Gauge fixing for sequence-function relationships
Open accessPreprint — published as 10.1371/journal.pcbi.1012818
Abstract
Quantitative models of sequence-function relationships are ubiquitous in computational biology, e.g., for modeling the DNA binding of transcription factors or the fitness landscapes of proteins. Interpreting these models, however, is complicated by the fact that the values of model parameters can often be changed without affecting model predictions. Before the values of model parameters can be meaningfully interpreted, one must remove these degrees of freedom (called “gauge freedoms” in physics) by imposing additional constraints (a process called “fixing the gauge”). However, strategies for fixing the gauge of sequence-function relationships have received little attention. Here we derive an analytically tractable family of gauges for a large class of sequence-function relationships. These gauges are derived in the context of models with all-order interactions, but an important subset of these gauges can be applied to diverse types of models, including additive models, pairwise-interaction models, and models with higher-order interactions. Many commonly used gauges are special cases of gauges within this family. We demonstrate the utility of this family of gauges by showing how different choices of gauge can be used both to explore complex activity landscapes and to reveal simplified models that are approximately correct within localized regions of sequence space. The results provide practical gauge-fixing strategies and demonstrate the utility of gauge-fixing for model exploration and interpretation. Significance Statement: Computational biology relies heavily on mathematical models that predict biological activities from DNA, RNA, or protein sequences. Interpreting the parameters of these models, however, remains difficult. Here we address a core challenge for model interpretation-the presence of ‘gauge freedoms’, i.e., ways of changing model parameters without affecting model predictions. The results unify commonly used methods for eliminating gauge freedoms and show how these methods can be used to simplify complex models in localized regions of sequence space. This work thus overcomes a major obstacle in the interpretation of quantitative sequence-function relationships.
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Feinauer, C., & Borgonovo, E. (2022) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2022.12.12.520028 · Cited by 4 · see citing works
Symmetry, gauge freedoms, and the interpretability of sequence-function relationships
Posfai, A., McCandlish, D. M., & Kinney, J. B. (2025) · Physical Review Research · doi:10.1103/physrevresearch.7.023005 · Cited by 4 · see citing works
Self-citationEpistasis facilitates functional evolution in an ancient transcription factorPreprint
Metzger, B. D., Park, Y., Starr, T. N., & Thornton, J. W. (2024) · eLife · doi:10.7554/elife.88737.2 · Cited by 3 · see citing works
Protein evolvability under rewired genetic codesPreprint
Rozhoňová, H., Martí‐Gómez, C., McCandlish, D. M., & Payne, J. L. (2023) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2023.06.20.545706 · Cited by 2 · see citing works
Self-citationSasse, A., Chikina, M. D., & Mostafavi, S. (2023) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2023.11.10.566588 · Cited by 1 · see citing works
Flagged first, then by citations
3 of the 104 references couldn’t be matched to a known paper.
