I wish to investigate how a virus could be attenuated through targeted mutagenesis by a priori prediction of attenuating amino acid mutations.
In particular, I am seeking bioinformatic solutions where the protein sequence of an exisiting live vaccine strais has been compared against it wild-type counterparts to identify key attenuating mutations and the key attenuating mutation(s) can be deduced without wet-lab site directed mutagenesis.
I presume surface antigens, such as the spike protein in COVID-10 would be the proteins of interest, but I'm open to suggestions.
I am a computer scientist looking to investigate the mutational parameter space of viral protein sequence data. A key approach I am considering is to use a mutational correlation/covariance matrix and then remove convolution artefacts. I hope this will identify compensatory mutations that will maintain a local interaction for example within a fold, providing a starting point for identifying attenuating mutations.
This may have application for example to COVID-19 and the goal is to produce an efficacious vaccine.