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Machine Learning Approaches for Differentiating Thermophilic and Mesophilic Lipases

Year 2024, Volume: 11 Issue: 4, 701 - 710, 30.12.2024
https://doi.org/10.54287/gujsa.1558391

Abstract

Differentiating thermophilic proteins from their mesophilic counterparts presents a significant challenge, yet achieving this distinction is crucial for the rational design of more stable proteins. In this study, a systematic analysis was performed on 3,715 unreviewed bacterial lipase enzymes obtained from the UniProt web server and screened according to their Tm values. Furthermore, a tree was constructed using the MEGA 11 program and lipase sequences from different families were selected. The final dataset consists of 88 mesophilic proteins and 123 thermophilic proteins were used. We found that Ile, Leu, aliphatic index, hydropathy, aliphatic amino acids, hydrophobic amino acids, tiny amino acids, and small amino acids are the key variables distinguishing thermophilic from mesophilic lipase proteins. These findings suggest that amino acid composition is crucial in differentiating these two groups.

References

  • Ahmed, Z., Zulfiqar, H., Tang, L., & Lin, H. (2022). A statistical analysis of the sequence and structure of thermophilic and non-thermophilic proteins. International Journal of Molecular Sciences, 23(17), 10116. https://doi.org/10.3390/ijms231710116
  • Alataş, E., Tanyıldızı Kökkülünk, H., Tanyıldızı, H., & Alcın, G. (2023). Treatment prediction with machine learning in prostate cancer patients. Computer Methods in Biomechanics and Biomedical Engineering, 1–9. https://doi.org/10.1080/10255842.2023.2298364
  • Albayrak, A., & Sezerman, U. O. (2012). Discrimination of thermophilic and mesophilic proteins using reduced amino acid alphabets with n-grams. Current Bioinformatics, 7(2), 152-158. https://doi.org/10.2174/157489312800604435
  • Ai, H., Zhang, L., Zhang, J., Cui, T., Chang, A. K., & Liu, H. (2018). Discrimination of thermophilic and mesophilic proteins using support vector machine and decision tree. Current Proteomics, 15(5), 374-383. https://doi.org/10.2174/1570164615666180718143606
  • Capriotti, E., Fariselli, P., & Casadio, R. (2004). A neural-network-based method for predicting protein stability changes upon single point mutations. Bioinformatics, 20, 63-68. https://doi.org/10.1093/bioinformatics/bth928
  • Capriotti, E., Fariselli, P., & Casadio, R. (2005). I-Mutant2.0: predicting stability changes upon mutation from the protein sequence or structure. Nucleic Acids Research, 33(2), 306-310. https://doi.org/10.1093/nar/gki375
  • Chakravorty, D., Faheem Khan, M., & Patra, S. (2017). Thermostability of proteins revisited through machine learning methodologies: From nucleotide sequence to structure. Current Biotechnology, 6(1), 39-49. https://doi.org/10.2174/2211550105666151222183232
  • Charoenkwan, P., Chotpatiwetchkul, W., Lee, V. S., Nantasenamat, C., & Shoombuatong, W. (2021). A novel sequence-based predictor for identifying and characterizing thermophilic proteins using estimated propensity scores of dipeptides. Scientific Reports, 11(1), 23782. https://doi.org/10.1038/s41598-021-03293-w
