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New chemical descriptors relevant for the design of biologically active peptides. A multivariate characterization of 87 amino acids.
Umeå universitet, Samhällsvetenskapliga fakulteten, Enheten för demografi och åldrandeforskning (CEDAR).ORCID-id: 0000-0001-9188-5518
1998 (Engelska)Ingår i: Journal of Medicinal Chemistry, ISSN 0022-2623, E-ISSN 1520-4804, Vol. 41, nr 14, s. 2481-2491, artikel-id 9651153Artikel i tidskrift (Refereegranskat) Published
Abstract [en]

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.

Ort, förlag, år, upplaga, sidor
Washington DC: American Chemical Society (ACS), 1998. Vol. 41, nr 14, s. 2481-2491, artikel-id 9651153
Nyckelord [en]
chemical descriptors, amino acids, sequence-activity modeling, characterization
Nationell ämneskategori
Medicin och hälsovetenskap
Forskningsämne
organisk kemi
Identifikatorer
URN: urn:nbn:se:umu:diva-142520DOI: 10.1021/jm9700575OAI: oai:DiVA.org:umu-142520DiVA, id: diva2:1161925
Konferens
1998 Jul 2;41(14):2481-91.
Tillgänglig från: 2017-12-01 Skapad: 2017-12-01 Senast uppdaterad: 2018-06-09
Ingår i avhandling
1. Deciphering sequence data: A multivariate approach
Öppna denna publikation i ny flik eller fönster >>Deciphering sequence data: A multivariate approach
1997 (Engelska)Doktorsavhandling, sammanläggning (Övrigt vetenskapligt)
Abstract [en]

In this thesis, attention has been focused on the quantitative description of nucleic acids, proteins and peptides. The strategy was to use multivariate chemometrical methods for improving the understanding of the complex structural codes of these kinds of biological molecules. Tools have been developed that enable quantitative modelling of biological molecules, i.e. models based on data that quantitatively describes their properties. The advantage of such models is that they provide interpretations in terms of chemical characteristics for complex features such as similarity, dissimilarity and potency.

By a multivariate physical-chemical characterization of the building blocks of nucleic acids and proteins, i.e. nucleosides and amino acids, descriptive scales have been developed, so called principal properties. The scales give a description of the intrinsic properties of these building blocks. The multivariate characterization results in a multi-property matrix. A principal component analysis of the multi-property matrix gives a small number of latent variables which are considered as the principal properties of the characterized molecules.

The principal property scales may be used for a wide range of different purposes, such as detecting trends and groupings in large sequence data sets, and for analyzing quantitative relationships between structure and function. In statistical experimental design, the descriptors are well suited as design variables to select combinations of amino acids in such a way that they span a wide range of properties.

The use of these principal property descriptors is demonstrated in the quantitative modelling of relationships between structure and activity of various peptide series, DNA-promoters and in the quantitative modelling of transfer ribonucleic acid sequence data (tRNA).

Ort, förlag, år, upplaga, sidor
Umeå: Solfjädern Offset AB, 1997. s. 76
Nyckelord
Principal properties, amino acids, nucleotides, tRNA, DNA, multivariate data analysis, sequence analysis, QSAR, quantitative sequence activity relationships
Nationell ämneskategori
Organisk kemi
Identifikatorer
urn:nbn:se:umu:diva-142699 (URN)91-7191-337-8 (ISBN)
Disputation
1997-06-06, N320, Naturvetarhuset, 90187, Umeå, 14:00 (Svenska)
Opponent
Handledare
Tillgänglig från: 2023-02-03 Skapad: 2017-12-08 Senast uppdaterad: 2023-02-03Bibliografiskt granskad

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Sandberg, Maria

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