Protein engineering

Protein engineering is the scientific process of creating useful or valuable proteins. Scientists achieve this by designing unnatural polypeptides, which are long chains of amino acids. Often, they do this by altering the amino acid sequences found in nature. This field is quite young, as researchers are still learning how proteins fold and recognize one another. This discipline has vast applications in medicine and industrial bioprocessing. By 2017, the market for protein engineering products and services reached an estimated $168 billion.

There are two primary strategies used in protein engineering: rational design and directed evolution. These two methods are not mutually exclusive. In fact, researchers often apply both to reach their goals. Rational design relies on a specific plan based on known information. Directed evolution mimics the process of natural selection to find better versions of a protein. While rational design is often inexpensive and technically easy, directed evolution can produce superior results.

In rational protein design, a scientist uses detailed knowledge of a protein's structure and function. They make specific changes to the sequence to achieve a desired effect. This method uses site-directed mutagenesis, which is a well-developed way to change specific parts of a protein. However, a major drawback is that detailed structural knowledge is often unavailable. Even when it is available, it is hard to predict how mutations will work. This is because structural information usually provides only a static picture of a protein.

To overcome a lack of structural data, scientists use multiple sequence alignment. This technique involves aligning the target protein sequence with other related sequences. This process can reveal which amino acids are conserved across different species. Conserved amino acids are often important for the protein's function. Identifying these can help scientists find "hot spot" amino acids for mutation. Tools like Clustal omega can align up to 190,000 sequences at once. Other methods include MAFFT, which uses fast Fourier transform, and T-Coffee, which is noted for being highly accurate.

Another way to approach rational design is through structural prediction. There are four main classes of prediction methods: ab initio, fragment-based, homology modeling, and protein threading. Ab initio methods involve free modeling without using any template information. They aim to predict the native structure by finding the global minimum of free energy. Fragment-based methods use databases to match homologous structures to new sequences. Homology modeling, or comparative modeling, uses a known template sequence to model a query sequence. Finally, protein threading is used when a reliable homologue cannot be found.

Directed evolution offers a different path by using random mutagenesis. Scientists might use error-prone PCR or sequence saturation mutagenesis to create many variants. They then use a selection regime to pick the versions with the desired traits. This process can be repeated through multiple rounds of mutation and selection. Sometimes, they use a process called DNA shuffling. This mixes and matches pieces of successful variants, much like recombination during sexual reproduction. This method is powerful because it requires no prior knowledge of the protein's structure.

Despite its success, directed evolution has specific requirements and drawbacks. It requires high-throughput screening to manage the large number of variants. This means scientists must test many different proteins to find the best ones. This process often requires expensive robotic equipment to automate the work. Additionally, not all protein activities can be screened for easily. However, the results are often surprising. Mutations that cause desired changes are sometimes completely unexpected by the researchers.

Protein engineering also explores multivalent binding to improve how proteins interact with targets. Multivalent binding uses multiple binding domains in a single molecule. This increases the likelihood of interactions through avidity effects. Avidity, or effective affinity, can be much higher than the sum of individual affinities. This makes it a cost-effective tool for targeted binding. A common example of multivalent proteins is antibodies. Scientists are currently conducting extensive research into bispecific antibodies for various medical uses.