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  • 1.
    Forssén, Patrik
    et al.
    Karlstad University, Faculty of Health, Science and Technology (starting 2013), Department of Engineering and Chemical Sciences (from 2013).
    Multia, Evgen
    University of Helsinki, Finland.
    Samuelsson, Jörgen
    Karlstad University, Faculty of Health, Science and Technology (starting 2013), Department of Engineering and Chemical Sciences (from 2013).
    Andersson, Marie
    Karlstad University, Faculty of Health, Science and Technology (starting 2013), Department of Engineering and Chemical Sciences (from 2013).
    Aastrup, Teodor
    Attana AB, Sweden.
    Altun, Samuel
    Attana AB, Sweden.
    Wallinder, Daniel
    Attana AB, Sweden.
    Wallbing, Linus
    Attana AB, Sweden.
    Liangsupree, Thanaporn
    University of Helsinki, Finland.
    Riekkola, Marja-Liisa
    University of Helsinki, Finland.
    Fornstedt, Torgny
    Karlstad University, Faculty of Health, Science and Technology (starting 2013), Department of Engineering and Chemical Sciences (from 2013).
    Reliable Strategy for Analysis of Complex Biosensor Data2018In: Analytical Chemistry, ISSN 0003-2700, E-ISSN 1520-6882, Vol. 90, no 8, p. 5366-5374Article in journal (Refereed)
    Abstract [en]

    When using biosensors, analyte biomolecules of several different concentrations are percolated over a chip with immobilized ligand molecules that form complexes with analytes. However, in many cases of biological interest, e.g., in antibody interactions, complex formation steady-state is not reached. The data measured are so-called sensorgram, one for each analyte concentration, with total complex concentration vs time. Here we present a new four-step strategy for more reliable processing of this complex kinetic binding data and compare it with the standard global fitting procedure. In our strategy, we first calculate a dissociation graph to reveal if there are any heterogeneous interactions. Thereafter, a new numerical algorithm, AIDA, is used to get the number of different complex formation reactions for each analyte concentration level. This information is then used to estimate the corresponding complex formation rate constants by fitting to the measured sensorgram one by one. Finally, all estimated rate constants are plotted and clustered, where each cluster represents a complex formation. Synthetic and experimental data obtained from three different QCM biosensor experimental systems having fast (close to steady-state), moderate, and slow kinetics (far from steady-state) were evaluated using the four-step strategy and standard global fitting. The new strategy allowed us to more reliably estimate the number of different complex formations, especially for cases of complex and slow dissociation kinetics. Moreover, the new strategy proved to be more robust as it enables one to handle system drift, i.e., data from biosensor chips that deteriorate over time.

  • 2.
    Lipponen, Katriina
    et al.
    Univ Helsinki, Dept Chem, Analyt Chem Lab, FIN-00014 Helsinki, Finland..
    Stege, Patricia W.
    Univ Helsinki, Dept Chem, Analyt Chem Lab, FIN-00014 Helsinki, Finland.;Natl Univ San Luis, CONICET, Dept Chem, INQUISAL,Lab Analyt Chem, San Luis, Argentina..
    Cilpa, Geraldine
    Univ Helsinki, Dept Chem, Analyt Chem Lab, FIN-00014 Helsinki, Finland..
    Samuelsson, Jorgen
    Karlstad University, Faculty of Technology and Science, Department of Chemistry and Biomedical Sciences.
    Fornstedt, Torgny
    Karlstad University, Faculty of Technology and Science, Department of Chemistry and Biomedical Sciences. Karlstad Univ, Dept Chem & Biomed Sci, SE-65188 Karlstad, Sweden.;Uppsala Univ, Dept Phys & Analyt Chem, SE-75124 Uppsala, Sweden..
    Riekkola, Marja-Liisa
    Univ Helsinki, Dept Chem, Analyt Chem Lab, FIN-00014 Helsinki, Finland..
    Three Different Approaches for the Clarification of the Interactions between Lipoproteins and Chondroitin-6-sulfate2011In: Analytical Chemistry, ISSN 0003-2700, E-ISSN 1520-6882, Vol. 83, no 15, p. 6040-6046Article in journal (Refereed)
    Abstract [en]

