Natural Science
Vol.12 No.08(2020), Article ID:102448,12 pages
10.4236/ns.2020.128047

Showcase to Illustrate How the Web-Server iSulf_Wide-PseAAC Is Working

Kuo-Chen Chou

Gordon Life Science Institute, Boston, MA, USA

Correspondence to: Kuo-Chen Chou,

Copyright © 2020 by author(s) and Scientific Research Publishing Inc.

This work is licensed under the Creative Commons Attribution International License (CC BY 4.0).

http://creativecommons.org/licenses/by/4.0/

Received: August 16, 2020 ; Accepted: August 23, 2020 ; Published: August 26, 2020

ABSTRACT

Current coronavirus pandemic has endangered the entire mankind life. The reported cases are increasing exponentially. Information of protein post-translational modification (PTM) can provide useful clues to develop antiviral drugs. According to our recent works, the PTM prediction can be significantly improved by widening the samples of training dataset. Based on such an idea, a new predictor called “iSulf_Wide-PseAAC” has been developed. Its accuracy is overwhelmingly higher than its counterparts. To maximize the convenience for most experimental scientists, a user-friendly web-server for the new predictor has been established at http://121.36.221.79/Isulf_Pse/, which will become a very useful tool for fighting pandemic coronavirus and save the mankind of this planet.

Keywords:

Pandemic Coronavirus, 5-Step Rule, PseAAC, Learning at Wide Region, Webserver

Recently, a very powerful method has been established to predict S-sulfonylation sites in proteins by Wide learning approach [1]. To show how the webserver is working, do the following.

Step 1. Open the webserver at http://121.36.221.79/Isulf_Pse/, and you will see the top page of the predictor on your computer screen, as shown in Figure 1. Click on the Read Me button to see a brief introduction about iSulf_Wide-PseAAC predictor and the caveat when using it.

Step 2. Either type or copy/paste the query protein sequences into the input box as is shown at the center of Figure 2. The input sequence should be in the FASTA format. 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 corresponding predicted results, which 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. Of course, the more number of query proteins and the longer of each sequence, the more time it is usually needed.

Step 4. As shown on the lower panel of Figure 1, you may also choose the prediction by entering your desired input file via the Browse button. The input file should also be in FASTA format, but it can contain as many protein sequences as you want.

Figure 1. The toppage of webserver after Step 2.

Step 5. Click on the Citation button to find the relevant papers that document the detailed development and algorithm of iSulf_Wide-PseAAC.

Step 6. Click on the Data button to download the benchmark datasets used to train and test the iSulf_Wide-PseAAC predictor.

Note. To obtain the predicted result with the anticipated success rate, the entire sequence of the query rather than its fragment should be used as an input. A sequence with less than 50 amino acid residues is generally deemed as a fragment.

It is anticipated that the Web Server will be very useful because the vast majority of biological scientists can easily get their desired results without the need to go through the complicated equations in [1] that were presented just for the integrity in developing the predictor

Also, note 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 Papers presented for developing a new sequence-analyzing method or statistical predictor by observing the guidelines of Chou’s 5-step rules have the following notable merits: 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.” The Chou’s 5-steps rule has been widely and increasingly concurred by many scientists (see, e.g., [2 - 52].

Figure 2. The outcome after Step 3.

It has not escaped our notice that during the development of iSulf_Wide-PseAAC webserver, the approach of general pseudo amino acid components [53] or PseAAC [54] had been utilized and hence its accuracy would be much higher than its counterparts, as concurred by many investigators (see, e.g., [2 , 5 , 8 , 13 - 16 , 23 , 32 , 41 - 43 , 55 - 118]).

CONFLICTS OF INTEREST

The author declares no conflicts of interest regarding the publication of this paper.

