Department of Clinical Microscopy, Faculty of Medical Technology, Mahidol University, Bangkok 10700, Thailand.
Center for Research and Innovation, Faculty of Medical Technology, Mahidol University, Bangkok 10700, Thailand.
Int J Mol Sci. 2019 Jun 17;20(12):2950. doi: 10.3390/ijms20122950.
Cancer remains one of the major causes of death worldwide. Angiogenesis is crucial for the pathogenesis of various human diseases, especially solid tumors. The discovery of anti-angiogenic peptides is a promising therapeutic route for cancer treatment. Thus, reliably identifying anti-angiogenic peptides is extremely important for understanding their biophysical and biochemical properties that serve as the basis for the discovery of new anti-cancer drugs. This study aims to develop an efficient and interpretable computational model called TargetAntiAngio for predicting and characterizing anti-angiogenic peptides. TargetAntiAngio was developed using the random forest classifier in conjunction with various classes of peptide features. It was observed via an independent validation test that TargetAntiAngio can identify anti-angiogenic peptides with an average accuracy of 77.50% on an objective benchmark dataset. Comparisons demonstrated that TargetAntiAngio is superior to other existing methods. In addition, results revealed the following important characteristics of anti-angiogenic peptides: (i) disulfide bond forming Cys residues play an important role for inhibiting blood vessel proliferation; (ii) Cys located at the C-terminal domain can decrease endothelial formatting activity and suppress tumor growth; and (iii) Cyclic disulfide-rich peptides contribute to the inhibition of angiogenesis and cell migration, selectivity and stability. Finally, for the convenience of experimental scientists, the TargetAntiAngio web server was established and made freely available online.
癌症仍然是全球主要的死亡原因之一。血管生成对于各种人类疾病的发病机制至关重要，尤其是实体瘤。抗血管生成肽的发现为癌症治疗提供了一种有前途的治疗途径。因此，可靠地识别抗血管生成肽对于了解它们的生物物理和生化特性非常重要，这些特性是发现新的抗癌药物的基础。本研究旨在开发一种名为 TargetAntiAngio 的高效且可解释的计算模型，用于预测和表征抗血管生成肽。TargetAntiAngio 使用随机森林分类器与各种肽特征类别结合开发。通过独立验证测试观察到，TargetAntiAngio 可以在客观基准数据集上以平均准确率 77.50%识别抗血管生成肽。比较表明，TargetAntiAngio 优于其他现有方法。此外，结果揭示了抗血管生成肽的以下重要特征：(i) 形成二硫键的 Cys 残基对于抑制血管增殖起着重要作用；(ii) Cys 位于 C 末端结构域可以降低内皮形成活性并抑制肿瘤生长；和 (iii) 环二硫键丰富的肽有助于抑制血管生成和细胞迁移、选择性和稳定性。最后，为了方便实验科学家，建立了 TargetAntiAngio 网络服务器，并免费在线提供。
