How the Artificial Intelligence Tool iHyd-PseAAC is Working in Predicting the Hydroxyproline and Hydroxylysine in Proteins Volume 4 - Issue 1

Kuo-Chen Chou*

• Gordon Life Science Institute, Boston, United States of America

Received: March 10, 2020;   Published: March 13, 2020

*Corresponding author: Kuo-Chen Chou Gordon Life Science Institute, Boston, Massachusetts 02478, United States of America

DOI: 10.32474/PRJFGS.2020.04.000179

Abstract PDF

In 2014 a very powerful AI (artificial intelligence) tool has been established for identifying hydroxyproline and hydroxylysine sites in proteins, two of the important post modifications in proteins [1]. To see how the web-server is working, please do the following.
a) Step 1. Open the web server at the site and you will see the top page of the predictor on your computer screen, as shown in Figure1. Click on the Read Me button to see a brief introduction about iHyd-PseAAC predictor and the caveat when using it
b) Step 2. Either type or copy/paste the query protein sequences into the input box at the center of Figure1. The protein sequences should be in FASTA format. The input examples can be seen by clicking on the Example button right above the input box.

Figure 1: The top-page of the web-server iHyd-PseAAC.

c) Step 3. Click on the Submit button to see the predicted result. For instance, if you use the protein sequences in the Example window as the input, after a few seconds, you will see the corresponding predicted results, which is fully consistent with experiment observations.
d) Step 4. Click on the Citation button to find the relevant paper that documents the detailed development and algorithm of iHyd-PseAAC.
e) Step 5. Click on the Data button to download the benchmark dataset used to train and test the iHyd-PseAAC predictor.
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 [2]. 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 see e.g. [3,4] and three comprehensive review papers [5-7]: (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. It has not escaped our notice that during the development of iDNA6mA-PseKNC web-server, the approach of general pseudo amino acid components [8] or PseAAC [9] had been utilized and hence its accuracy would be much higher than its counterparts, as concurred by many investigators see, e.g. [10- 13]. For the marvelous and awesome roles of the “5-steps rule” in driving proteome, genome analyses and drug development, see a series of recent papers [14-35] where the rule and its wide applications have been very impressively presented from various aspects or at different angles.

