Protein sequence design with a learned potential
Open accessAbstract
The task of protein sequence design is central to nearly all rational protein engineering problems, and enormous effort has gone into the development of energy functions to guide design. Here, we investigate the capability of a deep neural network model to automate design of sequences onto protein backbones, having learned directly from crystal structure data and without any human-specified priors. The model generalizes to native topologies not seen during training, producing experimentally stable designs. We evaluate the generalizability of our method to a de novo TIM-barrel scaffold. The model produces novel sequences, and high-resolution crystal structures of two designs show excellent agreement with in silico models. Our findings demonstrate the tractability of an entirely learned method for protein sequence design.
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Machine learning sequence design approaches have been developed previously, but have focused on the monomer design problem, achieve lower native sequence recoveries, and with the exception of a TIM barrel design study have not been extensively validated using crystallography and cryoEM.
“Machine learning sequence design approaches have been developed previously (1–6, 6a), notably the previously described message passing method on which ProteinMPNN is based, but have focused on the monomer design problem, achieve lower native sequence recoveries, and with the exception of a TIM barrel design study (6) have not been extensively validated using crystallography and cryoEM to evaluate”
From Robust deep learning–based protein sequence design using ProteinMPNN (2022) · cites this paper as [6]
Split into 2 atoms — 1 confirmed, 1 partially supported, 0 disputed, 0 misattributed, 0 unverifiable. Expand the claim to see per-atom verdicts.
Experimental — accuracy not yet measuredThe 2.5 Å crystal structure of L056 has a global fold similar to predictions, with Cα RMSD 2.9 Å from trRosetta and 2.3 Å from WT T4 lysozyme.
“We determined a 2.5 Å resolution crystal of L056 (see Methods, Figure 3e). The global fold was similar to predictions, with a Cα RMSD of 2.9 Å from the backbone structure predicted by trRosetta and 2.3 Å RMSD from a WT T4 Lysozyme structure59,60.”
From Large language models generate functional protein sequences across diverse families (2023) · cites this paper as [60]
Jin et al. developed a model that generates both the sequence and structure of a CDR-H3 loop in an autoregressive manner.
“For instance, Jin et al. [69] developed a model that generates both the sequence and structure of a CDR-H3 loop in an autoregressive manner, similar to work conducted by the Huang group [70] on general protein sequence design.”
From Computational and artificial intelligence-based methods for antibody development (2023) · cites this paper as [70]
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Verkuil, R., Kabeli, O., Du, Y., Wicky, B. I. M., Milles, L. F., Dauparas, J., Baker, D., Ovchinnikov, S., Sercu, T., & Rives, A. (2022) · doi:10.1101/2022.12.21.521521 · Cited by 181 · see citing works
Computational and artificial intelligence-based methods for antibody development
Kim, J., McFee, M., Fang, Q., Abdin, O., & Kim, P. M. (2023) · Trends in Pharmacological Sciences · doi:10.1016/j.tips.2022.12.005 · Cited by 178 · see citing works
Protein sequence design by conformational landscape optimization
Norn, C., Wicky, B. I. M., Juergens, D., Liu, S., Kim, D., Tischer, D., Koepnick, B., Anishchenko, I., Players, F., Baker, D., et al. (2021) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.2017228118 · Cited by 144 · see citing works
Deep learning techniques have significantly impacted protein structure prediction and protein design
Pearce, R., & Zhang, Y. (2021) · Current Opinion in Structural Biology · doi:10.1016/j.sbi.2021.01.007 · Cited by 127 · see citing works
Ig-VAE: Generative modeling of protein structure by direct 3D coordinate generation
Eguchi, R. R., Choe, C. A., & Huang, P.-S. (2022) · PLOS Computational Biology · doi:10.1371/journal.pcbi.1010271 · Cited by 141 · see citing works
Self-citationProtein design via deep learning
Ding, W., Nakai, K., & Gong, H. (2022) · Briefings in Bioinformatics · doi:10.1093/bib/bbac102 · Cited by 66 · see citing works
- Authors
- Anand, N., Eguchi, R., Mathews, I. I., Perez, C. P., Derry, A., Altman, R. B., & Huang, P.-S.
- Affiliations
- Stanford University (US)
- Stanford Synchrotron Radiation Lightsource (US)
- Corresponding author
- Huang, Po-Ssu
- ORCID
- Venue
- Nature Communications
- Published
- 2022-02-08
- Volume
- 13
- Issue
- 1
- Pages
- 746-746
- ISSN
- 2041-1723 (ISSN-L)
- PMC
- PMC8826426
- OpenAlex
- W4210861939
- Cited by
- 171
- References
- 74
- Citations by year
- 2026: 21 · 2025: 26 · 2024: 46 · 2023: 40 · 2022: 25 · 2021: 7 · 2020: 6
- FWCI
- 11.18
- Type
- journal article
- Language
- en
- Open access
- gold
- doi.org
- Locations
- Nature Communications (publishedVersion, cc-by)
- PubMed Central (publishedVersion, cc-by)
- PubMed (publishedVersion)
- OSTI OAI (U.S. Department of Energy Office of Scientific and Technical Information) (submittedVersion)
- RePEc: Research Papers in Economics (submittedVersion)
- DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
- Funders
- U.S. Department of Energy (76SF00515, DE–AC02–76SF00515, AC02‐76SF00515, No. DE-AC02–76SF00515, Contract No. DE-AC02-76SF00515, P30-GM133894, DE-AC02-)
- National Institutes of Health (DE-AC02-76SF00515, LM012409, GM102365, GM61374)
- Office of Science (AC02-76SF00515, DE-AC02-76SF0051 5, DE-AC02, 76SF00515)
- Chemistry, Engineering and Medicine for Human Health, Stanford University
- National Institute of General Medical Sciences (R01GM102365, GM 61374, U01GM061374, R24GM061374, DE-AC02-76SF00515, T32GM120007, GM102365, P30GM133894)
- U.S. National Library of Medicine (LM012409, T32LM012409)
- Basic Energy Sciences (DE-AC02, 76SF00515, Contract No. DE-AC02-76SF00515, AC02-76SF00515, DE-AC-02-76SF00515)
- Advanced Scientific Computing Research (DE-AC02-76SF00515)
- Biological and Environmental Research (DE- AC0276SF00515, P30GM133894)
- SLAC National Accelerator Laboratory (DEAC02−76SF00515, AC02‐76SF00515, Contract No. DE-AC02-76SF00515)
- 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
- Physical Sciences › Materials Science › Materials Chemistry › Enzyme Structure and Function
- Keywords
- protein sequence design, deep neural network, energy function, crystallographic data, de novo protein design, TIM barrel, rational protein design, protein stability
- MeSH terms
- Protein Domains, Deep Learning, Amino Acid Sequence, Computer Simulation, Models, Molecular, Protein Engineering, Protein Folding, Crystallography, X-Ray
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
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