High performance system for signal peptide prediction: SOSUIsignal

Gomi, Masahiro; Sonoyama, Masashi; Mitaku, Shigeki

doi:10.1273/cbij.4.142

Cited by 89 publications

(88 citation statements)

References 10 publications

(10 reference statements)

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“…We used the SOSUIsignal program (Gomi et al 2004; http://www.bp.nuap.nagoya-u.ac.jp/sosui/sosuisignal/ sosuisignal submit.html) and NetNGlyc 1.0 server (http:// www.cbs.dtu.dk/services/NetNGlyc/) to predict N-terminal signal peptides, transmembrane regions, and potential Nglycosylation sites. To predict secondary structures, we used the New Joint method (Ito et al 1997; http://www.cbrc.jp/papia-cgi/ssp query.pl?query=seq).…”

Section: Protein Structure Predictionmentioning

confidence: 99%

“…Importantly, we found no amino acids unique to lungfish or sturgeon GHRs at these functional sites, despite their unique hormone receptor set. The SOSUIsignal program (Gomi et al 2004) classified all the receptors as proteins with N-terminal signal peptides and single membrane-spanning domains (with the exceptions of seabream GHRs of black seabream and gilthead seabream and carp SLR; Fig. 6a).…”

Section: Lineage-specific Features Of the Vertebrate Ghr/slrs: From Tmentioning

confidence: 99%

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Evolution of Receptors for Growth Hormone and Somatolactin in Fish and Land Vertebrates: Lessons from the Lungfish and Sturgeon Orthologues

Fukamachi

Meyer

2007

J Mol Evol

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Two cognate hormones, growth hormone (GH) and somatolactin (SL), control several important physiological processes in vertebrates. Knowledge about GH and its receptor (GHR) has accumulated over the last decades. However, much less is known about SL and its receptor (SLR). SL is found only in fish (including lungfish), suggesting that it was present in the common ancestor of vertebrates, but was lost secondarily in the lineage leading to land vertebrates after the lungfish branched off. SLR was suggested to be a duplicated copy of GHR acquired only in teleosts via the fish-specific genome duplication (FSGD). This scenario (i.e., the existence of SL but not SLR in the vertebrate ancestors) is intriguing but contested. In this study, we first evaluated the plausibility of this scenario through synteny analyses and found that the loci for GHR and SLR are located in syntenic genomic positions, whereas the loci for GH and SL are not. Next, we cloned GHRs of lungfish and sturgeon, which possess SL but did not undergo the FSGD (i.e., they should not possess SLR). Their phylogenetic positions in the GHR/SLR gene tree further support the fish-specific scenario for the GHR SLR duplication. Interestingly, their sequences share greater similarity with teleost SLRs and reptilian/amphibian GHRs than with the GHRs of mammals, birds, and teleosts. On the basis of these results, we discuss the validity of the nomenclature of the teleost-specific copy of GHR as SLR and an ancestral receptor(s) for SL before the evolution of SLR during the FSGD.

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Section: Protein Structure Predictionmentioning

confidence: 99%

Section: Lineage-specific Features Of the Vertebrate Ghr/slrs: From Tmentioning

confidence: 99%

Evolution of Receptors for Growth Hormone and Somatolactin in Fish and Land Vertebrates: Lessons from the Lungfish and Sturgeon Orthologues

Fukamachi

Meyer

2007

J Mol Evol

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“…Nonclassical secreted proteins should obtain a neural network score (NN-score) exceeding the normal threshold of 0.6, but at the same time be predicted not to contain a signal peptide. We selected 318 hypothetical protein sequences with NN-scores greater than or equal to 0.9 but without a signal peptide, and submitted them to the SOSUI software (http://sosui.proteome.bio.tuat.ac.jp/ sosuiframe0.html) to delete membrane proteins [13]. Then, 249 protein sequences were investigated by Proteome Analyst's subcellular localization server (http://pasub.cs.ualberta.ca:8080/pa/Subcellular) to get proteins with an extracellular probability of more than 30%.…”

Section: Methodsmentioning

confidence: 99%

A novel recently evolved gene C19orf24 encodes a non-classical secreted protein

Wang¹,

Zhou

Liu

et al. 2006

Cellular and Molecular Biology Letters

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“…In relation to the true signal peptidase I cleavage site position, this type of postprocessing may lead to cleavage site shifted downstream [13]. Gomi et al [14] used clustering based signal sequences and signal peptide scoring indices to discriminate signal peptides from globular proteins and predict their cleavage scores.…”

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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High performance system for signal peptide prediction: SOSUIsignal

Cited by 89 publications

References 10 publications

Evolution of Receptors for Growth Hormone and Somatolactin in Fish and Land Vertebrates: Lessons from the Lungfish and Sturgeon Orthologues

Evolution of Receptors for Growth Hormone and Somatolactin in Fish and Land Vertebrates: Lessons from the Lungfish and Sturgeon Orthologues

A novel recently evolved gene C19orf24 encodes a non-classical secreted protein

Signal peptide discrimination and cleavage site identification using SVM and NN

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