Antibody-SGM, a Score-Based Generative Model for Antibody Heavy-Chain Design

计算机科学 计算生物学 蛋白质设计 序列(生物学) 蛋白质工程 功能(生物学) 蛋白质结构 生物 遗传学 生物化学
作者
Xuezhi Xie,Pedro A. Valiente,Jin Sub Lee,Ji‐Sun Kim,Philip M. Kim
出处
期刊:Journal of Chemical Information and Modeling [American Chemical Society]
标识
DOI:10.1021/acs.jcim.4c00711
摘要

Traditional computational methods for antibody design involved random mutagenesis followed by energy function assessment for candidate selection. Recently, diffusion models have garnered considerable attention as cutting-edge generative models, lauded for their remarkable performance. However, these methods often focus solely on the backbone or sequence, resulting in the incomplete depiction of the overall structure and necessitating additional techniques to predict the missing component. This study presents Antibody-SGM, an innovative joint structure-sequence diffusion model that addresses the limitations of existing protein backbone generation models. Unlike previous models, Antibody-SGM successfully integrates sequence-specific attributes and functional properties into the generation process. Our methodology generates full-atom native-like antibody heavy chains by refining the generation to create valid pairs of sequences and structures, starting with random sequences and structural properties. The versatility of our method is demonstrated through various applications, including the design of full-atom antibodies, antigen-specific CDR design, antibody heavy chains optimization, validation with Alphafold3, and the identification of crucial antibody sequences and structural features. Antibody-SGM also optimizes protein function through active inpainting learning, allowing simultaneous sequence and structure optimization. These improvements demonstrate the promise of our strategy for protein engineering and significantly increase the power of protein design models.
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