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arXiv · 2512.24192

SeedProteo: Accurate De Novo All-Atom Design of Protein Binders

Wei Qu·Yiming Ma·Fei Ye·Chan Lu·Yi Zhou·Kexin Zhang·Lan Wang·Minrui Gui·Quanquan Gu
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Abstract

We present SeedProteo, a diffusion-based model for de novo all-atom protein design. We demonstrate how to repurpose a cutting-edge folding architecture into a powerful generative design framework by effectively integrating self-conditioning features. Extensive benchmarks highlight the model's capabilities across two distinct tasks: in unconditional generation, SeedProteo exhibits superior length generalization and structural diversity, maintaining robustness for long sequences and complex topologies; in binder design, it achieves state-of-the-art performance among open-source methods, attaining the highest in-silico design success rates, structural diversity and novelty. Finally, we validate SeedProteo through wet-lab assays on two therapeutic targets, achieving hit rates of 70%-80% and picomolar-level binding affinities, establishing leading results. To facilitate community adoption, we provide public access to SeedProteo via a webserver (https://seedfold.io/proteinDesign).

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Wei Qu, Yiming Ma, Fei Ye, Chan Lu, Yi Zhou, Kexin Zhang, Lan Wang, Minrui Gui, Quanquan Gu. 2025-12-30. SeedProteo: Accurate De Novo All-Atom Design of Protein Binders. https://arxiv.org/abs/2512.24192

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