Proteina: Scaling Flow-based Protein Structure Generative Models
Tomas Geffner Kieran Didi Zuobai Zhang Danny Reidenbach Zhonglin Cao Jason Yim Mario Geiger Christian Dallago Emine Kucukbenli Arash Vahdat Karsten Kreis
Recently, diffusion- and flow-based generative models of protein structures have emerged as a powerful tool for de novo protein design. Here, we develop *Proteina*, a new large-scale flow-based protein backbone generator that utilizes hierarchical fold class labels for conditioning and relies on a tailored scalable transformer architecture with up to $5\times$ as many parameters as previous models. To meaningfully quantify performance, we introduce a new set of metrics that directly measure the distributional similarity of generated proteins with reference sets, complementing existing metrics. We further explore scaling training data to millions of synthetic protein structures and explore improved training and sampling recipes adapted to protein backbone generation. This includes fine-tuning strategies like LoRA for protein backbones, new guidance methods like classifier-free guidance and autoguidance for protein backbones, and new adjusted training objectives. Proteina achieves state-of-the-art performance on de novo protein backbone design and produces diverse and designable proteins at unprecedented length, up to 800 residues. The hierarchical conditioning offers novel control, enabling high-level secondary-structure guidance as well as low-level fold-specific generation.
protein structure generation, de novo protein design, flow matching, fold class conditioning
We present a novel flow-based protein backbone generative model that uses a new scalable transformer architecture and conditions on fold class labels.