ArticleeLife2022
Heterogeneity of the GFP fitness landscape and data-driven protein design.
Article in eLife, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 33 papers.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
33 citing papers in PubMed.
- Fitness translocation: improving variant effect prediction with biologically-grounded data augmentation.Bioinformatics (Oxford, England) · 2026Article
- Inference of fitness landscapes with heterogeneous patterns of epistasis across sites.bioRxiv : the preprint server for biology · 2026Article
- How far can you go? Extrapolating values of catalytic activity from known protein landscapes in natural and directed evolution.Chemical Society reviews · 2026Review
- Scalable and cost-efficient custom gene library assembly from oligopools.Science advances · 2026Article
- Rapid directed evolution guided by protein language models and epistatic interactions.Science (New York, N.Y.) · 2026Article
- Descent from a common ancestor restricts exploration of protein sequence space.Proceedings of the National Academy of Sciences of the United States of America · 2026Article
- Physical Mechanisms of an Unconventional Green Fluorescent Protein Indicator for Chloride.The journal of physical chemistry. B · 2026Article
- Epimutations: raw material for evolution?The EMBO journal · 2026Review
- A gene-based model of fitness and its implications for genetic variation: linkage disequilibrium.Genetics · 2025Article
- Learning sequence-function relationships with scalable, interpretable Gaussian processes.bioRxiv : the preprint server for biology · 2025Article
- A Cyanobacterial Screening Platform for Rubisco Mutant Variants.ACS synthetic biology · 2025Article
- A gene-based model of fitness and its implications for genetic variation: Linkage disequilibrium.bioRxiv : the preprint server for biology · 2025Article
- Pervasive Divergence in Protein Thermostability is Mediated by Both Structural Changes and Cellular Environments.Molecular biology and evolution · 2025Article
- Article
- Importance of higher-order epistasis in protein sequence-function relationships.bioRxiv : the preprint server for biology · 2025Article
- Resolving discrepancies between chimeric and multiplicative measures of higher-order epistasis.Nature communications · 2025Article
- A map of the rubisco biochemical landscape.Nature · 2025Article
- MMRT: MultiMut Recursive Tree for predicting functional effects of high-order protein variants from low-order variants.Computational and structural biotechnology journal · 2025Article
- Protein stability models fail to capture epistatic interactions of double point mutations.Protein science : a publication of the Protein Society · 2025Article
- Protein stability models fail to capture epistatic interactions of double point mutations.bioRxiv : the preprint server for biology · 2024Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
10 authors.
Funding
Abstract
Studies of protein fitness landscapes reveal biophysical constraints guiding protein evolution and empower prediction of functional proteins. However, generalisation of these findings is limited due to scarceness of systematic data on fitness landscapes of proteins with a defined evolutionary relationship. We characterized the fitness peaks of four orthologous fluorescent proteins with a broad range of sequence divergence. While two of the four studied fitness peaks were sharp, the other two were considerably flatter, being almost entirely free of epistatic interactions. Mutationally robust proteins, characterized by a flat fitness peak, were not optimal templates for machine-learning-driven protein design - instead, predictions were more accurate for fragile proteins with epistatic landscapes. Our work paves insights for practical application of fitness landscape heterogeneity in protein engineering.
Indexed as
Identifiers
What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.