  • Christensen, N. J., & Kepp, K. P. (2013). Stability mechanisms of a thermophilic laccase probed by molecular dynamics. PloS One, 8(4), e61985. https://doi.org/10.1371/journal.pone.0061985
  • Dao, F.-Y., Yang, H., Su, Z.-D., Yang, W., Wu, Y., Hui, D., Chen, W., Tang, H., & Lin, H. (2017). Recent advances in conotoxin classification by using machine learning methods. Molecules, 22(7), 1057. https://doi.org/10.3390/molecules22071057
  • Das, R., & Gerstein, M. (2000). The stability of thermophilic proteins: a study based on comprehensive genome comparison. Functional & Integrative Genomics, 1(1), 76-88. https://doi.org/10.1007/s101420000003
  • Ding, Y., Cai, Y., Zhang, G., & Xu, W. (2004). The influence of dipeptide composition on protein thermostability. FEBS Letters, 569(1-3), 284-288. https://doi.org/10.1016/j.febslet.2004.06.009
  • Ding, Y. R., Cai, Y. J., Sun, J., & Xu, B. W. (2010). Identifying the mesophilic and thermophilic proteins from their amino acid composition with ν-support vector machines. Journal of Algorithms & Computational Technology, 4(3), 335-348. https://doi.org/10.1260/1748-3018.4.3.335
  • Dominy, B. N., Minoux, H., & Brooks III, C. L. (2004). An electrostatic basis for the stability of thermophilic proteins. Proteins: Structure, Function, and Bioinformatics, 57(1), 128-141. https://doi.org/10.1002/prot.20190
  • Feng, C., Ma, Z., Yang, D., Li, X., Zhang, J., & Li, Y. (2020). A method for prediction of thermophilic protein based on reduced amino acids and mixed features. Frontiers in Bioengineering and Biotechnology, 8, 285. https://doi.org/10.3389/fbioe.2020.00285
  • Gromiha, M. M. (2007). Prediction of protein stability upon point mutations. Biochemical Society Transactions, 35(6), 1569-1573. https://doi.org/10.1042/BST0351569
  • Gromiha, M. M., & Suresh, M. X. (2008). Discrimination of mesophilic and thermophilic proteins using machine learning algorithms. Proteins: Structure, Function, and Bioinformatics, 70(4), 1274-1279. https://doi.org/10.1002/prot.21616
  • Gromiha, M. M., Thomas, S., & Santhosh, C. (2002). Role of cation-π interactions in the stability of thermophilic proteins. Preparative Biochemistry and Biotechnology, 32(4), 355-362. https://doi.org/10.1081/PB-120015459
  • Hussian, C. H. A. C., & Leong, W. Y. (2023). Thermostable enzyme research advances: a bibliometric analysis. Journal of Genetic Engineering and Biotechnology, 21(1), 37. https://doi.org/10.1186/s43141-023-00494-w
  • Ikai, A. (1980). Thermostability and aliphatic index of globular proteins. The Journal of Biochemistry, 88(6), 1895-1898. https://doi.org/10.1093/oxfordjournals.jbchem.a133168
  • Kotsiantis, S. B. (2013). Decision trees: a recent overview. Artificial Intelligence Review, 39(4), 261-283. https://doi.org/10.1007/s10462-011-9272-4
  • Ku, T., Lu, P., Chan, C., Wang, T., Lai, S., Lyu, P., & Hsiao, N. (2009). Predicting melting temperature directly from protein sequences. Computational Biology and Chemistry, 33(6), 445-450. https://doi.org/10.1016/j.compbiolchem.2009.10.002 (The Tm Index program is available at http://tm.life.nthu.edu.tw/)
