Calculating sequence divergence score for a protein from identity or similarity score?
This is a very tentative answer as I haven't done anything like this, but it's a learning experience for me and I hope it could be helpful to get a more knowledgeable response started.
I don't think you can do this.  Per Wang, 2009 and Yona, 2002, divergence scores could be Kullback-Leibler divergence or Jensen-Shannon divergence, but both papers opt for the latter.  Divergence scores are based on &quot;empirical probability distributions between the 20 amino acids&quot;.  BLOSUM or PAM matrices may be used.  Simply counting the identical or similar residues wouldn't contain as much information.  Wang, 2004 compared JensenShannon to numerous other scoring methods in the context of PSI-BLAST.
The similarity score is related to the divergence score:  Score = 0.5(1-D)(1+S) where D is the divergence score and S is the significance score. It is equation #15 in Wang, 2009 paper.  If you could safely assume the significance score for some alignments was near 1 (no chance similarities) then maybe you could say D = 1 - similarity score.  Unfortunately, judging by the glossary because I haven't downloaded any of the datasets, for the Ensembl data similarity is merely &quot;How well one sequence matches another determined by calculation by an alignment program of identical and conserved residues/nucleotides.&quot;  So from that I take it they may just be counting up the &quot;conserved&quot; amino acids in the old fashioned way, but don't know that.