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How the artificial intelligence tool iSNO-PseAAC is working in predicting the cysteine S-nitrosylation sites in proteins

Kuo-Chen Chou

Gordon Life Science Institute, Boston, Massachusetts 02478, United States of America

E-mail : bhuvaneswari.bibleraaj@uhsm.nhs.uk

DOI: 10.15761/JSCRM.1000137

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In 2013 a very powerful AI (artificial intelligence) tool has been established for identifying cysteine S-nitrosylation sites in proteins, which is one of the important post modifications in proteins [1].

To see how the web-server is working, please do the following.

Step 1. Open the web server at http://app.aporc.org/iSNO-PseAAC/ and you will see the top page of the predictor on your computer screen, as show in Figure 1. Click on the Read Me button to see a brief introduction about iSNO-PseAAC predictor and the caveat when using it.

Figure 1. A semi-screenshot for the top-page of the iSNO-PseAAC web-server at http://app.aporc.org/iSNO-PseAAC (Adapted from [1]with permission)

Step 2. Either type or copy/paste the query protein sequences into the input box shown at the center of Figure 1. The input sequence should be in the FASTA format. A sequence in FASTA format consists of a single initial line beginning with a greater-than symbol (“>”) in the first column, followed by lines of sequence data. The words right after the “>” symbol in the single initial line are optional and only used for the purpose of identification and description. All lines should be no longer than 120 characters and usually do not exceed 80 characters. The sequence ends if another line starting with a “>” appears; this indicates the start of another sequence. Example sequences in FASTA format can be seen by clicking on the Example button right above the input box.

Step 3. Click on the Submit button to see the predicted result. For example, if you use the query protein sequences in the Example window as the input, after clicking the Submit button, you will see on your screen the predicted SNO site positions and the corresponding sequences segments as formulated by Equation 1. All these results are fully consistent with the experimentally verified results. It takes about a few seconds for the above computation before the predicted results appear on the computer screen; the more number of query proteins and longer of each sequence, the more time it is usually needed.

Step 4. Click on the Citation button to find the relevant papers that document the detailed development and algorithm of iSNO-PseAAC.

Step 5. Click on the Data button to download the benchmark datasets used to train and test the iSNO-PseAAC predictor.

It is instructive to point out that the web-server predictor has been developed by strictly observing the guidelines of “Chou’s 5-steps rule” and hence have the following notable merits (see, e.g., [2-29] and three comprehensive review papers [30-32]): (1) crystal clear in logic development, (2) completely transparent in operation, (3) easily to repeat the reported results by other investigators, (4) with high potential in stimulating other sequence-analyzing methods, and (5) very convenient to be used by the majority of experimental scientists.  

Moreover, it has not escaped our notice that during the development of iSNO-PseAAC web-server, the approach of general pseudo amino acid components [33] or PseAAC [34] had been utilized and hence its accuracy would be much higher than its counterparts, as concurred by many investigators [1-6,8-11,13,18,26,30,32-300].

It is anticipated that iSNO-PseAAC may become a very useful high throughput tool for conducting proteome analysis as well as drug development.

For the remarkable and awesome roles of the “5-steps rule” in driving proteome, genome analyses and drug development, see a series of recent papers [31,32,291,301-309] where the rule and its wide applications have been very impressively presented from various aspects or at different angles.

