Prediction of protein signal sequences and their cleavage sites by statistical rulers

Liu, Hui; Yang, Jie; Ling, Jianguo; Chou, Kuo‐Chen

doi:10.1016/j.bbrc.2005.10.046

Cited by 31 publications

(13 citation statements)

References 49 publications

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“…Alanine and glycine are frequently found at the Ϫ1 position of an SPase cleavage site, whereas the Ϫ3 position is often occupied by alanine, serine, or valine (24). We therefore considered whether the consensus AXA site at Ala 39 or Ala 55 might be recognized by an SPase (Fig.…”

Section: The Signal Peptide C Region and Its Flanking Residues 67-78 mentioning

confidence: 99%

The Nrf3 Transcription Factor Is a Membrane-bound Glycoprotein Targeted to the Endoplasmic Reticulum through Its N-terminal Homology Box 1 Sequence

Zhang

Kobayashi

Yamamoto

et al. 2009

Journal of Biological Chemistry

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Transcription factor Nrf3 (NF-E2 p45-related factor 3) is targeted to the endoplasmic reticulum (ER). Mouse Nrf3 is subject to proteolysis, Asn glycosylation, and deglycosylation reactions. It is synthesized as a ϳ96-kDa protein that is subsequently converted into isoforms of ϳ90, 80, and 70 kDa. In the ER, the ϳ90-kDa glycoprotein is predominant and gives rise to ϳ80-and ϳ70-kDa isoforms. The ϳ90-and ϳ80-kDa polypeptides were observed in the nuclear envelope, whereas the ϳ70-kDa isoform was detected primarily in the nucleoplasm. Our experiments showed the N-terminal homology box 1 (NHB1, residues 12-31) is part of a tripartite signal peptide sequence, comprising n, h, and c regions. The h region (residues 12-23) was demonstrated to target Nrf3 to the ER and is necessary for its Asn glycosylation. The n region (residues 1-11) controlled the abundance of the ϳ90-kDa glycoprotein. The c region (residues 24 -39) was found to contain a signal peptidase cleavage site that is responsible for production of the ϳ90-kDa mature Nrf3 glycoprotein from a ϳ96-kDa precursor. We have found that Nrf3 is activated by the ER stressors tunicamycin and brefeldin A, and that NHB1 is required for this response. Amino acids between the c region and NHB2 (residues 76 -100) controlled the proteolytic processing of mouse Nrf3 into cleavage products of ϳ80-kDa (glycated) and ϳ70-kDa (non-glycated); by contrast, human Nrf3 lacked a signal peptidase cleavage site between its c region and NHB2. Lastly, data are presented suggesting that the NHB2 sequence in mouse Nrf3 may regulate the topology of the transcription factor within the ER membrane.Nuclear factor-erythroid 2 p45-related factor 3 (Nrf3) 2 is a member of the cap "n" collar (CNC) subfamily of basic-region leucine zipper (bZIP) transcription factors (1, 2), which also includes Nrf1, Nrf2, and the NF-E2 p45 subunit. Like other members of the CNC bZIP family, Nrf3 can bind to the antioxidant response element (ARE, with a core consensus sequence 5Ј-TGA(C/G)nnnGC-3Ј) (1-5) and is therefore presumably involved in the regulation of antioxidant and detoxification genes, such as those for the glutamate cysteine ligase catalytic and modifier subunits, glutathione S-transferase (GST) isoenzymes and NAD(P)H:quinone oxidoreductase 1 (NQO1).The physiological functions of Nrf3 remain elusive. It is expressed in the liver and a wide variety of other tissues, but is particularly abundant in the placenta (1, 6, 7). As Nrf3 Ϫ/Ϫ mice have no obvious phenotype (8), it is possible that its loss can be compensated by Nrf1 and/or Nrf2. Interestingly, it has been found that the expression of Gclc, Gclm, and Nqo1 genes is not completely abolished in double Nrf1 Ϫ/Ϫ ::Nrf2 Ϫ/Ϫ knockout mice (9), suggesting that the residual transcriptional activation may be mediated by Nrf3. This hypothesis is supported by the observation that Nrf3 mRNA levels were elevated between 4-and 5-fold in skin from Nrf2 Ϫ/Ϫ mice, when compared with that in wild-type mice (10).We and others have used bioinformatics to gain an insight ...

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Section: The Signal Peptide C Region and Its Flanking Residues 67-78 mentioning

confidence: 99%

The Nrf3 Transcription Factor Is a Membrane-bound Glycoprotein Targeted to the Endoplasmic Reticulum through Its N-terminal Homology Box 1 Sequence

Zhang

Kobayashi

Yamamoto

et al. 2009

Journal of Biological Chemistry

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“…Among these three, however, the jackknife test is deemed as the most rigorous and objective one, as discussed by a comprehensive review [Chou and Zhang, 1995]. Therefore, jackknife test has been used by more and more investigators [Zhou, 1998;Yuan, 1999;Feng, 2001;Hua and Sun, 2001;Zhou and AssaMunt, 2001;Chou, 2001b;Luo et al, 2002;Pan et al, 2003;Zhou and Doctor, 2003;Liu et al, 2005b;Wang et al, 2005;Shen and Chou, 2005a,b;Shen et al, 2005a,b;Xiao et al, 2005a,c] in examining the power of various predictors. During jackknifing, each protein in the dataset is in turn singled out as a tested protein and all the rule-parameters are calculated based on the remaining proteins.…”

