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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 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.

SUBMITTER: McDermott JE

PROVIDER: S-EPMC6557245 | biostudies-literature | 2019

REPOSITORIES: biostudies-literature

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

McDermott Jason E JE Cort John R JR Nakayasu Ernesto S ES Pruneda Jonathan N JN Overall Christopher C Adkins Joshua N JN

PeerJ 20190607

<h4>Background</h4>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.<h4>Methods</h4>To allow identification of novel bac ...[more]

PMID: 31211016

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Project description:BackgroundDegrons are short linear motifs, bound by E3 ubiquitin ligase to target protein substrates to be degraded by the ubiquitin-proteasome system. Mutations leading to deregulation of degron functionality disrupt control of protein abundance due to mistargeting of proteins destined for degradation and often result in pathologies. Targeting degrons by small molecules also emerges as an exciting drug design strategy to upregulate the expression of specific proteins. Despite their essential function and disease targetability, reliable identification of degrons remains a conundrum. Here, we developed a deep learning-based model named Degpred that predicts general degrons directly from protein sequences.ResultsWe showed that the BERT-based model performed well in predicting degrons singly from protein sequences. Then, we used the deep learning model Degpred to predict degrons proteome-widely. Degpred successfully captured typical degron-related sequence properties and predicted degrons beyond those from motif-based methods which use a handful of E3 motifs to match possible degrons. Furthermore, we calculated E3 motifs using predicted degrons on the substrates in our collected E3-substrate interaction dataset and constructed a regulatory network of protein degradation by assigning predicted degrons to specific E3s with calculated motifs. Critically, we experimentally verified that a predicted SPOP binding degron on CBX6 prompts CBX6 degradation and mediates the interaction with SPOP. We also showed that the protein degradation regulatory system is important in tumorigenesis by surveying degron-related mutations in TCGA.ConclusionsDegpred provides an efficient tool to proteome-wide prediction of degrons and binding E3s singly from protein sequences. Degpred successfully captures typical degron-related sequence properties and predicts degrons beyond those from previously used motif-based methods, thus greatly expanding the degron landscape, which should advance the understanding of protein degradation, and allow exploration of uncharacterized alterations of proteins in diseases. To make it easier for readers to access collected and predicted datasets, we integrated these data into the website http://degron.phasep.pro/ .

Project description:The PINK1 (phosphatase and tensin homologue-induced putative kinase 1)/Parkin-dependent mitochondrial quality control pathway mediates the clearance of damaged organelles, but appears to be disrupted in Parkinson's disease (PD) [Springer and Kahle (2011) Autophagy 7, 266-278]. Upon mitochondrial stress, PINK1 activates the E3 ubiquitin (Ub) ligase Parkin through phosphorylation of the Ub-like (UBL) domain of Parkin and of the small modifier Ub itself at a conserved residue [Sauvé and Gehring (2014) Cell Res. 24, 1025-1026]. Recently resolved partial crystal structures of Parkin showed a 'closed', auto-inhibited conformation, consistent with its notoriously weak enzymatic activity at steady state [Wauer and Komander (2013) EMBO J. 32, 2099-2112; Riley et al. (2013) Nat. Commun. 4, 1982; Trempe et al. (2013) Science 340, 1451-1455; Spratt et al. (2013) Nat. Commun. 4, 1983]. It has thus become clear that Parkin must undergo major structural rearrangements in order to unleash its catalytic functions. Recent published findings derived from X-ray structures and molecular modelling present a complete structural model of human Parkin at an all-atom resolution [Caulfield et al. (2014) PLoS Comput. Biol. 10, e1003935]. The results of the combined in silico simulations-based and experimental assay-based study indicates that PINK1-dependent Ser65 phosphorylation of Parkin is required for its activation and triggering of 'opening' conformations. Indeed, the obtained structures showed a sequential release of Parkin's intertwined domains and allowed docking of an Ub-charged E2 coenzyme, which could enable its enzymatic activity. In addition, using cell-based screening, select E2 enzymes that redundantly, cooperatively or antagonistically regulate Parkin's activation and/or enzymatic functions at different stages of the mitochondrial autophagy (mitophagy) process were identified [Fiesel et al. (2014) J. Cell Sci. 127, 3488-3504]. Other work that aims to pin-point the particular pathogenic dysfunctions of Parkin mis-sense mutations have been recently disseminated (Fabienne C. Fiesel, Thomas R. Caulfield, Elisabeth L. Moussaud-Lamodiere, Daniel F.A.R. Dourado, Kotaro Ogaki, Owen A. Ross, Samuel C. Flores, and Wolfdieter Springer, submitted). Such a structure-function approach provides the basis for the dissection of Parkin's regulation and a targeted drug design to identify small-molecule activators of this neuroprotective E3 Ub ligase.

Dataset Information

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

Publications

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

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