Seminal fluid proteins (SFPs) have been shown to affect the physiology,behaviour and immune responses of mated females in some species. Thisopen window for manipulation of female’s fitness allows the possibility forcomplex evolutionary dynamics between the SFPs and proteins of femalesthat would counter the effects of the former, the female reproductive proteins (FRPs). Also, the bean beetles of the Bruchinae subfamily are pests to pre-ferred species of plant hosts. The hosts have a great variety of secondary defensive metabolites between them and to detoxify those compounds, each beetle species is expected to have a well adapted arsenal of digestive proteinsfor a specific host. I carried out a comparative study with four species of bean beetles with the aim to identify patterns of selection in the proteins mentioned. Expression data for one of those species, Callosobruchus maculatus, has allowed to identify its SFPs, FRPs and digestive proteins and with orthology inference I identified their orthologues in the other three species. Then I estimated theratio of non-synonymous to synonymous substitution rates (ω) for each protein by using codeML of the PAML package and used them as a proxy for estimating selection. FRPs had about the same ω values as conserved genes found across the Arthropod phylum, while the SFPs and digestive proteins hadhigher ω values, indicating more relaxed purifying selection. I also performed tests of positive selection and have identified 92 digestive proteins, 9 FRPs and 26 SFPs as potential targets for future functional work. Finally, I examined the scenario of co-evolution between SFPs and FRPs because of direct interaction. By correlating branch-specific ω values for each possible pairs of proteins I found that SFPs are associated on average more with FRPs than with digestive or conserved genes, as expected. The same was true for the FRPs. Also I examined the possibility of factors contributing to the association such as expression levels, sex-biased expression and protein function. Using linear regression models I found that expression levels and proteinfunction do predict in some degree the ω estimates and could thus also affectthe correlations examined. High gene expression levels reduce the overall ωvalues of genes, also known as E-R anticorrelation. Sex-biased expression does not affect the overall ω values, but does affect the intensity of the E-R anticorrelation, with it being less prominent in male-biased genes and more prominent towards female-biased genes.