laura J. Broederdorf, and Joseph G. Graham. 2012, Bacterial Type IV Secretion Systems: Versatile Virulence Machines. Future Microbiol . 7(2): 241--257.

[2]

Daniel E. Voth, Paul A. Beare, Dale Howe, Uma M. Sharma, Georgios Samoilis, Diane C. Cockrell, Anders Omsland, and Robert A. Heinzen. 2010. The Coxiella burnetii Cryptic Plasmid Is Enriched in Genes Encoding Type IV Secretion System Substrate. Journal of Bacteriology, 193, 1493--1503.

[3]

Damien F. Meyer, Christophe Noroy, Amal Moumene, Sylvain Raffaele Emmanuel Albina and Nathalie Vachiery. 2013. Searching algorithm for type IV secretion system effectors 1.0: a tool for predicting type IV effectors and exploring their genomic context. Nucleic Acids Research, 41, 9218--9229.

[4]

Lingyun Zou, Chonghan Nan, and Fuquan Hu. 2013. Accurate prediction of bacterial type IV secreted effectors using amino acid composition and PSSM profiles. Bioinformatics 29, 3135--3142.

[5]

L. Yu, Y. Guo, Y. Li, G. Li, M. Li, J. Luo, W. Xiong, and W. Qin. 2010. SecretP: identifying bacterial secreted proteins by fusing new features into Chou's pseudo-amino acid composition. Journal of Theoretical Biology, 267, 1--6.

[6]

David Burstein, Tal Zusman, Elena Degtyar, Ram Viner, Gil Segal and Tal Pupko. 2009. Genome-Scale Identification of Legionella pneumophila Effectors Using a Machine Learning Approach. PLoS Pathogens, 5.

[7]

Y. Wang, X. Wei, H. Bao, and S. Liu. 2014. Prediction of bacterial type IV secreted effectors by C-terminal features. BMC Genomics, 15--50.

[8]

Ziv Lifshitz, David Burstein, Kierstyn Schwartz, Howard A. Shuman, Tal Pupko and Gil Segal. 2014. Identification of Novel Coxiella burnetii Icm/Dot Effectors and Genetic Analysis of Their Involvement in Modulating a MitogenActivated Protein Kinase Pathway. Infection and Immunity, 82, 3740--3752.

[9]

Chen Chen, Simran Banga, Katja Mertens, Mary M. Weber, Ivana Gorbaslieva, Yunhao Tan, Zhao-Qing Luo, and James E. Samuel. 2010. Large-scale identification and translocation of type IV secretion substrates by Coxiella burnetii. PNAS, 107, 21755--21760.

[10]

K. Carey, H. Newton, A. Luhrmann, and C. R. Roy. 2011. The Coxiella burnetii Dot/Icm System Delivers a Unique Repertoire of Type IV Effectors into Host Cells and Is Required for Intracellular Replication. PLoS Pathogen,s 7.

[11]

Xiaoxiao Pan, Anja Lührmann, Ayano Satoh, Michelle A. Laskowski-Arce, and Craig R. Roy. 2008. Ankyrin Repeat Proteins Comprise a Diverse Family of Bacterial Type IV Effectors. NIH Public Access 320, 1651--1654.

[12]

Daniel E. Voth, Dale Howe, Paul A. Beare, Joseph P. Vogel, Nathan Unsworth, James E. Samuel, and Robert A. Heinzen. 2009. The Coxiella burnetii Ankyrin Repeat Domain-Containing Protein Family Is Heterogeneous, with C-Terminal Truncations That Influence Dot/Icm-Mediated Secretion. Journal of Bacteriology,191, 4232--4242.

[13]

Svetlana Lockwood, Daniel E. Voth, Paul A. Beare, Wendy C. Brown, Robert A. Heinzen and Shira L. Broschat. 2011. Identification of Anaplasma marginale Type IV Secretion System Effector Proteins. PLoS ONE, 6.

