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

McDermott, Jason; Cort, John; Nakayasu, Ernesto; Pruneda, Jonathan N.; Overall, Christopher C.; Adkins, Joshua N.

doi:10.7717/peerj.7055

Cited by 7 publications

(12 citation statements)

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“…The E3 ubiquitin ligase is an enzyme with specific recognition of substrate proteins, and is significant part of the ubiquitination pathway ( 16 , 17 ). The E3 ubiquitin ligase is mainly divided into three types: HECT, TING, and u-box domain families ( 18 ). Among them, HERC4 belongs to the HECT family, and its gene is located on chromosome 10q21.3.…”

Section: Introductionmentioning

confidence: 99%

SAV1, regulated by HERC4, inhibits the proliferation, migration, and invasion of hepatocellular carcinoma

Huang¹,

Tang²,

Sun³

et al. 2021

Transl Cancer Res TCR

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Background: Hepatic carcinoma is one of the most malignant cancers worldwide. Salvador 1 (SAV1) plays a key role in a variety of human carcinogenesis. This study investigated the role of SAV1 and HERC4 in hepatocellular carcinoma (HCC).Methods: SAV1 and HERC4 expressions in HCC tissues were examined using RT-qPCR assay. The regulatory effect of HERC4 on SAV1 was verified by co-immunoprecipitation (Co-IP), RT-qPCR, Western blot, and immunofluorescent assays in HEP3B and Huh 7 cell lines. In addition, functional experimental verification was performed through Edu staining, colony formation, and Transwell assay. Finally, Xenograft tumor model was finally used in nude mice.Results: Clinical features showed significant difference with SAV1 and HERC4 expression. HERC4 was found to be upregulated, while SAV1 was downregulated in HCC. Patients with high HERC4 or low SAV1 had a worse prognosis. Results showed that HERC4 could notably decreased the expression level of SAV1 in HCC cells. Our results showed that overexpression HERC4 could reverse the inhibitory effects of SAV1 on HCC cell proliferation, migration, and invasion. SAV1 overexpression repressed tumor growth and enhance caspase 3 expression. Conclusion:SAV1 can be directly downregulated by HERC4, indicating that the HERC4/SAV1 axis might have great promise for targeted therapies of HCC.

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Section: Introductionmentioning

confidence: 99%

SAV1, regulated by HERC4, inhibits the proliferation, migration, and invasion of hepatocellular carcinoma

Huang¹,

Tang²,

Sun³

et al. 2021

Transl Cancer Res TCR

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“…The desired recoding scheme (alphabet) and kmer length (k) are specified by the user. Six recoding alphabets are included ( Table 1 ) ( Arnold et al , 2009 ; Bacardit et al , 2009 ; McDermott et al , 2019 ; Yamada and Tomii, 2014 ), or the user may specify no recoding at all. Snekmer uses the specified alphabet and kmer length to calculate the potential kmer space of all possible kmer combinations.…”

Section: Resultsmentioning

confidence: 99%

“…At least one major annotation resource [Rapid Annotation using Subsystem Technology (RAST)] uses the kmer approach for protein annotation ( Edwards et al , 2012 ; Overbeek et al , 2014 ) and multiple newer tools, such as MMseqs2 ( Mirdita et al , 2019 ; Steinegger and Söding, 2017 ) and DIAMOND ( Buchfink et al , 2021 ) use kmers to increase the speed and sensitivity of searches in various ways. We previously extended the protein kmer approach via amino acid recoding (AAR), which uses chemical similarities between amino acids to simplify the sequence space ( McDermott et al , 2019 ). We found that a simple AAR grouping of amino acids by hydrophobicity or hydrophilicity performed well in classifying disparate families of ubiquitin ligase effector mimics from viral and bacterial pathogens ( McDermott et al , 2019 ).…”

