The Significant and Profound Impacts of Chou’s 5-Steps Rule

Kuo-Chen Chou

Gordon Life Science Institute, Boston, MA, USA.

DOI: 10.4236/ns.2020.129048                

Abstract

In this short review paper, the significant and profound impacts of the 5-steps rule have been briefly recalled with crystal clear convincingness.

Keywords

5-Step Rules, Significant Impacts, Profound Impacts, Curie Temperature

The “5-Steps Rule” or “5-Step Rules” was originally proposed by Kuo-Chen Chou in 2011 [1], named by many scientists as “Chou’s 5-steps rule” or “Chou’s 5-step rules”.

According to this rule, to develop a practically more useful statistical prediction method or predictor for genome or proteome analysis, one should observe the following five guidelines. 1) Construct or select a valid benchmark dataset to train and test the predictor. 2) Formulate the biological sequence samples with an effective mathematical expression that can truly reflect their intrinsic correlation with the target to be predicted. 3) Introduce or develop a powerful algorithm (or engine) to operate the prediction. 4) Properly perform cross-validation tests to objectively evaluate the anticipated accuracy of the predictor. 5) Establish a user-friendly webserver for the predictor that is accessible to the public. Ever since then, the 5-steps rule has been used by many scientists in developing various predictors for proteome or genome analyses.

Papers presented for developing a new sequence-analyzing method or statistical predictor by observing the guidelines of Chou’s 5-strp 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.

Therefore, the impacts of the 5-step rules are both significantly and profoundly (see, e.g., [2-49].

Moreover, the Chou’s 5-steps rule has been further extended to materials science for developing powerful method of detecting perovskite materials with higher Curie temperature as well [50].

Conflicts of Interest

The authors declare no conflicts of interest.

