Visual-like Template Diffusion: Boosting Single-Sequence Protein Structure Prediction by Adapting Image Diffusion Models
Abstract
Abstract Single-sequence protein structure prediction has drawn increasing attention due to the high computational costs associated with obtaining homologous information. Here, we propose a visual-like template diffusion method, named TDFold, to achieve accurate and highly efficient single-sequence 3D structure prediction for proteins. Given a protein sequence, TDFold initially generates high-quality inter-residue geometries (distances and orientations) as templates from a probabilistic diffusion perspective. Since inter-residue geometries can be encoded as multi-channel feature matrices (each channel for either the inter-residue distance or orientation correlation), analogous to image features, we construct an image-level template diffusion module by adapting the stable diffusion (SD) model from text-vision generation to sequence-template diffusion for proteins. Subsequently, a lightweight sequence-template co-evolved learning (SCL) network is constructed to facilitate accurate and efficient protein structure prediction. As a result, TDFold possesses three highlights: (i) better single-sequence prediction performance: TDFold greatly outperforms existing protein language models (PLMs), including ESMFold (also known as ESM2), OmegaFold, trRosettaX-Single, and RGN2, on homology-insufficient datasets such as Orphan and Orphan25, while also achieving promising results on the popular CASP14 and CASP15 benchmarks; (ii) low resource consumption: By utilizing the lightweight SCL…
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