An All-Atom Generative Model for Designing Protein Complexes
This work addresses a key gap in protein modeling for biologists and drug developers by enabling precise design of protein complexes, though it builds on existing single-chain methods.
The authors tackled the problem of modeling and designing multi-chain protein complexes, which are crucial for biological functions but underexplored compared to single-chain proteins, and introduced APM, an all-atom generative model that achieves state-of-the-art results in tasks like designing complexes from scratch, folding, and inverse-folding.
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.