Crystallization of proteins is a very delicate process, which is influenced by many known and unknown factors. Of tested factors, many factors are exclusively related to individual amino-acid characters such as molecular weight or protein characters such as protein length. It is considered necessary to test factors that combine both individual amino-acid characters and protein characters with respect to success rate of crystallization. In this study, two combined characters characterizing individual amino-acid character and protein character, amino acid distribution probability and future composition, were used to correlate the success rate of crystallization of proteins from Lactobacillus via modeling. The results obtained from logistic regression and neural network were compared against the results obtained from each of 533 individual amino-acid characters. This study demonstrated that the combined characters are involved in crystallization process and should be taken into account for predicting the success rate of crystallization process.
References
[1]
Smialowski, P., Schmidt, T., Cox, J., Kirschner, A. and Frishman, D. (2006) Will My Protein Crystallize? A Sequence-Based Predictor. Proteins, 62, 343-355. https://doi.org/10.1002/prot.20789
[2]
Slabinski, L., Jaroszewski, L., Rychlewski, L., Wilson, I.A., Lesley, S.A. and Godzik, A. (2007) XtalPred: A Web Server for Prediction of Protein Crystallizability. Bioinformatics, 23, 3403-3405.
https://doi.org/10.1093/bioinformatics/btm477
[3]
Chen, K., Kurgan, L. and Rahbari, M. (2007) Prediction of Protein Crystallization Using Collocation of Amino Acid Pairs. Biochemical and Biophysical Research Communications, 355, 764-759.
https://doi.org/10.1016/j.bbrc.2007.02.040
[4]
Overton, I.M., Padovani, G., Girolami, M.A. and Barton, G.J. (2008) ParCrys: A Parzen Window Density Estimation Approach to Protein Crystallization Propensity Prediction. Bioinformatics, 24, 901-907.
https://doi.org/10.1093/bioinformatics/btn055
[5]
Kurgan, L. and Mizianty, M.J. (2009) Sequence-Based Protein Crystallization Propensity Prediction for Structural Genomics: Review and Comparative Analysis. Natural Science, 1, 93-106.
https://doi.org/10.4236/ns.2009.12012
[6]
Kurgan, L., Razib, A.A., Aghakhani, S., Dick, S., Mizianty, M.J. and Jahandideh, S. (2009) CRYSTALP2: Sequence-Based Protein Crystallization Propensity Prediction. BMC Structural Biology, 9, 50.
https://doi.org/10.1186/1472-6807-9-50
[7]
Zucker, F.H., Stewart, C., dela Rosa, J., Kim, J., Zhang, L., Xiao, L., Ross, J., Napuli, A.J., Mueller, N., Castaneda, L.J., Nakazawa Hewitt, S.R., Arakaki, T.L., Larson, E.T., Subramanian, E., Verlinde, C.L., Fan, E., Buckner, F.S., Van Voorhis, W.C., Merritt, E.A. and Hol, W.G. (2010) Prediction of Protein Crystallization Outcome Using a Hybrid Method. Journal of Structural Biology, 171, 64-73. https://doi.org/10.1186/1472-6807-9-50
[8]
Wang, H., Feng, L., Webb, G.I., Kurgan, L., Song, J. and Lin, D. (2018) Critical Evaluation of Bioinformatics Tools for the Prediction of Protein Crystallization Propensity. Briefings in Bioinformatics, 19, 838-852.
https://doi.org/10.1093/bib/bbx018
[9]
Wu, G. and Yan, S.M. (2002) Randomness in the Primary Structure of Protein: Methods and Implications. Molecular Biology Today, 3, 55-69.
[10]
Wu, G. and Yan, S. (2006) Mutation Trend of Hemagglutinin of Influenza A Virus: A Review from Computational Mutation Viewpoint. Acta Pharmacologica Sinica, 27, 513-526.
https://doi.org/10.1111/j.1745-7254.2006.00329.x
[11]
Wu, G. and Yan, S. (2008) Lecture Notes on Computational Mutation. Nova Science Publishers, New York.
[12]
Wu, G. and Yan, S. (2010) Creation and Application of Computational Mutation. Journal of Guangxi Academy of Sciences, 17, 145-150.
[13]
Seddik, H.A., Bendali, F., Gancel, F., Fliss, I., Spano, G. and Drider, D. (2019) Lactobacillus plantarum and Its Probiotic and Food Potentialities. Probiotics and Antimicrobial Proteins, 9, 111-122.
https://doi.org/10.1007/s12602-017-9264-z
[14]
Salas-Jara, M.J., Ilabaca, A., Vega, M. and García, A. (2016) Biofilm Forming Lactobacillus: New Challenges for the Development of Probiotics. Microorganisms, 4, pii: E35. https://doi.org/10.3390/microorganisms4030035
[15]
Martín, R., Miquel, S., Ulmer, J., Kechaou, N., Langella, P. and Bermúdez-Humarán, L.G. (2013) Role of Commensal and Probiotic Bacteria in Human Health: A Focus on Inflammatory Bowel Disease. Microbial Cell Factories, 12, 71. https://doi.org/10.1186/1475-2859-12-71
[16]
Chen, L., Oughtred, R., Berman, H.M. and Westbrook, J. (2004) TargetDB: A Target Registration Database for Structural Genomics Projects. Bioinformatics, 20, 2860-2862. https://doi.org/10.1093/bioinformatics/bth300
[17]
Yan, S. and Wu, G. (2011) Possible Random Mechanism in Crystallization Evidenced in Proteins from Plasmodium falciparum. Crystal Growth & Design, 11, 4198-4204. https://doi.org/10.1021/cg200814k
[18]
Yan, S. and Wu, G. (2012) Correlating Dynamic Amino Acid Properties with Success Rate of Crystallization of Proteins from Bacteroides vulgatus. Crystal Research and Technology, 47, 511-516.
