表面张力
肽
肺表面活性物质
化学
氨基酸
异亮氨酸
胶束
回归
亮氨酸
生物系统
随机森林
色谱法
数学
热力学
有机化学
统计
物理
水溶液
计算机科学
生物化学
生物
人工智能
作者
Fabián Ricardo,Paola Ruiz Puentes,Luis H. Reyes,Juan C. Cruz,Óscar Álvarez,Diego Pradilla
标识
DOI:10.1016/j.ces.2022.118208
摘要
The surface tension at the critical micelle concentration (STCMC) is an important descriptor for surfactants in applications including cosmetics, pharmaceuticals and food. A predictive STCMC Random Forest model was trained with 691 conventional surfactants and 9 amino acids. The model was evaluated by fivefold cross-validation, and the prediction power was tested for peptides by direct comparison with the experimental STCMC values found in the literature or measured in this work. Predictions were also conducted for short peptide permutations. The estimated STCMC approached the experimental values as the peptide length decreased, suggesting a strong influence of secondary structures for longer sequences where the developed algorithm fails to make robust predictions. Concerning the short peptide permutations, the model estimated lower STCMC values as the carbon number increased within the hydrophobic portions. This highlights the importance of the hydrophobic amino acids leucine, isoleucine, and phenylalanine in peptides with attractive surfactant properties.
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