Identifying antioxidant proteins by using optimal dipeptide compositions
Abstract
Antioxidant proteins are a kind of molecules that can terminate cellular and DNA damages caused by free radical intermediates. The use of antioxidant proteins for prevention of diseases has been intensively studied in recent years. Thus, accurate identification of antioxidant proteins is essential for understanding their roles in pharmacology. In this study, a support vector machine-based predictor called AodPred was developed for identifying antioxidant proteins. In this predictor, the sequence was formulated by using the optimal 3-gap dipeptides obtained by using feature selection method. It was observed by jackknife cross-validation test that AodPred can achieve an overall accuracy of 74.79 % in identifying antioxidant proteins. As a user-friendly tool, AodPred is freely accessible at http://lin.uestc.edu.cn/server/AntioxiPred . To maximize the convenience of the vast majority of experimental scientists, a step-by-step guide is provided on how to use the web server to obtain the desired results.
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Most works in bioinformatics use SVM for classification.
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Cites
Cd-hit: Accelerated for clustering the next-generation sequencing data
Fu, L., Niu, B., Zhu, Z., Wu, S., & Li, W. (2012) · Bioinformatics · doi:10.1093/bioinformatics/bts565 · Cited by 11,175 · see citing works
Free radicals, antioxidants and functional foods: impact on human health
Lobo, V, Patil, A., Phatak, A., & Chandra, N. (2010) · Pharmacognosy Reviews · doi:10.4103/0973-7847.70902 · Cited by 5,435 · see citing works
Free Radicals, Antioxidants in Disease and Health
Department of Pharmacy, L. S. P. C. H., Pham-Huy, L. A., He, H., Department of Analytical Chemistry, C. P. U., Pham-Huyc, C., & Laboratory of Toxicology, F. O. P. (2008) · International Journal of Biomedical Science · doi:10.59566/ijbs.2008.4089 · Cited by 3,058 · see citing works
Prediction of protein cellular attributes using pseudo‐amino acid composition
Chou, K. (2001) · Proteins Structure Function and Bioinformatics · doi:10.1002/prot.1035 · Cited by 1,947 · see citing works
Some remarks on protein attribute prediction and pseudo amino acid composition
Chou, K.-C. (2011) · Journal of Theoretical Biology · doi:10.1016/j.jtbi.2010.12.024 · Cited by 1,225 · see citing works
Data mining in bioinformatics using Weka
Frank, E., Hall, M., Trigg, L., Holmes, G., & Witten, I. H. (2004) · Bioinformatics · doi:10.1093/bioinformatics/bth261 · Cited by 885 · see citing works
Role of oxygen free radicals in cancer development
Dreher, D., & Junod, A. F. (1996) · European Journal of Cancer · doi:10.1016/0959-8049(95)00531-5 · Cited by 853 · see citing works
iRSpot-PseDNC: identify recombination spots with pseudo dinucleotide composition
Chen, W., Feng, P., Lin, H., & Chou, K. (2013) · Nucleic Acids Research · doi:10.1093/nar/gks1450 · Cited by 611 · see citing works
Self-citationGuo, S.-H., Deng, E.-Z., Xu, L.-Q., Ding, H., Lin, H., Chen, W., & Chou, K.-C. (2014) · Bioinformatics · doi:10.1093/bioinformatics/btu083 · Cited by 375 · see citing works
Self-citationPlasma antioxidants and longevity: a study on healthy centenarians
Mecocci, P., Polidori, M. C., Troiano, L., Cherubini, A., Cecchetti, R., Pini, G., Straatman, M., Monti, D., Stahl, W., Sies, H., et al. (2000) · Free Radical Biology and Medicine · doi:10.1016/s0891-5849(00)00246-x · Cited by 306 · see citing works
Fenton reaction - controversy concerning the chemistry
Barbusiński, K. (2009) · Ecological Chemistry and Engineering S-chemia I Inzynieria Ekologiczna S · Cited by 303 · see citing works
Feng, P., Chen, W., Lin, H., & Chou, K. (2013) · Analytical Biochemistry · doi:10.1016/j.ab.2013.05.024 · Cited by 294 · see citing works
Self-citationFree radicals and redox signalling in cardiovascular disease
Shah, A. M., & Channon, K. M. (2004) · Heart · doi:10.1136/hrt.2003.029389 · Cited by 192 · see citing works
Naïve Bayes Classifier with Feature Selection to Identify Phage Virion Proteins
