An all-atom protein generative model

• 成果类型: Article
• 署名作者: Chu, Alexander E.; Kim, Jinho; Cheng, Lucy; El Nesr, Gina; Xu, Minkai; Shuai, Richard W.; Huang, Po-Ssu
• 署名单位: Stanford University; Stanford University; Stanford University; Stanford University; Alphabet Inc.; DeepMind; Google Incorporated
• 刊物名称: PROCEEDINGS OF THE NATIONAL ACADEMY OF SCIENCES OF THE UNITED STATES OF AMERICA
• ISSN/ISSBN: 0027-9095
• DOI: 10.1073/pnas.2311500121
• 发表日期: 2024-07-02
• 关键词: design

摘要

Proteins mediate their functions through chemical interactions; modeling these interactions, which are typically through sidechains, is an important need in protein design. However, constructing an all-atom generative model requires an appropriate scheme for managing the jointly continuous and discrete nature of proteins encoded in the structure and sequence. We describe an all-atom diffusion model of protein structure, Protpardelle, which represents all sidechain states at once as a superposition state; superpositions defining a protein are collapsed into individual residue types and conformations during sample generation. When combined with sequence design methods, our model is able to codesign all-atom protein structure and sequence. Generated proteins are of good quality under the typical quality, diversity, and novelty metrics, and sidechains reproduce the chemical features and behavior of natural proteins. Finally, we explore the potential of our model to conduct all-atom protein design and scaffold functional motifs in a backbone- and rotamer-free way.