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  • 1. Eriksson, Lennart
    et al.
    Andersson, Patrik
    Umeå University, Faculty of Science and Technology, Department of Chemistry.
    Johansson, Erik
    Tysklind, Mats
    Umeå University, Faculty of Science and Technology, Department of Chemistry.
    Sandberg, Maria
    Wold, Svante
    Umeå University, Faculty of Science and Technology, Department of Chemistry.
    The Constrained Principal Property (CPP) Space in QSAR---Directional and Non-Directional Modelling Approaches2000In: Molecular Modeling and Prediction of Bioactivity, Kluwer Academic/Plenum Publishers , 2000, p. 65-70Chapter in book (Other academic)
  • 2.
    Sandberg Hiltunen, Maria
    Umeå University, Faculty of Social Sciences, Centre for Demographic and Ageing Research (CEDAR).
    A multivariate characterization of tRNA nucleosides1996In: Journal of Chemometrics, ISSN 0886-9383, E-ISSN 1099-128X, Vol. 10, no 5-6, p. 493-508Article in journal (Refereed)
    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.

  • 3.
    Sandberg Hiltunen, Maria
    Umeå University, Faculty of Social Sciences, Centre for Demographic and Ageing Research (CEDAR).
    A new approach to quantify and analyse tRNA sequence dataManuscript (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.

  • 4.
    Sandberg Hiltunen, Maria
    Umeå University, Faculty of Social Sciences, Centre for Demographic and Ageing Research (CEDAR).
    A Multivariate Characterization of tRNAnucleosides1996In: Journal of Chemometrics, ISSN 0886-9383, E-ISSN 1099-128X, Vol. 10, p. 493-508Article in journal (Refereed)
  • 5.
    Sandberg Hiltunen, Maria
    Umeå University, Faculty of Social Sciences, Centre for Demographic and Ageing Research (CEDAR).
    DNA and peptide sequences and chemical processes multivariately modelled by principal component analysis and partial least-squares projections to latent structures1993In: Analytica Chimica Acta, ISSN 0003-2670, E-ISSN 1873-4324, Vol. 277, p. 239-253Article in journal (Refereed)
    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.

  • 6.
    Sandberg Hiltunen, Maria
    Umeå University, Faculty of Social Sciences, Centre for Demographic and Ageing Research (CEDAR).
    Multivariate process and quality monitoring applied to anelectrolysis process: Part I. Process supervision with multivariate control charts1998In: Chemometrics and Intelligent Laboratory Systems, Vol. 42, p. 221-231Article in journal (Refereed)
    Abstract [en]

    Multivariate statistical process control (MSPC) is applied to an electrolysis process. The process produces extremely pure copper, and to monitor its quality the levels of eight metal impurities were recorded twice a day. These quality data are analysed adopting an (1) `intuitive' univariate approach, and (2) with multivariate techniques. It is demonstrated that the univariate analysis gives confusing results with regards to outlier detection, while the multivariate approach identifies two types of outliers. Moreover, it is shown how the results from the multivariate principal component analysis (PCA) method can be displayed graphically in multivariate control charts. Multivariate Shewhart, cumulative sum (CUSUM) and exponentially weighted moving average (EWMA) control charts are used and compared. Also, an informationally powerful control chart, the simultaneous scores monitoring and residual tracking (SMART) chart, is introduced and used.

  • 7.
    Sandberg Hiltunen, Maria
    Umeå University, Faculty of Social Sciences, Centre for Demographic and Ageing Research (CEDAR).
    Andersson, Patrik (Contributor)
    Tysklind, Mats (Contributor)
    Wold, Svante (Contributor)
    The Constrained Principal Property (CPP) Space in QSAR: Directional and Non-Directional Modelling Approaches2000In: 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)
  • 8.
    Sandberg Hiltunen, Maria
    Umeå University, Faculty of Social Sciences, Centre for Demographic and Ageing Research (CEDAR).
    The evolutionary transition from uracil to thymine balances the genetic code1996In: Journal of Chemometrics, ISSN 0886-9383, E-ISSN 1099-128X, Vol. 10, p. 163-170Article in journal (Refereed)
    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.

  • 9.
    Sandberg Hiltunen, Maria
    et al.
    Umeå University, Faculty of Science and Technology, Department of Chemistry.
    Hjorth Alifrangis, Lene
    The Royal Danish School of Pharmacy.
    Christensen, Inge
    The Royal Danish School of Pharmacy.
    Structure-Property Model for Membrane Partitioning of Oligopeptides2000In: Journal of Medicinal Chemistry, ISSN 0022-2623, E-ISSN 1520-4804, Vol. 43, no 1, p. 103-113Article in journal (Refereed)
    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.

  • 10.
    Sandberg, Maria
    Umeå University, Faculty of Social Sciences, Centre for Demographic and Ageing Research (CEDAR).
    New chemical descriptors relevant for the design of biologically active peptides. A multivariate characterization of 87 amino acids.1998In: Journal of Medicinal Chemistry, ISSN 0022-2623, E-ISSN 1520-4804, Vol. 41, no 14, p. 2481-2491, article id 9651153Article in journal (Refereed)
    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.

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