I have simulated in silico how to clone a large protein (1900bp)
Please, any suggestions on what measures to consider before doing wet lab to clone and express this protein?
Thank you for your insightful feedback
Sure, vector type, taget cell line/type, useful promoter, fragment length, etc.
This strongly depends on you experimental goals.
Lum Abienwi Ambe
It depends on what you intend to achieve.
To clone in a gene of interest (GOI) into a vector you need to consider a few things: Primer design for your GOI, PCR amplification of GOI, verification of amplicon, plasmid vector to be used (resistance, size, copy number, MCS), restriction enzymes for digesting plasmid and insert, ligation and transformation into suitable competent cells, verification of potential clones (gel-electrophoresis, digests, colony PCR and sequencing), transform clones into a host strain and transformants analysed using growth assays, western blots etc
These steps were carried out for cloning of my GOI into a pTrc vector
From my experience, I always make sure I have a restriction enzyme site on either end of the insert to check post cloning, this allows you to double check that the sequence is properly inserted, also I design qPCR primers where one primer is outside the cloning site and the second primer is found within the insert. This allows you to check orientation and another way to check to make sure your insert is integrated within the plasmid. If you plan on cloning into cells, Id suggest a fluorescent protein to track the plasmid.
@Andreas Eisenriech, thank you for your response, I am cross checking and rechecking all of that
@ Mimi Asogwa, thanks for reiterating on the steps to take.I am using pet15b and bl21cells. This will help me to not miss out on a step within my experiment
@Shawn Krueger thank you. your suggestion looks like a trick to getting it rightly the first time. ) :
You must first identify the variables that significantly influence your cloning results. Then, any of the optimization approaches especially response surface methodology or artificial neural network could be applied using either particle swarm optimization (PSO) or genetic algorithm (GA) algorithms.
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