Cited by
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Efficient epistasis inference via higher-order covariance matrix factorization
Shimagaki, K. S., & Barton, J. P. (2025) · Genetics · doi:10.1093/genetics/iyaf118 · Cited by 10 · see citing works
Toward generalizable and interpretable AI in regulatory genomics
NAGAI, M., Murphy, A. E., Rizzo, K., & Koo, P. K. (2026) · Nature Genetics · doi:10.1038/s41588-026-02670-3 · Cited by 6 · see citing works
Structural ontogeny of protein-protein interactions
Yang, A., Jiang, H., Jude, K. M., Akpinaroglu, D., Allenspach, S., Li, A. J., Bowden, J., Perez, C. P., Liu, L. D., Huang, P., et al. (2026) · Science · doi:10.1126/science.adx6931 · Cited by 4 · see citing works
Symmetry, gauge freedoms, and the interpretability of sequence-function relationships
Posfai, A., McCandlish, D. M., & Kinney, J. B. (2025) · Physical Review Research · doi:10.1103/physrevresearch.7.023005 · Cited by 4 · see citing works
Self-citationLearning sequence-function relationships with scalable, interpretable Gaussian processesPreprint
Zhou, J., Martí‐Gómez, C., Petti, S., & McCandlish, D. M. (2025) · eLife · doi:10.7554/elife.108964.1 · Cited by 3 · see citing works
Self-citationGaugefixer: overcoming parameter non-identifiability in models of sequence-function relationships
Martí‐Gómez, C., McCandlish, D. M., & Kinney, J. B. (2026) · BMC Bioinformatics · doi:10.1186/s12859-026-06605-x · Cited by 2 · see citing works
Self-citationLearning sequence-function relationships with scalable, interpretable Gaussian processesPreprint
Zhou, J., Martí‐Gómez, C., Petti, S., & McCandlish, D. M. (2025) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2025.08.15.670613 · Cited by 1 · see citing works
Self-citationLearning sequence-function relationships with scalable, interpretable Gaussian processesPreprint
Zhou, J., Martí‐Gómez, C., Petti, S., & McCandlish, D. M. (2025) · eLife · doi:10.7554/elife.108964 · Cited by 1 · see citing works
Self-citationCounting-based inference of mutant growth rates from pooled sequencing across growth regimesPreprint
Sezer, D., & Toprak, E. (2025) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2025.10.10.681719
Sethi, P., & Zhou, J. (2026) · eLife · doi:10.7554/elife.108005.2
Self-citationOn inferring epistatic complexity in protein sequence-function landscapes
Dupic, T., Phillips, A. M., & Desai, M. M. (2026) · Genetics · doi:10.1093/genetics/iyag250
- Authors
- Posfai, A., Zhou, J., McCandlish, D. M., & Kinney, J. B.
- Affiliations
- Cold Spring Harbor Laboratory (US)
- Corresponding author
- Kinney, Justin B.
- Venue
- PLoS Computational Biology
- Publisher
- openRxiv
- Published
- 2024-05-13
- Volume
- 21
- Issue
- 3
- Pages
- e1012818-e1012818
- ISSN
- 1553-734X (ISSN-L)
- PMC
- PMC11957564
- OpenAlex
- W4408658210
- Cited by
- 11
- References
- 104
- Citations by year
- 2026: 5 · 2025: 6
- FWCI
- 3.29
- Type
- journal article
- Language
- en
- Open access
- gold
- doi.org
- Licence
- cc-by
- Locations
- PLoS Computational Biology (publishedVersion, cc-by)
- bioRxiv (Cold Spring Harbor Laboratory) (acceptedVersion, cc-by)
- Europe PMC (PubMed Central) (publishedVersion, cc-by)
- PubMed Central (publishedVersion, cc-by)
- PubMed (publishedVersion)
- Cold Spring Harbor Laboratory Institutional Repository (Cold Spring Harbor Laboratory) (acceptedVersion)
- Cold Spring Harbor Laboratory Institutional Repository (Cold Spring Harbor Laboratory) (submittedVersion)
- PubMed Central (submittedVersion, cc-by)
- RePEc: Research Papers in Economics (submittedVersion)
- DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
- Funders
- Alfred P. Sloan Foundation
- University of Florida
- Cold Spring Harbor Laboratory
- National Institutes of Health (1r35gm154908-01)
- College of Liberal Arts and Sciences, University of Florida
- National Human Genome Research Institute (R01HG011787)
- National Institute of General Medical Sciences (GM154908, R35GM154908, R35GM133777, R35GM133613)
- Topics
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Gene Regulatory Network Analysis
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Protein Structure and Dynamics
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Genomics and Chromatin Dynamics
- Keywords
- gauge fixing, sequence-function relationships, degrees of freedom, pairwise interaction model, higher-order interactions, additive models, activity landscape, model interpretation, transcription factor-DNA binding, protein fitness landscapes
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.
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