The recent worldwide spreading of pneumonia-causing virus, such as Coronavirus, COVID-19, and H1N1, has been endangering the life of human beings all around the world. In order to really understand the biological process within a cell level and provide useful clues to develop antiviral drugs, information of Gram positive bacteria protein subcellular localization is vitally important. In view of this, a CNN based protein subcellular localization predictor called “pLoc_Deep-mGpos” was developed. The predictor is particularly useful in dealing with the multi-sites systems in which some proteins may simultaneously occur in two or more different organelles that are the current focus of pharmaceutical industry. The global absolute true rate achieved by the new predictor is over 99% and its local accuracy is around 92% - 99%. Both are transcending other existing state-of-the-art predictors significantly. To maximize the convenience for most experimental scientists, a user-friendly web-server for the new predictor has been established at http://www.jci-bioinfo.cn/pLoc_Deep-mGpos/ , which will become a very powerful tool for developing effective drugs to fight pandemic coronavirus and save the mankind of this planet.
KeywordsPandemic CoronavirusMulti-Label SystemGram Positive ProteinsLearning at Deeper LevelFive-Steps RulePseAAC
Ehrlich, J.S., Hansen, M.D. and Nelson, W.J. (2002) Spatio-Temporal Regulation of Rac1 Localization and Lamellipodia Dynamics during Epithelial Cell-Cell Adhesion. Developmental Cell, 3, 259-270. https://doi.org/10.1016/S1534-5807(02)00216-2
Glory, E. and Murphy, R.F. (2007) Automated Subcellular Location Determination and High-Throughput Microscopy. Developmental Cell, 12, 7-16. https://doi.org/10.1016/j.devcel.2006.12.007
Chou, K.C. (2015) Impacts of Bioinformatics to Medicinal Chemistry. Medicinal Chemistry, 11, 218-234. https://doi.org/10.2174/1573406411666141229162834
Xiao, X., Cheng, X., Chen, G., Mao, Q. and Chou, K.C. (2019 pLoc_bal-mGpos: Predict Subcellular Localization of Gram-Positive Bacterial Proteins by Quasi-Balancing Training Dataset and PseAAC. Genomics, 111, 886-892. https://doi.org/10.1016/j.ygeno.2018.05.017
Nakai, K. and Kanehisa, M. (1992) A Knowledge Base for Predicting Protein Localization Sites in Eukaryotic Cells. Genomics, 14, 897-911. https://doi.org/10.1016/S0888-7543(05)80111-9
Cedano, J., Aloy, P., Perez-Pons, J.A. and Querol, E. (1997) Relation between Amino Acid Composition and Cellular Location of Proteins. Journal of Molecular Biology, 266, 594-600. https://doi.org/10.1006/jmbi.1996.0804
Reinhardt, A. and Hubbard, T. (1998) Using Neural Networks for Prediction of the Subcellular Location of Proteins. Nucleic Acids Research, 26, 2230-2236. https://doi.org/10.1093/nar/26.9.2230
Chou, K.C. and Shen, H.B. (2007) Recent Progresses in Protein Subcellular Location Prediction. Analytical Biochemistry, 370, 1-16. https://doi.org/10.1016/j.ab.2007.07.006
Chou, K.C., Wu, Z.C. and Xiao, X. (2011) iLoc-Euk: A Multi-Label Classifier for Predicting the Subcellular Localization of Singleplex and Multiplex Eukaryotic Proteins. PLoS One, 6, e18258. https://doi.org/10.1371/journal.pone.0018258
Mandal, M., Mukhopadhyay, A. and Maulik, U. (2015) Prediction of Protein Subcellular Localization by Incorporating Multiobjective PSO-Based Feature Subset Selection into the General Form of Chou’s PseAAC. Medical & Biological Engineering & Computing, 53, 331-344. https://doi.org/10.1007/s11517-014-1238-7
Maxwell, A., Li, R., Yang, B., Weng, H., Ou, A., Hong, H., Zhou, Z., Gong, P. and Zhang, C. (2017) Deep Learning Architectures for Multi-Label Classification of Intelligent Health Risk Prediction. BMC Bioinformatics, 18, 523. https://doi.org/10.1186/s12859-017-1898-z
