A BIOINFORMATICS MODULE FOR UNDERGRADUATE BIOCHEMISTRY: LEVERAGING ALPHAFOLD2 TO TEACH PROTEIN STRUCTURE PREDICTION
Main Article Content
Keywords
AlphaFold2, Protein Structure Prediction, Artificial Intelligence, Undergraduate Biochemistry Education, Bioinformatics Integration
Abstract
AlphaFold2 uses artificial intelligence to predict the detailed three-dimensional shape of proteins using information from their amino acids. Because it is very accurate and efficient, plus it benefits many biochemistry research areas, protein structure modeling should be featured in undergraduate biochemistry courses. An instructional example was produced to present AlphaFold2 to students in an advanced biochemistry laboratory. This module focused on helping students build the models of proteins produced by the genome of the recent global outbreak, for which there were no published, experimental structures. We sought to assess how the module changed student knowledge in biochemistry and to make it possible for educators at all bioinformatics levels to add AlphaFold2-based exercises into their classes.
References
1.Maveyraud L, Mourey L. Protein X-ray crystallography and drug discovery. Molecules. 2020;25(5). Epub 20200225. doi: 10.3390/molecules25051030 ; PubMed Central PMCID: PMC7179213. [DOI] [PMC free article] [PubMed] [Google Scholar]
2.Hu Y, Cheng K, He L, Zhang X, Jiang B, Jiang L, et al. NMR-based methods for protein analysis. Anal Chem. 2021;93(4):1866–79. doi: 10.1021/acs.analchem.0c03830 [DOI] [PubMed] [Google Scholar]
3.Peplow M. Cryo-electron microscopy reaches resolution milestone. ACS Cent Sci. 2020;6(8):1274–7. doi: 10.1021/acscentsci.0c01048 [DOI] [PMC free article] [PubMed] [Google Scholar]
4.McLaughlin KJ. Developing a macromolecular crystallography driven CURE. Struct Dyn. 2021;8(2):020406. Epub 20210330. doi: 10.1063/4.0000089 ; PubMed Central PMCID: PMC8012065. [DOI] [PMC free article] [PubMed] [Google Scholar]
5.Jumper J, Evans R, Pritzel A, Green T, Figurnov M, Ronneberger O, et al. Applying and improving AlphaFold at CASP14. Proteins: Struct. Funct. Bioinf. 2021;89(12):1711–21. doi: 10.1002/prot.26257 [DOI] [PMC free article] [PubMed] [Google Scholar]
6.Jumper J, Evans R, Pritzel A, Green T, Figurnov M, Ronneberger O, et al. Highly accurate protein structure prediction with AlphaFold. Nature. 2021;596(7873):583–9. Epub 20210715. doi: 10.1038/s41586-021-03819-2 ; PubMed Central PMCID: PMC8371605. [DOI] [PMC free article] [PubMed] [Google Scholar]
7.Pereira J, Simpkin AJ, Hartmann MD, Rigden DJ, Keegan RM, Lupas AN. High-accuracy protein structure prediction in CASP14. Proteins: Struct. Funct. Bioinf. 2021;89(12):1687–99. doi: 10.1002/prot.26171 [DOI] [PubMed] [Google Scholar]
8.Fontana P, Dong Y, Pi X, Tong AB, Hecksel CW, Wang L, et al. Structure of cytoplasmic ring of nuclear pore complex by integrative cryo-EM and AlphaFold. Science. 2022;376(6598):eabm9326. Epub 20220610. doi: 10.1126/science.abm9326 ; PubMed Central PMCID: PMC10054137. [DOI] [PMC free article] [PubMed] [Google Scholar]
9.Borkakoti N, Thornton JM. AlphaFold2 protein structure prediction: Implications for drug discovery. Curr Opin. Struct Biol. 2023;78:102526. Epub 20230106. doi: 10.1016/j.sbi.2022.102526 ; PubMed Central PMCID: PMC7614146. [DOI] [PMC free article] [PubMed] [Google Scholar]
10.Adams WK, Perkins KK, Podolefsky NS, Dubson M, Finkelstein ND, Wieman CE. New instrument for measuring student beliefs about physics and learning physics: The Colorado Learning Attitudes about Science Survey. Physical Review Special Topics. Phys Educ Res. 2006;2(1):010101. doi: 10.1103/PhysRevSTPER.2.010101 [DOI] [Google Scholar]
11.Adams WK, Wieman CE, Perkins KK, Barbera J. Modifying and validating the Colorado Learning Attitudes about Science Survey for Use in Chemistry. J Chem Educ. 2008;85(10):1435. doi: 10.1021/ed085p1435 [DOI] [Google Scholar]
12.Semsar K, Knight JK, Birol G, Smith MK. The Colorado Learning Attitudes about Science Survey (CLASS) for Use in Biology. CBE—Life Sci Educ. 2011;10(3):268–78. doi: 10.1187/cbe.10-10-0133 [DOI] [PMC free article] [PubMed] [Google Scholar]
13.Dorn B, Tew AE. Becoming experts: measuring attitude development in introductory computer science. Proceeding of the 44th ACM technical symposium on computer science education. Denver, Colorado, USA: Association for Computing Machinery; 2013. p. 183–8.
