Deep learning (DL) is currently revolutionizing peptide drug development due to both computational advances and the substantial recent expansion of digitized biological data. However, progress in oligopeptide drug development has been limited, likely due to the lack of suitable datasets and difficulty in identifying informative features to use as inputs for DL models. Here, we utilized an unsupervised deep learning model to learn a semantic pattern based on the intrinsically disordered regions of ~171 known osteogenic proteins. Subsequently, oligopeptides were generated from this semantic pattern based on Monte Carlo simulation, followed by in vivo functional characterization. A five amino acid oligopeptide (AIB5P) had strong bone-formation-promoting effects, as determined in multiple mouse models (e.g., osteoporosis, fracture, and osseointegration of implants). Mechanistically, we showed that AIB5P promotes osteogenesis by binding to the integrin α5 subunit and thereby activating FAK signaling. In summary, we successfully established an oligopeptide discovery strategy based on a DL model and demonstrated its utility from cytological screening to animal experimental verification.

由于计算的进步和最近数字化生物数据的大量扩展，深度学习 (DL) 目前正在彻底改变肽药物的开发。然而，寡肽药物开发的进展有限，这可能是由于缺乏合适的数据集以及难以识别用作 DL 模型输入的信息特征。在这里，我们利用无监督深度学习模型来学习基于约 171 种已知成骨蛋白的内在无序区域的语义模式。随后，基于蒙特卡罗模拟从这种语义模式生成寡肽，然后进行体内功能表征。一种五氨基酸寡肽 (AIB5P) 具有强烈的骨形成促进作用，如在多个小鼠模型中所确定的（例如，骨质疏松症、骨折、和植入物的骨整合）。从机制上讲，我们发现 AIB5P 通过与整合素 α5 亚基结合从而激活 FAK 信号传导来促进成骨。总之，我们成功地建立了基于 DL 模型的寡肽发现策略，并展示了其从细胞学筛选到动物实验验证的实用性。