What is it about?

While many theoretical or computer based models of molecular evolution exist, not all of them have been proven to accurately predict real-life outcomes. In this study, the researchers developed and tested a new model called SEEC, which learns how amino acids are evolutionarily interconnected from natural protein families to predict how sequences might evolve. They tested the model by creating variations of a specific protein in bacteria and found that the newly evolved proteins remained functional or performed better than the original ones even after a large number of mutations. The study suggests that SEEC could be a useful tool for studying genetic changes over time in populations, understanding how new functions develop, and potentially aiding in the development of vaccines.

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Why is it important?

Understanding evolutionary dynamics is crucial for unraveling the complexities of life on Earth, from the development of different species to the emergence of new functions in proteins. Scientific exploration in this field is also fundamental to understanding the tree of life and the common ancestry of species. Additionally, computational models of evolution have practical applications in various fields, including medicine and industry. Understanding protein evolution can aid in the development of drugs, vaccines, and other medical interventions. It can also inform strategies for improving industrial processes, such as the production of enzymes or other biotechnological applications. Similarly, experimentally validating computational or theoretical models ensures that they accurately represent real-world scenarios. We validated our computational model by experimentally testing its predictions in proteins of the beta-lactamase family that allowed bacteria to survive. This is essential for building confidence in the reliability of these models and their ability to generate meaningful insights on how evolutionary processes shape life on earth. Furthermore, the ability of computational models to explore the dynamics of how proteins obtain new functional roles over time and characterize viral fitness landscapes has potential implications for vaccine development. By understanding how viruses and their building blocks, e.g. proteins, evolve and adapt, researchers can develop more effective strategies for preventing and treating infectious diseases.

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This page is a summary of: In vivo functional phenotypes from a computational epistatic model of evolution, Proceedings of the National Academy of Sciences, January 2024, Proceedings of the National Academy of Sciences,
DOI: 10.1073/pnas.2308895121.
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