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  71. 71. Zhang, S.L. (2015) Accurate Prediction of Protein Structural Classes by Incorporating PSSS and PSSM into Chou’s General PseAAC. Chemometrics and Intelligent Laboratory Systems (CHEMOLAB), 142, 28-35. https://doi.org/10.1016/j.chemolab.2015.01.004

  72. 72. Fan, G.L., Zhang, X.Y., Liu, Y.L., Nang, Y. and Wang, H. (2015) DSPMP: Discriminating Secretory Proteins of Malaria Parasite by Hybridizing Different Descriptors of Chou’s Pseudo Amino Acid Patterns. Journal of Computational Chemistry, 36, 2317-2327. https://doi.org/10.1002/jcc.24210

  73. 73. Ali, F. and Hayat, M. (2015) Classification of Membrane Protein Types Using Voting Feature Interval in Combination with Chou’s Pseudo Amino Acid Composition. Journal of Theoretical Biology, 384, 78-83. https://doi.org/10.1016/j.jtbi.2015.07.034

  74. 74. Tang, H., Chen, W. and Lin, H. (2016) Identification of Immunoglobulins Using Chou’s Pseudo Amino Acid Composition with Feature Selection Technique. Molecular BioSystems, 12, 1269-1275. https://doi.org/10.1039/C5MB00883B

  75. 75. Ahmad, K., Waris, M. and Hayat, M. (2016) Prediction of Protein Submitochondrial Locations by Incorporating Dipeptide Composition into Chou’s General Pseudo Amino Acid Composition. The Journal of Membrane Biology, 249, 293-304. https://doi.org/10.1007/s00232-015-9868-8

  76. 76. Behbahani, M., Mohabatkar, H. and Nosrati, M. (2016) Analysis and Comparison of Lignin Peroxidases between Fungi and Bacteria Using Three Different Modes of Chou’s General Pseudo Amino Acid Composition. Journal of Theoretical Biology, 411, 1-5. https://doi.org/10.1016/j.jtbi.2016.09.001

  77. 77. Fan, G.L., Liu, Y.L. and Wang, H. (2016) Identification of Thermophilic Proteins by Incorporating Evolutionary and Acid Dissociation Information into Chou’s General Pseudo Amino Acid Composition. Journal of Theoretical Biology, 407, 138-142. https://doi.org/10.1016/j.jtbi.2016.07.010

  78. 78. Ju, Z., Cao, J.Z. and Gu, H. (2016) Predicting Lysine Phosphoglycerylation with Fuzzy SVM by Incorporating k-Spaced Amino Acid Pairs into Chou’s General PseAAC. Journal of Theoretical Biology, 397, 145-150. https://doi.org/10.1016/j.jtbi.2016.02.020

  79. 79. Tiwari, A.K. (2016) Prediction of G-Protein Coupled Receptors and Their Subfamilies by Incorporating Various Sequence Features into Chou’s General PseAAC. Computer Methods and Programs in Biomedicine, 134, 197-213. https://doi.org/10.1016/j.cmpb.2016.07.004

  80. 80. Xu, C., Sun, D., Liu, S. and Zhang, Y. (2016) Protein Sequence Analysis by Incorporating Modified Chaos Game and Physicochemical Properties into Chou’s General Pseudo Amino Acid Composition. Journal of Theoretical Biology, 406, 105-115. https://doi.org/10.1016/j.jtbi.2016.06.034

  81. 81. Zou, H.L. and Xiao, X. (2016) Classifying Multifunctional Enzymes by Incorporating Three Different Models into Chou’s General Pseudo Amino Acid Composition. The Journal of Membrane Biology, 249, 561-567. https://doi.org/10.1007/s00232-016-9904-3

  82. 82. Yu, B., Li, S., Qiu, W.Y., Chen, C., Chen, R.X., Wang, L., Wang, M.H. and Zhang, Y. (2017) Accurate Prediction of Subcellular Location of Apoptosis Proteins Combining Chou’s PseAAC and PsePSSM Based on Wavelet Denoising. Oncotarget, 8, 107640-107665. https://doi.org/10.18632/oncotarget.22585

  83. 83. Mousavizadegan, M. and Mohabatkar, H. (2018) Computational Prediction of Antifungal Peptides via Chou’s PseAAC and SVM. Journal of Bioinformatics and Computational Biology, 16, Article ID: 1850016. https://doi.org/10.1142/S0219720018500166