References

[1]	Chou, K.C. (2011) Some Remarks on Protein Attribute Prediction and Pseudo Amino Acid Composition (50th Anniversary Year Review, 5-Steps Rule). Journal of Theoretical Biology, 273, 236-247. https://doi.org/10.1016/j.jtbi.2010.12.024
[2]	Butt, A.H. and Khan, Y.D. (2018) Prediction of S-Sulfenylation Sites Using Statistical Moments Based Features via Chou’s 5-Step Rule. International Journal of Peptide Research and Therapeutics (IJPRT), 17, 711-738.
[3]	Awais, M., Hussain, W., Khan, Y.D., Rasool, N., Khan, S.A. and Chou, K.C. (2019) 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 Transactions on Computational Biology and Bioinformatics, 19, 1-21. https://doi.org/10.1109/TCBB.2019.2919025
[4]	Barukab, O., Khan, Y.D., Khan, S.A. and Chou, K.C. (2019) iSulfoTyr-PseAAC: Identify Tyrosine Sulfation Sites by Incorporating Statistical Moments via Chou’s 5-Steps Rule and Pseudo Components. Current Genomics, 20, 306-320. https://doi.org/10.2174/1389202920666190819091609
[5]	Butt, A.H. and Khan, Y.D. (2019) Prediction of S-Sulfenylation Sites Using Statistical Moments Based Features via Chou’s 5-Step Rule. International Journal of Peptide Research and Therapeutics (IJPRT), 19, 71-82. https://doi.org/10.1007/s10989-019-09931-2
[6]	Chen, Y. and Fan, X. (2019) Use Chou’s 5-Steps Rule to Reveal Active Compound and Mechanism of Shuangsheng Pingfei San on Idiopathic Pulmonary Fibrosis. Current Molecular Medicine, 19, 511-563.
[7]	Du, X., Diao, Y., Liu, H. and Li, S. (2019) MsDBP: Exploring DNA-Binding Proteins by Integrating Multi-Scale Sequence Information via Chou’s 5-Steps Rule. Journal of Proteome Research, 18, 3119-3132. https://doi.org/10.1021/acs.jproteome.9b00226
[8]	Dutta, A., Dalmia, A., R, A., Singh, K.K. and Anand, A. (2019) Using the Chou’s 5-Steps Rule to Predict Splice Junctions with Interpretable Bidirectional Long Short-Term Memory Networks. Computers in Biology and Medicine, 116, Article ID: 103558. https://doi.org/10.1016/j.compbiomed.2019.103558
[9]	Ehsan, A., Mahmood, M.K., Khan, Y.D., Barukab, O.M., Khan, S.A. and Chou, K.C. (2019) iHyd-PseAAC (EPSV): Identify Hydroxylation Sites in Proteins by Extracting Enhanced Position and Sequence Variant Feature via Chou’s 5-Step Rule and General Pseudo Amino Acid Composition. Current Genomics, 20, 124-133. https://doi.org/10.2174/1389202920666190325162307
[10]	Hussain, W., Khan, S.D., Rasool, N., Khan, S.A. and Chou, K.C. (2019) SPalmitoylC-PseAAC: A Sequence- Based Model Developed via Chou’s 5-Steps Rule and General PseAAC for Identifying S-Palmitoylation Sites in Proteins. Analytical Biochemistry, 568, 14-23. https://doi.org/10.1016/j.ab.2018.12.019
[11]	Hussain, W., Khan, Y.D., Rasool, N., Khan, S.A. and Chou, K.C. (2019) SPrenylC-PseAAC: A Sequence-Based Model Developed via Chou’s 5-Steps Rule and General PseAAC for Identifying S-Prenylation Sites in Proteins. Journal of Theoretical Biology, 468, 1-11. https://doi.org/10.1016/j.jtbi.2019.02.007
[12]	Jun, Z. and Wang, S.Y. (2019) Identify Lysine Neddylation Sites Using Bi-Profile Bayes Feature Extraction via the Chou’s 5-Steps Rule and General Pseudo Components. Current Genomics, 20, 592-601. https://doi.org/10.2174/1389202921666191223154629
[13]	Khan, S., Khan, M., Iqbal, N., Hussain, T., Khan, S.A. and Chou, K.C. (2019) A Two-Level Computation Model Based on Deep Learning Algorithm for Identification of piRNA and Their Functions via Chou’s 5-Steps Rule. Human Genetics, 19, 756-799. https://doi.org/10.1007/s10989-019-09887-3
