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Unified rational protein engineering with sequence-based deep representation learning
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Abstract
Rational protein engineering requires a holistic understanding of protein function. Here, we apply deep learning to unlabeled amino-acid sequences to distill the fundamental features of a protein into a statistical representation that is semantically rich and structurally, evolutionarily and biophysically grounded. We show that the simplest models built on top of this unified representation (UniRep) are broadly applicable and generalize to unseen regions of sequence space. Our data-driven approach predicts the stability of natural and de novo designed proteins, and the quantitative function of molecularly diverse mutants, competitively with the state-of-the-art methods. UniRep further enables two orders of magnitude efficiency improvement in a protein engineering task. UniRep is a versatile summary of fundamental protein features that can be applied across protein engineering informatics.
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“They also learn summary representations, powerful features that can be used to better capture sequence relationships and link sequence to function via transfer learning 27,28,32-35.”
From Learning the protein language: Evolution, structure, and function (2021) · cites this paper as [28]
In the citing paper
“Since then, various language model-based protein embedding methods have been applied to these and other protein prediction problems through transfer learning, including protein phenotype prediction 28,32-34, residue-residue contact prediction 32,70, fold recognition 33, protein-protein 71,72 and protein-drug interaction prediction 35,73.”
From Learning the protein language: Evolution, structure, and function (2021) · cites this paper as [28]
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“In recent years, deep learning architectures that were originally developed for natural language processing (NLP) tasks, such as translating a sentence from one language into another, have been applied successfully to the creation of numerical protein representations from amino acid sequences 17 , 29 .”
From Turnover number predictions for kinetically uncharacterized enzymes using machine and deep learning (2023) · cites this paper as [29]
In the citing paper
“These fingerprints are used together with a 1900-dimensioanl enzyme representation, called UniRep vector 29 , as the input for a gradient boosted decision tree model 34 to predict the K M value for an enzyme-substrate pair.”
From Turnover number predictions for kinetically uncharacterized enzymes using machine and deep learning (2023) · cites this paper as [29]
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“For the kinase and monoamine receptor subsets, the dimensions of the protein descriptors were reduced and consisted of the concatenation of the UniRep final cell state, final hidden state and average of hidden states each with 64 dimensions, early stopping was set to 20 epochs and batch size decreased to 64.”
InfluentialAdvancing microbial production through artificial intelligence-aided biology
Gong, X., Zhang, J., Gan, Q., Teng, Y., Hou, J., Lyu, Y., Liu, Z., Wu, Z., Dai, R., Zou, Y., et al. (2024) · Biotechnology Advances · doi:10.1016/j.biotechadv.2024.108399 · Cited by 85
Enzymes in biotechnology: Critical platform technologies for bioprocess development
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Deep-Learning Resources for Studying Glycan-Mediated Host-Microbe Interactions
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“, 2020) and proteins (Alley et al., 2019), because information in these sequences is order dependent, and non-neighboring residues can have meaningful interactions.”
BackgroundMethodInfluentialHuang, Q., Chen, A. T., Chan, K. Y., Sorensen, H., Barry, A. J., Azari, B., Zheng, Q., Beddow, T., Zhao, B., Tobey, I. G., et al. (2023) · PLoS Biology · doi:10.1371/journal.pbio.3002112 · Cited by 85
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Predicting the subcellular location of prokaryotic proteins with DeepLocPro
Moreno, J., Nielsen, H., Winther, O., & Teufel, F. (2024) · Bioinformatics · doi:10.1093/bioinformatics/btae677 · Cited by 76
“The neural network itself is agnostic to the organism group of origin, relying on the pLM’s ability to correctly infer the origin organism group from the sequence alone (Alley et al. 2019, Elnaggar et al. 2022, Teufel et al. 2022).”
Assessing antibody and nanobody nativeness for hit selection and humanization with AbNatiV
Ramon, A., Ali, M., Atkinson, M., Saturnino, A., Didi, K., Visentin, C., Ricagno, S., Xu, X., Greenig, M., & Sormanni, P. (2024) · Nature Machine Intelligence · doi:10.1038/s42256-023-00778-3 · Cited by 83
“The embedding of the VQ-VAE, and possibly also the last hidden layer of the decoder, can be seen as an effective way to distil the fundamental features of antibody variable domains into a representation that is semantically rich and structurally, evolutionarily and biophysically grounded 62 .”
AlphaDesign: A de novo protein design framework based on AlphaFold
Jendrusch, M., Korbel, J. O., & Sadiq, S. K. (2021) · doi:10.1101/2021.10.11.463937 · Cited by 65
“Designs are then improved using gradient-based [71–73], gradient-free [69, 74, 76] or neural-network based [75] optimisation.”
MethodSystematic multi-trait AAV capsid engineering for efficient gene delivery
Eid, F.-E., Chen, A. T., Chan, K. Y., Huang, Q., Zheng, Q., Tobey, I. G., Pacouret, S., Brauer, P. P., Keyes, C., Powell, M. M., et al. (2024) · Nature Communications · doi:10.1038/s41467-024-50555-y · Cited by 85
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“In the case of ML3-9, H3B1 and B1D2+A1 we observe a poor dynamic range from fitting a K Dapp , presumably due to poor expression or folding.”
