@article {Platt141499, author = {Alexander Platt and Claudia C Weber and David A Liberles}, title = {Protein Evolution Depends on Multiple Distinct Population Size Parameters}, elocation-id = {141499}, year = {2017}, doi = {10.1101/141499}, publisher = {Cold Spring Harbor Laboratory}, abstract = {That population size affects the fate of new mutations arising in genomes, modulating both how frequently they arise and how efficiently natural selection is able to filter them, is well established. It is therefore clear that these distinct roles for population size that characterize different processes should affect the evolution of proteins and need to be carefully defined. Empirical evidence is consistent with a role for demography in influencing protein evolution, supporting the idea that functional constraints alone do not determine the composition of coding sequences.Given that the relationship between population size, mutant fitness and fixation probability has been well characterized, estimating fitness from observed substitutions is well within reach with well-formulated models. Molecular evolution research has, therefore, increasingly begun to leverage concepts from population genetics to quantify the selective effects associated with different classes of mutation. However, in order for this type of analysis to provide meaningful information about the intra- and inter-specific evolution of coding sequences, a clear definition of concepts of population size, what they influence, and how they are best parameterized is essential.Here, we present an overview of the many distinct concepts that {\textquotedblleft}population size{\textquotedblright} and {\textquotedblleft}effective population size{\textquotedblright} may refer to, what they represent for studying proteins, and how this knowledge can be harnessed to produce better specified models of protein evolution.}, URL = {https://www.biorxiv.org/content/early/2017/05/30/141499}, eprint = {https://www.biorxiv.org/content/early/2017/05/30/141499.full.pdf}, journal = {bioRxiv} }