LAUSR

The ability to perform de novo protein design will allow researchers to expand the variety of available proteins. By designing synthetic structures computationally, they can utilise more structures than those available in the Protein Data Bank, design structures that are not found in nature, or direct the design of proteins to acquire a specific desired structure. While some researchers attempt to design proteins from first physical and thermodynamic principals, we decided to attempt to test whether it is possible to perform de novo helical protein design ofjust the backbone statistically using machine learning by building a model that uses a long short-term memory (LSTM) generative adversarial network (GAN) architecture. The LSTM-based GAN model used only theφandψangles of each residue from an augmented dataset of only helical protein structures. Though the network’s generated backbone structures were not perfect, they were idealised and evaluated post generation where the non-ideal structures were filtered out and the adequate structures kept. The results were successful in developing a logical, rigid, compact,helical protein backbone topology. This paper is a proof of concept that shows it is possible to generate a novel helical backbone topology using an LSTM-GAN architecture using only theφandψangles as features. The next step is to attempt to use these backbone topologies and sequence design them to form complete protein structures.