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interpreting the recommended mutations in a one-by-one context #13

@avilella

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@avilella

Hi,

I've been using this software for a while now, and I thought of a scheme for which I could attempt to have a "validated" or "rejected" tag for each of the somatic hypermutations of a monoclonal antibody, in the following way:

Given a monoclonal antibody observed in a single cell, I list each of the mutations with respect to the germline V-genes, and produce mAb protein sequences that are identical to the observed except for 1 aminoacid, in which the mutation has been reverted back to the germline (a.k.a 'step-1 mAb').

Then in parallel I then:
(1) submit this step-1 mAb to efficient-evolution/bin/recommend.py
(2) submit the mAb as it was observed the same way

Then I count how many time does the software mark the mutation as:
(1) 'rejected': when it suggests that the original mAb should mutate towards the step-1 mab.
(2) 'validated': when it suggests that the step-1 mAb should mutate towards the original mab.

Doing this for a collection of mAbs, I obtain many more 'rejected' mutations than I get 'validated', in about a 10 to 1 ratio, and I wonder if this is expected?
Should I put a limit to which mutations recommended by the software I take into account? E.g. by using the numbers in the output? Any recommendations?

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