ArticleMolecular biology and evolution2020
Protein Structural Information and Evolutionary Landscape by In Vitro Evolution.
Article in Molecular biology and evolution, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 18 papers.
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Who cites it
18 citing papers in PubMed.
- Functional bottlenecks can emerge from non-epistatic underlying traits.PLoS computational biology · 2026Article
- Optimal sequencing depth for measuring the concentrations of molecular barcodes.Nucleic acids research · 2025Article
- Emergent time scales of epistasis in protein evolution.Proceedings of the National Academy of Sciences of the United States of America · 2024Article
- In vivo functional phenotypes from a computational epistatic model of evolution.Proceedings of the National Academy of Sciences of the United States of America · 2024Article
- Inference of annealed protein fitness landscapes with AnnealDCA.PLoS computational biology · 2024Article
- Latent generative landscapes as maps of functional diversity in protein sequence space.Nature communications · 2023Article
- Inferring protein fitness landscapes from laboratory evolution experiments.PLoS computational biology · 2023Article
- Uncovering the promiscuous activity of IL-6 proteins: A multi-dimensional analysis of phylogeny, classification and residue conservation.Protein science : a publication of the Protein Society · 2022Article
- Slow Protein Dynamics Elicits New Enzymatic Functions by Means of Epistatic Interactions.Molecular biology and evolution · 2022Article
- Allostery: Allosteric Cancer Drivers and Innovative Allosteric Drugs.Journal of molecular biology · 2022Review
- Allostery, and how to define and measure signal transduction.Biophysical chemistry · 2022Review
- Modeling Sequence-Space Exploration and Emergence of Epistatic Signals in Protein Evolution.Molecular biology and evolution · 2022Article
- Editorial: Molecular evolution: You learn from your mistakes.Frontiers in molecular biosciences · 2022Article
- AMaLa: Analysis of Directed Evolution Experiments via Annealed Mutational Approximated Landscape.International journal of molecular sciences · 2021Article
- Unsupervised Inference of Protein Fitness Landscape from Deep Mutational Scan.Molecular biology and evolution · 2021Article
- Aggregation is a Context-Dependent Constraint on Protein Evolution.Frontiers in molecular biosciences · 2021Article
- Deep Mutational Scanning of Viral Glycoproteins and Their Host Receptors.Frontiers in molecular biosciences · 2021Review
- Not Enough Natural Data? Sequence and Ye Shall Find.Frontiers in molecular biosciences · 2020Article
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Authors and funding
5 authors.
Funding
Abstract
Protein structure is tightly intertwined with function according to the laws of evolution. Understanding how structure determines function has been the aim of structural biology for decades. Here, we have wondered instead whether it is possible to exploit the function for which a protein was evolutionary selected to gain information on protein structure and on the landscape explored during the early stages of molecular and natural evolution. To answer to this question, we developed a new methodology, which we named CAMELS (Coupling Analysis by Molecular Evolution Library Sequencing), that is able to obtain the in vitro evolution of a protein from an artificial selection based on function. We were able to observe with CAMELS many features of the TEM-1 beta-lactamase local fold exclusively by generating and sequencing large libraries of mutational variants. We demonstrated that we can, whenever a functional phenotypic selection of a protein is available, sketch the structural and evolutionary landscape of a protein without utilizing purified proteins, collecting physical measurements, or relying on the pool of natural protein variants.
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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.