Efficient protein structure generation with sparse denoising models

Abstract

Generating designable protein backbones has become an integral part of machine learning-assisted approaches to protein design. Together with sequence design and structure predictor-based filtering, it forms the backbone of the computational protein design pipeline. However, current protein structure generators face important limitations for large proteins and require retraining for protein design tasks unseen during model training. To address the first issue, we introduce salad, a family of sparse all-atom denoising models for protein backbone generation. Our models are notably faster than the state-of-the-art while matching or improving designability and diversity, and generate designable structures for protein lengths up to 1,000 amino acids. To address the second issue, we combine salad with structure-editing, a strategy for expanding the capability of protein denoising models to unseen tasks. We apply our approach to a variety of protein design tasks, from motif-scaffolding to multi-state protein design, demonstrating the flexibility of salad and structure-editing.