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Protein Engineering Information

Protein engineering is the process of developing useful or valuable proteins. It is a young discipline, with much research taking place into the understanding of protein folding and recognition for protein design principles.

There are two general strategies for protein engineering, rational design and directed evolution. These techniques are not mutually exclusive; researchers will often apply both. In the future, more detailed knowledge of protein structure and function, as well as advancements in high-throughput technology, may greatly expand the capabilities of protein engineering. Eventually, even unnatural amino acids may be incorporated thanks to a new method that allows the inclusion of novel amino acids in the genetic code.

Contents

Rational design of proteins

Main article: Protein design

In rational protein design, the scientist uses detailed knowledge of the structure and function of the protein to make desired changes. This generally has the advantage of being inexpensive and technically easy, since site-directed mutagenesis techniques are well-developed. However, its major drawback is that detailed structural knowledge of a protein is often unavailable, and even when it is available, it can be extremely difficult to predict the effects of various mutations.

Computational protein design algorithms seek to identify novel amino acid sequences that are low in energy when folded to the pre-specified target structure. While the sequence-conformation space that needs to be searched is large, the most challenging requirement for computational protein design is a fast, yet accurate, energy function that can distinguish optimal sequences from similar suboptimal ones.

Directed evolution

Main article: Directed evolution

In directed evolution, random mutagenesis is applied to a protein, and a selection regime is used to pick out variants that have the desired qualities. Further rounds of mutation and selection are then applied. This method mimics natural evolution and generally produces superior results to rational design.[citation needed] An additional technique known as DNA shuffling mixes and matches pieces of successful variants in order to produce better results. This process mimics the recombination that occurs naturally during sexual reproduction. The great advantage of directed evolution is that it requires no prior structural knowledge of a protein, nor is it necessary to be able to predict what effect a given mutation will have. Indeed, the results of directed evolution experiments are often surprising in that desired changes are often caused by mutations that were not expected to have that effect. The drawback is that they require high-throughput, which is not feasible for all proteins. Large amounts of recombinant DNA must be mutated and the products screened for desired qualities. The sheer number of variants often requires expensive robotic equipment to automate the process. Furthermore, not all desired activities can be easily screened for.

Examples of engineered proteins

Using computational methods, a protein with a novel fold has been designed, known as Top7,[1] as well as sensors for unnatural molecules.[2] The engineering of fusion proteins has yielded rilonacept, a pharmaceutical which has secured FDA approval for the treatment of cryopyrin-associated periodic syndrome.

Another computational method, IPRO, successfully engineered the switching of cofactor specificity of Candida boidinii xylose reductase.[3] Iterative Protein Redesign and Optimization (IPRO) redesigns proteins to increase or give specificity to native or novel substrates and cofactors. This is done by repeatedly randomly perturbing the backbones of the proteins around specified design positions, identifying the lowest energy combination of rotamers, and determining if the new design has a lower binding energy than previous ones. The iterative nature of this process allows IPRO to make additive mutations to the protein sequence that collectively improve the specificity towards the desired substrates and/or cofactors. Details on how to download the software implemented in Python and experimental testing of predictions are outlined in the following paper.[4]

See also

References

  1. ^ Kuhlman, Brian; Dantas, Gautam; Ireton, Gregory C.; Varani, Gabriele; Stoddard, Barry L. & Baker, David (2003), "Design of a Novel Globular Protein Fold with Atomic-Level Accuracy", Science 302 (5649): 1364–1368, doi:10.1126/science.1089427, PMID 14631033
  2. ^ Looger, Loren L.; Dwyer, Mary A.; Smith, James J. & Hellinga, Homme W. (2003), "Computational design of receptor and sensor proteins with novel functions", Nature 423 (6936): 185–190, doi:10.1038/nature01556, PMID 12736688
  3. ^ Khoury, GA; Fazelinia, H; Chin, JW; Pantazes, RJ; Cirino, PC; Maranas, CD (October 2009), "Computational design of Candida boidinii xylose reductase for altered cofactor specificity", Protein Science 18 (10): 2125–38, doi:10.1002/pro.227, PMC 2786976, PMID 19693930, http://www.pubmedcentral.nih.gov/articlerender.fcgi?tool=pmcentrez&artid=2786976
  4. ^ Khoury, GA; Fazelinia, H; Chin, JW; Pantazes, RJ; Cirino, PC; Maranas, CD (October 2009), "Computational design of Candida boidinii xylose reductase for altered cofactor specificity", Protein Science 18 (10): 2125–38, doi:10.1002/pro.227, PMC 2786976, PMID 19693930, http://www.pubmedcentral.nih.gov/articlerender.fcgi?tool=pmcentrez&artid=2786976

External links

Categories: Proteins

 

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