Designing human Sphingosine-1-phosphate lyases using a temporal Dirichlet variational autoencoder
Enzymatic deficiencies cause the accumulation of toxic levels of substrates in a cell and are associated with life-threatening pathologies. Restoring physiological enzymes levels by injecting a recombinant version of the defective enzyme could provide a viable therapeutic option. However, these enzyme replacement therapies have had limited success, as the recombinant enzymes are less catalytically active, cause immune response and are difficult to manufacture. Moreover, the vast sequence design space makes finding enzymes with desired therapeutic properties extremely challenging. Here, we present a new enzyme engineering framework, which builds on recent advances in deep learning, variational calculus and natural language processing, to design variants of human enzymes with biochemical features comparable to the wild type protein as a way to rapidly build targeted libraries for downstream screening. We applied our method to design variants of human Sphyngosine-1-phosphate lyase (HsS1PL) as potential therapeutic treatments for nephrotic syndrome type 14 (NPHS14), and characterized their biochemical properties through extensive sequence and molecular dynamics analyses.