Chen Yu, Chen Long, Wu Jinyang, Xu Xiaofeng, Yang Chengshuai, Zhang Yong, Chen Xinrong, Lin Kaili, Zhang Shilei
Department of Oral and Cranio-maxillofacial Surgery, Shanghai Ninth People's Hospital, Shanghai Jiao Tong University School of Medicine; College of Stomatology, Shanghai Jiao Tong University; National Center for Stomatology; National Clinical Research Center for Oral Diseases; Shanghai Key Laboratory of Stomatology; Shanghai Research Institute of Stom, Shanghai, 200011, China.
Stomatology Hospital, School of Stomatology, Zhejiang University School of Medicine, Zhejiang Provincial Clinical Research Center for Oral Diseases, Key Laboratory of Oral Biomedical Research of Zhejiang Province, Cancer Center of Zhejiang University, Engineering Research Center of Oral Biomaterials and Devices of Zhejiang Province, Hangzhou, China.
Bioact Mater. 2024 Dec 9;46:37-54. doi: 10.1016/j.bioactmat.2024.11.011. eCollection 2025 Apr.
Angiogenesis is imperative for bone regeneration, yet the conventional cytokine therapies have been constrained by prohibitive costs and safety apprehensions. It is urgent to develop a safer and more efficient therapeutic alternative. Herein, utilizing the methodologies of Deep Learning (DL) and Natural Language Processing (NLP), we proposed a paradigm algorithm that amalgamates with a variant, , to deftly discern potential pro-angiogenic peptides from intrinsically disordered regions (IDRs) of 262 related proteins, where are fertile grounds for developing safer and highly promising bioactive peptides. After the evaluation of the candidate oligopeptides, one tripeptide, PSP, emerged as particularly notable for its exceptional ability to stimulate the vascularization of endothelial cells (ECs), enhance vascular-osteo communication, and then boost the osteogenic differentiation of bone marrow stem cells (BMSCs), evidenced in mouse critical-sized cranial model. Moreover, we found that PSP serves as a 'priming' agent, activating the body's innate ability to produce Osteolectin (Oln) prompting ECs to release small extracellular vesicles (sEVs) enriched with Oln to facilitate bone formation. In summary, our study established a precise and efficient composite model of DL and NLP to screen bioactive peptides, opening an avenue for the development of various peptide-based therapeutic strategies applicable to a broader range of diseases.
血管生成对于骨再生至关重要，然而传统的细胞因子疗法受到高昂成本和安全担忧的限制。开发一种更安全、更有效的治疗替代方案迫在眉睫。在此，我们利用深度学习（DL）和自然语言处理（NLP）方法，提出了一种范式算法，该算法与一种变体相结合，以巧妙地从262种相关蛋白质的内在无序区域（IDRs）中识别潜在的促血管生成肽，这些区域是开发更安全、极具前景的生物活性肽的沃土。在对候选寡肽进行评估后，一种三肽PSP脱颖而出，因其在小鼠临界大小颅骨模型中具有刺激内皮细胞（ECs）血管化、增强血管与骨的通讯以及促进骨髓干细胞（BMSCs）成骨分化的特殊能力而备受关注。此外，我们发现PSP作为一种“启动”剂，激活机体产生骨凝集素（Oln）的先天能力，促使ECs释放富含Oln的小细胞外囊泡（sEVs）以促进骨形成。总之，我们的研究建立了一个精确有效的DL和NLP复合模型来筛选生物活性肽，为开发适用于更广泛疾病的各种基于肽的治疗策略开辟了一条途径。
