Mathematical modelling of insect neuropeptide potencies. Are quantitatively predictive models possible?

Area of Science:

• Insect Endocrinology
• Biochemistry
• Computational Biology

Background:

• Adipokinetic hormones (AKHs) regulate lipid and carbohydrate metabolism in insects.
• Understanding the structure-activity relationships (SAR) of AKH analogues is crucial for various applications.

Purpose of the Study:

• To develop mathematical models predicting the potency of adipokinetic hormone analogues in Locusta migratoria.
• To explore the utility of partial least squares (PLS) regression for quantitative structure-activity relationship (QSAR) studies in insect hormones.

Main Methods:

• Lipid mobilization assay (in vivo) and acetate uptake assay (in vitro) were used to determine hormone potencies.
• Sixty-nine natural and synthetic adipokinetic hormone analogues were tested.
• Amino acid sequence variations were described using continuous descriptor scales (z(1)', z(2)', and z(3)').
• Partial least squares (PLS) regression was employed to model hormone potencies based on descriptor scales.

Main Results:

• Mathematical models were constructed using PLS regression, correlating peptide structure with potency.
• Predictive models achieved correlations (r(2) values) up to 0.73 between predicted and actual potencies.
• The study demonstrates the potential of PLS for quantitative SAR in adipokinetic hormones.

Conclusions:

• Partial least squares regression is a viable method for modeling adipokinetic hormone potencies in Locusta migratoria.
• The developed approach can aid in predicting the activity of novel hormone structures.
• This work contributes to optimizing peptide datasets for future structure-activity prediction studies.