  • Kumar, S., Tsai, C.-J., & Nussinov, R. (2000). Factors enhancing protein thermostability. Protein Engineering, Design and Selection, 13(3), 179-191. https://doi.org/10.1093/protein/13.3.179
  • Kumar, M., Thakur, V., & Raghava, G. P. S. (2008). COPid: composition based protein identification. In Silico Biology, 8(2), 121-128. (Calculate Composition of Whole Protein is available at https://webs.iiitd.edu.in/raghava/COPid/whole_comp.html)
  • Liang, H.-K., Huang, C.-M., Ko, M.-T., & Hwang, J.-K. (2005). Amino acid coupling patterns in thermophilic proteins. Proteins: Structure, Function, and Bioinformatics, 59(1), 58-63. https://doi.org/10.1002/prot.20386
  • Li, W. F., Zhou, X. X., & Lu, P. (2005). Structural features of thermozymes. Biotechnology advances, 23(4), 271-281. https://doi.org/10.1016/j.biotechadv.2005.01.002
  • Li, Y., Zhang, J., Tai, D., Russell Middaugh, C., Zhang, Y., & Fang, J. (2012). PROTS: A fragment-based protein thermo-stability potential. Proteins: Structure, Function, and Bioinformatics, 80(1), 81-92. https://doi.org/10.1002/prot.23163
  • Lin, H., & Chen, W. (2011). Prediction of thermophilic proteins using feature selection technique. Journal of Microbiological Methods, 84(1), 67-70. https://doi.org/10.1016/j.mimet.2010.10.013
  • Liu, Y., Wang, Y., & Zhang, J. (2012, September 14-16). New machine learning algorithm: Random Forest. In: B. Liu, M. Ma, & J. Chang (Eds.), Proceedings of the Information Computing and Applications (pp. 246-252), Chengde, China. https://doi.org/10.1007/978-3-642-34062-8_32
  • Loladze, V. V., Ibarra-Molero, B., Sanchez-Ruiz, J. M., & Makhatadze, G. I. (1999). Engineering a thermostable protein via optimization of charge−charge interactions on the protein surface. Biochemistry, 38(50), 16419-16423. https://doi.org/10.1021/bi992271w
  • Marabotti, A., Scafuri, B., & Facchiano, A. (2021). Predicting the stability of mutant proteins by computational approaches: An overview. Briefings in Bioinformatics, 22(3), bbaa074. https://doi.org/10.1093/bib/bbaa074
  • Montanucci, L., Fariselli, P., Martelli, P. L., & Casadio, R. (2008). Predicting protein thermostability changes from sequence upon multiple mutations. Bioinformatics, 24(13), i190-i195. https://doi.org/10.1093/bioinformatics/btn166
  • Mrozek, D., & Małysiak-Mrozek, B. (2011). An improved method for protein similarity searching by alignment of fuzzy energy signatures. International Journal of Computational Intelligence Systems, 4(1), 75-88. https://doi.org/10.2991/ijcis.2011.4.1.7
  • Pack, S. P., & Yoo, Y. J. (2004). Protein thermostability: structure-based difference of amino acid between thermophilic and mesophilic proteins. Journal of Biotechnology, 111(3), 269-277. https://doi.org/10.1016/j.jbiotec.2004.01.018
  • Ponnuswamy, P. K., Muthusamy, R., & Manavalan, P. (1982). Amino acid composition and thermal stability of proteins. International Journal of Biological Macromolecules, 4(3), 186-190. https://doi.org/10.1016/0141-8130(82)90049-6
  • Razvi, A., & Scholtz, J. M. (2006). Lessons in stability from thermophilic proteins. Protein Science, 15(7), 1569-1578. https://doi.org/10.1110/ps.062130306
