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:

McDermott, Jason E. ^[1]; Cort, John R. ^[2]; Nakayasu, Ernesto S. ^[2]; Pruneda, Jonathan N. ^[3]; Overall, Christopher ^[4]; Adkins, Joshua N. ^[2]

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+ Show Author Affiliations

1. Biological Sciences Division, Pacific Northwest National Laboratory, Richland, WA, United States of America, Department of Molecular Microbiology and Immunology, Oregon Health & Science University, Portland, OR, United States of America
2. Biological Sciences Division, Pacific Northwest National Laboratory, Richland, WA, United States of America
3. Department of Molecular Microbiology and Immunology, Oregon Health & Science University, Portland, OR, United States of America
4. Center for Brain Immunology and Glia, University of Virginia, Charlottesville, United States of America

Publication Date:

Fri Jun 07 00:00:00 EDT 2019

Research Org.:

Pacific Northwest National Laboratory (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; e7055

Grant/Contract Number:  

AC05-76RLO01830; AC05-76RL01830

Resource Type:

Published Article

Journal Name:

PeerJ

Additional Journal Information:

Journal Name: PeerJ Journal Volume: 7; Journal ID: ISSN 2167-8359

Publisher:

PeerJ

Country of Publication:

United States

Language:

English

Subject:

59 BASIC BIOLOGICAL SCIENCES; 97 MATHEMATICS AND COMPUTING; machine learning; pathogens; effectors; prediction; protein sequence