# Calculating sequence divergence score for a protein from identity or similarity score?

I have % identity and % similarity scores for ~50K protein alignments, that I fetched from Ensembl Compara database. The issue is that I wanted to have divergence scores instead. So in order to calculate divergence scores, I first looked for the conventional method to calculate divergence score from protein alignments. But turns out, most of the methods/tools calculate % identity and % similarity scores instead of divergence scores.

(1) So I wonder what is the conventional method to calculate divergence scores from protein alignments.

(2) Could I simply calculate % sequence divergence as 100 - % identity or 100- % similarity score? If that's ok, should I prefer 100 - % identity or 100- % similarity?

Any suggestions from experts are welcome.

Update: As suggested in a comment below, I should mention that the average % identity score is more than 70. I hope this information would be helpful.

• It depends on how similar your proteins are, the quality of the alignements. Measuring distance between distant sequences is harder than for very similar sequences. – reuns Feb 27 at 13:50
• The sequences of the proteins very similar. Average % identity is >70. Thanks for suggesting, in the updated question, I mention this. Also, I started the bounty, in case you may know the answer! – Ramirez Feb 27 at 22:58
• Since you don't want to use similarity or identity calculating divergence as 1-(similarity | identity) won't do what you want. What exactly you want to find? Why you can't use similarity or identity – Hachiloni Feb 28 at 6:49
• @Hachiloni, yes, indeed the answer of @Mike Serfas below, suggests that the 100 - (% identity) may not be the right way to calculate the % divergence. However, similarity score could be used, if some condition is met. Well, that's what I was asking i.e. if I could calculate divergence score from similarity|identity scores. – Ramirez Feb 28 at 17:55

• thanks for the answer, it indeed clears things out. Especially the equation i.e. Score = 0.5(1-D)(1+S) seems to be very relevant to what I wanted to do. I wonder if the condition i.e. significance score for some alignments was near 1 (no chance similarities) is satisfied in the case of the alignments I have. Could you please point to a reference where I can get more details regarding this equation? – Ramirez Feb 28 at 18:08