[14]	Khan, Z.U., Ali, F., Khan, I.A., Hussain, Y. and Pi, D. (2019) iRSpot-SPI: Deep Learning-Based Recombination Spots Prediction by Incorporating Secondary Sequence Information Coupled with Physio-Chemical Properties via Chou’s 5-Step Rule and Pseudo Components. Chemometrics and Intelligent Laboratory Systems (CHEMOLAB), 189, 169-180. https://doi.org/10.1016/j.chemolab.2019.05.003
[15]	Lan, J., Liu, J., Liao, C., Merkler, D.J., Han, Q. and Li, J. (2019) A Study for Therapeutic Treatment against Parkinson’s Disease via Chou’s 5-Steps Rule. Current Topics in Medicinal Chemistry, 19, 2318-2333. https://doi.org/10.2174/1568026619666191019111528
[16]	Le, N.Q.K. (2019) iN6-methylat (5-Step): Identifying DNA N(6)-methyladenine Sites in Rice Genome Using Continuous Bag of Nucleobases via Chou’s 5-Step Rule. Molecular Genetics and Genomics: MGG, 294, 1173- 1182. https://doi.org/10.1007/s00438-019-01570-y
[17]	Le, N.Q.K., Yapp, E.K.Y., Ho, Q.T., Nagasundaram, N., Ou, Y.Y. and Yeh, H.Y. (2019) iEnhancer-5Step: Identifying Enhancers Using Hidden Information of DNA Sequences via Chou’s 5-Step Rule and Word Embedding. Analytical Biochemistry, 571, 53-61. https://doi.org/10.1016/j.ab.2019.02.017
[18]	Le, N.Q.K., Yapp, E.K.Y., Ou, Y.Y. and Yeh, H.Y. (2019) iMotor-CNN: Identifying Molecular Functions of Cytoskeleton Motor Proteins Using 2D Convolutional Neural Network via Chou’s 5-Step Rule. Analytical Biochemistry, 575, 17-26. https://doi.org/10.1016/j.ab.2019.03.017
[19]	Liang, R., Xie, J., Zhang, C., Zhang, M., Huang, H., Huo, H., Cao, X. and Niu, B. (2019) Identifying Cancer Targets Based on Machine Learning Methods via Chou’s 5-Steps Rule and General Pseudo Components. Current Topics in Medicinal Chemistry, 19, 2301-2317. https://doi.org/10.2174/1568026619666191016155543
[20]	Liang, Y. and Zhang, S. (2019) Identifying DNase I Hypersensitive Sites Using Multi-Features Fusion and F-Score Features Selection via Chou’s 5-Steps Rule. Biophysical Chemistry, 253, Article ID: 106227. https://doi.org/10.1016/j.bpc.2019.106227
[21]	Liu, Z., Dong, W., Jiang, W. and He, Z. (2019) csDMA: An Improved Bioinformatics Tool for Identifying DNA 6 mA Modifications via Chou’s 5-Step Rule. Scientific Reports, 9, Article No. 13109. https://doi.org/10.1038/s41598-019-49430-4
[22]	Malebary, S.J., Rehman, M.S.U. and Khan, Y.D. (2019) iCrotoK-PseAAC: Identify Lysine Crotonylation Sites by Blending Position Relative Statistical Features According to the Chou’s 5-Step Rule. PLoS ONE, 14, e0223993. https://doi.org/10.1371/journal.pone.0223993
[23]	Nazari, I., Tahir, M., Tayari, H. and Chong, K.T. (2019) iN6-Methyl (5-step): Identifying RNA N6-methyla- denosine Sites Using Deep Learning Mode via Chou’s 5-Step Rules and Chou’s General PseKNC. Chemometrics and Intelligent Laboratory Systems (CHEMOLAB), 193, Article ID: 103811. https://doi.org/10.1016/j.chemolab.2019.103811
[24]	Ning, Q., Ma, Z. and Zhao, X. (2019) dForml(KNN)-PseAAC: Detecting Formylation Sites from Protein Sequences Using K-Nearest Neighbor Algorithm via Chou’s 5-Step Rule and Pseudo Components. Journal of Theoretical Biology, 470, 43-49. https://doi.org/10.1016/j.jtbi.2019.03.011
[25]	Tahir, M., Tayara, H. and Chong, K.T. (2019) iDNA6mA (5-Step Rule): Identification of DNA N6-methyla- denine Sites in the Rice Genome by Intelligent Computational Model via Chou’s 5-Step Rule. CHEMOLAB, 189, 96-101. https://doi.org/10.1016/j.chemolab.2019.04.007
