Abstract
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.
Abstract (translated)
生物学家经常需要蛋白质抑制剂，原因多种多样，包括将其作为研究工具来理解生物过程，以及应用到农业、医疗保健等领域的社会问题中。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，防止它们与其配体蛋白结合，并增强免疫细胞对抗异常细胞的功能。抑制剂的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。人工智能的进步现在提供了一个机会，使抑制剂的发现比以往任何时候都要智能化。虽然在计算机辅助抑制剂发现方面已经进行了大量的研究，但这些研究主要集中在序列到结构的映射、逆向映射或生物活性预测上，使得生物学家很难利用这类工具。相反，我们的工作提出了一种新的计算机辅助抑制剂发现方法：从头生成具有口袋意识的肽结构和序列网络。我们的方法包括两个连续扩散模型，用于端到端的结构生成和序列预测。通过利用主链原子之间的角度和二面角关系，我们确保了肽结构的E(3)-不变性表示。结果表明，我们的方法在性能上可与最先进的模型相媲美，突显了其在具有口袋意识的肽设计方面的潜力。这项工作为使用受体特异性肽生成进行精确药物发现提供了新的途径。
URL
https://arxiv.org/abs/2410.21335
