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AI-enabled alkaline-resistant evolution of protein to apply in mass production
Open accessPreprint — published as 10.7554/elife.102788 · Paper page
Abstract
Artificial intelligence (AI) models have been used to study the compositional regularities of proteins in nature, enabling it to assist in protein design to improve the efficiency of protein engineering and reduce manufacturing cost. However, in industrial settings, proteins are often required to work in extreme environments where they are relatively scarce or even non-existent in nature. Since such proteins are almost absent in the training datasets, it is uncertain whether AI model possesses the capability of evolving the protein to adapt extreme conditions. Antibodies are crucial components of affinity chromatography, and they are hoped to remain active at the extreme environments where most proteins cannot tolerate. In this study, we applied an advanced large language model (LLM), the Pro-PRIME model, to improve the alkali resistance of a representative antibody, a VHH antibody capable of binding to growth hormone. Through two rounds of design, we ensured that the selected mutant has enhanced functionality, including higher thermal stability, extreme pH resistance, and stronger affinity, thereby validating the generalized capability of the LLM in meeting specific demands. To the best of our knowledge, this is the first LLM-designed protein product, which is successfully applied in mass production.
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Accurate structure prediction of biomolecular interactions with AlphaFold 3
Abramson, J., Adler, J., Dunger, J., Evans, R., Green, T., Pritzel, A., Ronneberger, O., Willmore, L., Ballard, A. J., Bambrick, J., et al. (2024) · Nature · doi:10.1038/s41586-024-07487-w · Cited by 15,861
Evolutionary-scale prediction of atomic level protein structure with a language model
Lin, Z., Akin, H., Rao, R., Hie, B., Zhu, Z., Lu, W., Smetanin, N., Verkuil, R., Kabeli, O., Shmueli, Y., et al. (2022) · doi:10.1101/2022.07.20.500902 · Cited by 5,990
Deep learning-based kcat prediction enables improved enzyme-constrained model reconstruction
Li, F., Yuan, L., Lu, H., Li, G., Chen, Y., Engqvist, M. K. M., Kerkhoven, E. J., & Nielsen, J. (2022) · Nature Catalysis · doi:10.1038/s41929-022-00798-z · Cited by 466
Learning the language of viral evolution and escape
Hie, B., Zhong, E. D., Berger, B., & Bryson, B. (2021) · Science · doi:10.1126/science.abd7331 · Cited by 361
Rao, R., Liu, J., Verkuil, R., Meier, J., Canny, J. F., Abbeel, P., Sercu, T., & Rives, A. (2021) · doi:10.1101/2021.02.12.430858 · Cited by 350
Rapid protein stability prediction using deep learning representations
Blaabjerg, L. M., Kassem, M. M., Good, L. L., Jonsson, N., Cagiada, M., Johansson, K. E., Boomsma, W., Stein, A., & Lindorff-Larsen, K. (2023) · eLife · doi:10.7554/elife.82593 · Cited by 177
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Research works printed but not linked: 32
- Abraham, 2015, GROMACS: High performance molecular simulations through multi-level parallelism from laptops to supercomputers, SoftwareX, 1–2, 19, 10.1016/j.softx.2015.06.001 · DOI · Paper page
- Bjelkmar, 2010, Implementation of the CHARMM force field in GROMACS: analysis of protein stability effects from correction maps, virtual interaction sites, and water models, Journal of Chemical Theory and Computation, 6, 459, 10.1021/ct900549r · DOI
- Bussi, 2007, Canonical sampling through velocity rescaling, The Journal of Chemical Physics, 126, 10.1063/1.2408420 · DOI
- Essmann, 1995, A smooth particle mesh Ewald method, The Journal of Chemical Physics, 103, 8577, 10.1063/1.470117 · DOI
- Gülich, 2002, Engineering streptococcal protein G for increased alkaline stability, Protein Engineering, 15, 835, 10.1093/protein/15.10.835 · DOI
- Hess, 2008, GROMACS 4: algorithms for highly efficient, load-balanced, and scalable molecular simulation, Journal of Chemical Theory and Computation, 4, 435, 10.1021/ct700301q · DOI
- Jaenicke, 1991, Protein stability and molecular adaptation to extreme conditions, European Journal of Biochemistry, 202, 715, 10.1111/j.1432-1033.1991.tb16426.x · DOI
- Jang, 2022, Applications of artificial intelligence to enzyme and pathway design for metabolic engineering, Current Opinion in Biotechnology, 73, 101, 10.1016/j.copbio.2021.07.024 · DOI
- Jiang, 2024, A general temperature-guided language model to design proteins of enhanced stability and activity, Science Advances, 10, 10.1126/sciadv.adr2641 · DOI
- Khersonsky, 2018, Automated design of efficient and functionally diverse enzyme repertoires, Molecular Cell, 72, 178, 10.1016/j.molcel.2018.08.033 · DOI
- Li, 2023, SESNet: sequence-structure feature-integrated deep learning method for data-efficient protein engineering, Journal of Cheminformatics, 15, 10.1186/s13321-023-00688-x · DOI
- Linhult, 2004, Improving the tolerance of a protein a analogue to repeated alkaline exposures using a bypass mutagenesis approach, Proteins, 55, 407, 10.1002/prot.10616 · DOI
- Lovelock, 2022, The road to fully programmable protein catalysis, Nature, 606, 49, 10.1038/s41586-022-04456-z · DOI
