| Abstract
| - A quantitative structure−activity (affinity) relationship (QSAR) study is carried out to model the proton,sodium, copper, and silver cation affinities of α-amino acids (AA). Stepping multiple linear regression (MLR),partial least squares (PLS), and artificial neural network (ANN) approaches are applied to elucidate the multiplefactors affecting these affinities. The MLR and PLS models reveal that the variation in proton affinity isattributed to the highest electrophilic superdelocalizability of nitrogen (major) and the number of rotatablebonds (minor) in AA. The noncovalent interactions, especially ion−dipole interactions, are responsible forthe changes in Na+ affinity. The ionization potential, dipole moment of the side chain, and degree of linearityare the properties of AA that give the best correlation with the Cu+ and Ag+ affinities. The ANN models aredeveloped to study the relationships (linear or nonlinear) between the molecular descriptors and bindingaffinities. The ANN models show higher predictive power. The QSAR models are used to study the bindingforms of AA (neutral vs zwitterionic) upon protonation/cationization. To our knowledge, this is the firstattempt to carry out a QSAR study on protonated/cationized ΑΑ to elucidate their binding properties. Invirtue of the Na+ affinity ANN model, the Na+ affinities of dihydroxyphenylalanine (DOPA) were predicted.This work may pave the way for the success of applying similar approaches to peptides or proteins (with AAas the building blocks) in the future.
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