Optimizing protein function / fitness is among one of the most challenging tasks that a lab can undergo. Whether the objective is to merely create functional homologs or improved versions of existing proteins, modern laboratory techniques tend to be expensive, time consuming, and leave much to be desired.
But what if I told you that it was now possible to effortlessly optimize most proteins on your computer and at effectively no cost. Thanks to recent advances in deep learning and our platform Neurosnap, end-to-end in-silico mutagenesis is now a reality and better than ever. In this article we take the brightest natural Fluorescent Protein, AausFP1, and create 100 mutants and predict their structures using AlphaFold2. All data from our experiment is also publicly available for scrutiny.
https://neurosnap.ai/blog/post/62db42ce394c1138048dc3ed
All results from the blog post are available within this repository for free.
Want to try out the experiment yourself? Go to neurosnap.ai and try us out!