Protein Expression Optimization Process

protein expression optimization process

Traditional Protein Expression Optimization Process

An ad-hoc protein expression optimization process is unlikely to achieve the company objectives. Protein expression optimization is typically done one plasmid at a time based on experience and intuition. The scientist in charge will add a tag, use a codon optimization algorithm, or change the promoter. The problem with this approach is that’s its often more guided by cloning strategies than by the nature of the data it generates. It is unlikely to lead to data suitable for analysis.

Generally, successful optimization strategies require to carefully balance different control parameters (transcription, translation, plasmid copy numbers) that often interfere with each other. Optimizing protein expression cannot be achieved by maximizing every possible control parameter.

Data-Driven Protein Expression Optimization

We are proposing an alternative that relies on a rigorous experimental design and extensive use of gene synthesis/dna assembly. The process starts with a formalization of the optimization objective. Are we trying to maximize the expression of one protein, reduce the cost of a biomanufacturing process, or maximize the expression of a complex?

In a second step, we formalize the vector design options available to the client. We help them think outside the box by suggesting strategies that may not have considered. We help them formalize these strategies in GenoCAD. Upon completion of this phase, we use GenoCAD combinatorial design tool to estimate the size the genetic design space available to the project. At this point, the number of possible expression vectors largely exceeds experimental capabilities.

It is common for people in this situation to worry about the cost of gene synthesis or the labor involved in assembling large numbers of expression vectors. However, collecting expression data is generally the limiting steps. We help our clients design characterization strategies that can be performed quickly and inexpensively on a high number of expression vectors.

At this point, it becomes possible to design an entire experimental workflow that includes the assembly of a large number of plasmids and the collection of expression data. This leads to a dataset that can be used to build a mathematical model of how the different design parameters contribute to the vector performance.

This model is then used to predict the performance of vectors that were not included in the original dataset. Vectors predicted to have superior performance will then be assembled and characterized. A few iteration of this product development cycle may be necessary to achieve the target performance.

Throughout this process, we provide your team with the informatics/computational biology/bioinformatics support that its needs to complete their project.

Schedule a phone call or send us an email to learn how GenoFAB can help you.