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Title: Prediction of bacterial E3 ubiquitin ligase effectors using reduced amino acid peptide fingerprinting

Abstract

Background Although pathogenic Gram-negative bacteria lack their own ubiquitination machinery, they have evolved or acquired virulence effectors that can manipulate the host ubiquitination process through structural and/or functional mimicry of host machinery. Many such effectors have been identified in a wide variety of bacterial pathogens that share little sequence similarity amongst themselves or with eukaryotic ubiquitin E3 ligases. Methods To allow identification of novel bacterial E3 ubiquitin ligase effectors from protein sequences we have developed a machine learning approach, the SVM-based Identification and Evaluation of Virulence Effector Ubiquitin ligases (SIEVE-Ub). We extend the string kernel approach used previously to sequence classification by introducing reduced amino acid (RED) alphabet encoding for protein sequences. Results We found that 14mer peptides with amino acids represented as simply either hydrophobic or hydrophilic provided the best models for discrimination of E3 ligases from other effector proteins with a receiver-operator characteristic area under the curve (AUC) of 0.90. When considering a subset of E3 ubiquitin ligase effectors that do not fall into known sequence based families we found that the AUC was 0.82, demonstrating the effectiveness of our method at identifying novel functional family members. Feature selection was used to identify a parsimonious set of 10more » RED peptides that provided good discrimination, and these peptides were found to be located in functionally important regions of the proteins involved in E2 and host target protein binding. Our general approach enables construction of models based on other effector functions. We used SIEVE-Ub to predict nine potential novel E3 ligases from a large set of bacterial genomes. SIEVE-Ub is available for download at https://doi.org/10.6084/m9.figshare.7766984.v1 or https://github.com/biodataganache/SIEVE-Ub for the most current version.« less

Authors:
ORCiD logo [1]; ORCiD logo [2]; ORCiD logo [2];  [3];  [4]; ORCiD logo [2]
  1. Pacific Northwest National Lab. (PNNL), Richland, WA (United States); Oregon Health & Science Univ., Portland, OR (United States)
  2. Pacific Northwest National Lab. (PNNL), Richland, WA (United States)
  3. Oregon Health & Science Univ., Portland, OR (United States)
  4. Univ. of Virginia, Charlottesville, VA (United States)
Publication Date:
Research Org.:
Pacific Northwest National Lab. (PNNL), Richland, WA (United States)
Sponsoring Org.:
USDOE
OSTI Identifier:
1525490
Alternate Identifier(s):
OSTI ID: 1544792
Report Number(s):
PNNL-SA-138492
Journal ID: ISSN 2167-8359
Grant/Contract Number:  
AC05-76RL01830; AC05-76RLO01830
Resource Type:
Journal Article: Published Article
Journal Name:
PeerJ
Additional Journal Information:
Journal Volume: 7; Journal ID: ISSN 2167-8359
Publisher:
PeerJ Inc.
Country of Publication:
United States
Language:
English
Subject:
59 BASIC BIOLOGICAL SCIENCES; 97 MATHEMATICS AND COMPUTING; machine learning; pathogens; effectors; prediction; protein sequence

Citation Formats

McDermott, Jason E., Cort, John R., Nakayasu, Ernesto S., Pruneda, Jonathan N., Overall, Christopher, and Adkins, Joshua N. Prediction of bacterial E3 ubiquitin ligase effectors using reduced amino acid peptide fingerprinting. United States: N. p., 2019. Web. doi:10.7717/peerj.7055.
McDermott, Jason E., Cort, John R., Nakayasu, Ernesto S., Pruneda, Jonathan N., Overall, Christopher, & Adkins, Joshua N. Prediction of bacterial E3 ubiquitin ligase effectors using reduced amino acid peptide fingerprinting. United States. doi:10.7717/peerj.7055.
McDermott, Jason E., Cort, John R., Nakayasu, Ernesto S., Pruneda, Jonathan N., Overall, Christopher, and Adkins, Joshua N. Fri . "Prediction of bacterial E3 ubiquitin ligase effectors using reduced amino acid peptide fingerprinting". United States. doi:10.7717/peerj.7055.
@article{osti_1525490,
title = {Prediction of bacterial E3 ubiquitin ligase effectors using reduced amino acid peptide fingerprinting},
author = {McDermott, Jason E. and Cort, John R. and Nakayasu, Ernesto S. and Pruneda, Jonathan N. and Overall, Christopher and Adkins, Joshua N.},
abstractNote = {Background Although pathogenic Gram-negative bacteria lack their own ubiquitination machinery, they have evolved or acquired virulence effectors that can manipulate the host ubiquitination process through structural and/or functional mimicry of host machinery. Many such effectors have been identified in a wide variety of bacterial pathogens that share little sequence similarity amongst themselves or with eukaryotic ubiquitin E3 ligases. Methods To allow identification of novel bacterial E3 ubiquitin ligase effectors from protein sequences we have developed a machine learning approach, the SVM-based Identification and Evaluation of Virulence Effector Ubiquitin ligases (SIEVE-Ub). We extend the string kernel approach used previously to sequence classification by introducing reduced amino acid (RED) alphabet encoding for protein sequences. Results We found that 14mer peptides with amino acids represented as simply either hydrophobic or hydrophilic provided the best models for discrimination of E3 ligases from other effector proteins with a receiver-operator characteristic area under the curve (AUC) of 0.90. When considering a subset of E3 ubiquitin ligase effectors that do not fall into known sequence based families we found that the AUC was 0.82, demonstrating the effectiveness of our method at identifying novel functional family members. Feature selection was used to identify a parsimonious set of 10 RED peptides that provided good discrimination, and these peptides were found to be located in functionally important regions of the proteins involved in E2 and host target protein binding. Our general approach enables construction of models based on other effector functions. We used SIEVE-Ub to predict nine potential novel E3 ligases from a large set of bacterial genomes. SIEVE-Ub is available for download at https://doi.org/10.6084/m9.figshare.7766984.v1 or https://github.com/biodataganache/SIEVE-Ub for the most current version.},
doi = {10.7717/peerj.7055},
journal = {PeerJ},
issn = {2167-8359},
number = ,
volume = 7,
place = {United States},
year = {2019},
month = {6}
}

Journal Article:
Free Publicly Available Full Text
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