Computational tools for designing and engineering biocatalysts

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Current computational tools to assist experimentalists for the design and engineering of proteins with desired catalytic properties are reviewed. The applications of these tools for de novo design of protein active sites, optimization of substrate access and product exit pathways, redesign of protein–protein interfaces, identification of neutral/advantageous/deleterious mutations in the libraries from directed evolution and stabilization of protein structures are described. Remarkable progress is seen in de novo design of enzymes catalyzing a chemical reaction for which a natural biocatalyst does not exist. Yet, constructed biocatalysts do not match natural enzymes in their efficiency, suggesting that more research is needed to capture all the important features of natural biocatalysts in theoretical designs.

Introduction

Enzymes are molecular machines that catalyze chemical reactions in living organisms. It is of great scientific interest and practical need to construct enzymes with new catalytic properties and enhanced stabilities. The methods of directed evolution, based on several rounds of mutagenesis in combination with efficient screening or selection, have been particularly successful in this effort owing to the high complexity of protein structures and our limited understanding of the protein structure–function relationships. Long-term efforts to assist directed evolution in focusing on the regions in protein structures relevant for the function as well as to design enzymes de novo, led to development of a large variety of computational tools. These theoretical tools are maturing and 2008 is to be remembered as the year when the grand challenge – de novo design of an enzyme catalyzing a chemical reaction for which a natural biocatalyst does not exist – has been met. In this review, we discuss progress in the development of tools for protein design and engineering in the past two years. Other recent reviews can provide additional background and viewpoints [1, 2, 3, 4, 5, 6, 7, 8].

Section snippets

Computational tools for de novo design of active sites

Computational de novo design relies on the introduction of amino acid residues essential for catalysis into the existing scaffolds. The underlining idea is that enzymes enhance chemical reactions by lowering an activation barrier due to stabilization of the transition state by the residues of the active site [9]. Initially, the transition state of the reaction and the idealized active site geometry is modeled using quantum mechanics. The library of protein scaffolds is then searched to

Computational tools for design of ligand exchange pathways

The traditional Emil Fisher's ‘lock–key’ model uses analogy between enzyme (lock) and substrate (key) to describe the need for a matching shape of a substrate in order to fit to the active site of an enzyme [20]. The preference of an enzyme for given substrates is attributed to the quality of the match between enzyme active site and transition states of individual substrates. Lock–key model, or its modified version, the induced-fit model [21], explains catalysis by an enzyme with an easily

Computational tools for design of protein–protein interfaces

Protein–protein interactions are key components of all signal transduction processes, and the methods to alter these interactions represent important tools for dissecting function of connectivities in signal networks. The program ORBIT, referred to above, has been used for computational design of ubiquitous messenger protein calmodulin [32]. Calmodulin responses to the different levels of Ca2+ and interacts with calmodulin-dependent protein kinase II and calcineurin in the cells. Using a

Computational tools for the assignment of neutral, advantageous and deleterious mutations

Directed evolution methods developed over the past 15 years are powerful in generating molecular diversity, which must be screened or selected for desired catalytic activity or new properties. It is becoming generally accepted that blind generation of large libraries and laborious screening is not an efficient way of obtaining a good biocatalyst [5]. Narrowing the sequence space using the structural information or identification of useful mutations by statistical analysis and their intentional

Computational tools for design of protein stability

Stability of proteins has been of great interest to protein engineers as well as practitioners implementing the use enzymes in industrial processes. Despite their many favorable properties, the marginal stability of enzymes in reaction media often has prevented or delayed their implementation for the industrial-scale synthesis of fine chemicals and pharmaceuticals [2]. A number of simple rules defining the stability of protein structures have been formulated over the years and can be found in

Outlook

In spite of the remarkable progress in de novo design, developed biocatalysts do not match natural catalysts in their efficiency. What is missing in current designs? Designs employing site-directed mutagenesis may not achieve precise stabilization of the transition states, where the differences in the distances at the picometer scale can be important [50••]. Protein backbone dynamics can also play a very important role in catalysis and are not included in current design methods. Existing de novo

References and recommended reading

Papers of particular interest, published within the period of review, have been highlighted as:

  • • of special interest

  • •• of outstanding interest

Acknowledgements

Research work of the authors is supported by the research grants from the Czech Ministry of Education (LC06010, MSM0021622412 and MSM0021622413) and the Czech Science Foundation (201/07/0927 and 203/08/0114). Financial support is greatly appreciated.

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