[14]

J. Kyte, RF Doolittle. 1982. A simple method for displaying the hydropathic character of a protein. Journal of Molecular Biology 157, 105--132.

[15]

David Burstein, Francisco Amaro, Tal Zusman, Ziv Lifshitz, Ofir Cohen, Jack A Gilbert, Tal Pupko, Howard A Shuman, and Gil Segal. 2016. Genomic analysis of 38 Legionella species identifies large and diverse effector repertoires. Nature Genetics, 48.

[16]

N.Y. Yu, J.R. Wagner, M.R. Laird, G. Melli, S. Rey, R. Lo, P. Dao, S.C. Sahinalp, M. Ester, L.J. Foster, F.S.L. Brinkman, 2010, PSORTb 3.0: Improved protein subcellular localization prediction with refined localization subcategories and predictive capabilities for all prokaryotes, Bioinformatics, 26(13):1608--1615.

Digital Library

[17]

Reynolds SM, Käll L, Riffle ME, Bilmes JA, Noble WS, 2008, Transmembrane Topology and Signal Peptide Prediction Using Dynamic Bayesian Networks. PLoS Comput Biol, 4(11): e1000213.

[18]

Jannick Dyrløv Bendtsen, Henrik Nielsen, Gunnar von Heijne and Søren Brunak, 2004. Improved prediction of signal peptides: SignalP 3.0. Journal of Molecular Biology, 340:783--795.

[19]

A. Krogh, B. Larsson, G. von Heijne, and E.L.L. Sonnhammer, 2001. Predicting transmembrane protein topology with a hidden Markov model: Application to complete genomes, Journal of Molecular Biology, 305(3), 567--580.

[20]

K Chen, LA Kurgan, 2007. PFRES: Protein Fold Classification by Using Evolutionary Information and Predicted Secondary Structure. Bioinformatics, 23(21), 2843--2850.

Digital Library

[21]

Price CT, Al-Quadan T, Santic M, Jones SC, Abu Kwaik Y, 2010. Exploitation of conserved eukaryotic host cell farnesylation machinery by an F-box effector of Legionella pneumophila. The Journal of Experimental Medicine, 207(8): 1713--26.

Cited By

Chen TWang XChu YWang YJiang MWei DXiong Y(2020)T4SE-XGB: Interpretable Sequence-Based Prediction of Type IV Secreted Effectors Using eXtreme Gradient Boosting AlgorithmFrontiers in Microbiology10.3389/fmicb.2020.58038211Online publication date: 24-Sep-2020
https://doi.org/10.3389/fmicb.2020.580382
Esna Ashari ZBrayton KBroschat S(2019)Using an optimal set of features with a machine learning-based approach to predict effector proteins for Legionella pneumophilaPLOS ONE10.1371/journal.pone.020231214:1(e0202312)Online publication date: 25-Jan-2019
https://doi.org/10.1371/journal.pone.0202312
Ashari ZBroschat S(2019)t-Tree and t-Forest: Decision Tree and Random Forest Algorithms Including the Relevance Factor with Applications in Bioinformatics2019 IEEE International Conference on Bioinformatics and Biomedicine (BIBM)10.1109/BIBM47256.2019.8983065(2779-2783)Online publication date: Nov-2019
https://doi.org/10.1109/BIBM47256.2019.8983065
Show More Cited By

Index Terms

Determining Optimal Features for Predicting Type IV Secretion System Effector Proteins for Coxiella burnetii

Recommendations

An insight into the molecular basis for convergent evolution in fish antifreeze Proteins

Antifreeze proteins (AFPs) prevent the growth of ice-crystals in order to enable certain organisms to survive under sub-zero temperature surroundings. These AFPs have evolved from different types of proteins without having any significant structural and ...
Prediction and molecular insights into fungal adhesins and adhesin like proteins
Graphical abstract

Display Omitted
Highlights
- Enhanced prediction of fungal adhesin proteins using voting ensembles.
- ...
Abstract
Adhesion is the foremost step in pathogenesis and biofilm formation and is facilitated by a special class of cell wall proteins known as adhesins. Formation of biofilms in catheters and other medical devices subsequently leads to ...
Predicting small ligand binding sites in proteins using backbone structure

Motivation: Specific non-covalent binding of metal ions and ligands, such as nucleotides and cofactors, is essential for the function of many proteins. Computational methods are useful for predicting the location of such binding sites when ...

