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.¹

A team of scientists engaged in protein engineering experiments in a laboratory, showcasing advanced research techniques.

Scientists employ protein engineering techniques to design and create proteins with useful properties for medicinal and industrial applications.

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Each protein contains a unique genetically encoded sequence of amino acids. Protein synthesis occurs through translation and is based on mRNA codons.² Scientists use recombinant DNA technology to modify codons and develop diverse proteins with potentially enriched activities.³

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.⁴

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.⁵ 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.⁶

Protein engineering strategies (rational design, directed evolution, semirational design, de novo design, and peptidomimetics) yield improved protein properties for different applications, including basic protein research, detergent and food industries, medicine, agriculture, tissue engineering, and nanotechnology.

Protein engineering strategies such as rational design, directed evolution, semirational design, de novo design, and peptidomimetics help scientists improve protein properties for a range of applications.

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Rational method

Rational design is the classical protein engineering method that involves site directed mutagenesis.⁷ 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.⁷ 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.⁸

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.⁷

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.⁹ 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.

Peptidomimetics

Peptidomimetics is the design and synthesis of metabolically stable peptide analogs that mimic or block natural enzyme or peptide functions.⁵ This approach employs a variety of biological techniques including solid phase synthesis of nonpeptide libraries that extend the range of amino acid sequences incorporated into engineered proteins.¹⁰ Peptidomimetics also uses combinatorial approaches that employ multiple synthetic biology techniques and result in rapid protein variant generation.

De novo protein design

In 2024, David Baker won the Nobel Prize in Chemistry for computational protein design and the prize was shared with Demis Hassabis, and John Jumper for protein structure prediction.

Scientists use de novo protein design to synthesize proteins with specific structural and functional properties from scratch.¹¹ For example, researchers use this strategy to generate proteins that fold into a particular topology, bind to a specific target, or contain a particular catalytic site. Machine learning models such as denoising diffusion probabilistic models (DDPM) enable photorealistic image generation to visualize protein folding and support de novo protein design.¹² Researchers have improved diffusion models by integrating powerful structure prediction methods such as RoseTTAFold (RF) and AlphaFold2. The RF-based diffusion model can design diverse functional proteins from simple molecular specifications.¹¹

Autonomous Protein Engineering Systems

Because the creation of new proteins with improved or novel functions can be slow and labor intensive, scientists have developed autonomous platforms to accelerate the process.¹³ These platforms computationally estimate mutation effects, particularly when screening by combinatorial techniques is difficult. Furthermore, screening methods such as mass spectrometry (MS), however efficient and specific they are, require time-consuming sample preparation steps. This shortcoming is overcome by recently developed autosamplers that use electrospray ionization (ESI) for fast sample preparation.¹⁴

Additionally, robot scientists and self-driving laboratories combine laboratory experiments and automated learning and reasoning to accelerate new biomolecule design. For example, the fully autonomous protein engineering platform Self-driving Autonomous Machines for Protein Landscape Exploration (SAMPLE) is equipped with AI programs that learn protein sequence–function relationships and design new proteins. Subsequently, a fully automated robotic system performs experiments to test the designed proteins and provide feedback.¹⁵

Protein Engineering Applications

As industrial enzymes are often sourced from mesophilic organisms, they are typically active in moderate reaction conditions.¹⁶ However, an ideal industrial enzyme must withstand harsh conditions such as extreme temperature, pH, and salinity. Scientists use protein engineering techniques to improve the properties of industrially important enzymes such as lipases, esterases, amylases, proteases, xylanases, and cellulases for high specificity, thermostability, and catalytic efficiency.¹⁷ There are numerous protein engineering applications, including biocatalysts for food and industry, medicine, and the environment. Additionally, remarkable progress in protein engineering over the past decade has improved therapeutics by enabling researchers to produce antivirals, vaccine antigens, and drug-delivery nanovehicles.¹⁸

Table 1: Protein engineering applications^19-22

Application Area	Examples of Engineered Enzymes and Proteins	Mutagenesis Approach	Mutant Properties
Basic protein science	GroEL minichapherones	Semirational approach	Stability
Detergent industry	Alkaline proteases	Site directed mutagenesis and/or random mutagenesis	High activity at alkaline pH and low temperatures
Food industry	α-amylase	Site directed mutagenesis	Thermostability
Medicine	Insulin	Site directed mutagenesis	Fast acting monomeric insulin
Agriculture	5-enolpyruvyl-shikimate-3-phosphatesynthase	EP-PCR	Enhanced kinetic properties and confer herbicide tolerance (glyphosate)
Tissue engineering	Elastin-like polypeptides (ELPs)	Site directed mutagenesis	Enhanced elasticity and self-assembling properties
Nanobiotechnology	Bacterial pili	Site directed mutagenesis	Highly-conductive protein nanowires

Protein Engineering Challenges

Protein design not only offers multiple opportunities in terms of applications, but also presents challenges due to knowledge gaps around folding mechanisms, which are the physiochemical principles underlying protein stability and interactions with the environment. Computational methods allow scientists to generate 3D protein structures, which help elucidate the process of protein folding; however, it is not easy to manipulate the factors that determine protein conformation for targeted purposes.

Furthermore, it is difficult to accurately predict the protein conformational changes that happen during the process of binding with other molecules.¹⁸ This information is vital to determine how designed proteins respond to the environment. Researchers focus on overcoming these challenges by using machine learning tools and computational design methods to generate new proteins with favorable properties.

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