Since it was proposed in 2011, the “5-steps rule” or “5-step rules” has been widely used in molecular biology, both theoretical and experimental. Its original source was usually referred by citing a review paper for celebrating the 50th anniversary year of Journal of Theoretical Biology .
Interestingly, no such a clear-cut term as “5-step” can be found in the entire aforementioned paper. Why? This is because: it is the idea of the “5-steps rule” that would become crystal clear after carefully reading through the whole paper. Accordingly, the paper  is actually the cradle of the “5-steps rule”.
2. THE ESSENCE OF 5-STEPS RULE
In order to quantitatively predict, or develop a useful predictor for, a molecular biology system, the following five guidelines should be observed: 1) select or construct a valid benchmark dataset to train and test the predictor; 2) represent the samples with an effective formulation that can truly reflect their intrinsic correlation with the target to be predicted; 3) introduce or develop a powerful algorithm to conduct the prediction; 4) properly perform cross-validation tests to objectively evaluate the anticipated prediction accuracy; 5) establish a user-friendly web-server for the predictor that is accessible to the public. The predictors established in compliance with these steps have the following notable merits: a) crystal clear in logic development; b) completely transparent in operation; c) easily to repeat the reported results by other investigators; d) with high potential in stimulating other predictors; e) very convenient to be used by the majority of experimental scientists.
3. RESULT AND DISCUSSION
It is without exaggeration to say that the “5-steps rule” has been used at a very deeper levels of many molecular biology systems, as clearly and remarkably indicated by a series of the following reports: 1) “prediction of S-sulfenylation sites , 2) “identify phosphohistidine sites in proteins by blending statistical moments and position relative features , 3) “identify tyrosine sulfation sites by incorporating statistical moments” , 4) “prediction of S-sulfenylation sites using statistical moments , 5) “reveal active compound and mechanism of shuangsheng pingfei san on idiopathic pulmonary fibrosis , 6) “exploring DNA-binding proteins by integrating multi-scale sequence information” , 7) “predict splice junctions with interpretable bidirectional long short-term memory networks” , 8) “identify hydroxylation sites in proteins by extracting enhanced position and sequence variant feature” , 9) “a sequence model for identifying S-palmitoylation sites in proteins” , 10) “a sequence-based model for identifying S-prenylation sites in proteins” , 11) “a two-level computation model based on deep learning algorithm for identification of piRNA and their functions , 12) “deep learning-based recombination spots prediction by incorporating secondary sequence information coupled with physio-chemical properties” , 13) “a study for therapeutic treatment against Parkinson’s disease” , 14) “identifying DNA N(6)-methyladenine sites in rice genome using continuous bag of nucleobases” , 15) “identifying enhancers using hidden information of DNA sequences” , 16) “identifying molecular functions of cytoskeleton motor proteins using 2D convolutional neural network” , 17) “identifying cancer targets based on machine learning methods” , 18) “identifying DNase I hypersensitive sites using multi-features fusion and F-score features selection” , 19) “an improved bioinformatics tool for identifying DNA 6 mA modifications” , 20) “identify lysine crotonylation sites by blending position relative statistical features” , 21) “identifying RNA N6-methyladenosine sites using deep learning mode” , 22) “detecting formylation sites from protein sequences using K-nearest neighbor algorithm” , 23) “identification of DNA N6-methyladenine sites in the rice genome by intelligent computational model” , 24) “calcium pattern assessment in patients with severe aortic stenosis” , 25) “identifying FL11 subtype by characterizing tumor immune microenvironment in prostate adenocarcinoma” , 26) “a sequence-based tool for the prediction and analysis of quorum sensing peptides” , 27) “evaluate the stability of tautomers: susceptibility of 2-[(Phenylimino)-methyl]-cyclohexane-1,3-diones to tautomerization based on the calculated Gibbs free energies” , 28) “prediction of lysine formylation sites using the composition of k-spaced amino acid pairs” , 29) “a two-level sequence-based predictor for identifying nuclear receptors and their families” , 30) “a two-layer predictor for identifying proteases and their types” , 31) “classifying anticancer peptides using discriminative intelligent model” , 32) “a tool for protein physicochemical descriptor generation” , 33) “model feedback in lung cancer” .
