Principal property values for six non-natural amino acids and their application to a structure–activity relationship for oxytocin peptide analogues

Abstract

. Can. J. Chem. 65, 1814Chem. 65, (1987. Previously derived principal property scales for the 20 coded amino acids have been extended to six noncoded amino acids, namely ornithine, norvaline, norleucine, 0-methylated threonine, a-amino butyric acid, and citrulline. This has been done first, for the 20 coded amino acids, by developing PLS models for the relations between the previously derived scales (z, , z 2 , and z3) and newly measured thin-layer chromatography data, proton nuclear magnetic resonance data, plus literature data describing amino acid "bulk" (van der Waals volume and molecular mass). Thereafter, z values for the noncoded amino acids were calculated by inserting the same measured data for these compounds into the PLS models. As a validation, the calculated z values were then used to describe the structural variation of a set of 60 oxytocin peptide analogues for which literature data on biological measurements existed. The models explain 7 1-81% of the variance of the biological data and are highly significant according to cross-validation. Prediction errors of about twice the estimated biological measurement error are obtained for the 11 peptide analogues containing noncoded amino acids. Chem. 65, 1814Chem. 65, (1987.Des Cchelles de propriCtCs principales qui avaient CtC dCveloppCes antkrieurement pour les 20 acides aminCs codes ont Ct C Ctendues i six acides aminis non-codCs, soit l'ornithine, la norvaline, la norleucine, la thrConine 0-mCthylCe, l'acide a-aminobutyrique et la citrulline. Dans un premier temps, on a dCveloppC des modkles PLS pour les 20 acides aminCs codes en se basant sur les relations entre les Cchelles developpCes anterieurement ( z l , z 2 et z3) et les nouvelles donnCes mesuries par chromatographie en couche mince et par resonance magnktique nuclCaire ainsi que des donnCes de la littCrature dCcrivant le ccvolume~) des acides aminCs (volume de van der Waals et masse molCculaire). Ensuite, on a calculC les valeurs z des acides amines non-codes en insirant les mCmes donnCes pour ces composCs dans les modkles PLS. Afin de valider nos conclusions, on a ensuite utilisC les valeurs z pour dkcrire la variation de structure d'un ensemble de 60 analogues du peptide oxytocine pour lesquels des donnCes relatives aux mesures biologiques existent dans la litttrature. Les modkles expliquent de 7 1 a 81% de la variance des donnCes biologiques et, d'aprks des contre-validations, elles sont trks significatives. Pour 11 des analogues peptidiques contenant des acides aminis qui ne sont pas cod&, on peut prCdire des erreurs qui sont le double de l'erreur CvaluCe pour les mesures biologiques.[Traduit par la revue]

QSAR modeling of peptide biological activity by coupling support vector machine with particle swarm optimization algorithm and genetic algorithm

“…Kidera et al [7] first coded the natural amino acids through 10 orthogonal factors derived from 188 reported properties. The work was followed by Hellberg et al [8][9][10][11] who developed principal properties, or z-scores, for each of 20 natural amino acids and a series of unnatural amino acids. The z-scores were extracted by principal component analysis (PCA) from the collections of experimental data, such as HPLC retention times, pK a , NMR-derived properties, and other measurable variables related to hydrophobicity, size, and electronic features.…”

Digital Coding of Amino Acids Based on Hydrophobic Index

“…Owing to its wide application, recently a web-server called PseAA was established at http://chou.med.harvard.edu/bioinf/PseAA/, by which users can generate many different types of PseAA composition as they wish. Here we shall introduce a new Information value for accessibilityalu; average fraction 35% [16]; Hydropathy index [17]; Surrounding hydrophobicity in folded form [18]; Average gain in surrounding hydrophobicity [19]; Principal property value [19].…”

A new descriptor of amino acids based on the three-dimensional vector of atomic interaction field

“…The application program GETTVAF, which is written in C language, implements transformation from results of quantum chemistry calculation into 3D-VAIF descriptors. 6 ], (1≤m≤n≤5).…”

“…Factor analysis was employed by Kidera et al [3] to choose 10 orthogonal factors from 188 kinds of properties of coded amino acids in order to predict advanced structures of proteins. A group of single property descriptors, Z-scale, were first obtained from 20 physicochemical properties of amino acids by Hellberg et al [4][5][6][7][8] who improved this scale by adding 9 HPLC retaining indexes later. Furthermore Sandberg et al [9] successfully generalized this scale to 87 kinds of amino acids including natural amino acids.…”