14.Marchler-Bauer A, Lu S, Anderson JB, Chitsaz F, Derbyshire MK, DeWeese-Scott C, et al. CDD: a Conserved Domain Database for the functional annotation of proteins. Nucleic Acids Res. 2011;39(Database issue):D225–9. Epub 20101124. doi: 10.1093/nar/gkq1189 ; PubMed Central PMCID: PMC3013737. [DOI] [PMC free article] [PubMed] [Google Scholar]
15.Madlung A. Assessing an effective undergraduate module teaching applied bioinformatics to biology students. PLoS Comput Biol. 2018;14(1):e1005872. Epub 20180111. doi: 10.1371/journal.pcbi.1005872 ; PubMed Central PMCID: PMC5764237. [DOI] [PMC free article] [PubMed] [Google Scholar]
16.Gauthier J, Vincent AT, Charette SJ, Derome N. A brief history of bioinformatics. Brief Bioinform. 2019;20(6):1981–96. doi: 10.1093/bib/bby063 . [DOI] [PubMed] [Google Scholar]
17.Wilson Sayres MA, Hauser C, Sierk M, Robic S, Rosenwald AG, Smith TM, et al. Bioinformatics core competencies for undergraduate life sciences education. PLoS ONE. 2018;13(6):e0196878. doi: 10.1371/journal.pone.0196878 [DOI] [PMC free article] [PubMed] [Google Scholar]
18.Mirdita M, Schütze K, Moriwaki Y, Heo L, Ovchinnikov S, Steinegger M. ColabFold: making protein folding accessible to all. Nat Methods. 2022;19(6):679–82. doi: 10.1038/s41592-022-01488-1 [DOI] [PMC free article] [PubMed] [Google Scholar]
19.Zhan L, Guo D, Chen G, Yang J. Effects of Repetition Learning on Associative Recognition Over Time: Role of the Hippocampus and Prefrontal Cortex. Front Hum Neurosci. 2018;12:277. Epub 20180711. doi: 10.3389/fnhum.2018.00277 ; PubMed Central PMCID: PMC6050388. [DOI] [PMC free article] [PubMed] [Google Scholar]
20.Paysan-Lafosse T, Blum M, Chuguransky S, Grego T, Pinto BL, Salazar Gustavo A, et al. InterPro in 2022. Nucleic Acids Res. 2022;51(D1):D418–D27. doi: 10.1093/nar/gkac993 [DOI] [PMC free article] [PubMed] [Google Scholar]
21.Hebert EJ, Grimsley GR, Hartley RW, Horn G, Schell D, Garcia S, et al. Purification of ribonucleases Sa, Sa2, and Sa3 after expression in Escherichia coli. Protein Expr Purif. 1997;11(2):162–8. doi: 10.1006/prep.1997.0776 . [DOI] [PubMed] [Google Scholar]
22.Shaw KL, Grimsley GR, Yakovlev GI, Makarov AA, Pace CN. The effect of net charge on the solubility, activity, and stability of ribonuclease Sa. Protein Sci. 2001;10(6):1206–15. doi: 10.1110/ps.440101 ; PubMed Central PMCID: PMC2374010. [DOI] [PMC free article] [PubMed] [Google Scholar]
23.Yakovlev GI, Mitkevich VA, Shaw KL, Trevino S, Newsom S, Pace CN, et al. Contribution of active site residues to the activity and thermal stability of ribonuclease Sa. Protein Sci. 2003;12(10):2367–73. doi: 10.1110/ps.03176803 ; PubMed Central PMCID: PMC2366910. [DOI] [PMC free article] [PubMed] [Google Scholar]
24.Pace CN, Hebert EJ, Shaw KL, Schell D, Both V, Krajcikova D, et al. Conformational stability and thermodynamics of folding of ribonucleases Sa, Sa2 and Sa3. J Mol Biol. 1998;279(1):271–86. doi: 10.1006/jmbi.1998.1760 . [DOI] [PubMed] [Google Scholar]
25.Nasari A, Le H, Lawrence R, He Z, Yang X, Krell M, et al. Benchmarking the Performance of Accelerators on National Cyberinfrastructure Resources for Artificial Intelligence / Machine Learning Workloads. Practice and Experience in Advanced Research Computing. Boston, MA, USA: Association for Computing Machinery; 2022. p. Article 19. [Google Scholar]
