Antibody design, a crucial task with significant implications across various disciplines such as therapeutics and biology, presents considerable challenges due to its intricate nature. In this paper, we tackle antigen-specific antibody design as a protein sequence-structure co-design problem, considering both rationality and functionality. Leveraging a pre-trained conditional diffusion model that jointly models sequences and structures of complementarity-determining regions (CDR) in antibodies with equivariant neural networks, we propose direct energy-based preference optimization to guide the generation of antibodies with both rational structures and considerable binding affinities to given antigens. Our method involves fine-tuning the pre-trained diffusion model using a residue-level decomposed energy preference. Additionally, we employ gradient surgery to address conflicts between various types of energy, such as attraction and repulsion. Experiments on RAbD benchmark show that our approach effectively optimizes the energy of generated antibodies and achieves state-of-the-art performance in designing high-quality antibodies with low total energy and high binding affinity, demonstrating the superiority of our approach.

翻译：抗体设计是一项具有重要意义的任务，在治疗学和生物学等多个学科中具有广泛的应用前景，但由于其复杂的特性而面临巨大挑战。本文将抗原特异性抗体设计视为一个蛋白质序列-结构协同设计问题，兼顾合理性与功能性。利用预训练的等变神经网络条件扩散模型（该模型可联合建模抗体互补决定区的序列与结构），我们提出了基于直接能量偏好优化的方法，以指导生成具有合理结构且与给定抗原具有显著结合亲和力的抗体。该方法通过残基层级分解的能量偏好对预训练扩散模型进行微调，并采用梯度手术技术解决吸引能、排斥能等不同类型能量之间的冲突。在RAbD基准上的实验结果表明，本方法能有效优化生成抗体的能量，在设计低总能量、高结合亲和力的高质量抗体方面达到了当前最优性能，充分证明了该方法的优越性。