Showcase to Illustrate How the Web-server iPTM-mLys is working

In 2016 a very powerful web-server predictor has been established for predicting multiple lysine PTM sites and their different types [1].

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

Step 1: Opening the web-server at http://www.jci-bioinfo.cn/iPTM-mLys, you will see the top page of iPTM-mLys on your computer screen, as shown in figure1. Click on the Read Me button to see a brief introduction about the predictor.

Figure 1. A semi-screenshot for the top page of the web server iPTM-mLys at http://www.jci-bioinfo.cn/iPTM-mLys. (Adapted from {Qiu, 2016 #3249} with permission).

Step 2: Either type or copy/paste your query protein sequences into the input box at the center of Fig.1. The input sequence should be in the FASTA format. For the examples of sequences in FASTA format, click the Example button right above the input box. 

Step 3: Click the Submit button to get the predicted result. For example, if you use the Example window as the input, the corresponding predicted results are quite consistent with experimental observations. 

Step 4: As shown on the lower panel of Fig.1, you may also choose the batch prediction by entering your e-mail address and your desired batch input file (in FASTA format of course) via the Browse button. To see the sample of batch input file, click on the button Batch-example.

Step 5: Click the Supporting Information button to download the benchmark dataset used in this study.

Step 6: Click the Citation button to find the relevant papers that document the detailed development and algorithm of iPTM-mLys.

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 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., [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.

It has not escaped our notice that during the development of iRNA-2methyl 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 [33–73][2–6, 8–11, 13, 18, 26, 30, 32, 74–301]

For the wonderful and awesome roles of the “5-steps rule” in driving proteome, genome analyses and drug development, see a series of recent papers [31, 32, 292, 302–311] where the rule and its wide applications have been very impressively presented from various aspects or at different angles.

