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Sandberg, Maria
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Publications (7 of 7) Show all publications
Sandberg Hiltunen, M., Hjorth Alifrangis, L. & Christensen, I. (2000). Structure-Property Model for Membrane Partitioning of Oligopeptides. Journal of Medicinal Chemistry, 43(1), 103-113
Open this publication in new window or tab >>Structure-Property Model for Membrane Partitioning of Oligopeptides
2000 (English)In: Journal of Medicinal Chemistry, ISSN 0022-2623, E-ISSN 1520-4804, Vol. 43, no 1, p. 103-113Article in journal (Refereed) Published
Abstract [en]

The aim of this study was to develop a structure-property model for membrane partitioningof oligopeptides using statistical design methods and multivariate data analysis. A set of 20tetrapeptides with optional N-methylations at residues 2 and 4 was designed by a D-optimaldesign procedure. After synthesis and purification, the membrane partitioning abilities of thepeptides were tested in two chromatographic systems with phospholipids as the stationaryphase: immobilized artificial membrane chromatography (IAM) and immobilized liposomechromatography (ILC). The relationship between these measures and three different sets ofcalculated descriptors was analyzed by partial least-squares projection to latent structures(PLS). The descriptors used were the molecular surface area, Molsurf parameters, and Volsurfparameters. All three models were of good statistical quality and supported that a largehydrogen-bonding potential and the presence of a negative charge impair membrane partitioning,whereas hydrophobic parameters promote partitioning. The findings are in accordancewith what has been found for absorption of known drugs and have implications for the designof peptide-like drugs with good oral bioavailability.

Place, publisher, year, edition, pages
American Chemical Society (ACS), 2000
National Category
Other Basic Medicine
Identifiers
urn:nbn:se:umu:diva-142733 (URN)DOI: 10.1021/jm9910932 (DOI)
Available from: 2017-12-08 Created: 2017-12-08 Last updated: 2018-06-09
Sandberg Hiltunen, M. (2000). The Constrained Principal Property (CPP) Space in QSAR: Directional and Non-Directional Modelling Approaches. In: Klaus Gundertofte , Flemming Steen Jørgensen , Klaus Gundertofte , Flemming Steen Jørgensen (Ed.), Molecular Modeling and Prediction of Bioactivity: (pp. 65-70). Dordrecht: Kluwer Academic/Plenum Publishers
Open this publication in new window or tab >>The Constrained Principal Property (CPP) Space in QSAR: Directional and Non-Directional Modelling Approaches
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2000 (English)In: Molecular Modeling and Prediction of Bioactivity / [ed] Klaus Gundertofte , Flemming Steen Jørgensen , Klaus Gundertofte , Flemming Steen Jørgensen, Dordrecht: Kluwer Academic/Plenum Publishers , 2000, p. 65-70Chapter in book (Refereed)
Place, publisher, year, edition, pages
Dordrecht: Kluwer Academic/Plenum Publishers, 2000
National Category
Bioinformatics and Systems Biology
Identifiers
urn:nbn:se:umu:diva-142734 (URN)0-306-46217-6 (ISBN)
Available from: 2017-12-08 Created: 2017-12-08 Last updated: 2018-06-09
Eriksson, L., Andersson, P., Johansson, E., Tysklind, M., Sandberg, M. & Wold, S. (2000). The Constrained Principal Property (CPP) Space in QSAR---Directional and Non-Directional Modelling Approaches. In: Molecular Modeling and Prediction of Bioactivity (pp. 65-70). Kluwer Academic/Plenum Publishers
Open this publication in new window or tab >>The Constrained Principal Property (CPP) Space in QSAR---Directional and Non-Directional Modelling Approaches
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2000 (English)In: Molecular Modeling and Prediction of Bioactivity, Kluwer Academic/Plenum Publishers , 2000, p. 65-70Chapter in book (Other academic)
Place, publisher, year, edition, pages
Kluwer Academic/Plenum Publishers, 2000
Identifiers
urn:nbn:se:umu:diva-9123 (URN)0-306-46217-6 (ISBN)
Available from: 2008-03-03 Created: 2008-03-03 Last updated: 2018-06-09Bibliographically approved
Sandberg Hiltunen, M. (1996). A multivariate characterization of tRNA nucleosides. Journal of Chemometrics, 10(5-6), 493-508
Open this publication in new window or tab >>A multivariate characterization of tRNA nucleosides
1996 (English)In: Journal of Chemometrics, ISSN 0886-9383, E-ISSN 1099-128X, Vol. 10, no 5-6, p. 493-508Article in journal (Refereed) 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.

