Protein–Protein Interactions More Conserved within Species than across Species
Figure 6
Ways of Calculating the Overlap between Two Y2H Datasets
(A) Identity-based overlap between Datasets 1 and 2 according to Equation 2. Note that we can only calculate this score if both datasets are from the same organism. Starting with the observed interaction C-E in Dataset 1, we are trying to find the exact same interaction in Dataset 2. The following situations might occur: (a) C and E are also observed to interact in Dataset 2. (b) C and E are not observed to interact in Dataset 2. (c) It is impossible for C and E to be interacting in Dataset 2 due to either of these two reasons: (i) Either C or E are not part of Dataset 2 or (ii) C and E are either both used as preys or both used as baits in Dataset 2. Repeating the above procedure for all other observed interactions in Datasets 1 and 2, we finally calculate the identity-based overlap by dividing the number of common interactions found in Datasets 1 and 2 by the total number of expected interactions (observed and not-observed).
(B) The same procedure as described above is applied to the two Datasets 1 and 3, which are now allowed to be from different organisms. The only difference to Equation 2 (A) is the usage of homology for comparing two PPIs instead of a binary decision scheme (PPIs identical or not-identical). Thus, starting with the interaction D-E from Dataset 1, we try to find possible homologous interactions (not only the identical PPI) in Dataset 3. The only two options in this example are D-E and D′-E (Dataset 3), which in our example are both observed in Dataset 3. Iterating through all observed interactions of Datasets 1 and 3 and summing up the expected interactions and the overlapping homologous interactions, we can then calculate the homology-based overlap (Equation 3). Note that any results from Equation 2 are not comparable to any results from Equation 3.