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A multivariate characterization of tRNA nucleosides
Umeå universitet, Samhällsvetenskapliga fakulteten, Enheten för demografi och åldrandeforskning (CEDAR).ORCID-id: 0000-0001-9188-5518
1996 (Engelska)Ingår i: Journal of Chemometrics, ISSN 0886-9383, E-ISSN 1099-128X, Vol. 10, nr 5-6, s. 493-508Artikel i tidskrift (Refereegranskat) Published
Abstract [en]

Twenty nucleosides occurring in transfer ribonucleic acid (tRNA) have been characterized using 21 experimentally determined (HPLC, TLC, NMR, etc.) and calculated (log P, van der Waals surface area, ionization potential, etc.) variables. Principal component analysis (PCA) was performed on the data set and four statistically significant components or principal properties (PPs) were extracted. The PPs described 68·4% of the variance in the data. The PP values are discussed in terms of similarity and dissimilarity among the nucleosides. The loading vectors from the PCA are used for an interpretation of the nature of the PP vectors. Application of the PPs in sequence-activity modelling is demonstrated with 25 DNA-promoter sequences originating from E. coli.

Ort, förlag, år, upplaga, sidor
1996. Vol. 10, nr 5-6, s. 493-508
Nyckelord [en]
Nucleosides, Multivariate characterization, QSAR, Principal properties, Principal component analysis
Nationell ämneskategori
Organisk kemi
Identifikatorer
URN: urn:nbn:se:umu:diva-142534DOI: 10.1002/(SICI)1099-128X(199609)10:5/6<493::AID-CEM447>3.0.CO;2-COAI: oai:DiVA.org:umu-142534DiVA, id: diva2:1161996
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 Hiltunen, Maria

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