Comments

Please enable JavaScript to view thecomments powered by Disqus.

Information & Contributors

Information

Published In

cover image ACM Conferences

ACM-BCB '17: Proceedings of the 8th ACM International Conference on Bioinformatics, Computational Biology,and Health Informatics

August 2017

800 pages

ISBN:9781450347228

DOI:10.1145/3107411

General Chairs:
Nurit Haspel
University of Massachusetts Boston, USA
,
Lenore J. Cowen
Tufts University, USA
,
Program Chairs:
Amarda Shehu
George Mason University, USA
,
Tamer Kahveci
University of Florida, USA
,
Giuseppe Pozzi
Politecnico di Milano, Italy

Copyright © 2017 ACM.

Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

Sponsors

SIGBio: ACM Special Interest Group on Bioinformatics

Publisher

Association for Computing Machinery

New York, NY, United States

Publication History

Published: 20 August 2017

Permissions

Request permissions for this article.

Request Permissions

Check for updates

Author Tags

Qualifiers

Research-article

Funding Sources

This research was supported by National Institutes of Health grant R01AI042792 and by the Carl M. Hansen Foundation.

Conference

BCB '17

Sponsor:

SIGBio

BCB '17: 8th ACM International Conference on Bioinformatics, Computational Biology, and Health Informatics

August 20 - 23, 2017

Massachusetts, Boston, USA

Acceptance Rates

ACM-BCB '17 Paper Acceptance Rate 42 of 132 submissions, 32%;

Overall Acceptance Rate 254 of 885 submissions, 29%

Contributors

Other Metrics

View Article Metrics

Bibliometrics & Citations

Bibliometrics

Article Metrics

4
Total Citations
View Citations
164
Total Downloads

Downloads (Last 12 months)5
Downloads (Last 6 weeks)0

Reflects downloads up to 19 Nov 2024

Other Metrics

View Author Metrics

Citations

Cited By

Chen TWang XChu YWang YJiang MWei DXiong Y(2020)T4SE-XGB: Interpretable Sequence-Based Prediction of Type IV Secreted Effectors Using eXtreme Gradient Boosting AlgorithmFrontiers in Microbiology10.3389/fmicb.2020.58038211Online publication date: 24-Sep-2020
https://doi.org/10.3389/fmicb.2020.580382
Esna Ashari ZBrayton KBroschat S(2019)Using an optimal set of features with a machine learning-based approach to predict effector proteins for Legionella pneumophilaPLOS ONE10.1371/journal.pone.020231214:1(e0202312)Online publication date: 25-Jan-2019
https://doi.org/10.1371/journal.pone.0202312
Ashari ZBroschat S(2019)t-Tree and t-Forest: Decision Tree and Random Forest Algorithms Including the Relevance Factor with Applications in Bioinformatics2019 IEEE International Conference on Bioinformatics and Biomedicine (BIBM)10.1109/BIBM47256.2019.8983065(2779-2783)Online publication date: Nov-2019
https://doi.org/10.1109/BIBM47256.2019.8983065
Esna Ashari ZDasgupta NBrayton KBroschat S(2018)An optimal set of features for predicting type IV secretion system effector proteins for a subset of species based on a multi-level feature selection approachPLOS ONE10.1371/journal.pone.019704113:5(e0197041)Online publication date: 9-May-2018
https://doi.org/10.1371/journal.pone.0197041

View Options

Login options

Check if you have access through your login credentials or your institution to get full access on this article.

Full Access

Get this Publication

View options

PDF

View or Download as a PDF file.

eReader

View online with eReader.

Media

Figures

Other

Tables

View Table of Contents