"Ideal correlations" for biological activity of peptides. (July 2019)

• Record Type: Journal Article
• Authors: Toropov, Andrey A.; Toropova, Alla P.; Leszczynska, Danuta; Leszczynski, Jerzy
• Is Part Of: Bio systems. Volume 181(2019)
• Page Start: 51
• Page End: 57
• Publication Date: 2019-07
• Subjects: Peptide -- ACE Inhibitory activity -- Antibacterial activity -- Quasi-SMILES -- Bioinformatics -- Monte Carlo method -- CORAL software; Biological systems -- Periodicals; Biology -- Periodicals; Biology -- Periodicals; Evolution -- Periodicals; Biologie -- Périodiques; Évolution -- Périodiques; 570
• DOI: 10.1016/j.biosystems.2019.04.008
• Languages: English
• ISSNs: 0303-2647

Abstract

Highlights: Predictive models for activity of peptides are suggested. These models are a mathematical function of amino acids of peptide. The Index of ideality of correlation ( IIC ) is a criterion of predictive potential. Applying of IIC improves statistical quality for validation set. Abstract: Sequences of one-symbol abbreviations of amino acids are applied as the basis to build up predictive model of Angiotensin converting enzyme (ACE) inhibitory activity of dipeptides and antibacterial activity of group of polypeptides. The developed models are one-variable correlations between biological activity and descriptors calculated with so-called correlation weights of amino acids. The numerical data on the correlation weights are obtained by the Monte Carlo method. The Index of Ideality of Correlation ( IIC ) is a mathematical function of (i) the determination coefficient; and (ii) sums of positive and negative values of "observed minus predicted" endpoints values. The obtained results confirm that IIC can be applied to improve predictive potential of models for ACE inhibitor activity of dipeptides and antibacterial activity of polypeptides.