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  120. H. Lin, H. Ding, Predicting ion channels and their types by the dipeptide mode of pseudo amino acid composition. Journal of Theoretical Biology 269 (2011) 64-69.
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  130. M. Shu, X. Cheng, Y. Zhang, Y. Wang, Y. Lin, L. Wang, Z. Lin, Predicting the Activity of ACE Inhibitory Peptides with a Novel Mode of Pseudo Amino Acid Composition. Protein & Peptide Letters 18 (2011) 1233-1243.
  131. D. Wang, L. Yang, Z. Fu, J. Xia, Prediction of thermophilic protein with pseudo amino Acid composition: an approach from combined feature selection and reduction. Protein & Peptide Letters 18 (2011) 684-689.
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  139. Y.L. Chen, Q.Z. Li, L.Q. Zhang, Using increment of diversity to predict mitochondrial proteins of malaria parasite: integrating pseudo amino acid composition and structural alphabet. Amino Acids 42 (2012) 1309-16.
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  145. L.Q. Li, Y. Zhang, L.Y. Zou, Y. Zhou, X.Q. Zheng, Prediction of Protein Subcellular Multi-Localization Based on the General form of Chou's Pseudo Amino Acid Composition. Protein & Peptide Letters 19 (2012) 375-387.
  146. B. Liao, Q. Xiang, D. Li, Incorporating Secondary Features into the General form of Chou's PseAAC for Predicting Protein Structural Class. Protein & Peptide Letters 19 (2012) 1133-1138.
  147. W.Z. Lin, J.A. Fang, X. Xiao, K.C. Chou, Predicting Secretory Proteins of Malaria Parasite by Incorporating Sequence Evolution Information into Pseudo Amino Acid Composition via Grey System Model. PLoS One 7 (2012) e49040.
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  154. Y.F. Qin, C.H. Wang, X.Q. Yu, J. Zhu, T.G. Liu, X.Q. Zheng, Predicting Protein Structural Class by Incorporating Patterns of Over- Represented k-mers into the General form of Chou's PseAAC. Protein & Peptide Letters 19 (2012) 388-397.
  155. L.Y. Ren, Y.S. Zhang, I. Gutman, Predicting the Classification of Transcription Factors by Incorporating their Binding Site Properties into a Novel Mode of Chou's Pseudo Amino Acid Composition. Protein & Peptide Letters 19 (2012) 1170-1176.
  156. X.Y. Sun, S.P. Shi, J.D. Qiu, S.B. Suo, S.Y. Huang, R.P. Liang, Identifying protein quaternary structural attributes by incorporating physicochemical properties into the general form of Chou's PseAAC via discrete wavelet transform. Molecular BioSystems 8 (2012) 3178-3184.
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  159. X.W. Zhao, Z.Q. Ma, M.H. Yin, Predicting protein-protein interactions by combing various sequence- derived features into the general form of Chou's Pseudo amino acid composition. Protein & Peptide Letters 19 (2012) 492-500.
  160. Zia-ur-Rehman, A. Khan, Identifying GPCRs and their Types with Chou's Pseudo Amino Acid Composition: An Approach from Multi-scale Energy Representation and Position Specific Scoring Matrix. Protein & Peptide Letters 19 (2012) 890-903.
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  162. T.H. Chang, L.C. Wu, T.Y. Lee, S.P. Chen, H.D. Huang, J.T. Horng, EuLoc: a web-server for accurately predict protein subcellular localization in eukaryotes by incorporating various features of sequence segments into the general form of Chou's PseAAC. Journal of Computer-Aided Molecular Design 27 (2013) 91-103.
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  165. G.L. Fan, Q.Z. Li, Discriminating bioluminescent proteins by incorporating average chemical shift and evolutionary information into the general form of Chou's pseudo amino acid composition. Journal of Theoretical Biology 334 (2013) 45-51.
  166. D.N. Georgiou, T.E. Karakasidis, A.C. Megaritis, A short survey on genetic sequences, Chou's pseudo amino acid composition and its combination with fuzzy set theory. The Open Bioinformatics Journal 7 (2013) 41-48.
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  170. C. Huang, J.Q. Yuan, Predicting protein subchloroplast locations with both single and multiple sites via three different modes of Chou's pseudo amino acid compositions. Journal of Theoretical Biology 335 (2013) 205-12.
  171. M. Khosravian, F.K. Faramarzi, M.M. Beigi, M. Behbahani, H. Mohabatkar, Predicting Antibacterial Peptides by the Concept of Chou's Pseudo amino Acid Composition and Machine Learning Methods. Protein & Peptide Letters 20 (2013) 180-186.
  172. H. Lin, C. Ding, L.-F. Yuan, W. Chen, H. Ding, Z.-Q. Li, F.-B. Guo, J. Huang, N.-N. Rao, Predicting subchloroplast locations of proteins based on the general form of Chou's pseudo amino acid  composition: Approached from optimal tripeptide composition. International Journal of Biomethmatics 6 (2013) 1350003.
