Generative artificial intelligence performs rudimentary structural biology modeling

Jordan, M. I. & Mitchell, T. M. Machine learning: Trends, perspectives, and prospects. Science 349, 255–260. https://doi.org/10.1126/science.aaa8415 (2015).ADS 
MathSciNet 
CAS 
PubMed 

Google Scholar 
Wang, H. et al. Scientific discovery in the age of artificial intelligence. Nature 620, 47–60. https://doi.org/10.1038/s41586-023-06221-2 (2023).ADS 
CAS 
PubMed 

Google Scholar 
Jumper, J. et al. Highly accurate protein structure prediction with alphafold. Nature 596, 583–589. https://doi.org/10.1038/s41586-021-03819-2 (2021).ADS 
CAS 
PubMed 
PubMed Central 

Google Scholar 
Baek, M. et al. Accurate prediction of protein structures and interactions using a three-track neural network. Science 373, 871–876. https://doi.org/10.1126/science.abj8754 (2021).ADS 
CAS 
PubMed 
PubMed Central 

Google Scholar 
Kryshtafovych, A., Schwede, T., Topf, M., Fidelis, K. & Moult, J. Critical assessment of methods of protein structure prediction (CASP)-round XIV. Proteins 89, 1607–1617. https://doi.org/10.1002/prot.26237 (2021).CAS 
PubMed 
PubMed Central 

Google Scholar 
Burley, S. K. & Berman, H. M. Open-access data: A cornerstone for artificial intelligence approaches to protein structure prediction. Structure 29, 515–520. https://doi.org/10.1016/j.str.2021.04.010 (2021).CAS 
PubMed 
PubMed Central 

Google Scholar 
Elnaggar, A. et al. ProtTrans: Toward understanding the language of life through self-supervised learning. IEEE Trans. Pattern Anal. Mach. Intell. 44, 7112–7127. https://doi.org/10.1109/TPAMI.2021.3095381 (2022).PubMed 

Google Scholar 
Chowdhury, R. et al. Single-sequence protein structure prediction using a language model and deep learning. Nat. Biotechnol. 40, 1617–1623. https://doi.org/10.1038/s41587-022-01432-w (2022).ADS 
CAS 
PubMed 
PubMed Central 

Google Scholar 
Lin, Z. et al. Evolutionary-scale prediction of atomic-level protein structure with a language model. Science 379, 1123–1130. https://doi.org/10.1126/science.ade2574 (2023).ADS 
MathSciNet 
CAS 
PubMed 

Google Scholar 
Bordin, N. et al. AlphaFold2 reveals commonalities and novelties in protein structure space for 21 model organisms. Commun. Biol. 6, 160. https://doi.org/10.1038/s42003-023-04488-9 (2023).CAS 
PubMed 
PubMed Central 

Google Scholar 
Bordin, N. et al. Novel machine learning approaches revolutionize protein knowledge. Trends Biochem. Sci. 48, 345–359. https://doi.org/10.1016/j.tibs.2022.11.001 (2023).CAS 
PubMed 
PubMed Central 

Google Scholar 
Mosalaganti, S. et al. AI-based structure prediction empowers integrative structural analysis of human nuclear pores. Science 376, eabm9506. https://doi.org/10.1126/science.abm9506 (2022).CAS 
PubMed 

Google Scholar 
Fontana, P. et al. Structure of cytoplasmic ring of nuclear pore complex by integrative cryo-EM and alphafold. Science 376, eabm9326. https://doi.org/10.1126/science.abm9326 (2022).CAS 
PubMed 
PubMed Central 

Google Scholar 
Read, R. J., Baker, E. N., Bond, C. S., Garman, E. F. & van Raaij, M. J. AlphaFold and the future of structural biology. Acta Crystallogr. F Struct. Biol. Commun. 79, 166–168. https://doi.org/10.1107/S2053230X23004934 (2023).CAS 
PubMed 
PubMed Central 

Google Scholar 
Edich, M., Briggs, D. C., Kippes, O., Gao, Y. & Thorn, A. The impact of AlphaFold2 on experimental structure solution. Faraday Discuss. 240, 184–195. https://doi.org/10.1039/d2fd00072e (2022).ADS 
CAS 
PubMed 
PubMed Central 