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. https://doi.org/10.1007/s10989-019-09887-3
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
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 (CHEMOLAB), 193, 103811. https://doi.org/10.1016/j.chemolab.2019.103811
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
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
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
Chou, K.C. (2001) Prediction of Protein Cellular Attributes Using Pseudo Amino Acid Composition. Proteins: Structure, Function, and Genetics (Erratum: ibid., 2001, Vol. 44, 60), 43, 246-255. https://doi.org/10.1002/prot.1035
Chou, K.C. (2005) Using Amphiphilic Pseudo Amino Acid Composition to Predict Enzyme Subfamily Classes. Bioinformatics, 21, 10-19. https://doi.org/10.1093/bioinformatics/bth466
Zhou, X.B., Chen, C., Li, Z.C. and Zou, X.Y. (2007) Using Chou’s Amphiphilic Pseudo Amino Acid Composition and Support Vector Machine for Prediction of Enzyme Subfamily Classes. Journal of Theoretical Biology, 248, 546-551. https://doi.org/10.1016/j.jtbi.2007.06.001
Zhang, S.W., Chen, W., Yang, F. and Pan, Q. (2008) Using Chou’s Pseudo Amino Acid Composition to Predict Protein Quaternary Structure: A Sequence-Segmented PseAAC Approach. Amino Acids, 35, 591-598. https://doi.org/10.1007/s00726-008-0086-x
Qiu, J.D., Huang, J.H., Liang, R.P. and Lu, X.Q. (2009) Prediction of G-Protein-Coupled Receptor Classes Based on the Concept of Chou’s Pseudo Amino Acid Composition: An Approach from Discrete Wavelet Transform. Analytical Biochemistry, 390, 68-73. https://doi.org/10.1016/j.ab.2009.04.009
Mohabatkar, H. (2010) Prediction of Cyclin Proteins Using Chou’s Pseudo Amino Acid Composition. Protein & Peptide Letters, 17, 1207-1214. https://doi.org/10.2174/092986610792231564
Qiu, J.D., Suo, S.B., Sun, X.Y., Shi, S.P. and Liang, R.P. (2011) OligoPred: A Web-Server for Predicting Homo-Oligomeric Proteins by Incorporating Discrete Wavelet Transform into Chou’s Pseudo Amino Acid Composition. Journal of Molecular Graphics & Modelling, 30, 129-134. https://doi.org/10.1016/j.jmgm.2011.06.014
Nanni, L., Lumini, A., Gupta, D. and Garg, A. (2012) Identifying Bacterial Virulent Proteins by Fusing a Set of Classifiers Based on Variants of Chou’s Pseudo Amino Acid Composition and on Evolutionary Information. IEEE-ACM Transaction on Computational Biolology and Bioinformatics, 9, 467-475. https://doi.org/10.1109/TCBB.2011.117
Khosravian, M., Faramarzi, F.K., Beigi, M.M., Behbahani, M. and Mohabatkar, H. (2013) Predicting Antibacterial Peptides by the Concept of Chou’s Pseudo Amino Acid Composition and Machine Learning Methods. Protein & Peptide Letters, 20, 180-186. https://doi.org/10.2174/092986613804725307
Kumar, R., Srivastava, A., Kumari, B. and Kumar, M. (2015) Prediction of Beta-Lactamase and Its Class by Chou’s Pseudo Amino Acid Composition and Support Vector Machine. Journal of Theoretical Biology, 365, 96-103. https://doi.org/10.1016/j.jtbi.2014.10.008
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
Zhang, S., Yang, K., Lei, Y. and Song, K. (2019) iRSpot-DTS: Predict Recombination Spots by Incorporating the Dinucleotide-Based Spare-Cross Covariance Information into Chou’s Pseudo Components. Genomics, 111, 1760-1770. https://doi.org/10.1016/j.ygeno.2018.11.031
Akbar, S., Rahman, A.U., Hayat, M. and Sohail, 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, 103912. https://doi.org/10.1016/j.chemolab.2019.103912