2.Hu Y, Cheng K, He L, Zhang X, Jiang B, Jiang L, et al. NMR-based methods for protein analysis. Anal Chem. 2021;93(4):1866–79. doi: 10.1021/acs.analchem.0c03830 [DOI] [PubMed] [Google Scholar]
3.Peplow M. Cryo-electron microscopy reaches resolution milestone. ACS Cent Sci. 2020;6(8):1274–7. doi: 10.1021/acscentsci.0c01048 [DOI] [PMC free article] [PubMed] [Google Scholar]
4.McLaughlin KJ. Developing a macromolecular crystallography driven CURE. Struct Dyn. 2021;8(2):020406. Epub 20210330. doi: 10.1063/4.0000089 ; PubMed Central PMCID: PMC8012065. [DOI] [PMC free article] [PubMed] [Google Scholar]
5.Jumper J, Evans R, Pritzel A, Green T, Figurnov M, Ronneberger O, et al. Applying and improving AlphaFold at CASP14. Proteins: Struct. Funct. Bioinf. 2021;89(12):1711–21. doi: 10.1002/prot.26257 [DOI] [PMC free article] [PubMed] [Google Scholar]
6.Jumper J, Evans R, Pritzel A, Green T, Figurnov M, Ronneberger O, et al. Highly accurate protein structure prediction with AlphaFold. Nature. 2021;596(7873):583–9. Epub 20210715. doi: 10.1038/s41586-021-03819-2 ; PubMed Central PMCID: PMC8371605. [DOI] [PMC free article] [PubMed] [Google Scholar]
7.Pereira J, Simpkin AJ, Hartmann MD, Rigden DJ, Keegan RM, Lupas AN. High-accuracy protein structure prediction in CASP14. Proteins: Struct. Funct. Bioinf. 2021;89(12):1687–99. doi: 10.1002/prot.26171 [DOI] [PubMed] [Google Scholar]
8.Fontana P, Dong Y, Pi X, Tong AB, Hecksel CW, Wang L, et al. Structure of cytoplasmic ring of nuclear pore complex by integrative cryo-EM and AlphaFold. Science. 2022;376(6598):eabm9326. Epub 20220610. doi: 10.1126/science.abm9326 ; PubMed Central PMCID: PMC10054137. [DOI] [PMC free article] [PubMed] [Google Scholar]
9.Borkakoti N, Thornton JM. AlphaFold2 protein structure prediction: Implications for drug discovery. Curr Opin. Struct Biol. 2023;78:102526. Epub 20230106. doi: 10.1016/j.sbi.2022.102526 ; PubMed Central PMCID: PMC7614146. [DOI] [PMC free article] [PubMed] [Google Scholar]
10.Adams WK, Perkins KK, Podolefsky NS, Dubson M, Finkelstein ND, Wieman CE. New instrument for measuring student beliefs about physics and learning physics: The Colorado Learning Attitudes about Science Survey. Physical Review Special Topics. Phys Educ Res. 2006;2(1):010101. doi: 10.1103/PhysRevSTPER.2.010101 [DOI] [Google Scholar]
11.Adams WK, Wieman CE, Perkins KK, Barbera J. Modifying and validating the Colorado Learning Attitudes about Science Survey for Use in Chemistry. J Chem Educ. 2008;85(10):1435. doi: 10.1021/ed085p1435 [DOI] [Google Scholar]
12.Semsar K, Knight JK, Birol G, Smith MK. The Colorado Learning Attitudes about Science Survey (CLASS) for Use in Biology. CBE—Life Sci Educ. 2011;10(3):268–78. doi: 10.1187/cbe.10-10-0133 [DOI] [PMC free article] [PubMed] [Google Scholar]
13.Dorn B, Tew AE. Becoming experts: measuring attitude development in introductory computer science. Proceeding of the 44th ACM technical symposium on computer science education. Denver, Colorado, USA: Association for Computing Machinery; 2013. p. 183–8.