References

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  181. 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.
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  197. 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.
  198. 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.
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  200. 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.
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  239. H.L. Zou, X. Xiao, Predicting the Functional Types of Singleplex and Multiplex Eukaryotic Membrane Proteins via Different Models of Chou’s Pseudo Amino Acid Compositions. J Membr Biol 249 (2016) 23–9.
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  241. Cheng, X. Xiao, K.C. Chou, pLoc-mPlant: predict subcellular localization of multi-location plant proteins via incorporating the optimal GO information into general PseAAC. Molecular BioSystems 13 (2017) 1722–1727.
  242. Cheng, X. Xiao, K.C. Chou, pLoc-mVirus: predict subcellular localization of multi-location virus proteins via incorporating the optimal GO information into general PseAAC. Gene (Erratum: ibid., 2018, Vol.644, 156–156) 628 (2017) 315–321.
  243. Ju, J.J. He, Prediction of lysine propionylation sites using biased SVM and incorporating four different sequence features into Chou’s PseAAC. J Mol Graph Model 76 (2017) 356–363.
  244. Ju, J.J. He, Prediction of lysine crotonylation sites by incorporating the composition of k-spaced amino acid pairs into Chou’s general PseAAC. J Mol Graph Model 77 (2017) 200–204.
  245. Khan, M. Hayat, S.A. Khan, N. Iqbal, Unb-DPC: Identify mycobacterial membrane protein types by incorporating un-biased dipeptide composition into Chou’s general PseAAC. J Theor Biol 415 (2017) 13–19.
  246. Liang, S. Zhang, Predict protein structural class by incorporating two different modes of evolutionary information into Chou’s general pseudo amino acid composition. J Mol Graph Model 78 (2017) 110–117.
  247. L.M. Liu, Y. Xu, K.C. Chou, iPGK-PseAAC: identify lysine phosphoglycerylation sites in proteins by incorporating four different tiers of amino acid pairwise coupling information into the general PseAAC. Med Chem 13 (2017) 552–559.
  248. P.K. Meher, T.K. Sahu, V. Saini, A.R. Rao, Predicting antimicrobial peptides with improved accuracy by incorporating the compositional, physico-chemical and structural features into Chou’s general PseAAC. Sci Rep 7 (2017) 42362.
  249. W.R. Qiu, B.Q. Sun, X. Xiao, D. Xu, K.C. Chou, iPhos-PseEvo: Identifying human phosphorylated proteins by incorporating evolutionary information into general PseAAC via grey system theory. Molecular Informatics 36 (2017) UNSP 1600010.
  250. W.R. Qiu, Q.S. Zheng, B.Q. Sun, X. Xiao, Multi-iPPseEvo: A Multi-label Classifier for Identifying Human Phosphorylated Proteins by Incorporating Evolutionary Information into Chou’s General PseAAC via Grey System Theory. Mol Inform 36 (2017) UNSP 1600085.
  251. Rahimi, M.R. Bakhtiarizadeh, A. Mohammadi-Sangcheshmeh, OOgenesis_Pred: A sequence-based method for predicting oogenesis proteins by six different modes of Chou’s pseudo amino acid composition. J Theor Biol 414 (2017) 128–136.
  252. Tripathi, P.N. Pandey, A novel alignment-free method to classify protein folding types by combining spectral graph clustering with Chou’s pseudo amino acid composition. J Theor Biol 424 (2017) 49–54.
  253. Xiao, X. Cheng, S. Su, Q. Nao, K.C. Chou, pLoc-mGpos: Incorporate key gene ontology information into general PseAAC for predicting subcellular localization of Gram-positive bacterial proteins. Natural Science 9 (2017) 331–349.
  254. Xu, L. Ge, Y. Zhang, M. Dehmer, I. Gutman, Prediction of therapeutic peptides by incorporating q-Wiener index into Chou’s general PseAAC. J Biomed Inform doi: 10.1016/j.jbi.2017.09.011 (2017).
  255. Xu, C. Li, K.C. Chou, iPreny-PseAAC: identify C-terminal cysteine prenylation sites in proteins by incorporating two tiers of sequence couplings into PseAAC. Med Chem 13 (2017) 544–551.
  256. Yu, S. Li, W.Y. Qiu, C. Chen, R.X. Chen, L. Wang, M.H. Wang, Y. Zhang, Accurate prediction of subcellular location of apoptosis proteins combining Chou’s PseAAC and PsePSSM based on wavelet denoising. Oncotarget 8 (2017) 107640–107665.
  257. Yu, L. Lou, S. Li, Y. Zhang, W. Qiu, X. Wu, M. Wang, B. Tian, Prediction of protein structural class for low-similarity sequences using Chou’s pseudo amino acid composition and wavelet denoising. J Mol Graph Model 76 (2017) 260–273.
  258. Ahmad, M. Hayat, MFSC: Multi-voting based Feature Selection for Classification of Golgi Proteins by Adopting the General form of Chou’s PseAAC components. J Theor Biol 463 (2018) 99–109.
  259. Akbar, M. Hayat, iMethyl-STTNC: Identification of N(6)-methyladenosine sites by extending the Idea of SAAC into Chou’s PseAAC to formulate RNA sequences. J Theor Biol 455 (2018) 205–211.
  260. Arif, M. Hayat, Z. Jan, 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. J Theor Biol 442 (2018) 11–21.