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Molecular qubits based on photogenerated spin-correlated radical pairs for quantum sensing
Mani, T. (2022) · Chemical Physics Reviews · doi:10.1063/5.0084072 · Cited by 73
Novel machine learning approaches revolutionize protein knowledge
Bordin, N., Dallago, C., Heinzinger, M., Kim, S., Littmann, M., Rauer, C., Steinegger, M., Rost, B., & Orengo, C. (2023) · Trends in Biochemical Sciences · doi:10.1016/j.tibs.2022.11.001 · Cited by 72
“…hardware such as modern personal computers or even laptops. pLMs improve the prediction of protein function Since the introduction of the fi rst general-purpose pLMs around 3 years ago [46,48,50,56], pLMs have been shown to astutely capture aspects of protein structure, function, and evolution just”
BackgroundStructure‐Based Drug Discovery with Deep Learning**
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GPU-accelerated homology search with MMseqs2
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Self-Supervised Contrastive Learning of Protein Representations By Mutual Information MaximizationPreprint
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Leveraging transformers‐based language models in proteome bioinformatics
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“Transformers-based NLPmodelshave recentlyattractedsignificant interest due to their superior performance compared to conventional machine learning models on diverse NLP tasks [9, 14, 15].”
BackgroundSelf-play reinforcement learning guides protein engineering
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“…(detailed in Supplementary Table 13) for the surrogate after evaluating several other architectures (recurrent neural network-based UniRep 48 , MuFacNet 17 , GP 25 and hybrid 49 in eUniRep 16 employed Markov chain Monte Carlo (MCMC) algorithms with simulated annealing to stochastically…”
MethodInfluentialBiophysics-based protein language models for protein engineering
Gelman, S., Johnson, B., Freschlin, C. R., Sharma, A., D’Costa, S., Peters, J., Gitter, A., & Romero, P. A. (2025) · Nature Methods · doi:10.1038/s41592-025-02776-2 · Cited by 77
“The pretrained METL-Local model successfully identifies known interacting positions in GB1’s dynamic β1-β2 loop region, with pairwise combinations of positions 7, 9 and 11 all ranking in the top 10% of predicted positional epistasis.”
Connecting chemistry and biology through molecular descriptors
Fernández‐Torras, A., Comajuncosa-Creus, A., Duran‐Frigola, M., & Aloy, P. (2021) · Current Opinion in Chemical Biology · doi:10.1016/j.cbpa.2021.09.001 · Cited by 74
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Harnessing generative AI to decode enzyme catalysis and evolution for enhanced engineering
Xie, W. J., & Warshel, A. (2023) · National Science Review · doi:10.1093/nsr/nwad331 · Cited by 69
“The representation of protein sequences organizes based on the organism origin and protein secondary structure [45 ].”
Iterative Refinement Graph Neural Network for Antibody Sequence-Structure Co-designPreprint
Jin, W., Wohlwend, J., Barzilay, R., & Jaakkola, T. (2021) · arXiv (Cornell University) · doi:10.48550/arxiv.2110.04624 · Cited by 127
Protein design via deep learning
Ding, W., Nakai, K., & Gong, H. (2022) · Briefings in Bioinformatics · doi:10.1093/bib/bbac102 · Cited by 66
Protein structure prediction by AlphaFold 2: are attention and symmetries all you need?
Bouatta, N., Sorger, P., & AlQuraishi, M. (2021) · Acta Crystallographica Section D Structural Biology · doi:10.1107/s2059798321007531 · Cited by 69
Self-citation
Metadata
- Authors
- Alley, E. C., Khimulya, G., Biswas, S., AlQuraishi, M., & Church, G. M.
- Affiliations
- Harvard University (US)
- Corresponding author
- Church, George M.
- Venue
- Nature Methods
- Published
- 2019-10-21
- Volume
- 16
- Issue
- 12
- Pages
- 1315-1322
- ISSN
- 1548-7091 (ISSN-L)
- 1548-7105
- PMC
- PMC7067682
- OpenAlex
- W2980789587
- Cited by
- 1,131
- References
- 80
- Citations by year
- 2026: 130 · 2025: 170 · 2024: 201 · 2023: 197 · 2022: 188 · 2021: 159 · 2020: 69 · 2019: 17
- FWCI
- 35.71
- Type
- journal article
- Language
- en
- Open access
- green
- doi.org
- Licence
- Locations
- Nature Methods (publishedVersion)
- bioRxiv (Cold Spring Harbor Laboratory) (acceptedVersion, cc-by-nc-nd)
- Research Square (acceptedVersion, cc-by)
- PubMed (publishedVersion)
- Europe PMC (PubMed Central) (acceptedVersion)
- PubMed Central (acceptedVersion)
- OSTI OAI (U.S. Department of Energy Office of Scientific and Technical Information) (submittedVersion)
- Funders
- Amazon Web Services Cloud Credits for Research
- Center for Effective Altruism
- U.S. Department of Health & Human Services | National Institutes of Health (T32HG002295, U54-CA225088)
- National Science Foundation GRFP Fellowship
- U.S. Department of Health & Human Services | NIH | National Institute of General Medical Sciences (P50GM107618)
- Topics
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Protein Structure and Dynamics
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Machine Learning in Bioinformatics
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › RNA and protein synthesis mechanisms
- Keywords
- rational protein design, protein stability prediction, de novo protein design, protein mutants, recurrent neural networks, protein function prediction
- MeSH terms
- Deep Learning, Amino Acid Sequence, Mutation, Protein Engineering, Protein Stability
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