https://doi.org/10.1002/crat.201200007
[19]
Yan, S. and Wu, G. (2012) Randomness in Crystallization of Proteins from Staphylococcus aureus. Protein & Peptide Letters, 19, 784-789. https://doi.org/10.2174/092986612800793190
[20]
Yan, S. and Wu, G. (2013) Association of Combined Features of Amino Acid and Protein with Crystallization Propensity of Proteins from Cytophaga hutchinsonii. Zeitschrift fur Kristallographie, 228, 250-254.
https://doi.org/10.1524/zkri.2013.1570
[21]
Yan, S., Wang, H. and Wu, G. (2013) Correlation of Combined Features of Amino Acid and Protein with Crystallization Propensity of Proteins from Caenorhabditis elegans. Guangxi Sciences, 20, 234-238.
[22]
Yan, S. and Wu, G. (2015) Predicting Crystallization Propensity of Proteins from Arabidopsis thaliana. Biological Procedures Online, 17, 16. https://doi.org/10.1186/s12575-015-0029-3
[23]
Feller, W. (1968) An Introduction to Probability Theory and Its Applications. Third Edition, Wiley, New York, Vol. 1.
[24]
http://www.gxas.cn/dp.htm
[25]
Wu, G. and Yan, S. (2005) Determination of Mutation Trend in Proteins by Means of Translation Probability between RNA Codes and Mutated Amino Acids. Biochemical and Biophysical Research Communications, 337, 692-700. https://doi.org/10.1016/j.bbrc.2005.09.106
[26]
Wu, G. and Yan, S. (2006) Determination of Mutation Trend in Hemagglutinins by Means of Translation Probability between RNA Codons and Mutated Amino Acids. Protein & Peptide Letters, 13, 601-609.
https://doi.org/10.2174/092986606777145779
[27]
Wu, G. and Yan, S. (2007) Translation Probability between RNA Codons and Translated Amino Acids, and Its Applications to Protein Mutations. In: Ostrovskiy M.H., Ed., Leading-Edge Messenger RNA Research Communications, Nova Science Publishers, New York, Chapter 3, 47-65.
[28]
http://www.gxas.cn/fc.htm
[29]
Kawashima, S., Pokarowski, P., Pokarowska, M., Kolinski, A., Katayama, T. and Kanehisa, M. (2008) AAindex: Amino Acid Index Database, Progress Report 2008. Nucleic Acids Research, 36, D202-D205.
https://doi.org/10.1093/nar/gkm998
[30]
Darby, N.J. and Creighton, T.E. (1993) Dissecting the Disulphide-Coupled Folding Pathway of Bovine Pancreatic Trypsin Inhibitor. Forming the First Disulphide Bonds in Analogues of the Reduced Protein. Journal of Molecular Biology, 232, 873-896. https://doi.org/10.1006/jmbi.1993.1437
[31]
Dwyer, D.S. (2005) Electronic Properties of Amino Acid Side Chains: Quantum Mechanics Calculation of Substituent Effects. BMC Chemical Biology, 5, 2. https://doi.org/10.1186/1472-6769-5-2
[32]
Chou, P.Y. and Fasman, G.D. (1978) Prediction of Secondary Structure of Proteins from Amino Acid Sequence. Advances in Enzymology and Related Subjects of Biochemistry, 47, 45-48.
[33]
Demuth, H. and Beale, M. (2001) Neural Network Toolbox for Use with MatLab. User’s Guide, Version 4.
[34]
MathWorks Inc. (1984-2001) MatLab—The Language of Technical Computing. Version 6.1.0.450, Release 12.1.
[35]
Alonzo, T. and Pepe, M.S. (2002) Distribution-Free ROC Analysis Using Binary Regression Techniques. Biostatistics, 3, 421-432. https://doi.org/10.1093/biostatistics/3.3.421
[36]
Cai, T.X., Pepe, M.S., Zheng, Y.Y., Lumley, T. and Jenny, N.S. (2006) The Sensitivity and Specificity of Markers for Event Times. Biostatistics, 7, 182-197. https://doi.org/10.1093/biostatistics/kxi047
[37]
Pepe, M., Longton, G. and Janes, H. (2009) Estimation and Comparison of Receiver Operating Characteristic Curves. Stata Journal, 9, 1. https://doi.org/10.1177/1536867X0900900101
[38]
Atchley, W.R., Zhao, J., Fernandes, A.D. and Druke, T. (2005) Solving the Protein Sequence Metric Problem. Proceedings of the National Academy of Sciences of the United States of America, 102, 6395-6400.
https://doi.org/10.1073/pnas.0408677102
[39]
Chou, K.C. (2011) Some Remarks on Protein Attribute Prediction and Pseudo Amino Acid Composition (50th Anniversary Year Review). Journal of Theoretical Biology, 273, 236-247.
https://doi.org/10.1016/j.jtbi.2010.12.024
[40]
Derewenda, Z.S. and Godzik, A. (2017) The “Sticky Patch” Model of Crystallization and Modification of Proteins for Enhanced Crystallizability. Methods in Molecular Biology, 1607, 77-115.
https://doi.org/10.1007/978-1-4939-7000-1_4
[41]
Altan, I., Charbonneau, P. and Snell, E.H. (2016) Computational Crystallization. Archives of Biochemistry and Biophysics, 602, 12-20. https://doi.org/10.1016/j.abb.2016.01.004
[42]
Cressey, D. and Callaway, E. (2017) Cryo-Electron Microscopy Wins Chemistry Nobel. Nature, 550, 167.
https://doi.org/10.1038/nature.2017.22738
