Abstract
Multivariate data analysis methods (Principal Component Analysis (PCA) and Partial Least Squares (PLS)) are applied to the analysis of the CoMFA (Comparative Molecular Field Analysis) data for several nucleic acids components. The data set includes nitrogenated bases, nucleosides, linear nucleotides, 3′, 5′-cyclic nucleotides and oligonucleotides. PCA is applied to study the structure of the CoMFA data and to detect possible outliers in the data set. PLS is applied to correlate the CoMFA data with either calculated AM1 proton affinities or with experimental pKa values. The possibility of making a prediction of pKa values directly from 3D structures of the monomers for polynucleotides is also shown. The influence of the superposition criteria and of conformational changes along the glycosidic bond on the pKa prediction are studied as well.
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Gargallo, R., Sotriffer, C.A., Liedl, K.R. et al. Application of multivariate data analysis methods to Comparative Molecular Field Analysis (CoMFA) data: Proton affinities and pKa prediction for nucleic acids components. J Comput Aided Mol Des 13, 611–623 (1999). https://doi.org/10.1023/A:1008005522776
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DOI: https://doi.org/10.1023/A:1008005522776