Predict and analyze S-nitrosylation modification sites with the mRMR and IFS approaches

Li, Biqing; Hu, Le-Le; Niu, Shen; Cai, Yu-Dong; Chou, Kuo‐Chen

doi:10.1016/j.jprot.2011.12.003

Cited by 69 publications

(78 citation statements)

References 70 publications

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“…Our analysis reveals the absence of a consensus sequence motif around the 2SC sites. This finding is consistent with results previously reported on other types of post-translational Cys modification including, for instance, nitrosylation [36,38,39], and oxidation to sulfenic acid [47]. However, significant differences at distinct positions were determined when both PFMs and TMs were compared.…”

Section: Tmsupporting

confidence: 92%

“…From the sets of protein sequences, all fragments having a Cys residue in the central position were extracted. Each sequence fragment represents a peptide of x amino acids upstream and downstream of the Cys residue and length = 1 + 2x amino acids, as described in previous studies on other PTM types [35][36][37][38][39][40]. Each fragment was assigned to a set according to the type of Cys residue: modifiable Cys (MC) or non-modifiable Cys (NMC).…”

Section: Analysis Of 2sc Sitesmentioning

confidence: 99%

“…Computational prediction of these sites could be a convenient and high-throughput strategy to generate helpful information for subsequent experimental verification. Methods for computationally predicting redox-active Cys residues with specific modifications have already been proposed [8,[33][34][35][36][37]. However, to date no method has been developed to predict 2SC sites in proteins, and the succinate proteome has never been studied in order to evaluate the specificity of the protein succination.…”

Section: Introductionmentioning

confidence: 99%

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A computational analysis of S-(2-succino)cysteine sites in proteins

Miglio

Sabatino

Veglia

et al. 2016

Biochimica et Biophysica Acta (BBA) - Proteins and Proteomics

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The adduction of fumaric acid to the sulfhydryl group of certain cysteine (Cys) residues in proteins via a Michaellike reaction leads to the formation of S-(2-succino)cysteine (2SC) sites. Although its role remains to be fully understood, this post-translational Cys modification (protein succination) has been implicated in the pathogenesis of diabetes/obesity and fumarate hydratase-related diseases. In this study, theoretical approaches to address sequence-and 3D-structure-based features possibly underlying the specificity of protein succination have been applied to perform the first analysis of the available data on the succinate proteome. A total of 182 succinated proteins, 205 modifiable, and 1750 non-modifiable sites have been examined. The rate of 2SC sites per protein ranged from 1 to 3, and the overall relative abundance of modifiable sites was 10.8%. Modifiable and nonmodifiable sites were not distinguishable when the hydrophobicity of the Cys-flaking peptides, the acid dissociation constant value of the sulfhydryl groups, and the secondary structure of the Cys-containing segments were compared. By contrast, significant differences were determined when the accessibility of the sulphur atoms and the amino acid composition of the Cys-flaking peptides were analysed. Based on these findings, a sequence-based score function has been evaluated as a descriptor for Cys residues. In conclusion, our results indicate that modifiable and non-modifiable sites form heterogeneous subsets when features often discussed to describe Cys reactivity are examined. However, they also suggest that some differences exist, which may constitute the baseline for further investigations aimed at the development of predictive methods for 2SC sites in proteins.

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Section: Tmsupporting

confidence: 92%

Section: Analysis Of 2sc Sitesmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

A computational analysis of S-(2-succino)cysteine sites in proteins

Miglio

Sabatino

Veglia

et al. 2016

Biochimica et Biophysica Acta (BBA) - Proteins and Proteomics

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“…In past it has been used extensively for annotating different protein features (Kumar and Raghava, 2009;Kumar et al, 2006;Kumar et al, 2014;Li et al, 2012;Mishra et al, 2007;Wang et al, 2011). The following equation was used to compute the amino acid composition of protein sequence.…”

Section: Amino Acid Compositionmentioning

confidence: 99%

Prediction of β-lactamase and its class by Chou’s pseudo-amino acid composition and support vector machine

Kumar

Srivastava

Kumari

et al. 2015

Journal of Theoretical Biology

135

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“…In most cases, machine learning methods with features are proposed to predict protein-protein interaction sites. Popular algorithms are Support Vector Machine (SVM) [4,14,15], Random Forest (RF) [11,[16][17][18], neural network (NN) [19] and so on. Chen et al [14] constructed an integrative profile by developing a support vector machine ensemble.…”

Section: Introductionmentioning

confidence: 99%

A novel feature extraction scheme for prediction of protein–protein interaction sites

Jing

2015

Mol. BioSyst.

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Identifying protein-protein interaction (PPI) sites plays an important and challenging role in some topics of biology. Although many methods have been proposed, this problem is still far away to be solved. Here, a feature selection approach with an 11-sliding window and random forest algorithm is proposed, which is called DX-RF. This method has achieved an accuracy of 88.79%, recall of 82.09%, and precision of 85.76% with top-ranked 34 features on the Hetero test dataset and has 91.6% accuracy, 89.2% precision, 83.54% recall with top-ranked 25 features set on the Homo test dataset. Compared to other methods, the results indicate that the DX-RF method has a strong ability to select relevance features to get a higher performance. Moreover, in order to further understand protein interactions, feature analysis in this study is also performed.

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Predict and analyze S-nitrosylation modification sites with the mRMR and IFS approaches

Cited by 69 publications

References 70 publications

A computational analysis of S-(2-succino)cysteine sites in proteins

A computational analysis of S-(2-succino)cysteine sites in proteins

Prediction of β-lactamase and its class by Chou’s pseudo-amino acid composition and support vector machine

A novel feature extraction scheme for prediction of protein–protein interaction sites

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