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Determination of the Gas-phase Acidities of Oligopeptides
Published on: June 24, 2013
Quantitative structure-activity (affinity) relationship (QSAR) study on protonation and cationization of alpha-amino
1Department of Chemistry, Open Laboratory of Chemical Biology of The Institute of Molecular Technology for Drug Discovery and Synthesis, The University of Hong Kong, Pokfulam Road, Hong Kong SAR, China. fmsiu@hkucc.hku.hk
The Journal of Physical Chemistry. A
|November 3, 2006
Summary
Quantitative structure-activity relationship (QSAR) models predict affinities of alpha-amino acids (AA) for protons and metal cations. Artificial neural networks (ANN) offer superior predictive power for these binding interactions.
Area of Science:
- Computational chemistry
- Biophysical chemistry
- Medicinal chemistry
Background:
- Alpha-amino acids (AA) are fundamental building blocks of peptides and proteins.
- Understanding cation binding affinities is crucial for various biological and chemical processes.
- Previous studies have not comprehensively modeled cation affinities of protonated/cationized alpha-amino acids.
Purpose of the Study:
- To develop quantitative structure-activity relationship (QSAR) models for proton, sodium (Na+), copper (Cu+), and silver (Ag+) cation affinities of alpha-amino acids (AA).
- To elucidate the molecular factors governing these binding affinities.
- To investigate the binding forms (neutral vs. zwitterionic) of AA upon protonation/cationization.
Main Methods:
- Application of stepping multiple linear regression (MLR), partial least squares (PLS), and artificial neural network (ANN) approaches.
- Utilizing molecular descriptors to correlate with cation binding affinities.
- Development of ANN models to capture complex, potentially nonlinear relationships.
Main Results:
- MLR and PLS models identified nitrogen's electrophilic superdelocalizability and rotatable bonds as key factors for proton affinity.
- Noncovalent interactions, particularly ion-dipole interactions, significantly influence Na+ affinity.
- Ionization potential, side-chain dipole moment, and linearity best correlated with Cu+ and Ag+ affinities.
- ANN models demonstrated higher predictive accuracy compared to MLR and PLS.
- QSAR models successfully predicted Na+ affinities for dihydroxyphenylalanine (DOPA).
Conclusions:
- This study presents the first QSAR investigation into the binding properties of protonated/cationized alpha-amino acids.
- The developed QSAR models provide insights into the factors governing cation affinities of AA.
- The findings suggest potential for applying similar QSAR approaches to larger biomolecules like peptides and proteins.
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