用3D曲线控制蛋白骨架生成，实现自由绘制与拖拽设计。
ProtPainter: Draw or Drag Protein via Topology-guided Diffusion
- 基于3D曲线草图生成蛋白骨架，分两阶段完成草图构建与生成。
- 在测试中生成的骨架拓扑契合度超0.8，可设计性得分超0.5。
- 支持直观交互式设计，适合结构生物学家与蛋白质工程人员。
近期蛋白骨架生成方法在结构、功能或物理约束下取得进展，但缺乏对拓扑结构的精细控制能力，限制了骨架空间的探索。本文提出ProtPainter，一种基于扩散模型的蛋白骨架生成方法，以3D曲线为条件。该方法分为两个阶段：首先通过CurveEncoder从曲线预测二级结构，用于参数化草图生成；其次，在去噪扩散概率建模（DDPM）中，草图引导骨架生成，并引入螺旋门控（Helix-Gating）融合调度策略控制缩放因子。为评估性能，我们提出首个拓扑条件蛋白生成基准，包含蛋白修复任务及新指标自洽拓扑适配度（scTF）。实验表明，ProtPainter能生成拓扑契合（scTF > 0.8）且可设计（scTM > 0.5）的骨架，绘制与拖拽任务展示了其灵活性与多功能性。
原文摘要 · Abstract (English)
Recent advances in protein backbone generation have achieved promising results under structural, functional, or physical constraints. However, existing methods lack the flexibility for precise topology control, limiting navigation of the backbone space. We present ProtPainter, a diffusion-based approach for generating protein backbones conditioned on 3D curves. ProtPainter follows a two-stage process: curve-based sketching and sketch-guided backbone generation. For the first stage, we propose CurveEncoder, which predicts secondary structure annotations from a curve to parametrize sketch generation. For the second stage, the sketch guides the generative process in Denoising Diffusion Probabilistic Modeling (DDPM) to generate backbones. During this process, we further introduce a fusion scheduling scheme, Helix-Gating, to control the scaling factors. To evaluate, we propose the first benchmark for topology-conditioned protein generation, introducing Protein Restoration Task and a new metric, self-consistency Topology Fitness (scTF). Experiments demonstrate ProtPainter's ability to generate topology-fit (scTF > 0.8) and designable (scTM > 0.5) backbones, with drawing and dragging tasks showcasing its flexibility and versatility.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。