    Two different experimental approaches were used for obtaining a comprehensive view and understanding of the interactions between apolipoprotein B-100 (ApoB-100) of low-density lipoprotein and apolipoprotein E (ApoE) of high-density lipoprotein and chondroitin-6-sulfate (C6S) of arterial proteoglycan. The techniques employed were partial filling affinity capillary electrophoresis (PF-ACE) and continuous flow quartz crystal inicrobalance (QCM). In addition, molecular dynamic (MD) simulations were used to provide a supportive visual insight into the interaction mechanism. A new tool for analysis of QCM-data was utilized, i.e., adsorption energy distribution calculations, which allowed a deeper understanding of the interactions, especially at different temperatures. The PF-ACE technique probed mainly the strong adsorption interactions whereas in the MD calculations short:- and long-range interactions could be distinguished. Although there are differences in the techniques, a pretty good agreement was achieved between the three approaches for the interaction of 19 amino acid peptide of ApoB with C6S giving log affinity constants of 4.66 by QCM, 5.02 by PP-ACE, and 7.39 by MD, and for 15 amino acid peptide of ApoE with C6S 5.34 by QCM, 5.28 by PT-ACE, and 4.60 by MD at physiological temperature 37.0 degrees C.

  • 3.
    Sandblad, Peter
    et al.
    Uppsala universitet, Institutionen för fysikalisk och analytisk kemi.
    Arnell, Robert
    Uppsala universitet, Institutionen för fysikalisk och analytisk kemi.
    Samuelsson, Jörgen
    UDepartment of Physical and Analytical Chemistry, BMC Box 599, SE-751 24, Uppsala, Sweden.
    Fornstedt, Torgny
    Department of Physical and Analytical Chemistry, BMC Box 599, SE-751 24, Uppsala, Sweden.
    Approach for reliable evaluation of drug proteins interactions using surface plasmon resonance technology2009In: Analytical Chemistry, ISSN 0003-2700, E-ISSN 1520-6882, Vol. 81, no 9, p. 3551-3559Article in journal (Refereed)
    Abstract [en]

    The surface plasmon resonance (SPR) biosensor was recently introduced to the analytical biochemical society for measuring small drug-protein interactions. However, the technique has many times been used without specifying the type of enantiomeric form of the chiral drug measured and/or with using a too narrow drug concentration range resulting in biased values of binding coefficients and sometimes even assumptions about single-site bindings although the binding in reality comprises a multisite interaction. In this study we will give guidelines for reliable experimental and methodological approaches to avoid these pitfalls. For this purpose, we also introduce a new tool, based on physical chemistry, to the sensor community; the calculation of the adsorption energy distribution (AED). The AED-calculations reveal the degree of heterogeneity directly from the SPR raw data and thus guide us into a narrower selection of probable models before the rival model fitting procedure. We demonstrate how to measure reliable equilibrium data for the two typically different cases: drug binding to (i) transport (plasma) proteins and to (ii) a target protein. Both the binding of the chiral beta-blocker propranolol to alpha(1)-acid glycoprotein (AGP) and that of the anticoagulant warfarin to human serum albumin were heterogeneous, with a few strong enantioselective sites and many weak nonselective sites. We also demonstrate how the multisite binding rapidly falsely turns to single-site as the concentration range is narrowed and how adding dimethyl sulfoxide to the buffer affects multisite drug-protein data. The binding of the enantiomers of the thrombin inhibitor melagatran was investigated on both thrombin and the transport proteins, revealing clear enantioselectivity for thrombin in favor of the active enantiomer, but almost similar binding properties for both enantiomers to the transport protein AGP. The AED-calculations verified that both these system has a unimodal energy distribution and are best described with a homogeneous adsorption model.

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