  • Rigoldi, F., Donini, S., Redaelli, A., Parisini, E., & Gautieri, A. (2018). Engineering of thermostable enzymes for industrial applications. APL Bioengineering, 2(1), 011501. https://doi.org/10.1063/1.4997367
  • Sahoo, R. K., Sanket, A. S., Gaur, M., Das, A., & Subudhi, E. (2019). Insight into the structural configuration of metagenomically derived lipase from diverse extreme environment. Biocatalysis and Agricultural Biotechnology, 22, 101404. https://doi.org/10.1016/j.bcab.2019.101404
  • Strickler, S. S., Gribenko, A. V., Gribenko, A. V., Keiffer, T. R., Tomlinson, J., Reihle, T., Loladze, V. V., & Makhatadze, G. I. (2006). Protein stability and surface electrostatics: a charged relationship. Biochemistry, 45(9), 2761-2766. https://doi.org/10.1021/bi0600143
  • Tamura, K., Stecher, G., & Kumar, S. (2021). MEGA11: molecular evolutionary genetics analysis version 11. Molecular Biology and Evolution, 38(7), 3022-3027. https://doi.org/10.1093/molbev/msab120
  • Tian, J., Wu, N., Chu, X., & Fan, Y. (2010). Predicting changes in protein thermostability brought about by single- or multi-site mutations. BMC Bioinformatics, 11, 1. https://doi.org/10.1186/1471-2105-11-370
  • Vardar-Yel, N., Tütüncü, H. E., & Sürmeli, Y. (2024). Lipases for targeted industrial applications, focusing on the development of biotechnologically significant aspects: A comprehensive review of recent trends in protein engineering. International Journal of Biological Macromolecules, 273, 132853. https://doi.org/10.1016/j.ijbiomac.2024.132853
  • Wang, X.-F., Gao, P., Liu, Y.-F., Li, H.-F., & Lu, F. (2020). Predicting thermophilic proteins by machine learning. Current Bioinformatics, 15(5), 493-502. https://doi.org/10.2174/1574893615666200207094357
  • Wijma, H. J., Floor, R. J., & Janssen, D. B. (2013). Structure-and sequence-analysis inspired engineering of proteins for enhanced thermostability. Current Opinion in Structural Biology, 23(4), 588-594. https://doi.org/10.1016/j.sbi.2013.04.008
  • Wu, L.-C., Lee, J.-X., Huang, H.-D., Liu, B.-J., & Horng, J.-T. (2009). An expert system to predict protein thermostability using decision tree. Expert Systems with Applications, 36(5), 9007-9014. https://doi.org/10.1016/j.eswa.2008.12.020
  • Zhang, G., & Fang, B. (2006a). Application of amino acid distribution along the sequence for discriminating mesophilic and thermophilic proteins. Process biochemistry, 41(8), 1792-1798. https://doi.org/10.1016/j.procbio.2006.03.026
  • Zhang, G., & Fang, B. (2006b). Discrimination of thermophilic and mesophilic proteins via pattern recognition methods. Process Biochemistry, 41(3), 552-556. https://doi.org/10.1016/j.procbio.2005.09.003
  • Zhang, G., & Fang, B. (2007). LogitBoost classifier for discriminating thermophilic and mesophilic proteins. Journal of Biotechnology, 127(3), 417-424. https://doi.org/10.1016/j.jbiotec.2006.07.020
  • Zhou, X.-X., Wang, Y.-B., Pan, Y.-J., & Li, W.-F. (2008). Differences in amino acids composition and coupling patterns between mesophilic and thermophilic proteins. Amino Acids, 34(1), 25-33. https://doi.org/10.1007/s00726-007-0589-x
Year 2024, Volume: 11 Issue: 4, 701 - 710, 30.12.2024
https://doi.org/10.54287/gujsa.1558391

