Natural language processing approach to model the secretion signal of type III effectors
Type III effectors are proteins injected by Gram-negative bacteria into eukaryotic hosts. In many plant and animal pathogens, these effectors manipulate host cellular processes to the benefit of the bacteria. Type III effectors are secreted by a type III secretion system that must “classify” each ba...
| Main Authors: | , , , , , , , |
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| Format: | Article |
| Language: | English |
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Frontiers Media S.A.
2022-10-01
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| Series: | Frontiers in Plant Science |
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| Online Access: | https://www.frontiersin.org/articles/10.3389/fpls.2022.1024405/full |
| _version_ | 1811246062138032128 |
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| author | Naama Wagner Michael Alburquerque Noa Ecker Edo Dotan Ben Zerah Michelle Mendonca Pena Neha Potnis Tal Pupko |
| author_facet | Naama Wagner Michael Alburquerque Noa Ecker Edo Dotan Ben Zerah Michelle Mendonca Pena Neha Potnis Tal Pupko |
| author_sort | Naama Wagner |
| collection | DOAJ |
| description | Type III effectors are proteins injected by Gram-negative bacteria into eukaryotic hosts. In many plant and animal pathogens, these effectors manipulate host cellular processes to the benefit of the bacteria. Type III effectors are secreted by a type III secretion system that must “classify” each bacterial protein into one of two categories, either the protein should be translocated or not. It was previously shown that type III effectors have a secretion signal within their N-terminus, however, despite numerous efforts, the exact biochemical identity of this secretion signal is generally unknown. Computational characterization of the secretion signal is important for the identification of novel effectors and for better understanding the molecular translocation mechanism. In this work we developed novel machine-learning algorithms for characterizing the secretion signal in both plant and animal pathogens. Specifically, we represented each protein as a vector in high-dimensional space using Facebook’s protein language model. Classification algorithms were next used to separate effectors from non-effector proteins. We subsequently curated a benchmark dataset of hundreds of effectors and thousands of non-effector proteins. We showed that on this curated dataset, our novel approach yielded substantially better classification accuracy compared to previously developed methodologies. We have also tested the hypothesis that plant and animal pathogen effectors are characterized by different secretion signals. Finally, we integrated the novel approach in Effectidor, a web-server for predicting type III effector proteins, leading to a more accurate classification of effectors from non-effectors. |
| first_indexed | 2024-04-12T14:48:14Z |
| format | Article |
| id | doaj.art-c05c8381f1244891aa73972208757a04 |
| institution | Directory Open Access Journal |
| issn | 1664-462X |
| language | English |
| last_indexed | 2024-04-12T14:48:14Z |
| publishDate | 2022-10-01 |
| publisher | Frontiers Media S.A. |
| record_format | Article |
| series | Frontiers in Plant Science |
| spelling | doaj.art-c05c8381f1244891aa73972208757a042022-12-22T03:28:35ZengFrontiers Media S.A.Frontiers in Plant Science1664-462X2022-10-011310.3389/fpls.2022.10244051024405Natural language processing approach to model the secretion signal of type III effectorsNaama Wagner0Michael Alburquerque1Noa Ecker2Edo Dotan3Ben Zerah4Michelle Mendonca Pena5Neha Potnis6Tal Pupko7The Shmunis School of Biomedicine and Cancer Research, George S. Wise Faculty of Life Sciences, Tel Aviv University, Tel Aviv, IsraelThe Shmunis School of Biomedicine and Cancer Research, George S. Wise Faculty of Life Sciences, Tel Aviv University, Tel Aviv, IsraelThe Shmunis School of Biomedicine and Cancer Research, George S. Wise Faculty of Life Sciences, Tel Aviv University, Tel Aviv, IsraelThe Shmunis School of Biomedicine and Cancer Research, George S. Wise Faculty of Life Sciences, Tel Aviv University, Tel Aviv, IsraelThe Shmunis School of Biomedicine and Cancer Research, George S. Wise Faculty of Life Sciences, Tel Aviv University, Tel Aviv, IsraelDepartment of Entomology and Plant Pathology, Auburn University, Auburn, AL, United StatesDepartment of Entomology and Plant Pathology, Auburn University, Auburn, AL, United StatesThe Shmunis School of Biomedicine and Cancer Research, George S. Wise Faculty of Life Sciences, Tel Aviv University, Tel Aviv, IsraelType III effectors are proteins injected by Gram-negative bacteria into eukaryotic hosts. In many plant and animal pathogens, these effectors manipulate host cellular processes to the benefit of the bacteria. Type III effectors are secreted by a type III secretion system that must “classify” each bacterial protein into one of two categories, either the protein should be translocated or not. It was previously shown that type III effectors have a secretion signal within their N-terminus, however, despite numerous efforts, the exact biochemical identity of this secretion signal is generally unknown. Computational characterization of the secretion signal is important for the identification of novel effectors and for better understanding the molecular translocation mechanism. In this work we developed novel machine-learning algorithms for characterizing the secretion signal in both plant and animal pathogens. Specifically, we represented each protein as a vector in high-dimensional space using Facebook’s protein language model. Classification algorithms were next used to separate effectors from non-effector proteins. We subsequently curated a benchmark dataset of hundreds of effectors and thousands of non-effector proteins. We showed that on this curated dataset, our novel approach yielded substantially better classification accuracy compared to previously developed methodologies. We have also tested the hypothesis that plant and animal pathogen effectors are characterized by different secretion signals. Finally, we integrated the novel approach in Effectidor, a web-server for predicting type III effector proteins, leading to a more accurate classification of effectors from non-effectors.https://www.frontiersin.org/articles/10.3389/fpls.2022.1024405/fulltype III secretion systemsecretion signalmachine learningnatural language processingeffectorspathogenomics |
| spellingShingle | Naama Wagner Michael Alburquerque Noa Ecker Edo Dotan Ben Zerah Michelle Mendonca Pena Neha Potnis Tal Pupko Natural language processing approach to model the secretion signal of type III effectors Frontiers in Plant Science type III secretion system secretion signal machine learning natural language processing effectors pathogenomics |
| title | Natural language processing approach to model the secretion signal of type III effectors |
| title_full | Natural language processing approach to model the secretion signal of type III effectors |
| title_fullStr | Natural language processing approach to model the secretion signal of type III effectors |
| title_full_unstemmed | Natural language processing approach to model the secretion signal of type III effectors |
| title_short | Natural language processing approach to model the secretion signal of type III effectors |
| title_sort | natural language processing approach to model the secretion signal of type iii effectors |
| topic | type III secretion system secretion signal machine learning natural language processing effectors pathogenomics |
| url | https://www.frontiersin.org/articles/10.3389/fpls.2022.1024405/full |
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