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  174. B. Liu, X. Wang, Q. Zou, Q. Dong, Q. Chen, Protein remote homology detection by combining Chou's pseudo amino acid composition and profile-based protein representation. Molecular Informatics 32 (2013) 775-782.
  175. H. Mohabatkar, M.M. Beigi, K. Abdolahi, S. Mohsenzadeh, Prediction of Allergenic Proteins by Means of the Concept of Chou's Pseudo Amino Acid Composition and a Machine Learning Approach. Medicinal Chemistry 9 (2013) 133-137.
  176. E. Pacharawongsakda, T. Theeramunkong, Predict Subcellular Locations of Singleplex and Multiplex Proteins by Semi-Supervised Learning and Dimension-Reducing General Mode of Chou's PseAAC. IEEE Transactions on Nanobioscience 12 (2013) 311-320.
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  178. A.N. Sarangi, M. Lohani, R. Aggarwal, Prediction of Essential Proteins in Prokaryotes by Incorporating Various Physico-chemical Features into the General form of Chou's Pseudo Amino Acid Composition. Protein Pept Lett 20 (2013) 781-95.
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  180. X. Wang, G.Z. Li, W.C. Lu, Virus-ECC-mPLoc: a multi-label predictor for predicting the subcellular localization of virus proteins with both single and multiple sites based on a general form of Chou's pseudo amino acid composition. Protein & Peptide Letters 20 (2013) 309-317.
  181. X. Xiao, J.L. Min, P. Wang, K.C. Chou, iCDI-PseFpt: Identify the channel-drug interaction in cellular networking with PseAAC and molecular fingerprints. Journal of Theoretical Biology 337C (2013) 71-79.
  182. N. Xiaohui, L. Nana, X. Jingbo, C. Dingyan, P. Yuehua, X. Yang, W. Weiquan, W. Dongming, W. Zengzhen, Using the concept of Chou's pseudo amino acid composition to predict protein solubility: An approach with entropies in information theory. Journal of Theoretical Biology 332 (2013) 211-217.
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  184. Y. Xu, X.J. Shao, L.Y. Wu, N.Y. Deng, K.C. Chou, iSNO-AAPair: incorporating amino acid pairwise coupling into PseAAC for predicting cysteine S-nitrosylation sites in proteins. PeerJ 1 (2013) e171.
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  186. Z. Hajisharifi, M. Piryaiee, M. Mohammad Beigi, M. Behbahani, H. Mohabatkar, Predicting anticancer peptides with Chou's pseudo amino acid composition and investigating their mutagenicity via Ames test. Journal of Theoretical Biology 341 (2014) 34-40.
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  191. L. Li, S. Yu, W. Xiao, Y. Li, M. Li, L. Huang, X. Zheng, S. Zhou, H. Yang, Prediction of bacterial protein subcellular localization by incorporating various features into Chou's PseAAC and a backward feature selection approach. Biochimie 104 (2014) 100-7.
  192. B. Liu, J. Xu, X. Lan, R. Xu, J. Zhou, X. Wang, K.C. Chou, iDNA-Prot|dis: identifying DNA-binding proteins by incorporating amino acid distance-pairs and reduced alphabet profile into the general pseudo amino acid composition. PLoS ONE 9 (2014) e106691.
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  195. W.R. Qiu, X. Xiao, K.C. Chou, iRSpot-TNCPseAAC: Identify recombination spots with trinucleotide composition and pseudo amino acid components. Int J Mol Sci  (IJMS) 15 (2014) 1746-1766.
  196. W.R. Qiu, X. Xiao, W.Z. Lin, K.C. Chou, iMethyl-PseAAC: Identification of Protein Methylation Sites via a Pseudo Amino Acid Composition Approach. Biomed Res Int (BMRI) 2014 (2014) 947416.
  197. Y. Xu, X. Wen, X.J. Shao, N.Y. Deng, K.C. Chou, iHyd-PseAAC: Predicting hydroxyproline and hydroxylysine in proteins by incorporating dipeptide position-specific propensity into pseudo amino acid composition. International Journal of Molecular Sciences (IJMS) 15 (2014) 7594-7610.
  198. Y. Xu, X. Wen, L.S. Wen, L.Y. Wu, N.Y. Deng, K.C. Chou, iNitro-Tyr: Prediction of nitrotyrosine sites in proteins with general pseudo amino acid composition. PLoS ONE 9 (2014) e105018.
  199. J. Zhang, P. Sun, X. Zhao, Z. Ma, PECM: Prediction of extracellular matrix proteins using the concept of Chou's pseudo amino acid composition. Journal of Theoretical Biology 363 (2014) 412-418.
  200. J. Zhang, X. Zhao, P. Sun, Z. Ma, PSNO: Predicting Cysteine S-Nitrosylation Sites by Incorporating Various Sequence-Derived Features into the General Form of Chou's PseAAC. Int J Mol Sci 15 (2014) 11204-19.
  201. L. Zhang, X. Zhao, L. Kong, Predict protein structural class for low-similarity sequences by evolutionary difference information into the general form of Chou's pseudo amino acid composition. J Theor Biol 355 (2014) 105-10.
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  203. S. Ahmad, M. Kabir, M. Hayat, Identification of Heat Shock Protein families and J-protein types by incorporating Dipeptide Composition into Chou's general PseAAC. Comput Methods Programs Biomed 122 (2015) 165-74.