Abstract

References

  • Ahmed, Z., Zulfiqar, H., Tang, L., & Lin, H. (2022). A statistical analysis of the sequence and structure of thermophilic and non-thermophilic proteins. International Journal of Molecular Sciences, 23(17), 10116. https://doi.org/10.3390/ijms231710116
  • Alataş, E., Tanyıldızı Kökkülünk, H., Tanyıldızı, H., & Alcın, G. (2023). Treatment prediction with machine learning in prostate cancer patients. Computer Methods in Biomechanics and Biomedical Engineering, 1–9. https://doi.org/10.1080/10255842.2023.2298364
  • Albayrak, A., & Sezerman, U. O. (2012). Discrimination of thermophilic and mesophilic proteins using reduced amino acid alphabets with n-grams. Current Bioinformatics, 7(2), 152-158. https://doi.org/10.2174/157489312800604435
  • Ai, H., Zhang, L., Zhang, J., Cui, T., Chang, A. K., & Liu, H. (2018). Discrimination of thermophilic and mesophilic proteins using support vector machine and decision tree. Current Proteomics, 15(5), 374-383. https://doi.org/10.2174/1570164615666180718143606
  • Capriotti, E., Fariselli, P., & Casadio, R. (2004). A neural-network-based method for predicting protein stability changes upon single point mutations. Bioinformatics, 20, 63-68. https://doi.org/10.1093/bioinformatics/bth928
  • Capriotti, E., Fariselli, P., & Casadio, R. (2005). I-Mutant2.0: predicting stability changes upon mutation from the protein sequence or structure. Nucleic Acids Research, 33(2), 306-310. https://doi.org/10.1093/nar/gki375
  • Chakravorty, D., Faheem Khan, M., & Patra, S. (2017). Thermostability of proteins revisited through machine learning methodologies: From nucleotide sequence to structure. Current Biotechnology, 6(1), 39-49. https://doi.org/10.2174/2211550105666151222183232
  • Charoenkwan, P., Chotpatiwetchkul, W., Lee, V. S., Nantasenamat, C., & Shoombuatong, W. (2021). A novel sequence-based predictor for identifying and characterizing thermophilic proteins using estimated propensity scores of dipeptides. Scientific Reports, 11(1), 23782. https://doi.org/10.1038/s41598-021-03293-w
  • Christensen, N. J., & Kepp, K. P. (2013). Stability mechanisms of a thermophilic laccase probed by molecular dynamics. PloS One, 8(4), e61985. https://doi.org/10.1371/journal.pone.0061985
  • Dao, F.-Y., Yang, H., Su, Z.-D., Yang, W., Wu, Y., Hui, D., Chen, W., Tang, H., & Lin, H. (2017). Recent advances in conotoxin classification by using machine learning methods. Molecules, 22(7), 1057. https://doi.org/10.3390/molecules22071057
  • Das, R., & Gerstein, M. (2000). The stability of thermophilic proteins: a study based on comprehensive genome comparison. Functional & Integrative Genomics, 1(1), 76-88. https://doi.org/10.1007/s101420000003
  • Ding, Y., Cai, Y., Zhang, G., & Xu, W. (2004). The influence of dipeptide composition on protein thermostability. FEBS Letters, 569(1-3), 284-288. https://doi.org/10.1016/j.febslet.2004.06.009
  • Ding, Y. R., Cai, Y. J., Sun, J., & Xu, B. W. (2010). Identifying the mesophilic and thermophilic proteins from their amino acid composition with ν-support vector machines. Journal of Algorithms & Computational Technology, 4(3), 335-348. https://doi.org/10.1260/1748-3018.4.3.335
  • Dominy, B. N., Minoux, H., & Brooks III, C. L. (2004). An electrostatic basis for the stability of thermophilic proteins. Proteins: Structure, Function, and Bioinformatics, 57(1), 128-141. https://doi.org/10.1002/prot.20190
  • Feng, C., Ma, Z., Yang, D., Li, X., Zhang, J., & Li, Y. (2020). A method for prediction of thermophilic protein based on reduced amino acids and mixed features. Frontiers in Bioengineering and Biotechnology, 8, 285. https://doi.org/10.3389/fbioe.2020.00285
  • Gromiha, M. M. (2007). Prediction of protein stability upon point mutations. Biochemical Society Transactions, 35(6), 1569-1573. https://doi.org/10.1042/BST0351569
  • Gromiha, M. M., & Suresh, M. X. (2008). Discrimination of mesophilic and thermophilic proteins using machine learning algorithms. Proteins: Structure, Function, and Bioinformatics, 70(4), 1274-1279. https://doi.org/10.1002/prot.21616