Chou, K.C. (2017) An Unprecedented Revolution in Medicinal Chemistry Driven by the Progress of Biological Science. Current Topics in Medicinal Chemistry, 17, 2337-2358. https://doi.org/10.2174/1568026617666170414145508
Du, P., Wang, X., Xu, C. and Gao, Y. (2012) PseAAC-Builder: A Cross-Platform Stand-Alone Program for Generating Various Special Chou’s Pseudo Amino Acid Compositions. Analytical Biochemistry, 425, 117-119. https://doi.org/10.1016/j.ab.2012.03.015
Cao, D.S., Xu, Q.S. and Liang, Y.Z. (2013) Propy: A Tool to Generate Various Modes of Chou’s PseAAC. Bioinformatics, 29, 960-962. https://doi.org/10.1093/bioinformatics/btt072
Du, P., Gu, S. and Jiao, Y. (2014) PseAAC-General: Fast Building Various Modes of General Form of Chou’s Pseudo Amino Acid Composition for Large-Scale Protein Datasets. International Journal of Molecular Sciences, 15, 3495-3506. https://doi.org/10.3390/ijms15033495
Chou, K.C. (2009) Pseudo Amino Acid Composition and Its Applications in Bioinformatics, Proteomics and System Biology. Current Proteomics, 6, 262-274. https://doi.org/10.2174/157016409789973707
Chen, W., Lei, T.Y., Jin, D.C., Lin, H. and Chou, K.C. (2014) PseKNC: A Flexible Web-Server for Generating Pseudo K-Tuple Nucleotide Composition. Analytical Biochemistry, 456, 53-60. https://doi.org/10.1016/j.ab.2014.04.001
Chen, W., Lin, H. and Chou, K.C. (2015) Pseudo Nucleotide Composition or PseKNC: An Effective Formulation for Analyzing Genomic Sequences. Molecular BioSystems, 11, 2620-2634. https://doi.org/10.1039/C5MB00155B
Cheng, X., Xiao, X. and Chou, K.C. (2018) pLoc_bal-mGneg: Predict Subcellular Localization of Gram-Negative Bacterial Proteins by Quasi-Balancing Training Dataset and General PseAAC. Journal of Theoretical Biology, 458, 92-102. https://doi.org/10.1016/j.jtbi.2018.09.005
Chou, K.C. (2019) Two kinds of Metrics for Computational Biology. Genomics. https://www.sciencedirect.com/science/article/pii/S0888754319304604?via%3Dihub
Chou, K.C. (2013) Some Remarks on Predicting Multi-Label Attributes in Molecular Biosystems. Molecular Biosystems, 9, 1092-1100. https://doi.org/10.1039/c3mb25555g
Song, J., Wang, Y., Li, F., Akutsu, T., Rawlings, N.D., Webb, G.I. and Chou, K.C. (2018) iProt-Sub: A Comprehensive Package for Accurately Mapping and Predicting Protease-Specific Substrates and Cleavage Sites. Brief in Bioinformatics, 20, 638-658. https://doi.org/10.1093/bib/bby028
Zhang, M., Li, F., Marquez-Lago, T.T., Leier, A., Fan, C., Kwoh, C.K., Chou, K.C., Song, J. and Jia, C. (2019) Multiply: A Novel Multi-Layer Predictor for Discovering General and Specific Types of Promoters. Bioinformatics, 35, 2957-2965. https://doi.org/10.1093/bioinformatics/btz016
Shen, H.B. and Chou, K.C. (2007) Hum-mPLoc: An Ensemble Classifier for Large-Scale Human Protein Subcellular Location Prediction by Incorporating Samples with Multiple Sites. Biochemical and Biophysical Research Communications (BBRC), 355, 1006-1011. https://doi.org/10.1016/j.bbrc.2007.02.071
Chou, K.C. and Shen, H.B. (2008) Cell-PLoc: A Package of Web Servers for Predicting Subcellular Localization of Proteins in Various Organisms. Nature Protocols, 3, 153-162. https://doi.org/10.1038/nprot.2007.494
Shen, H.B. and Chou, K.C. (2009) A Top-Down Approach to Enhance the Power of Predicting Human Protein Subcellular Localization: Hum-mPLoc 2.0. Analytical Biochemistry, 394, 269-274. https://doi.org/10.1016/j.ab.2009.07.046
Chou, K.C. and Shen, H.B. (2010) Cell-PLoc 2.0: An Improved Package of Web-Servers for Predicting Subcellular Localization of Proteins in Various Organisms. Natural Science, 2, 1090-1103. https://doi.org/10.4236/ns.2010.210136
