The process of protein directed evolution can be envisaged as navigation over high-dimensional optimisation landscapes with numerous local maxima. The performance of any strategy in navigating such a landscape is dependent on the ruggedness of that landscape.
The process of protein directed evolution can be envisaged as navigation over high-dimensional optimisation landscapes with numerous local maxima.
The performance of any strategy in navigating such a landscape is dependent on the ruggedness of that landscape.
However, this information is generally unavailable at the outset of an experiment.
The page is ready to read now. The fuller skim-friendly version will appear here automatically.
The process of protein directed evolution can be envisaged as navigation over high-dimensional optimisation landscapes with numerous local maxima. The performance of any strategy in navigating such a landscape is dependent on the ruggedness of that landscape. However, this information is generally unavailable at the outset of an experiment.
by Sebastian Towers, Jessica James, Harrison Steel, Idris Kempf Directed evolution is a method for engineering biological systems or components, such as proteins, wherein desired traits are optimised through iterative rounds of mutagenesis and selection of fit variants. Using theoretical NK landscapes and four empirical protein fitness landscapes, we demonstrate consistent in silico improvement upon the performance of fixed-parameter strategies, using a pipeline that could also be combined with emerging AI-based methods for driving directed evolution.
Open the app view to save this story, compare related coverage, and continue from the same source.