  84. 84. Zhao, W., Wang, L., Zhang, T.X., Zhao, Z.N. and Du, P.F. (2018) A Brief Review on Software Tools in Generating Chou’s Pseudo-Factor Representations for All Types of Biological Sequences. Protein & Peptide Letters, 25, 822-829. https://doi.org/10.2174/0929866525666180905111124

  85. 85. Sabooh, M.F., Iqbal, N., Khan, M., Khan, M. and Maqbool, H.F. (2018) Identifying 5-Methylcytosine Sites in RNA Sequence Using Composite Encoding Feature into Chou’s PseKNC. Journal of Theoretical Biology, 452, 1-9. https://doi.org/10.1016/j.jtbi.2018.04.037

  86. 86. Arif, M., Hayat, M. and Jan, Z. (2018) iMem-2LSAAC: A Two-Level Model for Discrimination of Membrane Proteins and Their Types by Extending the Notion of SAAC into Chou’s Pseudo Amino Acid Composition. Journal of Theoretical Biology, 442, 11-21. https://doi.org/10.1016/j.jtbi.2018.01.008

  87. 87. Akbar, S. and Hayat, M. (2018) iMethyl-STTNC: Identification of N(6)-Methyladenosine Sites by Extending the Idea of SAAC into Chou’s PseAAC to Formulate RNA Sequences. Journal of Theoretical Biology, 455, 205-211. https://doi.org/10.1016/j.jtbi.2018.07.018

  88. 88. Fu, X., Zhu, W., Liso, B., Cai, L., Peng, L. and Yang, J. (2018) Improved DNA-Binding Protein Identification by Incorporating Evolutionary Information into the Chou’s PseAAC. IEEE Access, 18, 43-66.

  89. 89. Pan, Y., Wang, S., Zhang, Q., Lu, Q., Su, D., Zuo, Y. and Yang, L. (2019) Analysis and Prediction of Animal Toxins by Various Chou’s Pseudo Components and Reduced Amino Acid Compositions. Journal of Theoretical Biology, 462, 221-229. https://doi.org/10.1016/j.jtbi.2018.11.010

  90. 90. Nosrati, M., Mohabatkar, H. and Behbahani, M. (2019) Introducing of an Integrated Artificial Neural Network and Chou’s Pseudo Amino Acid Composition Approach for Computational Epitope-Mapping of Crimean-Congo Haemorrhagic Fever Virus Antigens. International Immunopharmacology, 78, Article ID: 106020. https://doi.org/10.1016/j.intimp.2019.106020

  91. 91. Nosrati, M., Mohabatkar, H. and Behbahani, M. (2020) Introducing of an Integrated Artificial Neural Network and Chou’s Pseudo Amino Acid Composition Approach for Computational Epitope-Mapping of Crimean-Congo Haemorrhagic Fever Virus Antigens. International Immunopharmacology, 78, Article ID: 106020. https://doi.org/10.1016/j.intimp.2019.106020

  92. 92. Tahir, M., Hayat, M. and Khan, S.A. (2019) iNuc-ext-PseTNC: An Efficient Ensemble Model for Identification of Nucleosome Positioning by Extending the Concept of Chou’s PseAAC to Pseudo-Tri-Nucleotide Composition. Molecular Genetics and Genomics: MGG, 294, 199-210. https://doi.org/10.1007/s00438-018-1498-2

  93. 93. Tahir, M. and Hayat, M. (2016) iNuc-STNC: A Sequence-Based Predictor for Identification of Nucleosome Positioning in Genomes by Extending the Concept of SAAC and Chou’s PseAAC. Molecular BioSystems, 12, 2587-2593. https://doi.org/10.1039/C6MB00221H

  94. 94. Tahir, M., Tayara, H. and Chong, K.T. (2019) iRNA-PseKNC(2methyl): Identify RNA 2’-O-methylation Sites by Convolution Neural Network and Chou’s Pseudo Components. Journal of Theoretical Biology, 465, 1-6. https://doi.org/10.1016/j.jtbi.2018.12.034

