Molecular Diversity ( IF 3.9 ) Pub Date : 2021-11-21 , DOI: 10.1007/s11030-021-10350-z
Valeria V Kleandrova 1 , Julio A Rojas-Vargas 2 , Marcus T Scotti 3 , Alejandro Speck-Planche 3
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Abstract
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.
Graphical abstract
中文翻译：
用于肽发现的 PTML 建模：具有抗高血压活性的非溶血肽的计算机设计
摘要
高血压是一种影响全球数百万人的疾病。尽管目前的抗高血压药物疗效很高，但它们与严重的副作用有关。肽构成了针对高血压的化学疗法的有吸引力的选择，计算模型可以加速抗高血压肽的设计。然而，据我们所知，所有计算机模型仅预测肽的抗高血压活性，而忽略了它们对红细胞的固有毒性潜力。在这项工作中，我们报告了第一个基于序列的模型，该模型通过多层感知器网络 (SB-PTML-MLP) 结合了扰动理论和机器学习，从而能够同时筛选肽的抗高血压活性和血液毒性。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。通过严格遵循指导方针等解释，我们执行了两项任务。首先，我们选择了对提高抗高血压活性和降低血液毒性都有有利贡献的氨基酸。然后，我们组装了那些合适的氨基酸，虚拟地设计了 SB-PTML-MLP 模型预测的肽作为具有低血液毒性的抗高血压药物。科学文献中报道的在线计算工具进行的预测证实了 SB-PTML-MLP 模型作为设计有效和安全的抗高血压肽的工具的潜力。这里介绍的方法可以扩展到肽的其他药理应用。
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