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BMC Genomics. 2017 Apr 4;18(1):279. doi: 10.1186/s12864-017-3668-8.

Prediction of reversible disulfide based on features from local structural signatures.

Author information

1
School of Life Sciences and the State Key Laboratory of Agrobiotechnology, The Chinese University of Hong Kong, Shatin, New Territories, Hong Kong, SAR, China.
2
Department of Cell Biology and Genetics, School of Basic Medical Sciences, Shenzhen University Health Science Center, Nanhai Ave 3688, Shenzhen, 518060, China.
3
Department of Biology, Hong Kong Baptist University, Kowloon Tong, Kowloon, Hong Kong, SAR, China.
4
Centre of Reproduction, Development and Aging, Faculty of Health Sciences, University of Macau, Taipa, Macau, China.
5
School of Life Sciences and the State Key Laboratory of Agrobiotechnology, The Chinese University of Hong Kong, Shatin, New Territories, Hong Kong, SAR, China. djguo@cuhk.edu.hk.

Abstract

BACKGROUND:

Disulfide bonds are traditionally considered to play only structural roles. In recent years, increasing evidence suggests that the disulfide proteome is made up of structural disulfides and reversible disulfides. Unlike structural disulfides, reversible disulfides are usually of important functional roles and may serve as redox switches. Interestingly, only specific disulfide bonds are reversible while others are not. However, whether reversible disulfides can be predicted based on structural information remains largely unknown.

METHODS:

In this study, two datasets with both types of disulfides were compiled using independent approaches. By comparison of various features extracted from the local structural signatures, we identified several features that differ significantly between reversible and structural disulfides, including disulfide bond length, along with the number, amino acid composition, secondary structure and physical-chemical properties of surrounding amino acids. A SVM-based classifier was developed for predicting reversible disulfides. RESULTS: By 10-fold cross-validation, the model achieved accuracy of 0.750, sensitivity of 0.352, specificity of 0.953, MCC of 0.405 and AUC of 0.751 using the RevSS_PDB dataset. The robustness was further validated by using RevSS_RedoxDB as independent testing dataset. This model was applied to proteins with known structures in the PDB database. The results show that one third of the predicted reversible disulfide containing proteins are well-known redox enzymes, while the remaining are non-enzyme proteins. Given that reversible disulfides are frequently reported from functionally important non-enzyme proteins such as transcription factors, the predictions may provide valuable candidates of novel reversible disulfides for further experimental investigation.

CONCLUSIONS:

This study provides the first comparative analysis between the reversible and the structural disulfides. Distinct features remarkably different between these two groups of disulfides were identified, and a SVM-based classifier for predicting reversible disulfides was developed accordingly. A web server named RevssPred can be accessed freely from: http://biocomputer.bio.cuhk.edu.hk/RevssPred .

KEYWORDS:

Cysteine; Prediction; Redox; Reversible disulfide; RevssPred; SVM; Structural disulfide; Structural signature

PMID:
28376774
PMCID:
PMC5379614
DOI:
10.1186/s12864-017-3668-8
[Indexed for MEDLINE]
Free PMC Article

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