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Machine learning for functional protein design
Open accessAbstract
Notin, Rollins and colleagues discuss advances in computational protein design with a focus on redesign of existing proteins. Recent breakthroughs in AI coupled with the rapid accumulation of protein sequence and structure data have radically transformed computational protein design. New methods promise to escape the constraints of natural and laboratory evolution, accelerating the generation of proteins for applications in biotechnology and medicine. To make sense of the exploding diversity of machine learning approaches, we introduce a unifying framework that classifies models on the basis of their use of three core data modalities: sequences, structures and functional labels. We discuss the new capabilities and outstanding challenges for the practical design of enzymes, antibodies, vaccines, nanomachines and more. We then highlight trends shaping the future of this field, from large-scale assays to more robust benchmarks, multimodal foundation models, enhanced sampling strategies and laboratory automation.
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Cites
Highly accurate protein structure prediction with AlphaFold
Jumper, J., Evans, R., Pritzel, A., Green, T., Figurnov, M., Ronneberger, O., Tunyasuvunakool, K., Bates, R., Žídek, A., Potapenko, A., et al. (2021) · Nature · doi:10.1038/s41586-021-03819-2 · Cited by 43,764
Gene Ontology: tool for the unification of biology
Ashburner, M., Ball, C. A., Blake, J. A., Botstein, D., Butler, H., Cherry, J. M., Davis, A. P., Dolinski, K., Dwight, S. S., Eppig, J. T., et al. (2000) · Nature Genetics · doi:10.1038/75556 · Cited by 43,206
Learning Transferable Visual Models From Natural Language Supervision
Radford, A., Kim, J. W., Hallacy, C., Ramesh, A., Goh, G., Agarwal, S., Sastry, G., Askell, A., Mishkin, P., Clark, J., et al. (2021) · arXiv · doi:10.48550/arxiv.2103.00020 · Cited by 15,293
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
Geometric Deep Learning: Going beyond Euclidean data
Bronstein, M. M., Bruna, J., LeCun, Y., Szlam, A., & Vandergheynst, P. (2017) · IEEE Signal Processing Magazine · doi:10.1109/msp.2017.2693418 · Cited by 3,661
Rives, A., Meier, J., Sercu, T., Goyal, S., Lin, Z., Liu, J., Guo, D., Ott, M., Zitnick, C. L., Ma, J., et al. (2021) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.2016239118 · Cited by 3,286
Rives, A., Meier, J., Sercu, T., Goyal, S., Lin, Z., Liu, J., Guo, D., Ott, M., Zitnick, C. L., Ma, J., et al. (2019) · doi:10.1101/622803 · Cited by 3,280 · cited 1×
“This has led to a wide diversity of models, inspired by learning paradigms initially introduced in the natural language-processing literature, such as autoregressive modeling 20,52,61,141 , masked-language modeling 15,146,175,176 or seq2seq architectures 177,178 .”
Automatic Chemical Design Using a Data-Driven Continuous Representation of Molecules
Gómez-Bombarelli, R., Wei, J. N., Duvenaud, D., Hernández-Lobato, J. M., Sánchez-Lengeling, B., Sheberla, D., Aguilera-Iparraguirre, J., Hirzel, T. D., Adams, R. P., & Aspuru-Guzik, A. (2018) · ACS Central Science · doi:10.1021/acscentsci.7b00572 · Cited by 3,106
ChEMBL: towards direct deposition of bioassay data
Mendez, D., Gaulton, A., Bento, A. P., Chambers, J., De Veij, M., Félix, E., Magariños, M. P., Mosquera, J. F., Mutowo, P., Nowotka, M., et al. (2019) · Nucleic Acids Research · doi:10.1093/nar/gky1075 · Cited by 2,372
Watson, J. L., Juergens, D., Bennett, N. R., Trippe, B. L., Yim, J., Eisenach, H. E., Ahern, W., Borst, A. J., Ragotte, R. J., Milles, L. F., et al. (2022) · doi:10.1101/2022.12.09.519842 · Cited by 2,369
Research works printed but not linked: 19
- Nakatani, K. et al. Increase in the thermostability of Bacillus sp. strain TAR-1 xylanase using a site saturation mutagenesis library. Biosci. Biotechnol. Biochem. 82, 1715–1723 (2018). · doi:10.1080/09168451.2018.1495550
- Lee, J. S., Kim, J. & Kim, P. M. Score-based generative modeling for de novo protein design. Nat. Comput. Sci. 3, 382–392 (2023).
