Laboratory of Fundamental and Applied Research of Quality and Technology of Food Production, Moscow State University of Food Production, Volokolamskoe shosse 11, Moscow, Russian Federation, 125080.
Department of Chemistry, Faculty of Natural and Exact Sciences, University of Oriente, 90500, Santiago de Cuba, Cuba.
Mol Divers. 2022 Oct;26(5):2523-2534. doi: 10.1007/s11030-021-10350-z. Epub 2021 Nov 21.
Hypertension is a medical condition that affects millions of people worldwide. Despite the high efficacy of the current antihypertensive drugs, they are associated with serious side effects. Peptides constitute attractive options for chemical therapy against hypertension, and computational models can accelerate the design of antihypertensive peptides. Yet, to the best of our knowledge, all the in silico models predict only the antihypertensive activity of peptides while neglecting their inherent toxic potential to red blood cells. In this work, we report the first sequence-based model that combines perturbation theory and machine learning through multilayer perceptron networks (SB-PTML-MLP) to enable the simultaneous screening of antihypertensive activity and hemotoxicity of peptides. We have interpreted the molecular descriptors present in the model from a physicochemical and structural point of view. By strictly following such interpretations as guidelines, we performed two tasks. First, we selected amino acids with favorable contributions to both the increase of the antihypertensive activity and the diminution of hemotoxicity. Then, we assembled those suitable amino acids, virtually designing peptides that were predicted by the SB-PTML-MLP model as antihypertensive agents exhibiting low hemotoxicity. The potentiality of the SB-PTML-MLP model as a tool for designing potent and safe antihypertensive peptides was confirmed by predictions performed by online computational tools reported in the scientific literature. The methodology presented here can be extended to other pharmacological applications of peptides.
高血压是一种影响全球数百万人的医学病症。尽管当前的抗高血压药物具有很高的疗效，但它们也与严重的副作用有关。肽类化合物是治疗高血压的有吸引力的选择，计算模型可以加速抗高血压肽的设计。然而，据我们所知，所有的计算模型都只预测了肽类的抗高血压活性，而忽略了它们对红细胞固有的毒性潜力。在这项工作中，我们报告了第一个基于序列的模型，该模型结合了扰动理论和通过多层感知机网络（SB-PTML-MLP）的机器学习，从而能够同时筛选肽类的抗高血压活性和溶血毒性。我们从物理化学和结构的角度解释了模型中存在的分子描述符。通过严格遵循这些解释作为指导原则，我们执行了两项任务。首先，我们选择了对增加抗高血压活性和降低溶血毒性都有有利贡献的氨基酸。然后，我们将这些合适的氨基酸组装起来，实际上设计了一些由 SB-PTML-MLP 模型预测的肽类，这些肽类被预测为具有低溶血毒性的抗高血压药物。通过在线计算工具进行的预测，证实了 SB-PTML-MLP 模型作为设计有效且安全的抗高血压肽类药物的工具的潜力，这些在线计算工具在科学文献中有所报道。这里提出的方法可以扩展到肽类的其他药理学应用。