Feng, P., Ding, H., Chen, W., et al. (2013) · Computational and Mathematical Methods in Medicine · doi:10.1155/2013/530696 · Cited by 173 · see citing works
Self-citationPredicting ion channels and their types by the dipeptide mode of pseudo amino acid composition
Lin, H., & Ding, H. (2010) · Journal of Theoretical Biology · doi:10.1016/j.jtbi.2010.10.019 · Cited by 151 · see citing works
Self-citationAcalPred: A Sequence-Based Tool for Discriminating between Acidic and Alkaline Enzymes
Lin, H., Chen, W., & Ding, H. (2013) · PLoS ONE · doi:10.1371/journal.pone.0075726 · Cited by 122 · see citing works
Self-citationIdentification of Antioxidants from Sequence Information Using Naïve Bayes
Feng, P.-M., Lin, H., & Chen, W. (2013) · Computational and Mathematical Methods in Medicine · doi:10.1155/2013/567529 · Cited by 109 · see citing works
Self-citationBailey, D. M., Evans, K. A., James, P. E., McEneny, J., Young, I. S., Fall, L., Gutowski, M., Kewley, E. M., McCord, J. M., Møller, K., et al. (2008) · The Journal of Physiology · doi:10.1113/jphysiol.2008.159855 · Cited by 108 · see citing works
Prediction of Golgi-resident protein types by using feature selection technique
Ding, H., Guo, S.-H., Deng, E.-Z., et al. (2013) · Chemometrics and Intelligent Laboratory Systems · doi:10.1016/j.chemolab.2013.03.005 · Cited by 101 · see citing works
Ding, C., Yuan, L., Guo, S.-H., Lin, H., & Chen, W. (2012) · Journal of Proteomics · doi:10.1016/j.jprot.2012.09.006 · Cited by 89 · see citing works
Self-citationCoronary artery disease - free radical damage, antioxidant protection and the role of homocysteine
Maxwell, S. R. J. (2000) · Basic Research in Cardiology · doi:10.1007/s003950070012 · Cited by 87 · see citing works
Random Forest classification based on star graph topological indices for antioxidant proteins
Fernández-Blanco, E., Aguiar‐Pulido, V., Munteanu, C. R., & Dorado, J. (2012) · Journal of Theoretical Biology · doi:10.1016/j.jtbi.2012.10.006 · Cited by 56 · see citing works
Prediction of replication origins by calculating DNA structural properties
Chen, W., Feng, P., & Lin, H. (2012) · FEBS Letters · doi:10.1016/j.febslet.2012.02.034 · Cited by 55 · see citing works
Self-citationExploiting three kinds of interface propensities to identify protein binding sites
Liu, B., Wang, X., Lin, L., et al. (2009) · Computational Biology and Chemistry · doi:10.1016/j.compbiolchem.2009.07.001 · Cited by 27 · see citing works
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Xu, L., Liang, G., Shi, S., & Liao, C. (2018) · International Journal of Molecular Sciences · doi:10.3390/ijms19061773 · Cited by 95 · see citing works
- Authors
- Feng, P., Chen, W., & Lin, H.
- Affiliations
- North China University of Science and Technology (CN)
- University of Electronic Science and Technology of China (CN)
- Corresponding author
- Chen, Wei
- Lin, Hao
- Venue
- Interdisciplinary Sciences: Computational Life Sciences
- Published
- 2015-09-07
- Volume
- 8
- Issue
- 2
- Pages
- 186-191
- ISSN
- 1867-1462 (ISSN-L)
- 1913-2751
- OpenAlex
- W2101565577
- Cited by
- 60
- References
- 24
- Citations by year
- 2026: 1 · 2025: 3 · 2024: 10 · 2023: 5 · 2022: 6 · 2021: 14 · 2020: 9 · 2019: 2 · 2018: 8 · 2017: 1 · 2016: 1
- FWCI
- 1.45
- Type
- journal article
- Language
- en
- Open access
- closed
- Licence
- www.springer.com/tdm (tdm)
- Locations
- Interdisciplinary Sciences Computational Life Sciences (publishedVersion)
- PubMed (publishedVersion)
- Topics
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Machine Learning in Bioinformatics
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Protein Structure and Dynamics
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › vaccines and immunoinformatics approaches
- Keywords
- antioxidant proteins, protein classification, dipeptide composition, support vector machine, web server, feature selection, free radicals, protein sequence features
- MeSH terms
- Algorithms, Antioxidants, Dipeptides, Proteins, Computational Biology, Databases, Protein, Support Vector Machine
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