Parallel pathways for recruiting effector proteins determine centromere drive and suppression.

Selfish centromere DNA sequences bias their transmission to the egg in female meiosis. Evolutionary theory suggests that centromere proteins evolve to suppress costs of this "centromere drive." In hybrid mouse models with genetically different maternal and paternal centromeres, selfish centromere DNA exploits a kinetochore pathway to recruit microtubule-destabilizing proteins that act as drive effectors. We show that such functional differences are suppressed by a parallel pathway for effector recruitment by heterochromatin, which is similar between centromeres in this system. Disrupting the kinetochore pathway with a divergent allele of CENP-C reduces functional differences between centromeres, whereas disrupting heterochromatin by CENP-B deletion amplifies the differences. Molecular evolution analyses using Murinae genomes identify adaptive evolution in proteins in both pathways. We propose that centromere proteins have recurrently evolved to minimize the kinetochore pathway, which is exploited by selfish DNA, relative to the heterochromatin pathway that equalizes centromeres, while maintaining essential functions.

FREEDA: an automated computational pipeline guides experimental testing of protein innovation by detecting positive selection.

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 leads 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 (Finder of Rapidly Evolving Exons in De novo Assemblies) that provides a simple graphical user interface requiring only a gene name, integrates widely used molecular evolution tools to detect positive selection, and maps results onto protein structures predicted by AlphaFold. Applying FREEDA to >100 mouse centromere proteins, we find evidence of positive selection in intrinsically disordered regions of ancient domains, suggesting innovation of essential functions. As a proof-of-principle experiment, we show innovation in centromere binding of CENP-O. Overall, we provide an accessible computational tool to guide cell biology research and apply it to experimentally demonstrate functional innovation.

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

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 find statistical evidence of positive selection within loops and turns of ancient domains, suggesting innovation of essential functions. As a proof-of-principle experiment, we show innovation in centromere binding of mouse CENP-O. Overall, we provide an accessible computational tool to guide cell biology research and apply it to experimentally demonstrate functional innovation.