Amino Acids

, Volume 42, Issue 4, pp 1379–1386 | Cite as

Quantitative structure–activity relationship modeling of renin-inhibiting dipeptides

Original Article

Abstract

Partial least squares regression method was used to analyze a peptide dataset and construct inhibitory models for renin-inhibitory natural dipeptides. The models were computed with the renin-inhibitory activity as dependent variable (Y) and the peptide structural properties as predictors (X); validation was conducted using cross-validation and permutation tests. The amino acid descriptors were based on the 3- and 5-z scales of 20 coded amino acids to produce models that explained 71.6% of Y with a 33.8% predictive ability and 75.2% of Y with a predictive power of 50.8%, respectively. In both models, low molecular size amino acids with hydrophobic side chains were preferred at the N-terminus, while amino acids with bulky side chains were preferred at the C-terminus for potency. Based on the 5-z model, four Trp (W)-containing antihypertensive dipeptides (IW, LW, VW and AW) were predicted as the most potent renin inhibitors. The peptides were synthesized and in vitro inhibition assay showed that IW and LW inhibited 70% (IC50, 2.3 mM) and 37% renin activity at 3.2 mM, respectively, whereas VW and AW were inactive. There was no correlation between the observed renin-inhibitory activities and angiotensin-converting enzyme inhibitory activities of the dipeptides. We concluded that the structural similarities between isoleucine and leucine could have contributed to their distinct inhibitory activity when compared to alanine and valine. Therefore, IW may be a useful template for the development of advanced forms of highly active low molecular size antihypertensive peptides and peptidomimetics.

Keywords

Renin inhibitors Dipeptides Quantitative structure–activity relationship (QSAR) Partial least squares regression (PLS) Amino acid descriptors 

Notes

Acknowledgments

Operating and equipment research grants for this project were provided to Dr. RE Aluko by the Natural Sciences and Engineering Research Council of Canada (NSERC) and the Advanced Foods and Materials Network of Centre of Excellence (AFMNet) Canada. CC Udenigwe is a recipient of NSERC Alexander Graham Bell Canada PhD Scholarship.

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Copyright information

© Springer-Verlag 2011

Authors and Affiliations

  • Chibuike C. Udenigwe
    • 1
    • 2
  • Huan Li
    • 1
    • 2
  • Rotimi E. Aluko
    • 1
    • 2
  1. 1.Department of Human Nutritional SciencesUniversity of ManitobaWinnipegCanada
  2. 2.The Richardson Centre for Functional Foods and NutraceuticalsUniversity of ManitobaWinnipegCanada

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