"Ideal correlations" for biological activity of peptides

Andrey A Toropov; Alla P Toropova; Danuta Leszczynska; Jerzy Leszczynski

doi:10.1016/j.biosystems.2019.04.008

"Ideal correlations" for biological activity of peptides

Biosystems. 2019 Jul:181:51-57. doi: 10.1016/j.biosystems.2019.04.008. Epub 2019 Apr 25.

Authors

Andrey A Toropov¹, Alla P Toropova², Danuta Leszczynska³, Jerzy Leszczynski⁴

Affiliations

¹ Department of Environmental Health Science, Laboratory of Environmental Chemistry and Toxicology, Istituto di RicercheFarmacologiche Mario Negri IRCCS, Via La Masa 19, 20156 Milan, Italy.
² Department of Environmental Health Science, Laboratory of Environmental Chemistry and Toxicology, Istituto di RicercheFarmacologiche Mario Negri IRCCS, Via La Masa 19, 20156 Milan, Italy. Electronic address: alla.toropova@marionegri.it.
³ Interdisciplinary Nanotoxicity Center, Department of Civil and Environmental, Engineering, Jackson State University, 1325 Lynch Street, Jackson, MS 39217-0510, USA.
⁴ Interdisciplinary Nanotoxicity Center, Department of Chemistry, Physics and Atmospheric Sciences, Jackson State University, 1400 J. R. Lynch Street, P.O. Box 17910, Jackson, MS 39217, USA.

PMID: 31028832
DOI: 10.1016/j.biosystems.2019.04.008

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.

Keywords: ACE Inhibitory activity; Antibacterial activity; Bioinformatics; CORAL software; Monte Carlo method; Peptide; Quasi-SMILES.

MeSH terms

Amino Acid Sequence
Angiotensin-Converting Enzyme Inhibitors / chemistry
Angiotensin-Converting Enzyme Inhibitors / metabolism*
Animals
Humans
Models, Theoretical*
Monte Carlo Method
Peptides / chemistry
Peptides / genetics*
Peptides / metabolism*
Quantitative Structure-Activity Relationship

Substances

Angiotensin-Converting Enzyme Inhibitors
Peptides