Literature DB >> 10591108

Kinetic characterization of the interaction of the Z-fragment of protein A with mouse-IgG3 in a volume in chemical space.

K Andersson1, S Gülich, M Hämäläinen, P A Nygren, S Hober, M Malmqvist.   

Abstract

The kinetic rate parameters for the interaction between a single domain analogue of staphylococcal protein A (Z) and a mouse-IgG3 monoclonal antibody (MAb) were measured in Hepes buffer with different chemical additives. Five buffer ingredients (pH, NaCl, DMSO, EDTA, and KSCN) were varied simultaneously in 16 experiments following a statistical experimental plan. The 16 buffers thus spanned a volume in chemical space. A mathematical model, using data from the buffer composition, was developed and used to predict apparent kinetic parameters in five new buffers within the spanned volume. Association and dissociation parameters were measured in the new buffers, and these agreed with the predicted values, indicating that the model was valid within the spanned volume. The pattern of variation of the kinetic parameters in relation to buffer composition was different for association and dissociation, such that pH influenced both association and dissociation and NaCl influenced only dissociation. This indicated that the recognition mechanism (association) and the stability of the formed complex (dissociation) involve different binding forces, which can be further investigated by kinetic studies in systematically varied buffers.

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Year:  1999        PMID: 10591108     DOI: 10.1002/(sici)1097-0134(19991115)37:3<494::aid-prot16>3.0.co;2-f

Source DB:  PubMed          Journal:  Proteins        ISSN: 0887-3585


  3 in total

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Authors:  Nicolas Hugo; Virginie Lafont; Mervyn Beukes; Danièle Altschuh
Journal:  Protein Sci       Date:  2002-11       Impact factor: 6.725

2.  Unraveling the binding mechanism of trivalent tumor necrosis factor ligands and their receptors.

Authors:  Carlos R Reis; Aart H G van Assen; Wim J Quax; Robbert H Cool
Journal:  Mol Cell Proteomics       Date:  2010-09-17       Impact factor: 5.911

3.  Unbiased descriptor and parameter selection confirms the potential of proteochemometric modelling.

Authors:  Eva Freyhult; Peteris Prusis; Maris Lapinsh; Jarl E S Wikberg; Vincent Moulton; Mats G Gustafsson
Journal:  BMC Bioinformatics       Date:  2005-03-10       Impact factor: 3.169

  3 in total

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