TY - JOUR
T1 - Natural language processing approach to model the secretion signal of type III effectors
AU - Wagner, Naama
AU - Alburquerque, Michael
AU - Ecker, Noa
AU - Dotan, Edo
AU - Zerah, Ben
AU - Pena, Michelle Mendonca
AU - Potnis, Neha
AU - Pupko, Tal
N1 - Publisher Copyright: Copyright © 2022 Wagner, Alburquerque, Ecker, Dotan, Zerah, Pena, Potnis and Pupko.
PY - 2022/10/31
Y1 - 2022/10/31
N2 - 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.
AB - 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.
KW - effectors
KW - machine learning
KW - natural language processing
KW - pathogenomics
KW - secretion signal
KW - type III secretion system
UR - http://www.scopus.com/inward/record.url?scp=85141762402&partnerID=8YFLogxK
U2 - https://doi.org/10.3389/fpls.2022.1024405
DO - https://doi.org/10.3389/fpls.2022.1024405
M3 - مقالة
C2 - 36388586
SN - 1664-462X
VL - 13
JO - Frontiers in Plant Science
JF - Frontiers in Plant Science
M1 - 1024405
ER -