Introduction

Several millions of years of biological evolution have resulted in today's enzymes. They catalyze a given reaction with high specificity and enantioselectivity. But they are adjusted so perfectly to their physiological role that their activity and stability often do not match the desires of organic chemists. However Nature itself appears to provide a solution for this apparent dilemma: Natural Evolution produces a large number of variants by mutation and subsequently selects the "fittest" variant. This process can be mimicked in the test tube by using modern molecular biology methods of mutation and recombination. This collection of methods has been termed as Directed Evolution" and provides a powerful tool for the development of biocatalysts with novel properties without requiring knowledge on enzyme structures or catalytic mechanisms. By implementing recursive cycles of mutation and selection, one can optimize a protein for a particular process or new environmental parameter. Therefore, focus is put on the development of methods which are rapid, sensitive and cost-effective and preferably work at the microwell-plate, single colony or even single cell level.


Need for Directed Evolution

Natural enzymes are often not well suited for industrial applications like serving as catalysts in chemicals synthesis to additives for laundry detergents. Due to poor substrate solubility, breakdown of unstable products, or competing chemical reactions, the conditions for an enzyme reaction may be unsuitable for large-scale applications.

Reflecting their participation in complex biochemical networks inside living cells, enzymes are often inhibited by their own substrates or products, either of which may severely limit the productivity of a biocatalytic process. Evolution is usually the culprit: enzymes are optimized and often highly specialized for specific biological functions within the context of a living organism. Biotechnology, in contrast, needs enzymes which are stable and active over long periods of time, enzymes which are active in non-aqueous solvents and enzymes which can accept different substrates. These limitations have led the scientist to think of redesigning and synthesizing the biocatalysts having desired properties and Directed Evolution provides the solution to above limitations.


Process

Directed Evolution is an artificial technique used to design proteins using natural election rather than rational design. Since proteins are highly adaptable molecules, they evolve, and adapt at the molecular level. A typical directed evolution involves three steps:-

1. Diversification: - It involves the random mutation and/or recombination of genes encoding protein of interest in order to create a large library of gene variants. The degree of mutagenesis is critical to the success of directed evolution methods. If the mutagenesis is too heavy, then one will likely cripple the enzymes and the directed evolution will proceed slowly, if at all. Mutagenizing too little will also require a large number of screens to make any progress. Generally, the mutagenesis rate should be one base-pair of DNA per round of mutation. Accumulation of single point mutations will eventually lead to compensatory interactions which benefit the protein in the new environment. Methods of mutagenesis include error-prone PCR.
In brief, PCR is an automated way of amplifying the amount of a specific gene one has. If one runs a PCR process at a temperature or other physical condition where mistakes are introduced while copying the gene, then one has a system that naturally introduces mutations. Variations on this technique, such as DNA shuffling and sexual PCR allow for larger scale mutations, which can be particularly useful when trying to design proteins with new functions. The formation of chimeric proteins, which are made up from parts of other proteins, can sometimes fortuitously yield one protein that inherited the function of multiple sources.

2. Selection and screening: - Once the mutants are created the library of mutants is tested for the presence of mutants possessing the desired property using a screen or selection. Screens enable the researcher to identify and isolate high-performing mutants by hand, while selections automatically eliminate all nonfunctional mutants. The automation of screening is important. The more variants that can be screened at each step, the more efficient the selection process becomes. This is now possible with phage display, a technique that puts the protein to be designed
on the surface coat of a virus, the phage. Since the virus expresses the protein on the surface and contains the DNA that created that protein, we have a direct link between variant and its gene. Thus, we do not have to keep track of these things manually. The protein on the viral surface (hopefully) behaves as it would on its own. Thus, for example, one can test how well this protein binds to blood adhered to a filter. After running a huge population of phage over the filter, one can then wash off those that don't stick and keep the ones that do for the next round of
mutagenesis and screening. Once the number of variants is down to a manageable size, one can then use higher accuracy, lower throughput techniques to screen the final enzymes.