  95. 95. Zhang, L. and Kong, L. (2018) iRSpot-ADPM: Identify Recombination Spots by Incorporating the Associated Dinucleotide Product Model into Chou’s Pseudo Components. Journal of Theoretical Biology, 441, 1-8. https://doi.org/10.1016/j.jtbi.2017.12.025

  96. 96. Jiao, Y.S. and Du, P.F. (2017) Predicting Protein Submitochondrial Locations by Incorporating the Positional-Specific Physicochemical Properties into Chou’s General Pseudo-Amino Acid Compositions. Journal of Theoretical Biology, 416, 81-87. https://doi.org/10.1016/j.jtbi.2016.12.026

  97. 97. Ju, Z. and He, J.J. (2017) Prediction of Lysine Crotonylation Sites by Incorporating the Composition of k-Spaced Amino Acid Pairs into Chou’s General PseAAC. Journal of Molecular Graphics and Modelling, 77, 200-204. https://doi.org/10.1016/j.jmgm.2017.08.020

  98. 98. Khan, M., Hayat, M., Khan, S.A. and Iqbal, N. (2017) Unb-DPC: Identify Mycobacterial Membrane Protein Types by Incorporating Un-Biased Dipeptide Composition into Chou’s General PseAAC. Journal of Theoretical Biology, 415, 13-19. https://doi.org/10.1016/j.jtbi.2016.12.004

  99. 99. Liang, Y. and Zhang, S. (2017) Predict Protein Structural Class by Incorporating Two Different Modes of Evolutionary Information into Chou’s General Pseudo Amino Acid Composition. Journal of Molecular Graphics and Modelling, 78, 110-117. https://doi.org/10.1016/j.jmgm.2017.10.003

  100. 100. Meher, P.K., Sahu, T.K., Saini, V. and Rao, A.R. (2017) Predicting Antimicrobial Peptides with Improved Accuracy by Incorporating the Compositional, Physico-Chemical and Structural Features into Chou’s General PseAAC. Scientific Reports, 7, Article No. 42362. https://doi.org/10.1038/srep42362

  101. 101. Qiu, W.R., Zheng, Q.S., Sun, B.Q. and Xiao, X. (2017) Multi-iPPseEvo: A Multi-Label Classifier for Identifying Human Phosphorylated Proteins by Incorporating Evolutionary Information into Chou’s General PseAAC via Grey System Theory. Molecular Informatics, 36, UNSP 1600085. https://doi.org/10.1002/minf.201600085

  102. 102. Xu, C., Ge, L., Zhang, Y., Dehmer, M. and Gutman, I. (2017) Prediction of Therapeutic Peptides by Incorporating q-Wiener Index into Chou’s General PseAAC. Journal of Biomedical Informatics, 75, 63-69.

  103. 103. Butt, A.H., Rasool, N. and Khan, Y.D. (2018) Predicting Membrane Proteins and Their Types by Extracting Various Sequence Features into Chou’s General PseAAC. Molecular Biology Reports, 18, 39-58.

  104. 104. Ghauri, A.W., Khan, Y.D., Rasool, N., Khan, S.A. and Chou, K.C. (2018) pNitro-Tyr-PseAAC: Predict Nitrotyrosine Sites in Proteins by Incorporating Five Features into Chou’s General PseAAC. Current Pharmaceutical Design, 24, 4034-4043. https://doi.org/10.2174/1381612825666181127101039

  105. 105. Ju, Z. and Wang, S.Y. (2018) Prediction of Citrullination Sites by Incorporating k-Spaced Amino Acid Pairs into Chou’s General Pseudo Amino Acid Composition. Gene, 664, 78-83. https://doi.org/10.1016/j.gene.2018.04.055

  106. 106. Krishnan, M.S. (2018) Using Chou’s General PseAAC to Analyze the Evolutionary Relationship of Receptor Associated Proteins (RAP) with Various Folding Patterns of Protein Domains. Journal of Theoretical Biology, 445, 62-74. https://doi.org/10.1016/j.jtbi.2018.02.008

