The DOI matches its Crossref record.
Darwinian Evolution Can Follow Only Very Few Mutational Paths to Fitter Proteins
Abstract
Five point mutations in a particular β-lactamase allele jointly increase bacterial resistance to a clinically important antibiotic by a factor of ∼100,000. In principle, evolution to this high-resistance β-lactamase might follow any of the 120 mutational trajectories linking these alleles. However, we demonstrate that 102 trajectories are inaccessible to Darwinian selection and that many of the remaining trajectories have negligible probabilities of realization, because four of these five mutations fail to increase drug resistance in some combinations. Pervasive biophysical pleiotropy within the β-lactamase seems to be responsible, and because such pleiotropy appears to be a general property of missense mutations, we conclude that much protein evolution will be similarly constrained. This implies that the protein tape of life may be largely reproducible and even predictable.
Overview
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Integrity signals
Registry
Cites
Statistics
Verification
Claims the paper makes
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Self-check
Percentages, sums and reported differences re-computed from the paper’s own numbers.
The self-check needs the paper’s full text.
Citations
Cited by
Enzyme promiscuity: A mechanistic and evolutionary perspective
Tawfik, O. K. A. D. S. (2010) · Annual Review of Biochemistry · doi:10.1146/annurev-biochem-030409-143718 · Cited by 1,355
Blount, Z. D., Borland, C. Z., & Lenski, R. E. (2008) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.0803151105 · Cited by 840
Evolutionary paths to antibiotic resistance under dynamically sustained drug selection
Toprak, E., Veres, A., Michel, J.-B., Chait, R., Hartl, D. L., & Kishony, R. (2012) · Nature Genetics · doi:10.1038/ng.1034 · Cited by 764
Self-citationThe Molecular Diversity of Adaptive Convergence
Tenaillon, O., Rodríguez-Verdugo, A., Gaut, R. L., McDonald, P., Bennett, A. F., Long, A. D., & Gaut, B. S. (2012) · Science · doi:10.1126/science.1212986 · Cited by 815
Empirical fitness landscapes and the predictability of evolution
de Visser, J. A. G. M., & Krug, J. (2014) · Nature Reviews Genetics · doi:10.1038/nrg3744 · Cited by 715
Enzyme promiscuity: evolutionary and mechanistic aspects
Khersonsky, O., Roodveldt, C., & Tawfik, D. S. (2006) · Current Opinion in Chemical Biology · doi:10.1016/j.cbpa.2006.08.011 · Cited by 641
Contingency and determinism in evolution: Replaying life’s tape
Blount, Z. D., Lenski, R. E., & Losos, J. B. (2018) · Science · doi:10.1126/science.aam5979 · Cited by 590
High-resolution mapping of protein sequence-function relationships
Fowler, D. M., Araya, C. L., Fleishman, S. J., Kellogg, E. H., Stephany, J. J., Baker, D., & Fields, S. (2010) · Nature Methods · doi:10.1038/nmeth.1492 · Cited by 516
A Comprehensive Biophysical Description of Pairwise Epistasis throughout an Entire Protein Domain
Olson, C. A., Wu, N. C., & Sun, R. (2014) · Current Biology · doi:10.1016/j.cub.2014.09.072 · Cited by 408
Accelerated evolution of resistance in multidrug environments
Hegreness, M., Shoresh, N., Damian, D., Hartl, D., & Kishony, R. (2008) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.0805965105 · Cited by 300
Self-citationThe Causes and Consequences of Genetic Interactions (Epistasis)
Domingo, J., Baeza-Centurion, P., & Lehner, B. (2019) · Annual Review of Genomics and Human Genetics · doi:10.1146/annurev-genom-083118-014857 · Cited by 242
Stepwise Modification of a Modular Enhancer Underlies Adaptation in a Drosophila Population
Rebeiz, M., Pool, J. E., Kassner, V. A., Aquadro, C. F., & Carroll, S. B. (2009) · Science · doi:10.1126/science.1178357 · Cited by 249
Evidence that adaptation in Drosophila is not limited by mutation at single sites
Karasov, T., Messer, P. W., & Petrov, D. A. (2010) · PLoS Genetics · doi:10.1371/journal.pgen.1000924 · Cited by 257
Araya, C. L., Fowler, D. M., Chen, W., Muniez, I., Kelly, J. W., & Fields, S. (2012) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.1209751109 · Cited by 230
The Role of Conformational Dynamics and Allostery in Modulating Protein Evolution
Campitelli, P., Modi, T., Kumar, S., & Ozkan, S. B. (2020) · Annual Review of Biophysics · doi:10.1146/annurev-biophys-052118-115517 · Cited by 190
Antibiotic collateral sensitivity is contingent on the repeatability of evolution
Nichol, D., Rutter, J., Bryant, C., Hujer, A. M., Lek, S., Adams, M. D., Jeavons, P., Anderson, A. R. A., Bonomo, R. A., & Scott, J. G. (2019) · Nature Communications · doi:10.1038/s41467-018-08098-6 · Cited by 174
A Comparison of Methods to Measure Fitness in Escherichia coli
Wiser, M. J., & Lenski, R. E. (2015) · PLOS ONE · doi:10.1371/journal.pone.0126210 · Cited by 162
Comprehensive experimental fitness landscape and evolutionary network for small RNA
Jiménez, J. I., Xulvi-Brunet, R., Campbell, G. W., Turk-MacLeod, R., & Chen, I. A. (2013) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.1307604110 · Cited by 140
The Evolutionary Pathway to Virulence of an RNA Virus
Stern, A., Yeh, M. T., Zinger, T., Smith, M., Wright, C., Ling, G., Nielsen, R., Macadam, A., & Andino, R. (2017) · Cell · doi:10.1016/j.cell.2017.03.013 · Cited by 127
Identification of cis-suppression of human disease mutations by comparative genomics
Task Force for Neonatal Genomics, Jordan, D. M., Frangakis, S. G., Golzio, C., Cassa, C. A., Kurtzberg, J., Davis, E. E., Sunyaev, S. R., & Katsanis, N. (2015) · Nature · doi:10.1038/nature14497 · Cited by 115
Scanlan, P. D., Hall, A. R., Blackshields, G., Friman, V.-P., Davis, M. R., Goldberg, J. B., & Buckling, A. (2015) · Molecular Biology and Evolution · doi:10.1093/molbev/msv032 · Cited by 111
Casein and casein micelle structures, functions and diversity in 20 species.
