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

Abstract

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 in silico 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

Keywords:

• ACE
• biopeptides
• molecular docking
• MD simulation
• modeling
• enzyme inhibition

Acknowledgements

The work was partially conducted in the context of Bioinformatics Resources and Applications Facility (BRAF), C-DAC, Pune, India. Institute Computer Center, IIT-Roorkee and Bioinformatics Facility, Department of Biotechnology, IIT-Roorkee granted the provision of computational facilities and support.

Disclosure statement

No potential conflict of interest was reported by the authors.

Additional information

Funding

This research was supported by the Department of Biotechnology (DBT), Ministry of Science and Technology, Government of India [Grant number DBT/2015/IIT-R/325] to G.K.