Google Scholar 
Varadi, M. et al. AlphaFold protein structure database in 2024: Providing structure coverage for over 214 million protein sequences. Nucleic Acids Res. https://doi.org/10.1093/nar/gkad1011 (2023).PubMed Central 

Google Scholar 
Varadi, M. & Velankar, S. The impact of AlphaFold protein structure database on the fields of life sciences. Proteomics 23, e2200128. https://doi.org/10.1002/pmic.202200128 (2023).CAS 
PubMed 

Google Scholar 
Burley, S. K., Arap, W. & Pasqualini, R. Predicting proteome-scale protein structure with artificial intelligence. N. Engl. J. Med. 385, 2191–2194. https://doi.org/10.1056/NEJMcibr2113027 (2021).PubMed 

Google Scholar 
Brown, T. B. et al. Language models are few-shot learners. arXiv https://doi.org/10.48550/arXiv.2005.14165 (2020).
Google Scholar 
OpenAI. GPT-4 Technical Report. arXiv https://doi.org/10.48550/arXiv.2303.08774 (2023).OpenAI. Introducing ChatGPT, <https://openai.com/blog/chatgpt> (2022).Hirschberg, J. & Manning, C. D. Advances in natural language processing. Science 349, 261–266. https://doi.org/10.1126/science.aaa8685 (2015).ADS 
MathSciNet 
CAS 
PubMed 

Google Scholar 
Vaswani, A. et al. Attention is all you need. arXiv https://doi.org/10.48550/arXiv.1706.03762 (2017).
Google Scholar 
Webb, T., Holyoak, K. J. & Lu, H. Emergent analogical reasoning in large language models. Nat. Hum. Behav. 7, 1526–1541. https://doi.org/10.1038/s41562-023-01659-w (2023).PubMed 

Google Scholar 
Hagendorff, T., Fabi, S. & Kosinski, M. Human-like intuitive behavior and reasoning biases emerged in large language models but disappeared in ChatGPT. Nat. Comput. Sci. 3, 833–838. https://doi.org/10.1038/s43588-023-00527-x (2023).PubMed 
PubMed Central 

Google Scholar 
Yax, N., Anlló, H. & Palminteri, S. Studying and improving reasoning in humans and machines. Commun. Psychol. 2, 51. https://doi.org/10.1038/s44271-024-00091-8 (2024).
Google Scholar 
Boiko, D. A., MacKnight, R., Kline, B. & Gomes, G. Autonomous chemical research with large language models. Nature 624, 570–578. https://doi.org/10.1038/s41586-023-06792-0 (2023).ADS 
CAS 
PubMed 
PubMed Central 

Google Scholar 
Jablonka, K. M., Schwaller, P., Ortega-Guerrero, A. & Smit, B. Leveraging large language models for predictive chemistry. Nat. Mach. Intell. https://doi.org/10.1038/s42256-023-00788-1 (2024).
Google Scholar 
Savage, N. Drug discovery companies are customizing ChatGPT: Here’s how. Nat. Biotechnol. 41, 585–586. https://doi.org/10.1038/s41587-023-01788-7 (2023).CAS 
PubMed 

Google Scholar 
Wang, R., Feng, H. & Wei, G. W. ChatGPT in drug discovery: A case study on anticocaine addiction drug development with chatbots. J. Chem. Inform. Model. 63, 7189–7209. https://doi.org/10.1021/acs.jcim.3c01429 (2023).CAS 

Google Scholar 
Lubiana, T. et al. Ten quick tips for harnessing the power of ChatGPT in computational biology. PLoS Comput. Biol. 19, e1011319. https://doi.org/10.1371/journal.pcbi.1011319 (2023).CAS 
PubMed 
PubMed Central 

Google Scholar 
Shue, E. et al. Empowering beginners in bioinformatics with ChatGPT. Quant. Biol. 11, 105–108. https://doi.org/10.15302/j-qb-023-0327 (2023).PubMed 
PubMed Central 