3. Amplification: - The variants identified in the selection or screen are replicated many fold, enabling researchers to sequence their DNA in order to understand what mutations have occurred.


New mutagenic strategies for directed enzyme evolution

Despite the rapid growth of published examples of directed evolution, there is still a need for alternative and improved methods for the directed evolution of enzymes. Some improvements have been made in ligations by adopting PCR-based approaches and more recently by directing in vivo hypermutation with B cells to target genes delivered by retroviral infection. However, ligation free approaches for DNA shuffling have yet to be demonstrated.

Another strategy is to focus mutations in regions of the enzyme more likely to result in beneficial mutations. It is seen that directed evolution produces majority of mutations in regions that contribute to substrate binding, catalysis or the conformation and dynamics of the active site environment. More recently, a technique dubbed CASTing (combinatorial active-site saturation), in which pair-wise saturation mutagenesis of residues adjacent in sequence, was focused into the active site of a lipase from Pseudomonas aeruginosa, yielded a number of mutants active on substrates not previously accepted by the wild type.

One further promising approach for obtaining more efficient searches of sequence-space is the use of consensus-sequence data for constructing libraries.


New screening and selection strategies for directed evolution

The methods available for screening and selection from enzyme libraries have been reviewed. New screens are required to enable the identification of improved enzymes from larger libraries, and also to obtain the desired properties with generic methods that measure it directly. A frequent target for the directed evolution of enzymes is the improvement of thermostability and increased stability in organic solvents. Most screens for thermostability use indirect measures, such as resistance to thermoinactivation at high temperatures. Such a screen, though effective in many cases, is not a direct measure of protein stability and is unsuitable for proteins that are reversibly unfolded, or those that are likely to become reversibly unfolded upon mutation, thus leading to false positives. To enable a more direct screen for protein stability, the measurement of protein denaturation curves using tryptophan fluorescence in microplates has been explored.


Applications

Directed evolution has been successfully applied to building proteins that function in extremely high temperatures, high salt concentrations, organic solvents, toxic chemicals, and other non-protein-friendly environments. It's also been used to design proteins that catalyze steps in chemical reactions which are of great value in the synthesis of pharmaceuticals chemicals. Due to enzymatic specificity, the need for protecting in chemical reactions is reduced, increasing product yields during organic synthesis. Directed evolution is also used to predict genetic mutations that lead to antibiotic resistance.


Problems in Directed Evolution:

1. The desired function must be physically, biologically or evolutionarily feasible. This means that there exists a mutational pathway to get from here to there through ever-improving variants.

2. We must be able to make libraries of mutants complex enough to contain rare beneficial mutations. This means functional expression in a suitable microorganism such as E. coli.

3. We must have a rapid screen or selection that reflects the desired function. Whether directed evolution will solve a particular problem depends to some extent on how hard natural evolution has already worked at it. If a particular trait is already under selective pressure (e.g. catalytic activity on the biologically relevant substrate), it is unlikely that further improvements can be obtained in the laboratory by small mutational steps.

However, if biological function has imposed additional constraints, for example the trait is coupled to another trait that is also under selective pressure (e.g. high thermo stability), then this balance can be altered during laboratory evolution.


Future Prospective

The use of computational approaches in screening of directed evolution libraries has increased since past two years. Directed evolution takes the problem of protein design and uses the computational ability of natural mutation to select proteins of desired traits.

By copying natural mechanism, it is possible to improve current industrial and medical products and possibly identify new enzymes with novel catalytic functions and physical properties. The use of computational design has expanded to include the thermo stabilization of enzymes and the redesign of an enzyme active-site for improved catalytic activity. As computational processing power continues to increase, and protein modeling algorithms become further refined, computational design should soon be capable of tackling more complex enzyme mechanisms and also of dramatically refining experimental library approaches.

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