- Lutz, 2018, Protein engineering: past, present, and future, Protein Engineering: Methods and Protocols, 1685, 1, 10.1007/978-1-4939-7366-8_1 · DOI
- Madani, 2023, Large language models generate functional protein sequences across diverse families, Nature Biotechnology, 41, 1099, 10.1038/s41587-022-01618-2 · DOI · Paper page
- Minakuchi, 2013, Remarkable alkaline stability of an engineered protein A as immunoglobulin affinity ligand: C domain having only one amino acid substitution, Protein Science, 22, 1230, 10.1002/pro.2310 · DOI
- Narayanan, 2021, Machine learning for biologics: opportunities for protein engineering, developability, and formulation, Trends in Pharmacological Sciences, 42, 151, 10.1016/j.tips.2020.12.004 · DOI
- Palmer, 2008, Design of stability at extreme alkaline pH in streptococcal protein G, Journal of Biotechnology, 134, 222, 10.1016/j.jbiotec.2007.12.009 · DOI
- Parrinello, 1981, Polymorphic transitions in single crystals: A new molecular dynamics method, Journal of Applied Physics, 52, 7182, 10.1063/1.328693 · DOI
- Pinney, 2021, Parallel molecular mechanisms for enzyme temperature adaptation, Science, 371, 10.1126/science.aay2784 · DOI
- Qiu, 2023, Artificial intelligence-aided protein engineering: from topological data analysis to deep protein language models, Briefings in Bioinformatics, 24, 10.1093/bib/bbad289 · DOI
- Rives, 2021, Biological structure and function emerge from scaling unsupervised learning to 250 million protein sequences, PNAS, 118, 10.1073/pnas.2016239118 · DOI · Paper page
- Sternke, 2019, Consensus sequence design as a general strategy to create hyperstable, biologically active proteins, PNAS, 116, 11275, 10.1073/pnas.1816707116 · DOI
- Tan Y, Li M, Tan P, Zhou Z, Yu H, Fan G, Hong L. 2023. PETA: evaluating the impact of protein transfer learning with sub-word tokenization on downstream applications. arXiv. https://arxiv.org/abs/2310.17415. · arXiv
- Tokuriki, 2009, Protein dynamism and evolvability, Science, 324, 203, 10.1126/science.1169375 · DOI
- Wang, 2022, BayeStab: Predicting effects of mutations on protein stability with uncertainty quantification, Protein Science, 31, 10.1002/pro.4467 · DOI
- Weinreich, 2006, Darwinian evolution can follow only very few mutational paths to fitter proteins, Science, 312, 111, 10.1126/science.1123539 · DOI
- Weiß, 2018, Solid-phase agar plate assay for screening amine transaminases, Protein Engineering: Methods and Protocols, 1685, 283, 10.1007/978-1-4939-7366-8_17 · DOI
- Wijma, 2013, Structure- and sequence-analysis inspired engineering of proteins for enhanced thermostability, Current Opinion in Structural Biology, 23, 588, 10.1016/j.sbi.2013.04.008 · DOI
- Xia, 2021, Development of thermostable sucrose phosphorylase by semi-rational design for efficient biosynthesis of alpha-D-glucosylglycerol, Applied Microbiology and Biotechnology, 105, 7309, 10.1007/s00253-021-11551-0 · DOI
- Xu, 2020, Recent advances in the improvement of enzyme thermostability by structure modification, Critical Reviews in Biotechnology, 40, 83, 10.1080/07388551.2019.1682963 · DOI
- Zhou, 2024, Enhancing efficiency of protein language models with minimal wet-lab data through few-shot learning, Nature Communications, 15, 10.1038/s41467-024-49798-6 · DOI
Metadata
- Authors
- Kang, L., Wu, B., Zhou, B., Tan, P., Kang, Y. (., Yan, Y., Zong, Y., Li, S., Liu, Z., & Hong, L.
- Affiliations
- Shanghai Jiao Tong University (CN)
- Institute of Natural Sciences, Shanghai Jiao Tong University
- Shanghai Artificial Intelligence Laboratory (CN)
- Changchun GeneScience Pharmaceuticals Co., Ltd.
- Harbin Medical University (CN)
- School of Physics and Astronomy, Shanghai Jiao Tong University
- Corresponding author
- Liu, Zhuo
- Venue
- eLife
- Publisher
- eLife Sciences Publications, Ltd
- Published
- 2025-02-19
- Volume
- 13
- ISSN
- 2050-084X (ISSN-L)
- OpenAlex
- W4402383785
- Cited by
- 0
- References
- 6
- FWCI
- 0.00
- Type
- journal article
- Language
- en
- Open access
- gold
- doi.org
- Licence
- Locations
- eLife (publishedVersion, cc-by)
- bioRxiv (Cold Spring Harbor Laboratory) (acceptedVersion, cc-by-nc-nd)
- Funders
- National Natural Science Foundation of China (12204302)
- Computational Biology Program of Shanghai Science and Technology Commission (23JS1400600)
- Shanghai Pujiang Program (22PJ1406900)
- Startup Fund for Young Faculty at SJTU (SFYF at SJTU)
- Oceanic Interdisciplinary Program of Shanghai Jiao Tong University (SL2022MS018)
- Natural Science Foundation of Shanghai (23ZR1431700)
- Shanghai Jiao Tong University Scientific and Technological Innovation Funds (21X010200843)
- Science and Technology Innovation Key R&D Program of Chongqing (CSTB2022TIAD-STX0017)
- Topics
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Protein Structure and Dynamics
- Life Sciences › Immunology and Microbiology › Immunology › Monoclonal and Polyclonal Antibodies Research
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Machine Learning in Bioinformatics
- Keywords
- protein design, large language models, nanobodies, growth hormone, alkali resistance, thermal stability, binding affinity, affinity chromatography, protein engineering, mass production
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