Holt, C. (2016) · International Dairy Journal · doi:10.1016/j.idairyj.2016.01.004 · Cited by 95
Manrubia, S., Cuesta, J. A., Aguirre, J., Ahnert, S. E., Altenberg, L., Cano, A. V., Catalán, P., Diaz-Uriarte, R., Elena, S. F., García-Martín, J. A., et al. (2021) · Physics of Life Reviews · doi:10.1016/j.plrev.2021.03.004 · Cited by 88
Molecular Evolution of Transition Metal Bioavailability at the Host–Pathogen Interface
Antelo, G. T., Vila, A. J., Giedroc, D. P., & Capdevila, D. A. (2021) · Trends in Microbiology · doi:10.1016/j.tim.2020.08.001 · Cited by 63
Multiple scales of selection influence the evolutionary emergence of novel pathogens
Park, M., Loverdo, C., Schreiber, S. J., & Lloyd-Smith, J. O. (2013) · Philosophical Transactions of the Royal Society B: Biological Sciences · doi:10.1098/rstb.2012.0333 · Cited by 51
The genetic code constrains yet facilitates Darwinian evolution
Firnberg, E., & Ostermeier, M. (2013) · Nucleic Acids Research · doi:10.1093/nar/gkt536 · Cited by 36
Epistasis between promoter activity and coding mutations shapes gene evolvability
Cisneros, A. F., Gagnon-Arsenault, I., Dubé, A. K., Després, P. C., Kumar, P., Lafontaine, K., Pelletier, J. N., & Landry, C. R. (2023) · Science Advances · doi:10.1126/sciadv.add9109 · Cited by 33
Koonin, E. V., & Wolf, Y. I. (2010) · Biology Direct · doi:10.1186/1745-6150-5-64 · Cited by 28
“First, the available experimental studies, however limited, suggest that, although only a small fraction of the vast sequence space is open for evolution, the available trajectories are nevertheless numerous, so that evolution is far from deterministic [12,13].”
BackgroundStatistical Package for Growth Rates Made Easy
Mira, P., Barlow, M., Meza, J. C., & Hall, B. G. (2017) · Molecular Biology and Evolution · doi:10.1093/molbev/msx255 · Cited by 13
Investigating the determinants of performance in machine learning for protein fitness prediction
Sandhu, M., Mater, A. C., Matthews, D. S., Spence, M. A., Lenskiy, A. A., & Jackson, C. (2020) · doi:10.1101/2020.09.30.319780 · Cited by 8
Specialization Restricts the Evolutionary Paths Available to Yeast Sugar Transporters
Crandall, J. G., Zhou, X., Rokas, A., & Hittinger, C. T. (2024) · Molecular Biology and Evolution · doi:10.1093/molbev/msae228 · Cited by 7
AI-enabled alkaline-resistant evolution of protein to apply in mass production
Kang, L., Wu, B., Zhou, B., Tan, P., Kang, Y. (., Yan, Y., Zong, Y., Li, S., Liu, Z., & Hong, L. (2025) · eLife · doi:10.7554/elife.102788 · Cited by 7
A Vast Navigable Protein Sequence Subspace
Sinai, S., Gane, A., Jain, N., Bryant, D., Bileschi, M., Bashir, A., Church, G. M., Kelsic, E., & Colwell, L. (2026) · doi:10.64898/2026.10.18.759989
AI-enabled alkaline-resistant evolution of protein to apply in mass production
Kang, L., Wu, B., Zhou, B., Tan, P., Kang, Y. (., Yan, Y., Zong, Y., Li, S., Liu, Z., & Hong, L. (2025) · eLife · doi:10.7554/elife.102788.3
Metadata
- Authors
- Weinreich, D. M., Delaney, N. F., DePristo, M. A., & Hartl, D. L.
- Venue
- Science
- Published
- 2006-04-07
- Volume
- 312
- Issue
- 5770
- Pages
- 111-114
- ISSN
- 0036-8075 (ISSN-L)
- 1095-9203
- OpenAlex
- W2016666153
- Cited by
- 1,415
- References
- 0
- FWCI
- 32.69
- Type
- journal article
- Language
- en
- Open access
- closed
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 ›
Are you an author, or do you see a problem with this page? Request a review
Data sources
Bibliographic data: Semantic Scholar, OpenAlex, Crossref.
Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.