Google Scholar 
Karkera, N., Acharya, S. & Palaniappan, S. K. Leveraging pre-trained language models for mining microbiome-disease relationships. BMC Bioinform. 24, 290. https://doi.org/10.1186/s12859-023-05411-z (2023).
Google Scholar 
Xiao, Z. et al. Generative artificial intelligence GPT-4 accelerates knowledge mining and machine learning for synthetic biology. ACS Synth. Biol. 12, 2973–2982. https://doi.org/10.1021/acssynbio.3c00310 (2023).CAS 
PubMed 

Google Scholar 
Ille, A. M. & Mathews, M. B. AI interprets the central dogma and genetic code. Trends Biochem. Sci. 48, 1014–1018. https://doi.org/10.1016/j.tibs.2023.09.004 (2023).CAS 
PubMed 

Google Scholar 
Engh, R. A. & Huber, R. Accurate bond and angle parameters for X-ray protein structure refinement. Acta Crystallogr. Sect. A 47, 392–400. https://doi.org/10.1107/S0108767391001071 (1991).ADS 

Google Scholar 
Engh, R. A. & Huber, R. International Tables for Crystallography Volume F: Crystallography of Biological Macromolecules (Springer, 2001).
Google Scholar 
Berkholz, D. S., Shapovalov, M. V., Dunbrack, R. L. Jr. & Karplus, P. A. Conformation dependence of backbone geometry in proteins. Structure 17, 1316–1325. https://doi.org/10.1016/j.str.2009.08.012 (2009).CAS 
PubMed 
PubMed Central 

Google Scholar 
Shapovalov, M. V. & Dunbrack, R. L. Jr. A smoothed backbone-dependent rotamer library for proteins derived from adaptive kernel density estimates and regressions. Structure 19, 844–858. https://doi.org/10.1016/j.str.2011.03.019 (2011).CAS 
PubMed 
PubMed Central 

Google Scholar 
Fujii, N. & Saito, T. Homochirality and life. Chem. Rec. 4, 267–278. https://doi.org/10.1002/tcr.20020 (2004).CAS 
PubMed 

Google Scholar 
Mitchell J. B. O. & Smith, J. D‐amino acid residues in peptides and proteins. Proteins 50, 563–571. https://doi.org/10.1002/prot.10320 (2003).Meng, E. C. et al. UCSF chimeraX: Tools for structure building and analysis. Protein Sci. 32, e4792. https://doi.org/10.1002/pro.4792 (2023).CAS 
PubMed 
PubMed Central 

Google Scholar 
Doig, A. J. et al. Structure, stability and folding of the alpha-helix. Biochem. Soc. Symp. https://doi.org/10.1042/bss0680095 (2001).PubMed 

Google Scholar 
Eisenberg, D. The discovery of the alpha-helix and beta-sheet, the principal structural features of proteins. Proc. Natl. Acad. Sci. USA 100, 11207–11210. https://doi.org/10.1073/pnas.2034522100 (2003).ADS 
CAS 
PubMed 
PubMed Central 

Google Scholar 
Pauling, L., Corey, R. B. & Branson, H. R. The structure of proteins; two hydrogen-bonded helical configurations of the polypeptide chain. Proc. Natl. Acad. Sci. USA 37, 205–211. https://doi.org/10.1073/pnas.37.4.205 (1951).ADS 
CAS 
PubMed 
PubMed Central 

Google Scholar 
Pace, C. N. & Scholtz, J. M. A helix propensity scale based on experimental studies of peptides and proteins. Biophys. J. 75, 422–427. https://doi.org/10.1016/s0006-3495(98)77529-0 (1998).CAS 
PubMed 
PubMed Central 

Google Scholar 
Wolfram, S. ChatGPT Gets Its “Wolfram Superpowers”! https://writings.stephenwolfram.com/2023/03/chatgpt-gets-its-wolfram-superpowers/ (2023).Heinz, D. W., Baase, W. A. & Matthews, B. W. Folding and function of a T4 lysozyme containing 10 consecutive alanines illustrate the redundancy of information in an amino acid sequence. Proc. Natl. Acad. Sci. USA 89, 3751–3755. https://doi.org/10.1073/pnas.89.9.3751 (1992).ADS 
CAS 
PubMed 
PubMed Central 