  107. 107. Liang, Y. and Zhang, S. (2018) Identify Gram-Negative Bacterial Secreted Protein Types by Incorporating Different Modes of PSSM into Chou’s General PseAAC via Kullback-Leibler Divergence. Journal of Theoretical Biology, 454, 22-29. https://doi.org/10.1016/j.jtbi.2018.05.035

  108. 108. Mei, J., Fu, Y. and Zhao, J. (2018) Analysis and Prediction of Ion Channel Inhibitors by Using Feature Selection and Chou’s General Pseudo Amino Acid Composition. Journal of Theoretical Biology, 456, 41-48. https://doi.org/10.1016/j.jtbi.2018.07.040

  109. 109. Mei, J. and Zhao, J. (2018) Analysis and Prediction of Presynaptic and Postsynaptic Neurotoxins by Chou’s General Pseudo Amino Acid Composition and Motif Features. Journal of Theoretical Biology, 427, 147-153. https://doi.org/10.1016/j.jtbi.2018.03.034

  110. 110. Rahman, S.M., Shatabda, S., Saha, S., Kaykobad, M. and Sohel Rahman, M. (2018) DPP-PseAAC: A DNA-Binding Protein Prediction Model Using Chou’s General PseAAC. Journal of Theoretical Biology, 452, 22-34. https://doi.org/10.1016/j.jtbi.2018.05.006

  111. 111. Sankari, E.S. and Manimegalai, D.D. (2018) Predicting Membrane Protein Types by Incorporating a Novel Feature Set into Chou’s General PseAAC. Journal of Theoretical Biology, 455, 319-328. https://doi.org/10.1016/j.jtbi.2018.07.032

  112. 112. Srivastava, A., Kumar, R. and Kumar, M. (2018) BlaPred: Predicting and Classifying Beta-Lactamase Using a 3-Tier Prediction System via Chou’s General PseAAC. Journal of Theoretical Biology, 457, 29-36. https://doi.org/10.1016/j.jtbi.2018.08.030

  113. 113. Zhang, S. and Duan, X. (2018) Prediction of Protein Subcellular Localization with Oversampling Approach and Chou’s General PseAAC. Journal of Theoretical Biology, 437, 239-250. https://doi.org/10.1016/j.jtbi.2017.10.030

  114. 114. Adilina, S., Farid, D.M. and Shatabda, S. (2019) Effective DNA Binding Protein Prediction by Using Key Features via Chou’s General PseAAC. Journal of Theoretical Biology, 460, 64-78. https://doi.org/10.1016/j.jtbi.2018.10.027

  115. 115. Chen, G., Cao, M., Yu, J., Guo, X. and Shi, S. (2019) Prediction and Functional Analysis of Prokaryote Lysine Acetylation Site by Incorporating Six Types of Features into Chou’s General PseAAC. Journal of Theoretical Biology, 461, 92-101. https://doi.org/10.1016/j.jtbi.2018.10.047

  116. 116. Shen, Y., Tang, J. and Guo, F. (2019) Identification of Protein Subcellular Localization via Integrating Evolutionary and Physicochemical Information into Chou’s General PseAAC. Journal of Theoretical Biology, 462, 230-239. https://doi.org/10.1016/j.jtbi.2018.11.012

  117. 117. Wang, L., Zhang, R. and Mu, Y. (2019) Fu-SulfPred: Identification of Protein S-sulfenylation Sites by Fusing Forests via Chou’s General PseAAC. Journal of Theoretical Biology, 461, 51-58. https://doi.org/10.1016/j.jtbi.2018.10.046

  118. 118. Xiao, X., Cheng, X., Chen, G., Mao, Q. and Chou, K.C. (2019) pLoc_bal-mVirus: Predict Subcellular Localization of Multi-Label Virus Proteins by Chou’s General PseAAC and IHTS Treatment to Balance Training Dataset. Medicinal Chemistry, 15, 496-509. https://doi.org/10.2174/1573406415666181217114710

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