It is instructive to point out that in the systems of molecular biology there exist many multi-label ones where each of the individual constituents or samples considered may need two or more labels for distinction. For this kind of multi-label systems, two kinds of metrics are needed: one is the global set of metrics to indicate the global accuracy of the prediction method or predictor developed, while the other is the local metrics to indicate its local accuracy . For the concrete mathematical formulations of the two sets of metrics, as well as their biological implications, refer to a recent paper .
4. CONCLUSION AND PERSPECTIVE
The “5-steps rule” has played substantial roles in stimulating in-depth studies of molecular biology, both theoretical and experimental. It is indeed a remarkable and profound milestone for molecular biology.
Although at the present the reports in this regard from theoretical scientists are more than those from experimental scientists, it is anticipated that, with more experimental data available in future, this kind of reports from experimental scientists will be increasing as well. Particularly, the combined reports between experimental and theoretical approaches, or their compliments to each other, will increasingly appear.
It is anticipated that more impacts will be realized by the “5-steps rule”, as indicated by some very impressive papers [35 - 41] and a series of very recent papers (see, e.g., [42 - 59]).
 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.
 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.
 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.
 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.
 Dutta, A., Dalmia, 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.
 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.
 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.
 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.
 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. International Journal of Peptide Research and Therapeutics.
 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, 189, 169-180.
 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.
 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.
 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.
 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.
 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.
 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.
 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.
 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.
 Nazari, I., Tahir, M., Tayari, H. and Chong, K.T. (2019) iN6-Methyl (5-Step): Identifying RNA N6-Methyladenosine Sites Using Deep Learning Mode via Chou’s 5-Step Rules and Chou’s General PseKNC. Chemometrics and Intelligent Laboratory Systems, 193, Article ID: 103811.
 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.
 Tahir, M., Tayara, H. and Chong, K.T. (2019) iDNA6mA (5-Step Rule): Identification of DNA N6-Methyladenine Sites in the Rice Genome by Intelligent Computational Model via Chou’s 5-Step Rule. CHEMOLAB, 189, 96-101.
 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, 3769-3775.
 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.
 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.
 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.
 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.
 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.
 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.
 Akbar, S., Rahman, A.U. and Hayat, M. (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.
 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.
 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.
 Chou, K.C. (2019) Advance in Predicting Subcellular Localization of Multi-Label Proteins and Its Implication for Developing Multi-Target Drugs. Current Medicinal Chemistry, 26, 4918-4943.
 Chou, K.C. (2019) Two Kinds of Metrics for Computational Biology. Genomics.
 Chou, K.C. (2019) Proposing Pseudo Amino Acid Components Is an Important Milestone for Proteome and Genome Analyses. International Journal for Peptide Research and Therapeutics.
 Chou, K.C. (2019) An Insightful Recollection for Predicting Protein Subcellular Locations in Multi-Label Systems. Genomics.
 Chou, K.C. (2019) Progresses in Predicting Post-Translational Modification. International Journal of Peptide Research and Therapeutics.
 Chou, K.C. (2019) An Insightful Recollection since the Distorted Key Theory Was Born about 23 Years Ago. Genomics.
 Chou, K.C. (2019) Artificial Intelligence (AI) Tools Constructed via the 5-Steps Rule for Predicting Post-Translational Modifications. Trends in Artificial Intelligence (TIA), 3, 60-74.
 Liu, B., Gao, X. and Zhang, H. (2019) BioSeq-Analysis2.0: An Updated Platform for Analyzing DNA, RNA and Protein Sequences at Sequence Level and Residue Level Based on Machine Learning Approaches. Nucleic Acids Research, 47, e127.
 Chou, K.C. (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. International Journal of Nutrition Sciences, 4, 1037.
 Chou, K.C., Cheng, X. and Xiao, X. (2019) pLoc_bal-mEuk: Predict Subcellular Localization of Eukaryotic Proteins by General PseAAC and Quasi-Balancing Training Dataset. Medicinal Chemistry, 15, 472-485.
 Chou, K.C. (2019) The pLoc_bal-mAnimal Is a Powerful Artificial Intelligence Tool for Predicting the Subcellular Localization of Animal Proteins Based on Their Sequence Information Alone. Scientific Journal of Biometrics & Biostatistics, 2, 1-13.
 Chou, K.C. (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, 27-34.
 Chou, K.C. (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, 4.
 Chou, K.C. (2020) The pLoc_bal-mHum Is a Powerful Web-Serve for Predicting the Subcellular Localization of Human Proteins Purely Based on Their Sequence Information. Advances in Bioengineering and Biomedical Science Research, 3, 1-5.