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Structure-activity relationships study on biological activity of peptides as dipeptidyl peptidase IV inhibitors by chemometric modeling.

Paulina KęskaJoanna Stadnik
Published in: Chemical biology & drug design (2019)
The aim of this study is to identify the potential descriptors affecting the inhibitory activity of the peptides inhibiting dipeptidyl peptidase IV (DPP-IV). This study provides important information for assessing the biological activity of the new peptide sequences of food origin or making structural modifications to the current inhibitors to improve their performance. For this purpose, the chemometric method describing the relationship between the structure of food peptides and their biological activity (structure-activity relationship [SAR]) was used to theoretically predict the potential of bioactivity of peptides. Data on the physicochemical properties of amino acids in the dipeptides acting as inhibitors of DPP-IV were collected and analyzed for using these properties as descriptors in further analysis. A total of 252 dipeptide sequences with confirmed DPP-IV inhibitory activity available in the BIOPEP-UWM database were included in the analysis, and 16 descriptors defining individual amino acids (such as molecular weight, polarity, hydropathicity, bulkiness, buried residue, and acceptable and normalized frequency of alpha-helix and beta-sheet) were identified. Based on this information, a data matrix was constructed and used in the chemometric analysis (principal component analysis and multiple linear regression). From the SAR model created, a multiple regression equation was derived to predict the biological activity of the dipeptide DPP-IV inhibitors.
Keyphrases
  • amino acid
  • healthcare
  • human health
  • electronic health record
  • emergency department
  • risk assessment
  • deep learning
  • social media
  • dna binding
  • neural network
  • data analysis