14.Marchler-Bauer A, Lu S, Anderson JB, Chitsaz F, Derbyshire MK, DeWeese-Scott C, et al. CDD: a Conserved Domain Database for the functional annotation of proteins. Nucleic Acids Res. 2011;39(Database issue):D225–9. Epub 20101124. doi: 10.1093/nar/gkq1189 ; PubMed Central PMCID: PMC3013737. [DOI] [PMC free article] [PubMed] [Google Scholar]
15.Madlung A. Assessing an effective undergraduate module teaching applied bioinformatics to biology students. PLoS Comput Biol. 2018;14(1):e1005872. Epub 20180111. doi: 10.1371/journal.pcbi.1005872 ; PubMed Central PMCID: PMC5764237. [DOI] [PMC free article] [PubMed] [Google Scholar]
16.Gauthier J, Vincent AT, Charette SJ, Derome N. A brief history of bioinformatics. Brief Bioinform. 2019;20(6):1981–96. doi: 10.1093/bib/bby063 . [DOI] [PubMed] [Google Scholar]
17.Wilson Sayres MA, Hauser C, Sierk M, Robic S, Rosenwald AG, Smith TM, et al. Bioinformatics core competencies for undergraduate life sciences education. PLoS ONE. 2018;13(6):e0196878. doi: 10.1371/journal.pone.0196878 [DOI] [PMC free article] [PubMed] [Google Scholar]
18.Mirdita M, Schütze K, Moriwaki Y, Heo L, Ovchinnikov S, Steinegger M. ColabFold: making protein folding accessible to all. Nat Methods. 2022;19(6):679–82. doi: 10.1038/s41592-022-01488-1 [DOI] [PMC free article] [PubMed] [Google Scholar]
19.Zhan L, Guo D, Chen G, Yang J. Effects of Repetition Learning on Associative Recognition Over Time: Role of the Hippocampus and Prefrontal Cortex. Front Hum Neurosci. 2018;12:277. Epub 20180711. doi: 10.3389/fnhum.2018.00277 ; PubMed Central PMCID: PMC6050388. [DOI] [PMC free article] [PubMed] [Google Scholar]
20.Paysan-Lafosse T, Blum M, Chuguransky S, Grego T, Pinto BL, Salazar Gustavo A, et al. InterPro in 2022. Nucleic Acids Res. 2022;51(D1):D418–D27. doi: 10.1093/nar/gkac993 [DOI] [PMC free article] [PubMed] [Google Scholar]
21.Hebert EJ, Grimsley GR, Hartley RW, Horn G, Schell D, Garcia S, et al. Purification of ribonucleases Sa, Sa2, and Sa3 after expression in Escherichia coli. Protein Expr Purif. 1997;11(2):162–8. doi: 10.1006/prep.1997.0776 . [DOI] [PubMed] [Google Scholar]
22.Shaw KL, Grimsley GR, Yakovlev GI, Makarov AA, Pace CN. The effect of net charge on the solubility, activity, and stability of ribonuclease Sa. Protein Sci. 2001;10(6):1206–15. doi: 10.1110/ps.440101 ; PubMed Central PMCID: PMC2374010. [DOI] [PMC free article] [PubMed] [Google Scholar]
23.Yakovlev GI, Mitkevich VA, Shaw KL, Trevino S, Newsom S, Pace CN, et al. Contribution of active site residues to the activity and thermal stability of ribonuclease Sa. Protein Sci. 2003;12(10):2367–73. doi: 10.1110/ps.03176803 ; PubMed Central PMCID: PMC2366910. [DOI] [PMC free article] [PubMed] [Google Scholar]
24.Pace CN, Hebert EJ, Shaw KL, Schell D, Both V, Krajcikova D, et al. Conformational stability and thermodynamics of folding of ribonucleases Sa, Sa2 and Sa3. J Mol Biol. 1998;279(1):271–86. doi: 10.1006/jmbi.1998.1760 . [DOI] [PubMed] [Google Scholar]
25.Nasari A, Le H, Lawrence R, He Z, Yang X, Krell M, et al. Benchmarking the Performance of Accelerators on National Cyberinfrastructure Resources for Artificial Intelligence / Machine Learning Workloads. Practice and Experience in Advanced Research Computing. Boston, MA, USA: Association for Computing Machinery; 2022. p. Article 19. [Google Scholar]
Article Sidebar
Published
Dec 20, 2020
Article Details
How to Cite
Dr C Rathiga. (2020). A BIOINFORMATICS MODULE FOR UNDERGRADUATE BIOCHEMISTRY: LEVERAGING ALPHAFOLD2 TO TEACH PROTEIN STRUCTURE PREDICTION. Journal of Population Therapeutics and Clinical Pharmacology, 27(4), 153-159. https://doi.org/10.53555/f15twe94
Section
Articles
This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License.
All articles published in JPTCP are licensed under Copyright Creative Commons Attribution-NonCommercial 4.0 International License.
Author Biography
Dr C Rathiga
Associate Professor, Deportment of Biochemistry, Konaseema Institute of Medical Sciences & Research Foundation, Amalapuram, Andhra Pradesh, India
How to Cite
Dr C Rathiga. (2020). A BIOINFORMATICS MODULE FOR UNDERGRADUATE BIOCHEMISTRY: LEVERAGING ALPHAFOLD2 TO TEACH PROTEIN STRUCTURE PREDICTION. Journal of Population Therapeutics and Clinical Pharmacology, 27(4), 153-159. https://doi.org/10.53555/f15twe94