  261. A.H. Butt, N. Rasool, Y.D. Khan, Predicting membrane proteins and their types by extracting various sequence features into Chou’s general PseAAC. Mol Biol Rep doi:10.1007/s11033-018-4391-5 (2018).
  262. X. Cheng, X. Xiao, K.C. Chou, pLoc-mEuk: Predict subcellular localization of multi-label eukaryotic proteins by extracting the key GO information into general PseAAC. Genomics 110 (2018) 50–58.
  263. X. Cheng, X. Xiao, K.C. Chou, pLoc-mGneg: Predict subcellular localization of Gram-negative bacterial proteins by deep gene ontology learning via general PseAAC. Genomics 110 (2018) 231–239.
  264. X. Cheng, X. Xiao, K.C. Chou, pLoc-mHum: predict subcellular localization of multi-location human proteins via general PseAAC to winnow out the crucial GO information. Bioinformatics 34 (2018) 1448–1456.
  265. X. Cheng, X. Xiao, K.C. Chou, pLoc_bal-mGneg: predict subcellular localization of Gram-negative bacterial proteins by quasi-balancing training dataset and general PseAAC. Journal of Theoretical Biology 458 (2018) 92–102.
  266. X. Cheng, X. Xiao, K.C. Chou, pLoc_bal-mPlant: predict subcellular localization of plant proteins by general PseAAC and balancing training dataset Curr Pharm Des 24 (2018) 4013–4022.
  267. E. Contreras-Torres, Predicting structural classes of proteins by incorporating their global and local physicochemical and conformational properties into general Chou’s PseAAC. J Theor Biol 454 (2018) 139–145.
  268. X. Fu, W. Zhu, B. Liso, L. Cai, L. Peng, J. Yang, Improved DNA-binding protein identification by incorporating evolutionary information into the Chou’s PseAAC. IEEE Access 20 (2018) https://doi.org/10.1109/ACCESS.2018.2876656.
  269. A.W. Ghauri, Y.D. Khan, N. Rasool, S.A. Khan, K.C. Chou, pNitro-Tyr-PseAAC: Predict nitrotyrosine sites in proteins by incorporating five features into Chou’s general PseAAC. Curr Pharm Des 24 (2018) 4034–4043.
  270. F. Javed, M. Hayat, Predicting subcellular localizations of multi-label proteins by incorporating the sequence features into Chou’s PseAAC. Genomics https://doi.org/10.1016/j.ygeno.2018.09.004 (2018).
  271. Z. Ju, S.Y. Wang, Prediction of citrullination sites by incorporating k-spaced amino acid pairs into Chou’s general pseudo amino acid composition. Gene 664 (2018) 78–83.
  272. Y.D. Khan, N. Rasool, W. Hussain, S.A. Khan, K.C. Chou, iPhosT-PseAAC: Identify phosphothreonine sites by incorporating sequence statistical moments into PseAAC. Analytical Biochemistry 550 (2018) 109–116.
  273. Y.D. Khan, N. Rasool, W. Hussain, S.A. Khan, K.C. Chou, iPhosY-PseAAC: identify phosphotyrosine sites by incorporating sequence statistical moments into PseAAC. Mol Biol Rep 45 (2018) 2501–2509.
  274. M.S. Krishnan, Using Chou’s general PseAAC to analyze the evolutionary relationship of receptor associated proteins (RAP) with various folding patterns of protein domains. J Theor Biol 445 (2018) 62–74.
  275. Y. Liang, S. Zhang, Identify Gram-negative bacterial secreted protein types by incorporating different modes of PSSM into Chou’s general PseAAC via Kullback-Leibler divergence. J Theor Biol 454 (2018) 22–29.
  276. J. Mei, Y. Fu, J. Zhao, Analysis and prediction of ion channel inhibitors by using feature selection and Chou’s general pseudo amino acid composition. J Theor Biol 456 (2018) 41–48.
  277. J. Mei, J. Zhao, Prediction of HIV-1 and HIV-2 proteins by using Chou’s pseudo amino acid compositions and different classifiers. Sci Rep 8 (2018) 2359.
  278. J. Mei, J. Zhao, Analysis and prediction of presynaptic and postsynaptic neurotoxins by Chou’s general pseudo amino acid composition and motif features. J Theor Biol 427 (2018) 147–153.
  279. M. Mousavizadegan, H. Mohabatkar, Computational prediction of antifungal peptides via Chou’s PseAAC and SVM. J Bioinform Comput Biol (2018) 1850016.
  280. S.M. Rahman, S. Shatabda, S. Saha, M. Kaykobad, M. Sohel Rahman, DPP-PseAAC: A DNA-binding Protein Prediction model using Chou’s general PseAAC. J Theor Biol 452 (2018) 22–34.
  281. E.S. Sankari, D.D. Manimegalai, Predicting membrane protein types by incorporating a novel feature set into Chou’s general PseAAC. J Theor Biol 455 (2018) 319–328.
  282. A. Srivastava, R. Kumar, M. Kumar, BlaPred: predicting and classifying beta-lactamase using a 3-tier prediction system via Chou’s general PseAAC. J Theor Biol 457 (2018) 29–36.
  283. S. Zhang, X. Duan, Prediction of protein subcellular localization with oversampling approach and Chou’s general PseAAC. J Theor Biol 437 (2018) 239–250.
  284. S. Zhang, Y. Liang, Predicting apoptosis protein subcellular localization by integrating auto-cross correlation and PSSM into Chou’s PseAAC. J Theor Biol 457 (2018) 163–169.
  285. S. Adilina, D.M. Farid, S. Shatabda, Effective DNA binding protein prediction by using key features via Chou’s general PseAAC. J Theor Biol 460 (2019) 64–78.
  286. J. Ahmad, M. Hayat, MFSC: Multi-voting based feature selection for classification of Golgi proteins by adopting the general form of Chou’s PseAAC components. J Theor Biol 463 (2019) 99–109.
