Biologists frequently desire protein inhibitors for a variety of reasons, including use as research tools for understanding biological processes and application to societal problems in agriculture, healthcare, etc. Immunotherapy, for instance, relies on immune checkpoint inhibitors to block checkpoint proteins, preventing their binding with partner proteins and boosting immune cell function against abnormal cells. Inhibitor discovery has long been a tedious process, which in recent years has been accelerated by computational approaches. Advances in artificial intelligence now provide an opportunity to make inhibitor discovery smarter than ever before. While extensive research has been conducted on computer-aided inhibitor discovery, it has mainly focused on either sequence-to-structure mapping, reverse mapping, or bio-activity prediction, making it unrealistic for biologists to utilize such tools. Instead, our work proposes a new method of computer-assisted inhibitor discovery: de novo pocket-aware peptide structure and sequence generation network. Our approach consists of two sequential diffusion models for end-to-end structure generation and sequence prediction. By leveraging angle and dihedral relationships between backbone atoms, we ensure an E(3)-invariant representation of peptide structures. Our results demonstrate that our method achieves comparable performance to state-of-the-art models, highlighting its potential in pocket-aware peptide design. This work offers a new approach for precise drug discovery using receptor-specific peptide generation.

翻译：生物学家出于多种原因经常需要蛋白质抑制剂，包括将其作为理解生物过程的研究工具，以及应用于农业、医疗保健等社会问题。例如，免疫疗法依赖于免疫检查点抑制剂来阻断检查点蛋白，防止其与伴侣蛋白结合，从而增强免疫细胞对抗异常细胞的功能。抑制剂发现长期以来是一个繁琐的过程，近年来通过计算方法得以加速。人工智能的进步为抑制剂发现提供了前所未有的智能化机遇。尽管计算机辅助抑制剂发现已进行了广泛研究，但其主要集中于序列到结构映射、反向映射或生物活性预测，使得生物学家难以实际应用此类工具。相反，我们的工作提出了一种计算机辅助抑制剂发现的新方法：从头口袋感知肽结构与序列生成网络。我们的方法包含两个顺序扩散模型，用于端到端结构生成和序列预测。通过利用主链原子间的角度和二面角关系，我们确保了肽结构的E(3)不变表示。结果表明，我们的方法达到了与最先进模型相当的性能，凸显了其在口袋感知肽设计中的潜力。这项工作为利用受体特异性肽生成进行精准药物发现提供了一种新途径。