  • Gromiha, M. M., Thomas, S., & Santhosh, C. (2002). Role of cation-π interactions in the stability of thermophilic proteins. Preparative Biochemistry and Biotechnology, 32(4), 355-362. https://doi.org/10.1081/PB-120015459
  • Hussian, C. H. A. C., & Leong, W. Y. (2023). Thermostable enzyme research advances: a bibliometric analysis. Journal of Genetic Engineering and Biotechnology, 21(1), 37. https://doi.org/10.1186/s43141-023-00494-w
  • Ikai, A. (1980). Thermostability and aliphatic index of globular proteins. The Journal of Biochemistry, 88(6), 1895-1898. https://doi.org/10.1093/oxfordjournals.jbchem.a133168
  • Kotsiantis, S. B. (2013). Decision trees: a recent overview. Artificial Intelligence Review, 39(4), 261-283. https://doi.org/10.1007/s10462-011-9272-4
  • Ku, T., Lu, P., Chan, C., Wang, T., Lai, S., Lyu, P., & Hsiao, N. (2009). Predicting melting temperature directly from protein sequences. Computational Biology and Chemistry, 33(6), 445-450. https://doi.org/10.1016/j.compbiolchem.2009.10.002 (The Tm Index program is available at http://tm.life.nthu.edu.tw/)
  • Kumar, S., Tsai, C.-J., & Nussinov, R. (2000). Factors enhancing protein thermostability. Protein Engineering, Design and Selection, 13(3), 179-191. https://doi.org/10.1093/protein/13.3.179
  • Kumar, M., Thakur, V., & Raghava, G. P. S. (2008). COPid: composition based protein identification. In Silico Biology, 8(2), 121-128. (Calculate Composition of Whole Protein is available at https://webs.iiitd.edu.in/raghava/COPid/whole_comp.html)
  • Liang, H.-K., Huang, C.-M., Ko, M.-T., & Hwang, J.-K. (2005). Amino acid coupling patterns in thermophilic proteins. Proteins: Structure, Function, and Bioinformatics, 59(1), 58-63. https://doi.org/10.1002/prot.20386
  • Li, W. F., Zhou, X. X., & Lu, P. (2005). Structural features of thermozymes. Biotechnology advances, 23(4), 271-281. https://doi.org/10.1016/j.biotechadv.2005.01.002
  • Li, Y., Zhang, J., Tai, D., Russell Middaugh, C., Zhang, Y., & Fang, J. (2012). PROTS: A fragment-based protein thermo-stability potential. Proteins: Structure, Function, and Bioinformatics, 80(1), 81-92. https://doi.org/10.1002/prot.23163
  • Lin, H., & Chen, W. (2011). Prediction of thermophilic proteins using feature selection technique. Journal of Microbiological Methods, 84(1), 67-70. https://doi.org/10.1016/j.mimet.2010.10.013
  • Liu, Y., Wang, Y., & Zhang, J. (2012, September 14-16). New machine learning algorithm: Random Forest. In: B. Liu, M. Ma, & J. Chang (Eds.), Proceedings of the Information Computing and Applications (pp. 246-252), Chengde, China. https://doi.org/10.1007/978-3-642-34062-8_32
  • Loladze, V. V., Ibarra-Molero, B., Sanchez-Ruiz, J. M., & Makhatadze, G. I. (1999). Engineering a thermostable protein via optimization of charge−charge interactions on the protein surface. Biochemistry, 38(50), 16419-16423. https://doi.org/10.1021/bi992271w
  • Marabotti, A., Scafuri, B., & Facchiano, A. (2021). Predicting the stability of mutant proteins by computational approaches: An overview. Briefings in Bioinformatics, 22(3), bbaa074. https://doi.org/10.1093/bib/bbaa074
  • Montanucci, L., Fariselli, P., Martelli, P. L., & Casadio, R. (2008). Predicting protein thermostability changes from sequence upon multiple mutations. Bioinformatics, 24(13), i190-i195. https://doi.org/10.1093/bioinformatics/btn166
  • Mrozek, D., & Małysiak-Mrozek, B. (2011). An improved method for protein similarity searching by alignment of fuzzy energy signatures. International Journal of Computational Intelligence Systems, 4(1), 75-88. https://doi.org/10.2991/ijcis.2011.4.1.7
  • Pack, S. P., & Yoo, Y. J. (2004). Protein thermostability: structure-based difference of amino acid between thermophilic and mesophilic proteins. Journal of Biotechnology, 111(3), 269-277. https://doi.org/10.1016/j.jbiotec.2004.01.018