- Ingraham, J., Garg, V., Barzilay, R. & Jaakkola, T. Generative models for graph-based protein design. In Advances in Neural Information Processing Systems Vol. 32 (2019).
- Youssef, N. et al. Deep generative models predict SARS-CoV-2 spike infectivity and foreshadow neutralizing antibody escape. Preprint at bioRxiv https://doi.org/10.1101/2023.10.08.561389 (2023). · doi:10.1101/2023.10.08.561389
- Hesslow, D., Zanichelli, N., Notin, P., Poli, I. & Marks, D. RITA: a study on scaling up generative protein sequence models. Workshop on Computational Biology, ICML (2022).
- Munsamy, G., Lindner, S., Lorenz, P. & Ferruz, N. ZymCTRL: a conditional language model for the controllable generation of artificial enzymes. In Machine Learning for Structural Biology Workshop, NeurIPS (2022).
- Tagasovska, N. et al. A Pareto-optimal compositional energy-based model for sampling and optimization of protein sequences. Preprint at arXiv https://doi.org/10.48550/arXiv.2210.10838 (2022). · arXiv · doi:10.48550/arxiv.2210.10838
- Xu, M., Yuan, X., Miret, S. & Tang, J. ProtST: multi-modality learning of protein sequences and biomedical texts. In International Conference on Machine Learning Vol. 40 (PMLR, 2023).
- Darmawan, J. T., Gal, Y. & Notin, P. Sampling protein language models for functional protein design. In Generative AI and Biology (GenBio) Workshop, NeurIPS (2023).
- Kirjner, A. et al. Optimizing protein fitness using Gibbs sampling with graph-based smoothing. Preprint at arXiv https://doi.org/10.48550/arXiv.2307.00494 (2023). · arXiv · doi:10.48550/arxiv.2307.00494
- Truong, T. F. Jr. & Bepler, T. PoET: a generative model of protein families as sequences-of-sequences. Advances in Neural Information Processing Systems Vol. 36 (2023).
- Gruver, N. et al. Protein design with guided discrete diffusion. In Advances in Neural Information Processing Systems Vol. 36 (2023).
- Anand, N., Eguchi, R. & Huang, P.-S. Fully differentiable full-atom protein backbone generation. In Deep Generative Models for Highly Structured Data Workshop, ICLR (2019).
- Jing, B., Eismann, S., Suriana, P., Townshend, R. J. L. & Dror, R. Learning from protein structure with geometric vector perceptrons. In International Conference on Learning Representations Vol. 9 (2021).
- Gao, Z., Tan, C., Chacón, P. & Li, S. Z. PiFold: toward effective and efficient protein inverse folding. In International Conference on Learning Representations Vo. 11 (2023).
- Defferrard, M., Bresson, X. & Vandergheynst, P. Convolutional neural networks on graphs with fast localized spectral filtering. In Advances in Neural Information Processing Systems Vol. 29 (2016).
- Kipf, T. N. & Welling, M. Semi-supervised classification with graph convolutional networks. In International Conference on Learning Representations Vol. 5 (2017).
- Veličković, P. et al. Graph attention networks. In International Conference on Learning Representations Vol. 6 (2018).
- Notin, P., Hernández-Lobato, J. M. & Gal, Y. Improving black-box optimization in VAE latent space using decoder uncertainty. Adv. Neural Inf. Process. Syst. 34, 802–814 (2021).
Cited by
Evolutionary-scale enzymology enables exploration of a rugged catalytic landscape
(2025) · doi:10.1126/science.adu1058 · Cited by 42
Pseudo-perplexity in one fell swoop for protein fitness estimation
Kantroo, P., Wagner, G. P., & Machta, B. B. (2025) · PRX Life · doi:10.1103/zhx7-hcmm · Cited by 8
Cheng, L., Wei, T., Cui, X., Chen, H.-F., & Yu, Z. (2025) · Briefings in Bioinformatics · doi:10.1093/bib/bbaf391 · Cited by 1
Metadata
- Authors
- Notin, P., Rollins, N., Gal, Y., Sander, C., & Marks, D.
- Venue
- Nature Biotechnology
- Published
- 2024-02
- Volume
- 42
- Issue
- 2
- Pages
- 216-228
- ISSN
- 0733-222X (ISSN-L)
- 1087-0156
- 1546-1696
- OpenAlex
- W4391849830
- Cited by
- 270
- References
- 195
- FWCI
- 31.25
- Type
- journal article
- Language
- en
- Open access
- green
- Licence
- www.springernature.com/gp/researchers/text-and-data-mining (tdm)
- www.springernature.com/gp/researchers/text-and-data-mining (vor)
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