In Silico identification of angiotensin-converting enzyme inhibitory peptides from MRJP1.
Rana Adnan TahirAfsheen BashirMuhammad Noaman YousafAzka AhmedYasmine DaliSanaullah KhanSheikh Arslan SehgalPublished in: PloS one (2020)
Hypertension is considered as one of the most common diseases that affect human beings (both male and female) due to its high prevalence and also extending widely to both industrialize and developing countries. Angiotensin-converting enzyme (ACE) has a significant role in the regulation of blood pressure and ACE inhibition with inhibitory peptides is considered as a major target to prevent hypertension. In the current study, a blood pressure regulating honey protein (MRJP1) was examined to identify the ACE inhibitory peptides. The 3D structure of MRJP1 was predicted by utilizing the threading approach and further optimized by performing molecular dynamics simulation for 30 nanoseconds (ns) to improve the quality factor up to 92.43%. Root mean square deviation and root mean square fluctuations were calculated to evaluate the structural features and observed the fluctuations in the timescale of 30 ns. AHTpin server based on scoring vector machine of regression models, proteolysis and structural characterization approaches were implemented to identify the potential inhibitory peptides. The anti-hypertensive peptides were scrutinized based on the QSAR models of anti-hypertensive activity and the molecular docking analyses were performed to explore the binding affinities and potential interacting residues. The peptide "EALPHVPIFDR" showed the strong binding affinity and higher anti-hypertensive activity along with the global energy of -58.29 and docking score of 9590. The aromatic amino acids especially Tyr was observed as the key residue to design the dietary peptides and drugs like ACE inhibitors.
Keyphrases
- angiotensin converting enzyme
- blood pressure
- molecular docking
- amino acid
- angiotensin ii
- molecular dynamics simulations
- hypertensive patients
- heart rate
- endothelial cells
- protein protein
- molecular dynamics
- blood glucose
- risk assessment
- risk factors
- skeletal muscle
- dengue virus
- metabolic syndrome
- mass spectrometry
- climate change
- binding protein
- machine learning
- dna binding
- induced pluripotent stem cells
- protein kinase