Section: Introductionmentioning

confidence: 99%

“…We previously extended the protein kmer approach via amino acid recoding (AAR), which uses chemical similarities between amino acids to simplify the sequence space ( McDermott et al , 2019 ). We found that a simple AAR grouping of amino acids by hydrophobicity or hydrophilicity performed well in classifying disparate families of ubiquitin ligase effector mimics from viral and bacterial pathogens ( McDermott et al , 2019 ). This work demonstrated that the kmer AAR approach can be used as the basis for machine learning models capable of classifying sets of functionally related proteins with little sequence similarity.…”

Section: Introductionmentioning

confidence: 99%

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Snekmer: a scalable pipeline for protein sequence fingerprinting based on amino acid recoding

Chang

Nelson

Jerger

et al. 2023

Bioinformatics Advances

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Motivation The vast expansion of sequence data generated from single organisms and microbiomes has precipitated the need for faster and more sensitive methods to assess evolutionary and functional relationships between proteins. Representing proteins as sets of short peptide sequences (kmers) has been used for rapid, accurate classification of proteins into functional categories; however, this approach employs an exact-match methodology and thus may be limited in terms of sensitivity and coverage. We have previously used similarity groupings, based on the chemical properties of amino acids, to form reduced character sets and recode proteins. This amino acid recoding (AAR) approach simplifies the construction of protein representations in the form of kmer vectors, which can link sequences with distant sequence similarity and provide accurate classification of problematic protein families. Results Here we describe Snekmer, a software tool for recoding proteins into AAR kmer vectors and performing either (1) construction of supervised classification models trained on input protein families, or (2) clustering for de novo determination of protein families. We provide examples of the operation of the tool against a set of nitrogen cycling families originally collected using both standard hidden Markov models and a larger set of proteins from Uniprot and demonstrate that our method accurately differentiates these sequences in both operation modes. Availability and Implementation Snekmer is written in Python using Snakemake. Code and data used in this paper, along with tutorial notebooks, are available at http://github.com/PNNL-CompBio/Snekmer under an open source BSD-3 license. Supplementary information Supplementary data are available at Bioinformatics Advances online.

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“…AUC is defined as the area bounded by the receiver operating characteristic (ROC) curve and coordinate axis. The closer the AUC is to 1.0, the higher the authenticity of the detection method ( Bin et al, 2020 ; McDermott et al, 2019 ).…”

Section: Methodsmentioning

confidence: 99%

DLFF-ACP: prediction of ACPs based on deep learning and multi-view features fusion

Cao

Wang

Bin

et al. 2021

PeerJ

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An emerging type of therapeutic agent, anticancer peptides (ACPs), has attracted attention because of its lower risk of toxic side effects. However process of identifying ACPs using experimental methods is both time-consuming and laborious. In this study, we developed a new and efficient algorithm that predicts ACPs by fusing multi-view features based on dual-channel deep neural network ensemble model. In the model, one channel used the convolutional neural network CNN to automatically extract the potential spatial features of a sequence. Another channel was used to process and extract more effective features from handcrafted features. Additionally, an effective feature fusion method was explored for the mutual fusion of different features. Finally, we adopted the neural network to predict ACPs based on the fusion features. The performance comparisons across the single and fusion features showed that the fusion of multi-view features could effectively improve the model’s predictive ability. Among these, the fusion of the features extracted by the CNN and composition of k-spaced amino acid group pairs achieved the best performance. To further validate the performance of our model, we compared it with other existing methods using two independent test sets. The results showed that our model’s area under curve was 0.90, which was higher than that of the other existing methods on the first test set and higher than most of the other existing methods on the second test set. The source code and datasets are available at https://github.com/wame-ng/DLFF-ACP.

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

Cited by 7 publications

References 50 publications

SAV1, regulated by HERC4, inhibits the proliferation, migration, and invasion of hepatocellular carcinoma

SAV1, regulated by HERC4, inhibits the proliferation, migration, and invasion of hepatocellular carcinoma

Snekmer: a scalable pipeline for protein sequence fingerprinting based on amino acid recoding

DLFF-ACP: prediction of ACPs based on deep learning and multi-view features fusion

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