FREEDA: An automated computational pipeline guides experimental testing of protein innovation.

ABSTRACT: Cell biologists typically focus on conserved regions of a protein, overlooking innovations that can shape its function over evolutionary time. Computational analyses can reveal potential innovations by detecting statistical signatures of positive selection that lead to rapid accumulation of beneficial mutations. However, these approaches are not easily accessible to non-specialists, limiting their use in cell biology. Here, we present an automated computational pipeline FREEDA that provides a simple graphical user interface requiring only a gene name; integrates widely used molecular evolution tools to detect positive selection in rodents, primates, carnivores, birds, and flies; and maps results onto protein structures predicted by AlphaFold. Applying FREEDA to >100 centromere proteins, we

SUBMITTER: Dudka D 

PROVIDER: S-EPMC10292211 | biostudies-literature | 2023 Sep

REPOSITORIES: biostudies-literature