Section: Jackknife Testmentioning

confidence: 96%

Predicting protein subcellular location by fusing multiple classifiers

Chou

Shen

2006

J of Cellular Biochemistry

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One of the fundamental goals in cell biology and proteomics is to identify the functions of proteins in the context of compartments that organize them in the cellular environment. Knowledge of subcellular locations of proteins can provide key hints for revealing their functions and understanding how they interact with each other in cellular networking. Unfortunately, it is both time-consuming and expensive to determine the localization of an uncharacterized protein in a living cell purely based on experiments. With the avalanche of newly found protein sequences emerging in the post genomic era, we are facing a critical challenge, that is, how to develop an automated method to fast and reliably identify their subcellular locations so as to be able to timely use them for basic research and drug discovery. In view of this, an ensemble classifier was developed by the approach of fusing many basic individual classifiers through a voting system. Each of these basic classifiers was trained in a different dimension of the amphiphilic pseudo amino acid composition (Chou [2005] Bioinformatics 21: 10-19). As a demonstration, predictions were performed with the fusion classifier for proteins among the following 14 localizations: (1) cell wall, (2) centriole, (3) chloroplast, (4) cytoplasm, (5) cytoskeleton, (6) endoplasmic reticulum, (7) extracellular, (8) Golgi apparatus, (9) lysosome, (10) mitochondria, (11) nucleus, (12) peroxisome, (13) plasma membrane, and (14) vacuole. The overall success rates thus obtained via the resubstitution test, jackknife test, and independent dataset test were all significantly higher than those by the existing classifiers. It is anticipated that the novel ensemble classifier may also become a very useful vehicle in classifying other attributes of proteins according to their sequences, such as membrane protein type, enzyme family/sub-family, G-protein coupled receptor (GPCR) type, and structural class, among many others. The fusion ensemble classifier will be available at www.pami.sjtu.edu.cn/people/hbshen.

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“…However, SPs may also occur in the middle of a protein sequence or it's C-terminal [6]. Many researchers have investigated SP discriminations and cleavage sites identifications in human, plant, animal, eukaryotic, Grampositive and Gram-negative protein sequences [7][8][9][10], some of which are discussed below.…”

Section: Introductionmentioning

confidence: 99%

Signal peptide discrimination and cleavage site identification using SVM and NN

Kazemian

Yusuf

White

2014

Computers in Biology and Medicine

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This article appeared in a journal published by Elsevier. The attached copy is furnished to the author for internal non-commercial research and education use, including for instruction at the authors institution and sharing with colleagues.Other uses, including reproduction and distribution, or selling or licensing copies, or posting to personal, institutional or third party websites are prohibited. a b s t r a c tAbout 15% of all proteins in a genome contain a signal peptide (SP) sequence, at the N-terminus, that targets the protein to intracellular secretory pathways. Once the protein is targeted correctly in the cell, the SP is cleaved, releasing the mature protein. Accurate prediction of the presence of these short aminoacid SP chains is crucial for modelling the topology of membrane proteins, since SP sequences can be confused with transmembrane domains due to similar composition of hydrophobic amino acids. This paper presents a cascaded Support Vector Machine (SVM)-Neural Network (NN) classification methodology for SP discrimination and cleavage site identification. The proposed method utilises a dual phase classification approach using SVM as a primary classifier to discriminate SP sequences from Non-SP. The methodology further employs NNs to predict the most suitable cleavage site candidates. In phase one, a SVM classification utilises hydrophobic propensities as a primary feature vector extraction using symmetric sliding window amino-acid sequence analysis for discrimination of SP and Non-SP. In phase two, a NN classification uses asymmetric sliding window sequence analysis for prediction of cleavage site identification.The proposed SVM-NN method was tested using Uni-Prot non-redundant datasets of eukaryotic and prokaryotic proteins with SP and Non-SP N-termini. Computer simulation results demonstrate an overall accuracy of 0.90 for SP and Non-SP discrimination based on Matthews Correlation Coefficient (MCC) tests using SVM. For SP cleavage site prediction, the overall accuracy is 91.5% based on cross-validation tests using the novel SVM-NN model.

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Prediction of protein signal sequences and their cleavage sites by statistical rulers

Cited by 31 publications

References 49 publications

The Nrf3 Transcription Factor Is a Membrane-bound Glycoprotein Targeted to the Endoplasmic Reticulum through Its N-terminal Homology Box 1 Sequence

The Nrf3 Transcription Factor Is a Membrane-bound Glycoprotein Targeted to the Endoplasmic Reticulum through Its N-terminal Homology Box 1 Sequence

Predicting protein subcellular location by fusing multiple classifiers

Signal peptide discrimination and cleavage site identification using SVM and NN

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