  287. M. Awais, W. Hussain, Y.D. Khan, N. Rasool, S.A. Khan, K.C. Chou, iPhosH-PseAAC: Identify phosphohistidine sites in proteins by blending statistical moments and position relative features according to the Chou’s 5-step rule and general pseudo amino acid composition. IEEE/ACM Trans Comput Biol Bioinform https://doi.org/10.1109/TCBB.2019.2919025 or https://www.ncbi.nlm.nih.gov/pubmed/31144645 (2019).
  288. M. Behbahani, M. Nosrati, M. Moradi, H. Mohabatkar, Using Chou’s General Pseudo Amino Acid Composition to Classify Laccases from Bacterial and Fungal Sources via Chou’s Five-Step Rule. Applied Biochemistry and Biotechnology doi:10.1007/s12010-019-03141-8 (2019).
  289. A.H. Butt, N. Rasool, Y.D. Khan, Prediction of antioxidant proteins by incorporating statistical moments based features into Chou’s PseAAC. Journal of Theoretical Biology 473 (2019) 1–8.
  290. G. Chen, M. Cao, J. Yu, X. Guo, S. Shi, Prediction and functional analysis of prokaryote lysine acetylation site by incorporating six types of features into Chou’s general PseAAC. J Theor Biol 461 (2019) 92–101.
  291. K.C. Chou, An insightful 20-year recollection since the birth of pseudo amino acid components. Computers in Biology and Medicine in press (2019).
  292. K.C. Chou, Proposing pseudo amino acid components is an important milestone for proteome and genome analyses. International Journal for Peptide Research and Therapeutics (IJPRT) https://doi.org/10.1007/s10989-019-09910-7 or https://link.springer.com/article/10.1007%2Fs10989-019-09910-7 (2019).
  293. F. Javed, M. Hayat, Predicting subcellular localization of multi-label proteins by incorporating the sequence features into Chou’s PseAAC. Genomics 111 (2019) 1325–1332.
  294. S.J. Malebary, M.S.U. Rehman, Y.D. Khan, iCrotoK-PseAAC: Identify lysine crotonylation sites by blending position relative statistical features according to the Chou’s 5-step rule. PLoS One 14 (2019) e0223993.
  295. Q. Ning, Z. Ma, X. Zhao, dForml(KNN)-PseAAC: Detecting formylation sites from protein sequences using K-nearest neighbor algorithm via Chou’s 5-step rule and pseudo components. J Theor Biol 470 (2019) 43–49.
  296. M. Nosrati, H. Mohabatkar, M. Behbahani, 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 https://doi.org/10.1016/j.intimp.2019.106020 or https://www.sciencedirect.com/science/article/pii/S1567576919321277 (2019).
  297. Y. Shen, J. Tang, F. Guo, Identification of protein subcellular localization via integrating evolutionary and physicochemical information into Chou’s general PseAAC. J Theor Biol 462 (2019) 230–239.
  298. M. Tahir, M. Hayat, S.A. Khan, 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. Mol Genet Genomics 294 (2019) 199–210.
  299. L. Wang, R. Zhang, Y. Mu, Fu-SulfPred: Identification of Protein S-sulfenylation Sites by Fusing Forests via Chou’s General PseAAC. J Theor Biol 461 (2019) 51–58.
  300. X. Xiao, X. Cheng, G. Chen, Q. Mao, K.C. Chou, pLoc_bal-mVirus: Predict Subcellular Localization of Multi-Label Virus Proteins by Chou’s General PseAAC and IHTS Treatment to Balance Training Dataset. Med Chem 15 (2019) 496–509.
  301. Y.D. Khan, N. Amin, W. Hussain, N. Rasool, S.A. Khan, K.C. Chou, iProtease-PseAAC(2L): A two-layer predictor for identifying proteases and their types using Chou’s 5-step-rule and general PseAAC. Anal Biochem 588 (2020) 113477.
  302. K.C. Chou, Two kinds of metrics for computational biology. Genomics https://www.sciencedirect.com/science/article/pii/S0888754319304604?via%3Dihub (2019).
  303. K.C. Chou, An insightful recollection for predicting protein subcellular locations in multi-label systems. Genomics http://doi.org/10.1016/j.ygeno.2019.08.008 or https://www.sciencedirect.com/science/article/pii/S0888754319304604?via%3Dihub (2019).
  304. K.C. Chou, Progresses in predicting post-translational modification. International Journal of Peptide Research and Therapeutics (IJPRT) https:/doi.org/10.1007/s10989-019-09893-5 or https://link.springer.com/article/10.1007%2Fs10989-019-09893-5 (2019).
  305. K.C. Chou, Recent Progresses in Predicting Protein Subcellular Localization with Artificial Intelligence (AI) Tools Developed Via the 5-Steps Rule. Japanese Journal of Gastroenterology and Hepatology https:/doi.org/www.jjgastrohepto.org or https://www.jjgastrohepto.org/ (2019).
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  307. K.C. Chou, Artificial intelligence (AI) tools constructed via the 5-steps rule for predicting post-translational modifications. Trends in Artificial Inttelengence (TIA) 3 (2019) 60–74.
  308. K.C. Chou, Distorted Key Theory and Its Implication for Drug Development. Current Genomics http://www.eurekaselect.com/175823/article or http://www.eurekaselect.com/175823/article (2020).
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  310. K.C. Chou, An insightful recollection since the birth of Gordon Life Science Institute about 17 years ago. Advancement in Scientific and Engineering Research 4 (2019) 31–36.
  311. K.C. Chou, Gordon Life Science Institute: Its philosophy, achievements, and perspective. Annals of Cancer Therapy and Pharmacology 2 (2019) 001–26.