An evolution-based model for designing chorismate mutase enzymes
Abstract
Learning from evolution: Protein sequences contain information specifying their three-dimensional structure and function, and statistical analysis of families of sequences has been used to predict these properties. Building from sequence data, Russ et al. used statistical models that take into account conservation at amino acid positions and correlations in the evolution of pairs of amino acids to predict new artificial sequences that will have the properties of the protein family. For the chorismate mutase family of metabolic enzymes, the authors demonstrate experimentally that the artificial sequences display natural-like catalytic function. Because the models access an enormous space of diverse sequences, such evolution-based statistical approaches may guide the search for functional proteins with altered chemical activities. Science , this issue p. 440
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The model learned evolutionary conservation patterns without being trained on explicit alignment information.
“This indicates that the model has captured evolutionary conservation patterns without training on explicit alignment information such as with Potts models51, as implemented in direct coupling analysis7,52–55.”
From Large language models generate functional protein sequences across diverse families (2023) · cites this paper as [7]
No bmDCA artificial protein showed detectable lysozyme functionality.
“None of the bmDCA artificial proteins exhibited a detectable level of functionality, Fig. S10”
From Large language models generate functional protein sequences across diverse families (2023) · cites this paper as [7]
bmDCA could not fit three of the five lysozyme families and gave detectable expression for 30 of 50 proteins in the remaining two.
“bmDCA was unable to fit three out of the five lysozyme families, and exhibited 60% detectable expression (30/50 proteins) for the remaining two protein families.”
From Large language models generate functional protein sequences across diverse families (2023) · cites this paper as [7]
ProGen's model likelihoods align better with experimental assay data for CM and MDH than the original family-specific generation methods.
“In sum, ProGen’s model likelihoods are better aligned with experimentally-measured assay data on two diverse protein datasets—chorismate mutase and malate dehydrogenase—than the sequence generation methods from original studies specifically tailored for these families.”
From Large language models generate functional protein sequences across diverse families (2023) · cites this paper as [7]
On CM function data, ProGen log-likelihoods had AUC 0.85, significantly better than bmDCA's 0.78 (p<0.0001, two-tailed, n=1617).
“On CM function data, ProGen log-likelihoods had an area under the curve (AUC) of 0.85, significantly better (p<0.0001, two-tailed test, n=1617) than bmDCA, which had an AUC of 0.78 (Fig. 4c).”
From Large language models generate functional protein sequences across diverse families (2023) · cites this paper as [7]
A threshold of 0.42 norm relative enrichment approximately marks the cut-off between two modes in CM function data.
“We also applied thresholding at 0.42 norm relative enrichment (r.e.) to obtain binary labels for CM function, which roughly corresponds to the cut-off point between two modes that exist in CM function data, to be used for ROC curves, following the original study7.”
From Large language models generate functional protein sequences across diverse families (2023) · cites this paper as [7]
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“They also hold great potential for guiding protein design and engineering.(182,183) The success of these methods lies in their ability to capture dependencies between protein residues either by explicitly estimating inter-residue (pairwise) couplings(177,178) or by implicitly accounting for global sequence contexts.”
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Self-citationMachine Learning for Protein Engineering
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- Authors
- Russ, W. P., Figliuzzi, M., Stocker, C., Barrat-Charlaix, P., Socolich, M., Kast, P., Hilvert, D., Monasson, R., Cocco, S., Weigt, M., & Ranganathan, R.
- Affiliations
- The University of Texas Southwestern Medical Center (US)
- Centre National de la Recherche Scientifique (FR)
- ETH Zurich (CH)
- University of Chicago (US)
- Laboratoire de Physique de l’Ecole Normale Supérieure, PSL and CNRS, Paris, France.
- Corresponding author
- Russ, William P.
- Figliuzzi, Matteo
- Weigt, Martin
- Ranganathan, Rama
- ORCID
- Venue
- Science
- Published
- 2020-07-24
- Volume
- 369
- Issue
- 6502
- Pages
- 440-445
- ISSN
- 0036-8075 (ISSN-L)
- 1095-9203
- OpenAlex
- W3044778276
- Cited by
- 313
- References
- 56
- Citations by year
- 2026: 40 · 2025: 55 · 2024: 53 · 2023: 55 · 2022: 56 · 2021: 45 · 2020: 8 · 2012: 1
- FWCI
- 13.29
- Type
- journal article
- Language
- en
- Open access
- closed
- Locations
- Science (publishedVersion)
- PubMed (publishedVersion)
- HAL (submittedVersion)
- Funders
- National Institutes of Health (GM12345)
- Welch Foundation (I-1366)
- European Commission Directorate-General for Research and Innovation (734439)
- Agence Nationale de la Recherche (CE30-0021-01)
- Swiss National Science Foundation (310030M_182648, 310030B_176405)
- Topics
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Protein Structure and Dynamics
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Enzyme Catalysis and Immobilization
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › RNA and protein synthesis mechanisms
- Keywords
- chorismate mutase, rational enzyme design, protein evolution, complementation assay, aromatic amino acid biosynthesis, catalytic activity, generative models
- MeSH terms
- Amino Acid Sequence, Chorismate Mutase, Models, Genetic, Models, Statistical, Evolution, Molecular, Escherichia coli Proteins
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