Chou, K.C., Wu, Z.C. and Xiao, X. (2012) iLoc-Hum: Using Accumulation-Label Scale to Predict Subcellular Locations of Human Proteins with Both Single and Multiple Sites. Molecular Biosystems, 8, 629-641. https://doi.org/10.1039/C1MB05420A
Cheng, X., Xiao, X. and Chou, K.C. (2018) pLoc-mHum: Predict Subcellular Localization of Multi-Location Human Proteins via General PseAAC to Winnow out the Crucial GO Information. Bioinformatics, 34, 1448-1456. https://doi.org/10.1093/bioinformatics/btx711
Wang, X. and Li, G.Z. (2012) A Multi-Label Predictor for Identifying the Subcellular Locations of Singleplex and Multiplex Eukaryotic Proteins. PLoS One, 7, e36317. https://doi.org/10.1371/journal.pone.0036317
Huang, C. and Yuan, J. (2013) Using Radial Basis Function on the General Form of Chou’s Pseudo Amino Acid Composition and PSSM to Predict Subcellular Locations of Proteins with Both Single and Multiple Sites. Biosystems, 113, 50-57. https://doi.org/10.1016/j.biosystems.2013.04.005
Pacharawongsakda, E. and Theeramunkong, T. (2013) Predict Subcellular Locations of Singleplex and Multiplex Proteins by Semi-Supervised Learning and Dimension-Reducing General Mode of Chou’s PseAAC. IEEE Transactions on Nanobioscience, 12, 311-320. https://doi.org/10.1109/TNB.2013.2272014
Chen, W., Feng, P.M., Lin, H. and Chou, K.C. (2013) iRSpot-PseDNC: Identify Recombination Spots with Pseudo Dinucleotide Composition. Nucleic Acids Research, 41, e68. https://doi.org/10.1093/nar/gks1450
Chou, K.C. (2001) Using Subsite Coupling to Predict Signal Peptides. Protein Engineering, 14, 75-79. https://doi.org/10.1093/protein/14.2.75
Kabir, M. and Hayat, M. (2016) iRSpot-GAEnsC: Identifing Recombination Spots via Ensemble Classifier and Extending the Concept of Chou’s PseAAC to Formulate DNA Samples. Molecular Genetics and Genomics, 291, 285-296. https://doi.org/10.1007/s00438-015-1108-5
Liu, B., Wang, S., Long, R. and Chou, K.C. (2017) iRSpot-EL: Identify Recombination Spots with an Ensemble Learning Approach. Bioinformatics, 33, 35-41. https://doi.org/10.1093/bioinformatics/btw539
Liu, B., Weng, F., Huang, D.S. and Chou, K.C. (2018) iRO-3wPseKNC: Identify DNA Replication Origins by Three-Window-Based PseKNC. Bioinformatics, 34, 3086-3093. https://doi.org/10.1093/bioinformatics/bty312
Feng, P., Yang, H., Ding, H., Lin, H., Chen, W. and Chou, K.C. (2019) iDNA6mA-PseKNC: Identifying DNA N(6)-Methyladenosine Sites by Incorporating Nucleotide Physicochemical Properties into PseKNC. Genomics, 111, 96-102. https://doi.org/10.1016/j.ygeno.2018.01.005
Chou, K.C. (2020) The Development of Gordon Life Science Institute: Its Driving Force and Accomplishments. Natural Science, 12, 202-217. https://doi.org/10.4236/ns.2020.124018
Chou, K.C. (2020) The Most Important Ethical Concerns in Science. Natural Science, 12, 35-36. https://doi.org/10.4236/ns.2020.122005
Chou, K.C. (2020) Other Mountain Stones Can Attack Jade: The 5-Steps Rule. Natural Science, 12, 59-64. https://doi.org/10.4236/ns.2020.123011
Chou, K.C. (2020) The Problem of Elsevier Series Journals Online Submission by Using Artificial Intelligence. Natural Science, 12, 37-38. https://doi.org/10.4236/ns.2020.122006
Chou, K.C. (2020) Proposing 5-Steps Rule Is a Notable Milestone for Studying Molecular Biology. Natural Science, 12, 74-79. https://doi.org/10.4236/ns.2020.123011
Chou, K.C. (2020) Using Similarity Software to Evaluate Scientific Paper Quality Is a Big Mistake. Natural Science, 12, 42-58. https://doi.org/10.4236/ns.2020.123008
Chou, K.C. and Shen, H.B. (2009) Recent Advances in Developing Web-Servers for Predicting Protein Attributes. Natural Science, 1, 63-92. https://doi.org/10.4236/ns.2009.12011