DiffTopo: Fold exploration using coarse grained protein topology representations
Track: Machine learning: computational method and/or computational results
Keywords: deep learning; protein design; diffusion model; coarse-grained representation; generative model
TL;DR: We developed a new representation to describe protein topology and based on it we developed a generative model to sample protein space faster.
Abstract: A major challenge in the field of computational de novo protein design is the exploration of uncharted areas within protein structural space, i.e., generating “designable” protein structures that nature has not explored. However, the large degrees of freedom of protein structural backbones complicate the sampling process during protein design. In this work, we propose a new coarse grained protein structure representation method DiffTopo - an E(3) Equivariant 3D conditional diffusion model, which greatly increases the sampling efficiency. Combined with the RFdiffusion framework, novel protein folds can be generated rapidly, allowing for efficient exploration of the designable topology space. This opens up possibilities to solve the problem of generating new folds as well to functionalize de novo proteins through motif scaffolding, where functional or enzymatic sites can be introduced into novel protein frameworks.
Submission Number: 25
Loading