Please use this identifier to cite or link to this item: http://cmuir.cmu.ac.th/jspui/handle/6653943832/55220
Title: Multiparameter optimization method and enhanced production of secreted recombinant single-chain variable fragment against the HIV-1 P17 protein from Escherichia coli by fed-batch fermentation
Authors: Porntip Paopang
Watchara Kasinrerk
Chatchai Tayapiwatana
Phisit Seesuriyachan
Bordin Butr-Indr
Keywords: Biochemistry, Genetics and Molecular Biology
Issue Date: 2-Apr-2016
Abstract: © 2016, Taylor & Francis Group, LLC. The single-chain fragment variable (scFv) was used to produce a completely functional antigen-binding fragment in bacterial systems. The advancements in antibody engineering have simplified the method of producing Fv fragments and made it more efficient and generally relevant. In a previous study, the scFv anti HIV-1 P17 protein was produced by a batch production system, optimized by the sequential simplex optimization method. This study continued that work in order to enhance secreted scFv production by fed-batch cultivation, which supported high volumetric productivity and provided a large amount of scFvs for diagnostic and therapeutic research. The developments in cell culture media and process parameter settings were required to realize the maximum production of cells. This study investigated the combined optimization methods, Plackett–Burman design (PBD) and sequential simplex optimization, with the aim of optimize feed medium. Fed-batch cultivation with an optimal feeding rate was determined. The result demonstrated that a 20-mL/hr feeding rate of the optimized medium can increase cell growth, total protein production, and scFv anti-p17 activity by 4.43, 1.48, and 6.5 times more than batch cultivation, respectively. The combined optimization method demonstrated novel power tools for the optimization strategy of multiparameter experiments.
URI: https://www.scopus.com/inward/record.uri?partnerID=HzOxMe3b&scp=84968645187&origin=inward
http://cmuir.cmu.ac.th/jspui/handle/6653943832/55220
ISSN: 15322297
10826068
Appears in Collections:CMUL: Journal Articles

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