My rotation project in the Church lab was on structure prediction for bacterial T-box riboswitches (the goal was to classify which tRNA was recognized by the riboswitch; fortunately we only needed to predict the secondary structure for this goal). Even these were rather difficult to model. We looked into deep learning methods but ended up going with a HMM approach due to lack of structural data. And of course, these are ribo *switches*, which change structure upon ligand binding.
So it's very cool to see the progress that has been made since 2020.
My rotation project in the Church lab was on structure prediction for bacterial T-box riboswitches (the goal was to classify which tRNA was recognized by the riboswitch; fortunately we only needed to predict the secondary structure for this goal). Even these were rather difficult to model. We looked into deep learning methods but ended up going with a HMM approach due to lack of structural data. And of course, these are ribo *switches*, which change structure upon ligand binding.
So it's very cool to see the progress that has been made since 2020.
Amongst everyone else, I always look forwards to your comments the most