AbGPT：通过生成式语言建模进行从头抗体设计
AbGPT: De Novo Antibody Design via Generative Language Modeling
AI总结：
针对从头设计B细胞受体的难题，本文提出由基础蛋白质语言模型微调的AbGPT，并通过定制生成与过滤流程构建15,000条高质量BCR序列文库。
摘要：
适应性免疫应答主要由B细胞受体（BCRs）介导，凭借其多样性和抗原特异性，在有效中和病原体方面发挥着关键作用。由于BCRs结构复杂且结合需求多样，从头或从零开始设计BCRs一直具有挑战性。蛋白质语言模型（PLMs）在不依赖结构信息的情况下，在情境化表示和执行各种下游任务方面表现出显著性能。然而，这些模型往往缺乏对整个蛋白质空间的全面理解，从而限制了其在抗体设计中的应用。在本研究中，我们提出了Antibody Generative Pretrained Transformer（AbGPT），这是一个从基础PLM微调而来的模型，旨在实现更具信息依据的BCR序列设计。利用定制的生成和过滤流程，AbGPT成功生成了包含15,000条BCR序列的高质量文库，表明其对抗体库内的内在变异性和保守区域具有很强的理解。
英文摘要：
The adaptive immune response, largely mediated by B-cell receptors (BCRs), plays a crucial role for effective pathogen neutralization due to its diversity and antigen specificity. Designing BCRs de novo, or from scratch, has been challenging because of their complex structure and diverse binding requirements. Protein language models (PLMs) have shown remarkable performance in contextualizing and performing various downstream tasks without relying on structural information. However, these models often lack a comprehensive understanding of the entire protein space, which limits their application in antibody design. In this study, we introduce Antibody Generative Pretrained Transformer (AbGPT), a model fine-tuned from a foundational PLM to enable a more informed design of BCR sequences. Using a custom generation and filtering pipeline, AbGPT successfully generated a high-quality library of 15,000 BCR sequences, demonstrating a strong understanding of the intrinsic variability and conserved regions within the antibody repertoire.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
