Insights Into Protein Engineering: Methods and Applications

What Is Protein Engineering?


Protein engineering is a powerful biotechnological process that focuses on creating new enzymes or proteins and improving the functions of existing ones by manipulating their natural macromolecular architecture.

Each protein contains a unique genetically encoded sequence of amino acids. Protein synthesis occurs through translation and is based on mRNA codons. Scientists use recombinant DNA technology to modify codons and develop diverse proteins with potentially enriched activities.

Genetic engineering technologies that enable cloning of any gene found in nature and DNA chemical synthesis have immensely contributed to the protein engineering field. In addition, technological advancements such as x-ray crystallography and computer modeling help researchers design amino acid sequences that fold into precise 3D structures, synthesizing proteins with specific properties.



Protein Engineering Methods



Protein engineering encompasses multiple strategies including rational design, directed evolution, semirational design, peptidomimetics, and de novo protein design. Scientists use these strategies to develop novel proteins or optimize existing protein properties that are relevant to medicine and biotechnology. Researchers then screen newly developed protein variants to identify those with desirable functions. For this, they have developed efficient screening methods such as fluorescence activated cell sorting (FACS) and phage display technology to examine large libraries of synthetic proteins and enzymes.
Rational method

Rational design is the classical protein engineering method that involves site directed mutagenesis. Scientists perform specific point mutations via insertions or deletions in the coding sequence based on structural and functional knowledge of the target protein. Typically, they mutate coding regions that correspond to a protein’s activity.

A key limitation of the rational method is that researchers must know a protein’s structural, functional, and molecular information. Although the rational protein design approach offers an increased possibility of beneficial alterations, it is not easy to accurately predict the sequence-structure-function relationship, particularly at the single amino acid level. However, artificial intelligence (AI) has substantially improved protein structure prediction based on amino acid sequence, which is vital for rational design strategies and newer engineering methods, such as semirational and de novo protein design.

In comparison to other methods such as directed evolution, rational design is less time consuming as it does not require large library screening. Scientists use this strategy to engineer protein-based vaccines, antibodies, and enzymes with high thermal stability and catalytic efficiency to meet industrial demands.
Directed evolution

In 2018, Frances H. Arnold won the Nobel Prize in Chemistry for the directed evolution of enzymes. The prize was shared with George P. Smith and Sir Gregory P. Winter for the phage display of peptides and antibodies. The directed evolution method is a robust protein engineering technique that generates random mutations in a gene of interest, followed by rapid protein variant selection based on favorable properties for specific applications.

Scientists commonly use error-prone polymerase chain reaction (EP-PCR) to generate random mutations throughout a gene or gene region.7 This method does not require any prior information regarding the protein’s structure and mechanisms, as it mimics the process of natural evolution. The success of the directed evolution method lies in generating mutant libraries of significant size and diversity.
Semirational protein design

Semirational protein design is a combination of rational and directed evolution methods.

Scientists consider this strategy more effective because they can use computational or bioinformatic modeling to obtain information on the protein’s function and structure and, therefore, select the most promising protein region to change.

This results in a small but high-quality library. The semirational protein design approach provides researchers with an increased opportunity to select biocatalysts with a wider substrate range, specificity, selectivity, and stability without compromising on their catalytic efficiency.

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