  • Ponnuswamy, P. K., Muthusamy, R., & Manavalan, P. (1982). Amino acid composition and thermal stability of proteins. International Journal of Biological Macromolecules, 4(3), 186-190. https://doi.org/10.1016/0141-8130(82)90049-6
  • Razvi, A., & Scholtz, J. M. (2006). Lessons in stability from thermophilic proteins. Protein Science, 15(7), 1569-1578. https://doi.org/10.1110/ps.062130306
  • Rigoldi, F., Donini, S., Redaelli, A., Parisini, E., & Gautieri, A. (2018). Engineering of thermostable enzymes for industrial applications. APL Bioengineering, 2(1), 011501. https://doi.org/10.1063/1.4997367
  • Sahoo, R. K., Sanket, A. S., Gaur, M., Das, A., & Subudhi, E. (2019). Insight into the structural configuration of metagenomically derived lipase from diverse extreme environment. Biocatalysis and Agricultural Biotechnology, 22, 101404. https://doi.org/10.1016/j.bcab.2019.101404
  • Strickler, S. S., Gribenko, A. V., Gribenko, A. V., Keiffer, T. R., Tomlinson, J., Reihle, T., Loladze, V. V., & Makhatadze, G. I. (2006). Protein stability and surface electrostatics: a charged relationship. Biochemistry, 45(9), 2761-2766. https://doi.org/10.1021/bi0600143
  • Tamura, K., Stecher, G., & Kumar, S. (2021). MEGA11: molecular evolutionary genetics analysis version 11. Molecular Biology and Evolution, 38(7), 3022-3027. https://doi.org/10.1093/molbev/msab120
  • Tian, J., Wu, N., Chu, X., & Fan, Y. (2010). Predicting changes in protein thermostability brought about by single- or multi-site mutations. BMC Bioinformatics, 11, 1. https://doi.org/10.1186/1471-2105-11-370
  • Vardar-Yel, N., Tütüncü, H. E., & Sürmeli, Y. (2024). Lipases for targeted industrial applications, focusing on the development of biotechnologically significant aspects: A comprehensive review of recent trends in protein engineering. International Journal of Biological Macromolecules, 273, 132853. https://doi.org/10.1016/j.ijbiomac.2024.132853
  • Wang, X.-F., Gao, P., Liu, Y.-F., Li, H.-F., & Lu, F. (2020). Predicting thermophilic proteins by machine learning. Current Bioinformatics, 15(5), 493-502. https://doi.org/10.2174/1574893615666200207094357
  • Wijma, H. J., Floor, R. J., & Janssen, D. B. (2013). Structure-and sequence-analysis inspired engineering of proteins for enhanced thermostability. Current Opinion in Structural Biology, 23(4), 588-594. https://doi.org/10.1016/j.sbi.2013.04.008
  • Wu, L.-C., Lee, J.-X., Huang, H.-D., Liu, B.-J., & Horng, J.-T. (2009). An expert system to predict protein thermostability using decision tree. Expert Systems with Applications, 36(5), 9007-9014. https://doi.org/10.1016/j.eswa.2008.12.020
  • Zhang, G., & Fang, B. (2006a). Application of amino acid distribution along the sequence for discriminating mesophilic and thermophilic proteins. Process biochemistry, 41(8), 1792-1798. https://doi.org/10.1016/j.procbio.2006.03.026
  • Zhang, G., & Fang, B. (2006b). Discrimination of thermophilic and mesophilic proteins via pattern recognition methods. Process Biochemistry, 41(3), 552-556. https://doi.org/10.1016/j.procbio.2005.09.003
  • Zhang, G., & Fang, B. (2007). LogitBoost classifier for discriminating thermophilic and mesophilic proteins. Journal of Biotechnology, 127(3), 417-424. https://doi.org/10.1016/j.jbiotec.2006.07.020
  • Zhou, X.-X., Wang, Y.-B., Pan, Y.-J., & Li, W.-F. (2008). Differences in amino acids composition and coupling patterns between mesophilic and thermophilic proteins. Amino Acids, 34(1), 25-33. https://doi.org/10.1007/s00726-007-0589-x
There are 49 citations in total.

Details

Primary Language English
Subjects Bioinformatics and Computational Biology (Other)
Journal Section Biological Sciences
Authors

Nurcan Vardar Yel 0000-0003-0994-5871

Publication Date December 30, 2024
Submission Date September 30, 2024
Acceptance Date October 25, 2024
Published in Issue Year 2024 Volume: 11 Issue: 4

Cite

APA Vardar Yel, N. (2024). Machine Learning Approaches for Differentiating Thermophilic and Mesophilic Lipases. Gazi University Journal of Science Part A: Engineering and Innovation, 11(4), 701-710. https://doi.org/10.54287/gujsa.1558391