Why my protein does not express?

A common question from the wet lab is why I cannot express my protein. You have followed some of the the best practices, tricks and wet lab techniques:

  • Codon optimization.
  • Primer design, promoter, RBS.
  • Vector selection.
  • Fused protein expression
  • Fine-tuned the incubation procedure.

Modern laboratories offer diverse and sophisticated resource but, still, your protein does not express. Here, we would like to explore a few computational resources and approaches. They may help answer this common question and even provide numerous novel and surprising insights into protein expression process.

Computational approach

What you can learn about your protein expression using a computational approach and how.

Why computational? Because frequently the lab techniques have been all tried and exhausted. A computational approach may shed totally new light on your protein expression problem and give you valuable insight into the issue. Plus, the computational approach is among the most cost-effective, infinitely reusable, and simply easiest compared to manual laboratory experiments.

One of the greatest benefits of the computational approach to protein expression is gaining an integrated and system-wide view of the process. The user covers transcription, translation, and protein formation stages. Moreover, a single molecule experiment becomes accessible without major capital investment. Dynamic mRNA formation and 3D structure changes during the ribosome action can be incorporated into the analysis. tRNA tissue specifics and dynamic changes in the tRNA spectrum are included in the set of factors that influence protein expression yield.

The systemic view of protein expression

Most attempts to produce a protein follow a standard procedure for optimizing codons and selecting appropriate expression vectors. Codon optimization available from most commercial and open sources focuses predominantly on a static view of the nucleotide sequence and tRNA composition of the target host passively accepted by the scientific community. More sophisticated computational solutions may introduce dynamics of the tRNA abundance, miRNA action, and a significant number of diverse molecular mechanisms beyond static codon optimization algorithms.

Fast and reproducible cross-validation

Most of the features of mRNA and mRNA translation can be evaluated by a large set of diverse software tools. A remarkable advantage of this diversity is how easily you can cross-validate a computational result by running different tools and packages. Tools4miRs offers an excellent example of cross-validation when searching for miRNA target sites on mRNA.

Productivity, workflows and pipelines

Most researchers strive for productivity. Computational tools offer a great opportunity to run in a manual step-by-step workflow as well as an automated pipeline of computational tasks. High-performance computing integrates well with workflow management software and one open-source example is Pegasus.

Scroll Up