[26]	Wiktorowicz, A., Wit, A., Dziewierz, A., Rzeszutko, L., Dudek, D. and Kleczynski, P. (2019) Calcium Pattern Assessment in Patients with Severe Aortic Stenosis via the Chou’s 5-Steps Rule. Current Pharmaceutical Design, 25, 6-31. https://doi.org/10.2174/1381612825666190930101258
[27]	Yang, L., Lv, Y., Wang, S., Zhang, Q., Pan, Y., Su, D., Lu, Q. and Zuo, Y. (2019) Identifying FL11 Subtype by Characterizing Tumor Immune Microenvironment in Prostate Adenocarcinoma via Chou’s 5-Steps Rule. Genomics, 112, 1500-1515. https://doi.org/10.1016/j.ygeno.2019.08.021
[28]	Akbar, S., Rahman, A.U., Hayat, M., et al. (2020) cACP: Classifying Anticancer Peptides Using Discriminative Intelligent Model via Chou’s 5-Step Rules and General Pseudo Components. Chemometrics and Intelligent Laboratory (CHEMOLAB), 196, Article ID: 103912. https://doi.org/10.1016/j.chemolab.2019.103912
[29]	Akmal, M.A., Hussain, W., Rasool, N., Khan, Y.D., Khan, S.A. and Chou, K.C. (2020) Using Chou’s 5-Steps Rule to Predict O-Linked Serine Glycosylation Sites by Blending Position Relative Features and Statistical Moment. IEEE/ACM Transactions on Computational Biology and Bioinformatics, 1-12. https://doi.org/10.1109/TCBB.2020.2968441
[30]	Bouziane, H. and Chouarfia, A. (2020) Use of Chou’s 5-Steps Rule to Predict the Subcellular Localization of Gram-Negative and Gram-Positive Bacterial Proteins by Multi-Label Learning Based on Gene Ontology Annotation and Profile Alignment. Journal of Integrative Bioinformatics. https://doi.org/10.1515/jib-2019-0091
[31]	Charoenkwan, P., Schaduangrat, N., Nantasenamat, C., Piacham, T. and Shoombuatong, W. (2020) iQSP: A Sequence-Based Tool for the Prediction and Analysis of Quorum Sensing Peptides via Chou’s 5-Steps Rule and Informative Physicochemical Properties. International Journal of Molecular Sciences, 21, 75. https://doi.org/10.3390/ijms21010075
[32]	Charoenkwan, P., Schaduangrat, N., Nantasenamat, C., Piacham, T. and Shoombuatong, W. (2020) Correction: Shoombuatong, W., et al. iQSP: A Sequence-Based Tool for the Prediction and Analysis of Quorum Sensing Peptides via Chou’s 5-Steps Rule and Informative Physicochemical Properties. International Journal of Molecular Sciences, 21, 75. https://doi.org/10.3390/ijms21072629
[33]	Chen, Y. and Fan, X. (2020) Use of Chou’s 5-Steps Rule to Reveal Active Compound and Mechanism of Shuangshen Pingfei San on Idiopathic Pulmonary Fibrosis. Current Molecular Medicine, 20, 220-230. https://doi.org/10.2174/1566524019666191011160543
[34]	Dobosz, R., Mucko, J. and Gawinecki, R. (2020) Using Chou’s 5-Step Rule to Evaluate the Stability of Tautomers: Susceptibility of 2-[(Phenylimino)-methyl]-cyclohexane-1,3-diones to Tautomerization Based on the Calculated Gibbs Free Energies. Energies, 13, 183. https://doi.org/10.3390/en13010183
[35]	Du, L., Meng, Q., Jiang, H. and Li, Y. (2020) Using Evolutionary Information and Multi-Label Linear Discriminant Analysis to Predict the Subcellular Location of Multi-Site Bacterial Proteins via Chou’s 5-Steps Rule. IEEE Access, 8, 56452-56461. https://doi.org/10.1109/ACCESS.2020.2982160
[36]	Dutta, A., Dalmia, A., R, A., Singh, K.K. and Anand, A. (2020) Using the Chou’s 5-Steps Rule to Predict Splice Junctions with Interpretable Bidirectional Long Short-Term Memory Networks. Computers in Biology and Medicine, 116, Article ID: 103558. https://doi.org/10.1016/j.compbiomed.2019.103558