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  207. G.L. Fan, X.Y. Zhang, Y.L. Liu, Y. Nang, H. Wang, DSPMP: Discriminating secretory proteins of malaria parasite by hybridizing different descriptors of Chou's pseudo amino acid patterns. J Comput Chem 36 (2015) 2317-27.
  208. C. Huang, J.Q. Yuan, Simultaneously Identify Three Different Attributes of Proteins by Fusing their Three Different Modes of Chou's Pseudo Amino Acid Compositions. Protein Pept Lett 22 (2015) 547-56.
  209. J. Jia, Z. Liu, X. Xiao, K.C. Chou, iPPI-Esml: an ensemble classifier for identifying the interactions of proteins by incorporating their physicochemical properties and wavelet transforms into PseAAC. J Theor Biol 377 (2015) 47-56.
  210. Z. Ju, J.Z. Cao, H. Gu, iLM-2L: A two-level predictor for identifying protein lysine methylation sites and their methylation degrees by incorporating K-gap amino acid pairs into Chous general PseAAC. J Theor Biol 385 (2015) 50-7.
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  212. R. Kumar, A. Srivastava, B. Kumari, M. Kumar, Prediction of beta-lactamase and its class by Chou's pseudo amino acid composition and support vector machine. J Theor Biol 365 (2015) 96-103.
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  214. B. Liu, J. Xu, S. Fan, R. Xu, J. Jiyun Zhou, X. Wang, PseDNA-Pro: DNA-binding protein identification by combining Chou's PseAAC and physicochemical distance transformation. Molecular Informatics 34 (2015) 8-17  
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  219. R. Xu, J. Zhou, B. Liu, Y.A. He, Q. Zou, X. Wang, K.C. Chou, Identification of DNA-binding proteins by incorporating evolutionary information into pseudo amino acid composition via the top-n-gram approach. Journal of Biomolecular Structure & Dynamics (JBSD) 33 (2015) 1720-1730.
  220. M. Zhang, B. Zhao, X. Liu, Predicting industrial polymer melt index via incorporating chaotic characters into Chou's general PseAAC. Chemometrics and Intelligent Laboratory Systems (CHEMOLAB) 146 (2015) 232-240.
  221. S.L. Zhang, Accurate prediction of protein structural classes by incorporating PSSS and PSSM into Chou's general PseAAC. Chemometrics and Intelligent Laboratory Systems (CHEMOLAB) 142 (2015) 28-35.
  222. P.P. Zhu, W.C. Li, Z.J. Zhong, E.Z. Deng, H. Ding, W. Chen, H. Lin, Predicting the subcellular localization of mycobacterial proteins by incorporating the optimal tripeptides into the general form of pseudo amino acid composition. Mol Biosyst 11 (2015) 558-63.
  223. K. Ahmad, M. Waris, M. Hayat, Prediction of Protein Submitochondrial Locations by Incorporating Dipeptide Composition into Chou's General Pseudo Amino Acid Composition. J Membr Biol 249 (2016) 293-304.
  224. M. Behbahani, H. Mohabatkar, M. Nosrati, Analysis and comparison of lignin peroxidases between fungi and bacteria using three different modes of Chou's general pseudo amino acid composition. J Theor Biol 411 (2016) 1-5.
  225. G.L. Fan, Y.L. Liu, H. Wang, Identification of thermophilic proteins by incorporating evolutionary and acid dissociation information into Chou's general pseudo amino acid composition. J Theor Biol 407 (2016) 138-142.
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  227. J. Jia, Z. Liu, X. Xiao, B. Liu, K.C. Chou, pSuc-Lys: Predict lysine succinylation sites in proteins with PseAAC and ensemble random forest approach. Journal of Theoretical Biology 394 (2016) 223-230.
  228. J. Jia, Z. Liu, X. Xiao, B. Liu, K.C. Chou, iCar-PseCp: identify carbonylation sites in proteins by Monto Carlo sampling and incorporating sequence coupled effects into general PseAAC. Oncotarget 7 (2016) 34558-34570.
  229. J. Jia, L. Zhang, Z. Liu, X. Xiao, K.C. Chou, pSumo-CD: Predicting sumoylation sites in proteins with covariance discriminant algorithm by incorporating sequence-coupled effects into general PseAAC. Bioinformatics 32 (2016) 3133-3141.
  230. Y.S. Jiao, P.F. Du, Prediction of Golgi-resident protein types using general form of Chou's pseudo amino acid compositions: Approaches with minimal redundancy maximal relevance feature selection. J Theor Biol 402 (2016) 38-44.
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Editorial Information

Editor-in-Chief

Yoon Young Kim
Seoul National University

Article Type

Opinion Article

Publication history

Received: December 02, 2019
Accepted: December 20, 2019
Published: December 24, 2019

Copyright

©2019 Chou KC. This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.

Citation

Chou KC (2019) How the artificial intelligence tool iSNO-PseAAC is working in predicting the cysteine S-nitrosylation sites in proteins. J Stem Cell Res Med 4: DOI: 10.15761/JSCRM.1000137.

Corresponding author

Kuo-Chen Chou

Gordon Life Science Institute, Boston, Massachusetts 02478, United States of America

E-mail : bhuvaneswari.bibleraaj@uhsm.nhs.uk

Figure 1. A semi-screenshot for the top-page of the iSNO-PseAAC web-server at http://app.aporc.org/iSNO-PseAAC (Adapted from [1]with permission)