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Engineering Antibodies for Biosensor Technologies

Authors: Carl Nicholas Mayers; Neal Hopkins; Sarah A. Goodchild; Tracey Elizabeth Love;

Engineering Antibodies for Biosensor Technologies

Abstract

Publisher Summary The emphasis of this chapter is directed towards the progress made using genetically engineered antibodies that could provide solutions to many of the problems currently associated with the use of conventional antibodies in biosensor systems. The conventional antibodies used in biosensor systems, such as polyclonal, monoclonal, and digested antibodies, are discussed in this chapter. The development of recombinant antibody technology provides an alternative source of recognition elements with significant potential to satisfy the shortcomings of conventional antibody reagents. High affinity recombinant antibodies can be quickly selected from huge libraries displayed on various platforms. Use of recombinant antibodies also provides the ability to manipulate the sequence at the genetic level, to introduce novel features that could not be gained by immunization protocols. A significant advantage to the use of recombinant antibodies is that once a suitable antibody has been isolated, it does not have to represent a finished product. Characteristics such as increased affinity and stability in elevated temperatures and in the presence of solvents or denaturing buffers can be introduced by molecular engineering. The chapter also presents the type of biosensors, isolation and production of recombinant antibodies for use in the detection system, and engineering antibodies for enhanced utility in biosensor system.

Related Organizations
Keywords

Immunoglobulin Fab Fragments, Mice, Animals, Antibodies, Monoclonal, Immunoglobulins, Biosensing Techniques, Protein Engineering, Antibodies, Recombinant Proteins

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    citations
    This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
    21
    popularity
    This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.
    Top 10%
    influence
    This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
    Average
    impulse
    This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.
    Top 10%
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Found an issue? Give us feedback
citations
This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Citations provided by BIP!
popularity
This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.
BIP!Popularity provided by BIP!
influence
This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Influence provided by BIP!
impulse
This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.
BIP!Impulse provided by BIP!
21
Top 10%
Average
Top 10%
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