[37]	Ju, Z. and Wang, S.Y. (2020) Prediction of Lysine Formylation Sites Using the Composition of k-Spaced Amino Acid Pairs via Chou’s 5-Steps Rule and General Pseudo Components. Genomics, 112, 859-866. https://doi.org/10.1016/j.ygeno.2019.05.027
[38]	Kabir, M., Ahmad, S., Iqbal, M. and 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. Genomics, 112, 276-285. https://doi.org/10.1016/j.ygeno.2019.02.006
[39]	Khan, Y.D., Amin, N., Hussain, W., Rasool, N., Khan, S.A. and Chou, K.C. (2020) iProtease-PseAAC(2L): A Two-Layer Predictor for Identifying Proteases and Their Types Using Chou’s 5-Step-Rule and General PseAAC. Analytical Biochemistry, 588, Article ID: 113477. https://doi.org/10.1016/j.ab.2019.113477
[40]	Lin, W., Xiao, X., Qiu, W. and Chou, K.C. (2020) Use Chou’s 5-Steps Rule to Predict Remote Homology Proteins by Merging Grey Incidence Analysis and Domain Similarity Analysis. Natural Science, 12, 181-198. https://doi.org/10.4236/ns.2020.123016
[41]	Lu, W., Song, Z., Ding, Y., Wu, H., Cao, Y., Zhang, Y.L., et al. (2020) Use Chou’s 5-Step Rule to Predict DNA- Binding Proteins with Evolutionary Information. BioMed Research International, 2020, Article ID: 6984045. https://doi.org/10.1155/2020/6984045
[42]	Nguyen, D., Ho-Quang, T., Nguyen Quoc Khanh, L., Dinh-Phan, V. and Ou, Y.Y. (2020) Use Chou’s 5-Steps Rule with Different Word Embedding Types to Boost Performance of Electron Transport Protein Prediction Model. IEEE/ACM Transactions on Computational Biology and Bioinformatics. https://doi.org/10.1109/TCBB.2020.3010975
[43]	Pandey, R.P., Kumar, S., Ahmad, S., Vibhuti, A., Raj, V.S., Verma, A.K., Sharma, P. and Leal, E. (2020) Use Chou’s 5-Steps Rule to Evaluate Protective Efficacy Induced by Antigenic Proteins of Mycobacterium tuberculosis Encapsulated in Chitosan Nanoparticles. Life Sciences, 256, Article ID: 117961. https://doi.org/10.1016/j.lfs.2020.117961
[44]	Roy, T. and Bhattacharjee, P. (2020) A LabVIEW-Based Real-Time Modeling Approach via Chou’s 5-Steps Rule for Detection of Abnormalities in Cancer Cells. Gene Reports, Article ID: 100788. https://doi.org/10.1016/j.genrep.2020.100788
[45]	Song, C. and Yang, B. (2020) Use Chou’s 5-Step Rule to Classify Protein Modification Sites with Neural Network. Scientific Programming, 2020, Article ID: 8894633. https://doi.org/10.1155/2020/8894633
[46]	Song, C. and Yang, B. (2020) Use Chou’s 5-Step Rule to Classify Protein Modification Sites with Neural Network. Scientific Programming, 2020, Article ID: 8894633. https://doi.org/10.1155/2020/8894633
[47]	Vishnoi, S., Garg, P. and Arora, P. (2020) Physicochemical n-Grams Tool: A Tool for Protein Physicochemical Descriptor Generation via Chou’s 5-Step Rule. Chemical Biology & Drug Design, 95, 79-86. https://doi.org/10.1111/cbdd.13617
[48]	Vundavilli, H., Datta, A., Sima, C., Hua, J., Lopes, R. and Bittner, M. (2020) Using Chou’s 5-Steps Rule to Model Feedback in Lung Cancer. IEEE Journal of Biomedical and Health Informatics, 21, 2430-2438. https://doi.org/10.1109/JBHI.2019.2958042
[49]	Yang, L., Lv, Y., Wang, S., Zhang, Q., Pan, Y., Su, D., Lu, Q. and Zuo, Y. (2020) Identifying FL11 Subtype by Characterizing Tumor Immune Microenvironment in Prostate Adenocarcinoma via Chou’s 5-Steps Rule. Genomics, 112, 1500-1515. https://doi.org/10.1016/j.ygeno.2019.08.021
[50]	Zhai, X., Chen, M. and Lu, W. (2018) Accelerated Search for Perovskite Materials with Higher Curie Temperature Based on the Machine Learning Methods. Computational Materials Science, 151, 41-48. https://doi.org/10.1016/j.commatsci.2018.04.031