Design in the DARK: Learning Deep Generative Models for De Novo Protein Design

A bstract The design of novel protein sequences is providing paths towards the development of novel therapeutics and materials. At the forefront is the challenging field of de novo protein design, which looks to design protein sequences unlike those found in nature using general design methodologies. In this work, we develop a tool for de novo design, based on a deep generative sequence model, that rapidly samples novel protein sequences with diverse and ordered structures. To build this tool we develop a framework, called DARK, that trains the underlying generative model on an iteratively expanding set of synthetic sequences. The resulting model generalizes where models trained on natural sequences struggle and greatly improves on the efficiency of comparable sampling-based approaches. We further show how it can generate high quality candidates for de novo design problems and aid in the development of further novel design methods, in all, providing another step, amongst others, towards truly automated and intelligent protein design.