用原子级建模设计可结合的蛋白质复合物，突破多链蛋白生成瓶颈。
An All-Atom Generative Model for Designing Protein Complexes
- 基于原子级信息与多链数据，端到端生成蛋白质复合物结构。
- 支持从头设计、折叠与逆向折叠，零样本采样亦达顶尖性能。
- 适合蛋白工程、药物设计领域研究者使用，代码已开源。
蛋白质通常以复合物形式存在，与其他蛋白质或生物分子相互作用以执行特定生物学功能。单链蛋白建模研究已十分深入，如ESM系列和AlphaFold2等模型取得显著进展。然而，多链蛋白的研究与建模仍处于起步阶段，而它们对理解生物功能至关重要。针对这一挑战，我们提出APM（All-Atom Protein Generative Model），一种专为多链蛋白建模设计的生成模型。通过融合原子级信息并利用多链蛋白数据，APM能够精确建模链间相互作用，实现从头设计具备结合能力的蛋白质复合物。同时，该模型支持多链蛋白的折叠与逆向折叠任务。此外，经监督微调（SFT）后性能提升，并在部分任务中实现零样本采样，达到当前最优结果。代码已公开于https://github.com/bytedance/apm。
原文摘要 · Abstract (English)
Proteins typically exist in complexes, interacting with other proteins or biomolecules to perform their specific biological roles. Research on single-chain protein modeling has been extensively and deeply explored, with advancements seen in models like the series of ESM and AlphaFold2. Despite these developments, the study and modeling of multi-chain proteins remain largely uncharted, though they are vital for understanding biological functions. Recognizing the importance of these interactions, we introduce APM (All-Atom Protein Generative Model), a model specifically designed for modeling multi-chain proteins. By integrating atom-level information and leveraging data on multi-chain proteins, APM is capable of precisely modeling inter-chain interactions and designing protein complexes with binding capabilities from scratch. It also performs folding and inverse-folding tasks for multi-chain proteins. Moreover, APM demonstrates versatility in downstream applications: it achieves enhanced performance through supervised fine-tuning (SFT) while also supporting zero-shot sampling in certain tasks, achieving state-of-the-art results. We released our code at https://github.com/bytedance/apm.
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