xTrimoABFold：无需MSA的从头抗体结构预测
xTrimoABFold: De novo Antibody Structure Prediction without MSA
AI总结：
针对AlphaFold2依赖MSA、抗体预测效率低且CDR精度差的问题，该研究提出基于预训练抗体语言模型的xTrimoABFold，在精度（RMSD提升30%+）和速度（快151倍）上均超越现有方法，为从头抗体设计提供高效工具。
摘要：
在抗体工程领域，一项核心任务是设计全新抗体，使其互补位能以正确的表位结合特定抗原。理解抗体结构及其互补位有助于从机制层面理解其功能。因此，仅基于序列预测抗体结构始终是从头抗体设计中极具价值的问题。结构生物学领域的突破性成果AlphaFold2提供了一种基于蛋白质序列、结合计算成本高昂的协同进化多序列比对（MSAs）预测蛋白质结构的方案。然而，其计算效率偏低，且对抗体尤其是抗体互补决定区（CDRs）的预测精度不佳，限制了其在工业级高通量药物设计中的应用。为学习抗体的有效表征，我们通过Transformer模型，在来自观测抗体空间数据库的精选序列上训练了深度抗体语言模型（ALM）。我们还开发了名为xTrimoABFold的新型模型，基于预训练ALM以及高效的evoformer和结构模块，从抗体序列预测抗体结构。该模型通过最小化CDR上的领域特定焦点损失与帧对齐点损失的集成损失，在PDB中的抗体结构上进行端到端训练。xTrimoABFold的性能显著优于AlphaFold2及其他基于蛋白质语言模型的前沿模型（如OmegaFold、HelixFold-Single和IgFold），优势幅度极大（RMSD提升30%以上），同时推理速度比AlphaFold2快151倍。据我们所知，xTrimoABFold实现了当前最优的抗体结构预测效果。其在精度和效率上的双重提升使其成为从头抗体设计的宝贵工具，也有望推动免疫理论的进一步发展。
英文摘要：
In the field of antibody engineering, an essential task is to design a novel antibody whose paratopes bind to a specific antigen with correct epitopes. Understanding antibody structure and its paratope can facilitate a mechanistic understanding of its function. Therefore, antibody structure prediction from its sequence alone has always been a highly valuable problem for de novo antibody design. AlphaFold2, a breakthrough in the field of structural biology, provides a solution to predict protein structure based on protein sequences and computationally expensive coevolutionary multiple sequence alignments (MSAs). However, the computational efficiency and undesirable prediction accuracy of antibodies, especially on the complementarity-determining regions (CDRs) of antibodies limit their applications in the industrially high-throughput drug design. To learn an informative representation of antibodies, we employed a deep antibody language model (ALM) on curated sequences from the observed antibody space database via a transformer model. We also developed a novel model named xTrimoABFold to predict antibody structure from antibody sequence based on the pretrained ALM as well as efficient evoformers and structural modules. The model was trained end-to-end on the antibody structures in PDB by minimizing the ensemble loss of domain-specific focal loss on CDR and the frame-aligned point loss. xTrimoABFold outperforms AlphaFold2 and other protein language model based SOTAs, e.g., OmegaFold, HelixFold-Single, and IgFold with a large significant margin (30+\% improvement on RMSD) while performing 151 times faster than AlphaFold2. To the best of our knowledge, xTrimoABFold achieved state-of-the-art antibody structure prediction. Its improvement in both accuracy and efficiency makes it a valuable tool for de novo antibody design and could make further improvements in immuno-theory.
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。
