End-to-End deep structure generative model for protein design

Abstract

AO_SCPLOWBSTRACTC_SCPLOWDesigning protein with desirable structure and functional properties is the pinnacle of computational protein design with unlimited potentials in the scientific community from therapeutic development to combating the global climate crisis. However, designing protein macromolecules at scale remains challenging due to hard-to-realize structures and low sequence design success rate. Recently, many generative models are proposed for protein design but they come with many limitations. Here, we present a VAE-based universal protein structure generative model that can model proteins in a large fold space and generate high-quality realistic 3-dimensional protein structures. We illustrate how our model can enable robust and efficient protein design pipelines with generated conformational decoys that bridge the gap in designing structure conforming sequences. Specifically, sequences generated from our design pipeline outperform native fixed backbone design in 856 out of the 1,016 tested targets(84.3%) through AF2 validation. We also demonstrate our models design capability and structural pre-training potential by structurally inpainting the complementarity-determining regions(CDRs) in a set of monoclonal antibodies and achieving superior performance compared to existing methods.