1. Xu Y, Wen X, Shao XJ, Deng NY, Chou KC(2014) iHyd-PseAAC: Predicting hydroxyproline and hydroxylysine in proteins by incorporating dipeptide position-specific propensity into pseudo amino acid composition Int J Mol Sci15(5): 7594-7610.
2. Xu Y, Shao XJ, Wu LY, Deng NY, Chou KC(2013) iSNO-AAPair: incorporating amino acid pairwise coupling into PseAAC for predicting cysteine S-nitrosylation sites in proteins PeerJ 1: e171.
3. Barukab O, Khan YD,Khan SA, Chou KC(2019) iSulfoTyr-PseAAC: Identify tyrosine sulfation sites by incorporating statistical moments via Chou's 5-steps rule and pseudo components Current Genomicsp. 20(4).
4. Kabir M, Ahmad S, Iqbal M, Hayat M(2020) iNR-2L: A two-level sequence-based predictor developed via Chou's 5-steps rule and general PseAAC for identifying nuclear receptors and their families, Genomics112(1):276-285.
5. Chou KC (2011) Some remarks on protein attribute prediction and pseudo amino acid composition (50th Anniversary Year Review, 5-steps rule)J Theor Biol 273(1):236-247.
6. Chou KC (2019) Advance in predicting subcellular localization of multi-label proteins and its implication for developing multi-target drugs, Current Medicinal Chemistryp. 26(26).
7. Chou KC (2019) Impacts of pseudo amino acid components and 5-steps rule to proteomics and proteome analysis, Current Topics in Medicinak Chemistry (CTMC) (Special Issue edGP Zhou) p. 19(25).
8. Chou KC(2001)Prediction of protein cellular attributes using pseudo amino acid composition.Proteins43(3): 246-255.
9. Chou KC(2005) Using amphiphilic pseudo amino acid composition to predict enzyme subfamily classes. Bioinformatics21(1):10-19.
10. Hussain W, Khan YD, Rasool N, Khan SA, Chou KC (2019)SPalmitoylC-PseAAC: A sequence-based model developed via Chou's 5-steps rule and general PseAAC for identifying S-palmitoylation sites in proteins. AnalBiochem568:14-23.
11. Hussain W, Khan YD, Rasool N, Khan SA, Chou KC(2019) SPrenylC-PseAAC: A sequence-based model developed via Chou's 5-steps rule and general PseAAC for identifying S-prenylation sites in proteins. JTheorBiol 468:1-11.
12. Ilyas S, Hussain W, Ashraf A, Khan YD, Khan SA, et al. (2019)iMethylK-PseAAC: Improving accuracy for lysine methylation sites identification by incorporating statistical moments and position relative features into general PseAAC via Chou’s 5-steps rule. Current Genomics20(4):275-292.
13. Javed F, Hayat M(2019) Predicting subcellular localization of multi-label proteins by incorporating the sequence features into Chou's PseAAC. Genomics111(6): 1325-1332.
14. Chou KC(2019) cradle of Gordon Life Science Institute and its development and driving force. Int J Biol Genetics23(5):1-28.
15. Chou KC (2019) Showcase to illustrate how the web-server iDNA6mA-PseKNC is working. Journal of Pathology Research Reviews & Reports1: 1-15.
16. Chou KC(2019) The pLoc_bal-mPlant is a Powerful Artificial Intelligence Tool for Predicting the Subcellular Localization of Plant Proteins Purely based on their Sequence Information. Int J Nutr Sci 4: 1-4.
17. Chou KC, Cheng X, Xiao X (2019) pLoc_bal-mEuk: predict subcellular localization of eukaryotic proteins by general PseAAC and quasi-balancing training dataset. Med Chem 15(5): 472-485.
18. Chou KC (2019) Showcase to illustrate how the web-server iNitro-Tyr is working. Glo J of Com Sci and Infor Tec2: 1-16.
19. Chou KC(2019) Gordon Life Science Institute: Its philosophy, achievements, and perspective. Annals of Cancer Therapy and Pharmacology 2(2): 001-026.
20. Chou KC(2019) Showcase to Illustrate how the webserver pLoc_bal-mEuk Is working. Biomed J Sci & Tech Res24(2).
21. Chou KC(2020) The pLoc_bal-mGneg Predictor is a Powerful Web-Server for Identifying the Subcellular Localization of Gram-Negative Bacterial Proteins based on their Sequences Information Alone. ijSci 9(1): 27-34.
22. Chou KC(2020) How the artificial intelligence tool iRNA-2methyl is working for RNA 2’-Omethylation sites. Journal of Medical Care Research and Review3: 348-366.
23. Chou KC(2020) Showcase to illustrate how the web-server iKcr-PseEns is working. Journal of Medical Care Research and Review9 (1):85-95.
24. Chou KC(2020) The pLoc_bal-mVirus is a powerful artificial intelligence tool for predicting the subcellular localization of virus proteins according to their sequence information alone, J Gent & Genome.
25. Chou KC(2019) How the artificial intelligence tool iSNO-PseAAC is working in predicting the cysteine S-nitrosylation sites in proteinsJ Stem Cell Res Med pp.
26. Chou KC(2020) Showcase to illustrate how the web-server iRNA-Methyl is working. J Mol Genet3: 1-7.
27. Chou KC(2020) How the Artificial Intelligence Tool iRNA-PseU is Working in Predicting the RNA Pseudouridine Sites, Biomed J Sci & Tech Res 24(2).
28. Chou KC Showcase to illustrate how the web-server iSNO-AAPair is working, J Gent & Genome, 4 (2020).
29. Chou KC(2020) The pLoc_bal-mHum is a Powerful Web-Serve for Predicting the Subcellular Localization of Human Proteins Purely Based on Their Sequence Information. Adv Bioeng Biomed Sci Res 3(1).
30. Chou KC(2020) Showcase to Illustrate How the Web-server iPTM-mLys is working. Infotext Journal of Infectious Diseases and Therapy [IJID] pp.1-16.
31. Chou KC(2020) The pLoc_bal-mGpos is a powerful artificial intelligence tool for predicting the subcellular localization of Gram-positive bacterial proteins according to their sequence information alone. Glo J of Com Sci and Infor Tec 2: 01-13.
32. Chou KC Showcase to illustrate how the web-server iPreny-PseAAC is working, Glo J ofCom Sci and Infor Tec., 2 (2020) 01-15.
33. Chou KC(2020) Some illuminating remarks on molecular genetics and genomics as well as drug development. Molecular Genetics and Genomics 295(5): 261-274.
34. Chou KC(2020) The Problem of Elsevier Series Journals Online Submission by Using Artificial Intelligence, Natural Science 12(2): 37-38.
35. Chou KC(2020) The Most Important Ethical Concerns in Science. Natural Science 12(2): 35-36.