Google Scholar 
Zhao, Y. et al. Crystal structure of SARS-CoV-2 main protease in complex with protease inhibitor PF-07321332. Protein Cell 13, 689–693. https://doi.org/10.1007/s13238-021-00883-2 (2022).CAS 
PubMed 

Google Scholar 
Hammond, J. et al. Oral nirmatrelvir for high-risk, nonhospitalized adults with Covid-19. N. Engl. J. Med. 386, 1397–1408. https://doi.org/10.1056/NEJMoa2118542 (2022).CAS 
PubMed 

Google Scholar 
Chatterjee, S., Bhattacharya, M., Dhama, K., Lee, S. S. & Chakraborty, C. Resistance to nirmatrelvir due to mutations in the Mpro in the subvariants of SARS-CoV-2 omicron: Another concern?. Mol. Ther. Nucleic Acids 32, 263–266. https://doi.org/10.1016/j.omtn.2023.03.013 (2023).CAS 
PubMed 
PubMed Central 

Google Scholar 
Hu, Y. et al. Naturally occurring mutations of SARS-CoV-2 main protease confer drug resistance to nirmatrelvir. ACS Cent. Sci. 9, 1658–1669. https://doi.org/10.1021/acscentsci.3c00538 (2023).CAS 
PubMed 
PubMed Central 

Google Scholar 
Iketani, S. et al. Multiple pathways for SARS-CoV-2 resistance to nirmatrelvir. Nature 613, 558–564. https://doi.org/10.1038/s41586-022-05514-2 (2023).ADS 
CAS 
PubMed 

Google Scholar 
Zhou, Y. et al. Nirmatrelvir-resistant SARS-CoV-2 variants with high fitness in an infectious cell culture system. Sci. Adv. 8, eadd7197. https://doi.org/10.1126/sciadv.add7197 (2022).CAS 
PubMed 
PubMed Central 

Google Scholar 
Zuckerman, N. S., Bucris, E., Keidar-Friedman, D., Amsalem, M. & Brosh-Nissimov, T. Nirmatrelvir resistance-de novo E166V/L50V mutations in an immunocompromised patient treated with prolonged nirmatrelvir/ritonavir monotherapy leading to clinical and virological treatment failure-a case report. Clin. Infect. Dis. https://doi.org/10.1093/cid/ciad494 (2023).
Google Scholar 
Hirotsu, Y. et al. Multidrug-resistant mutations to antiviral and antibody therapy in an immunocompromised patient infected with SARS-CoV-2. Med 4(813–824), e814. https://doi.org/10.1016/j.medj.2023.08.001 (2023).CAS 

Google Scholar 
Eisenstein, M. A test of artificial intelligence. Nature https://doi.org/10.1038/d41586-023-02822-z (2023).PubMed 
PubMed Central 

Google Scholar 
Biever, C. ChatGPT broke the turing test—The race is on for new ways to assess AI. Nature 619, 686–689. https://doi.org/10.1038/d41586-023-02361-7 (2023).ADS 
CAS 
PubMed 

Google Scholar 
Chakraborty, C., Bhattacharya, M. & Lee, S. S. Artificial intelligence enabled ChatGPT and large language models in drug target discovery, drug discovery, and development. Mol. Ther. Nucleic Acids 33, 866–868. https://doi.org/10.1016/j.omtn.2023.08.009 (2023).CAS 
PubMed 
PubMed Central 

Google Scholar 
Gurwitz, D. & Shomron, N. Artificial intelligence utility for drug development: ChatGPT and beyond. Drug Dev. Res. https://doi.org/10.1002/ddr.22121 (2023).PubMed 

Google Scholar 
OpenAI. ChatGPT plugins. https://openai.com/blog/chatgpt-plugins#code-interpreter (2023).Mirdita, M. et al. ColabFold: Making protein folding accessible to all. Nat. Methods 19, 679–682. https://doi.org/10.1038/s41592-022-01488-1 (2022).CAS 
PubMed 
PubMed Central 

Google Scholar 
Schrodinger, LLC. The PyMOL Molecular Graphics System, Version 2.5.7. https://www.pymol.org/ (2023).

Hot Topics

Related Articles