five

Understanding structure-based dynamic interactions of antihypertensive peptides extracted from food sources

收藏
DataCite Commons2021-02-01 更新2024-07-28 收录
下载链接:
https://tandf.figshare.com/articles/dataset/Understanding_structure-based_dynamic_interactions_of_antihypertensive_peptides_extracted_from_food_sources/11841447
下载链接
链接失效反馈
官方服务:
资源简介:
Functional foods are emerging as essential healthy nutritional component due to their abundant wellbeing benefits. Especially the food-derived peptides are considered as key components for playing their biologically active roles. One such robust therapeutics that already exploited with food peptides that help treating high blood pressure via targeting Angiotensin-Converting Enzyme (ACE). This <i>in silico</i> study demonstrated the inhibitory potential of antihypertensive peptides derived from food sources. This study involves an intensive structure-based analysis of enzyme-peptide interactions using Molecular Dynamics (MD) simulations. Interestingly, this study will help us to get deeper understanding on how food peptides achieve successful inhibition of ACE. In this study, the peptide-enzyme complexes revealed two binding pockets, A and B, on either side of the active site Zn atom. Pocket B has a smaller binding site volume than pocket A, comprised of β-sheets and the active site opening cleft. The interface of the binding sites showed that the enzyme structure was negative to neutral charge, and the peptide structure was positive to neutral charge. The dynamics of complex structures of seven highly potential peptides were performed for 20 ns each at 300 K. Comparative analysis of RMSD, RMSF and binding energies show the enzyme-peptide complexes and the overall stability of apo-enzyme. Importantly, two peptides AFKAWAVAR and IWHHTF showed the highest variation in their RMSD as compared to the apo-enzyme. This study will further be useful for the assessment of the characteristics to predict novel inhibitory peptides that can be generated from food proteins. Communicated by Ramaswamy H. Sarma
提供机构:
Taylor & Francis
创建时间:
2020-02-12
5,000+
优质数据集
54 个
任务类型
进入经典数据集
二维码
社区交流群

面向社区/商业的数据集话题

二维码
科研交流群

面向高校/科研机构的开源数据集话题

数据驱动未来

携手共赢发展

商业合作