ArticleMolecular biology and evolution2014
A comprehensive, high-resolution map of a gene's fitness landscape.
Article in Molecular biology and evolution, 2014. The graph could read no effect estimate from its abstract, so it casts no vote on the map. An erratum has been issued. Cited by 184 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
184 citing papers in PubMed, 376 citations in OpenAlex.
- Synthetic Approaches as Empirical Tools for Testing Evolutionary Theory.Genome biology and evolution · 2026Review
- Predicting Enzyme Turnover Numbers and Enabling Rational Enzyme Evolution.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2026Article
- Synonymous mutations in essential genes infrequently produce fitness effects in human cell lines.Nucleic acids research · 2026Article
- Overestimating zero-shot fitness prediction: Broad benchmarks mask local failures and practical limitations.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
- VEFill: accurate and generalizable deep mutational scanning score imputation across protein domains.Molecular systems biology · 2026Article
- SynFit: Synergistic Contrastive Learning for Multi-Objective Protein Fitness Prediction and Optimization.bioRxiv : the preprint server for biology · 2026Article
- Machine learning framework for cost effective deep mutational scanning through targeted substitution profiling.BMC bioinformatics · 2026Article
- Environmental and mutational modulation of collateral fitness effects informs their mechanisms.Molecular biology and evolution · 2026Article
- CountESS: a flexible, graphical pipeline tool for deep mutational scanning analysis.bioRxiv : the preprint server for biology · 2026Article
- Origins and breadth of pairwise epistasis in an α-helix of β-lactamase TEM-1.Nature communications · 2026Article
- Multiplexed assays of variant effect for clinical variant interpretation.Nature reviews. Genetics · 2026Review
- ΦX174 bacteriophage viability predicted by protein biophysical modeling.bioRxiv : the preprint server for biology · 2025Article
- Functional synonymous mutations and their evolutionary consequences.Nature reviews. Genetics · 2025Review
- SAGE-prot: scoring-assisted generative exploration for multi-objective protein design.Briefings in bioinformatics · 2025Article
- Variant scoring tools for deep mutational scanning.Molecular systems biology · 2025Review
- A narrow range of transcript-error rates across the Tree of Life.Science advances · 2025Article
- Robust enzyme discovery and engineering with deep learning using CataPro.Nature communications · 2025Article
- MaveDB 2024: a curated community database with over seven million variant effects from multiplexed functional assays.Genome biology · 2025Article
- Tailoring industrial enzymes for thermostability and activity evolution by the machine learning-based iCASE strategy.Nature communications · 2025Article
124 more citing papers are in PubMed but not listed here.
Corrections and comments
- Erratum issued
Authors and funding
4 authors at 1 institution in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Mutations are central to evolution, providing the genetic variation upon which selection acts. A mutation's effect on the suitability of a gene to perform a particular function (gene fitness) can be positive, negative, or neutral. Knowledge of the distribution of fitness effects (DFE) of mutations is fundamental for understanding evolutionary dynamics, molecular-level genetic variation, complex genetic disease, the accumulation of deleterious mutations, and the molecular clock. We present comprehensive DFEs for point and codon mutants of the Escherichia coli TEM-1 β-lactamase gene and missense mutations in the TEM-1 protein. These DFEs provide insight into the inherent benefits of the genetic code's architecture, support for the hypothesis that mRNA stability dictates codon usage at the beginning of genes, an extensive framework for understanding protein mutational tolerance, and evidence that mutational effects on protein thermodynamic stability shape the DFE. Contrary to prevailing expectations, we find that deleterious effects of mutation primarily arise from a decrease in specific protein activity and not cellular protein levels.
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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.