Quantitative Structure−Activity (Affinity) Relationship (QSAR) Study on Protonation and Cationization of α-Amino Acids

Abstract

A quantitative structure-activity (affinity) relationship (QSAR) study is carried out to model the proton, sodium, copper, and silver cation affinities of alpha-amino acids (AA). Stepping multiple linear regression (MLR), partial least squares (PLS), and artificial neural network (ANN) approaches are applied to elucidate the multiple factors affecting these affinities. The MLR and PLS models reveal that the variation in proton affinity is attributed to the highest electrophilic superdelocalizability of nitrogen (major) and the number of rotatable bonds (minor) in AA. The noncovalent interactions, especially ion-dipole interactions, are responsible for the changes in Na+ affinity. The ionization potential, dipole moment of the side chain, and degree of linearity are the properties of AA that give the best correlation with the Cu+ and Ag+ affinities. The ANN models are developed to study the relationships (linear or nonlinear) between the molecular descriptors and binding affinities. The ANN models show higher predictive power. The QSAR models are used to study the binding forms of AA (neutral vs zwitterionic) upon protonation/cationization. To our knowledge, this is the first attempt to carry out a QSAR study on protonated/cationized AlphaAlpha to elucidate their binding properties. In virtue 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 AA as the building blocks) in the future.

MALDI Efficiency of Metabolites Quantitatively Associated with their Structural Properties: A Quantitative Structure–Property Relationship (QSPR) Approach

“…This result was intriguing because the shape of the molecules itself was relevant rather than electronic properties. It has been reported that cation affinities of amino acids were associated with degree of linearity [18], which is a direct index of the flexibility of molecule [19]. It was thus suggested that the shape properties of target compounds affect their interaction with other molecules to promote or inhibit their ionization.…”

Modeling resistance index of taxoids to MCF-7 cell lines using ANN together with electrotopological state descriptors

“…ANN In order to build reliable and predictive QSAR models, we adopted the ANN technique, which has been proven to have outstanding non-linear approximation ability [22,23,37] . A typical ANN consists of an input layer, a hidden layer, and an output layer.…”

“…The large amount of variables in E-state indices can fully represent the structure characters of molecules, such as information about non-covalent interactions, which may be important to the occurrence of anti-MDR activity. The artificial neural network (ANN), used as a modeling technique, has recently become a popular and powerful chemometric tool [21][22][23] . Compared with classical statistical methods, ANN-based approaches do not require preliminary knowledge of the mathematical form of the relationship between the variables [24] , which makes the ANN suitable for extrapolating the complex and unsure relationships between the biological phenomenon and the structure of the compounds.…”

Theoretically Predicted Descriptors Based Quantitative Structure Activity Relationship Study of the Activity of Acridines Against B-16 Melanoma

“…For the calculation of the quantum chemical molecular descriptor used in QSAR studies, semi empirical methods such as AM1 and PM3 mainly have been used (Saeed and Elias, 2010;Saeed et al, 2010a;2010b). However, DFT method has been used recently for the prediction of physiochemical and biological properties of organic molecules (Shaik et al, 2010;Siu and Che, 2006).…”