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In this study 87 amino acids (AA.s) have been characterized by 26 physicochemical descriptor variables. These descriptor variables include experimentally determined retention values in seven thin-layer chromatography (TLC) systems, three nuclear magnetic resonance (NMR) shift variables, and 16 calculated variables, namely six semiempirical molecular orbital indices, total, polar, and nonpolar surface area, van der Waals volume of the side chain, log P, molecular weight, and four indicator variables describing hydrogen bond donor and acceptor properties, and side chain charge. In the present study, the data from a previous characterization of 55 AA.s from our laboratory have been extended with data for 32 additional AA.s and 14 new descriptor variables. The new 32 AA.s were selected to represent both intermediate and more extreme physicochemical properties, compared to the 20 coded AA.s. The new extended and updated principal property scales, the z-scales, were calculated and aligned to previously reported z(old)-scales. The appropriateness of the extended z-scales were validated by the use in quantitative sequence-activity modeling (QSAM) of 89 elastase substrate analogues and in a QSAM of 29 neurotensin analogues.

GC-MS, HPLC, automatic amino acid analysis, high-resolution two-dimensional electrophoresis and capillary electrophoresis are suitable for the multicomponent analysis of body fluids and tissues. Manual interpretation of the complex metabolite and protein profiles thereby obtained is usually difficult, except in the case of metabolic disorders, where major deviations from the normal profiles often are observed. Implementation of multivariate data analysis makes it possible to retrieve diagnostic information that otherwise may be overlooked, as shown in this report where patients with leprosy have been examined. Urine samples were analysed by ion-exchange chromatography and by GC-MS to obtain profiles of amino acids and organic acids. Qualitative and quantitative information on 68 metabolites were then analysed by principal components analysis (PCA) and by partial least square models (PLS). Three different PLS dimensions were found (cross-validation) corresponding to controls (persons without leprosy), paucibacillary and multibacillary leprosy.

The 20 naturally occurring amino acids are characterized by 20 variables: pKNH2, pKCOOH, pI, molecular weight, substituent van der Waals volume, seven 1H and 13C nuclear magnetic resonance shift variables, and eight hydrophobicity-hydrophilicity scales. The 20-dimensional data set is reduced to a few new dimensions by principal components analysis. The three first principal components reveal relationships between the properties of the amino acids and the genetic code. Thus the amino acids coded for by adenosine (A), uracil (U), or cytosine (C) in their second codon position (corresponding to U, A, or G in the second anticodon position) are grouped in these components. No grouping was detected for the amino acids coded for by guanine (G) in the second codon position (corresponding to C in the second anticodon position). The results show that a relationship exists between the physical-chemical properties of the amino acids and which of the A (U), U (A), or C (G) nucleotide is used in the second codon (anticodon) position. The amino acids coded for by G (C) in the second codon (anticodon) position do not participate in this relationship.