Keywords
Nucleosides, Multivariate characterization, QSAR, Principal properties, Principal component analysis
National Category
Organic Chemistry
Identifiers
urn:nbn:se:umu:diva-142534 (URN)10.1002/(SICI)1099-128X(199609)10:5/6<493::AID-CEM447>3.0.CO;2-C (DOI)
Available from: 2017-12-01 Created: 2017-12-01 Last updated: 2018-06-09
Sandberg Hiltunen, M. (1996). The evolutionary transition from uracil to thymine balances the genetic code. Journal of Chemometrics, 10, 163-170
Open this publication in new window or tab >>The evolutionary transition from uracil to thymine balances the genetic code
1996 (English)In: Journal of Chemometrics, ISSN 0886-9383, E-ISSN 1099-128X, Vol. 10, p. 163-170Article in journal (Refereed) Published
Abstract [en]

A multivariate quantitative physicochemical characterization of the five bases adenine (A), cytosine (C), guanine (G), thymine (T) and uracil (U), followed by principal component analysis, shows that the relative dissimilarities between the bases of DNA (A, C, G and T) are almost the same (i.e. balanced). In contrast, mRNA (containing U instead of T) has a considerably larger relative physicochemical similarity between C and U than between all other pairs of bases and is therefore inherently more unbalanced. These results provide a physicochemical explanation of the presence of thymine instead of uracil as an element of DNA. The principal component scores enable a quantitative description of nucleic acid sequence data to be made for structure-activity modelling purposes.

Place, publisher, year, edition, pages
John Wiley & Sons, 1996
Keywords
multivariate characterization, DNA, nucleotides
National Category
Organic Chemistry
Identifiers
urn:nbn:se:umu:diva-142533 (URN)10.1002/(SICI)1099-128X(199603)10:2<163::AID-CEM415>3.0.CO;2-S (DOI)
Available from: 2017-12-01 Created: 2017-12-01 Last updated: 2018-06-09
Sandberg Hiltunen, M. (1993). DNA and peptide sequences and chemical processes multivariately modelled by principal component analysis and partial least-squares projections to latent structures. Analytica Chimica Acta, 277, 239-253
Open this publication in new window or tab >>DNA and peptide sequences and chemical processes multivariately modelled by principal component analysis and partial least-squares projections to latent structures
1993 (English)In: Analytica Chimica Acta, ISSN 0003-2670, E-ISSN 1873-4324, Vol. 277, p. 239-253Article in journal (Refereed) Published
Abstract [en]

Biopolymer sequences (e.g., DNA, RNA, proteins and polysaccharides) and chemical processes (e.g., a batch or continuous polymer synthesis run in a chemical plant) have close similarities from the modelling point of view. When a set of sequences or processes is characterized by multivariate data, a three-way data matrix is obtained. With sequences the position and with processes the time is one direction in this matrix. The multivariate modelling of this matrix by principal component analysis (PCA) or partial least-squares (PLS) methods for the following purposes is discussed: classification of sequences; quantitative relationships between sequence and biological activity or chemical properties; optimizing a sequence with respect to selected properties; process diagnostics; and quantitative relationships between process variables and product quality variables. To obtain good models, a number of problems have to be adequately dealt with: appropriate characterization of the sequence or process; experimental design (selecting sequences or process settings); transforming the three-way into a two-way matrix; and appropriate modelling and validation (modelling interactions, periodicities, “time series” structures and “neighbour effects”). A multivariate approach to sequence and process modelling using PCA and PLS projections to latent structures is discussed and illustrated with several sets of peptide and DNA promoter data.

Keywords
DNA, peptide sequences, multivariate, principal component analysis, partial least-squares projections to latent structures
National Category
Organic Chemistry
Identifiers
urn:nbn:se:umu:diva-142536 (URN)10.1016/0003-2670(93)80437-P (DOI)
Available from: 2017-12-01 Created: 2017-12-01 Last updated: 2018-06-09
Sandberg Hiltunen, M.A new approach to quantify and analyse tRNA sequence data.
Open this publication in new window or tab >>A new approach to quantify and analyse tRNA sequence data
(English)Manuscript (preprint) (Other academic)
Abstract [en]

A novel quantitative multivariate approach for describing and analyse tRNA sequence data is presented. This approach is based on a multivariate chemical description of each nucleoside in the sequence. 30 theoretically calculated descriptors were used to characterize 63 nucleosides, and principal component analysis was used to extract the main variation from this multivariate description. The resulting four principal properties were interpreted as (PPa) size/bulk of the nucleoside, (PPb) polarity/hydrophobicity of the nucleoside, (PPc) electronic properties of the nucleoside and (PPd)polarity and size of the ribose moiety. These principal properties may be used to translate the tRNA letter sequence data into a quantitative chemical representation. We demonstrate the use of this quantitative description with a multivariate analysis of a set of tRNA sequences. This analysis gives models that are interpretable in terms of wich sequence positions, and nucleoside properties that discriminate the different isoacceptors. This approach is applicable on all kinds of RNA sequence data and gives information that is complementary to current sequence analysis techniques.

National Category
Other Chemistry Topics Organic Chemistry
Research subject
Organic Chemistry
Identifiers
urn:nbn:se:umu:diva-142697 (URN)
Available from: 2017-12-08 Created: 2017-12-08 Last updated: 2018-06-09

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