Chapter 31 : Enzyme Engineering: Combining Computational Approaches with Directed Evolution

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This chapter begins with an overview of the synergy between rational design and screening or selection. The choice of a rational design approach requires consideration of the number of enzyme variants that can be evaluated experimentally. A review of fundamentals of enzyme catalysis from an energy landscape point of view is presented to illustrate why and where rational design could be applied. The bulk of the chapter follows with many examples from the literature of techniques that have worked. Throughout the discussion, mention of basic computational techniques needed to support design is made. It will be seen that it is often quite feasible to adapt an enzyme to many chemical process or organism-engineering needs. Catalytic effects that do not fall well into a thermodynamic viewpoint, such as vibrations or tunneling, are generally smaller, but could also be treated computationally with more development effort. Mutations to proline place a kink in the backbone that can also be stabilizing. The procedure of finding proline mutations can be automated by use of side-chain repacking software. The use of similar sequences to suggest mutations during the evolution of an enzyme is one of the more effective and easiest approaches to stabilization of proteins. Mutations at four positions designed from direct structure comparison helped convert an isocitrate dehydrogenase to an isopropylmalate dehydrogenase, although it was subsequently shown that random mutagenesis was more effective.

Citation: Clark L. 2010. Enzyme Engineering: Combining Computational Approaches with Directed Evolution, p 453-465. In Baltz R, Demain A, Davies J, Bull A, Junker B, Katz L, Lynd L, Masurekar P, Reeves C, Zhao H (ed), Manual of Industrial Microbiology and Biotechnology, Third Edition. ASM Press, Washington, DC. doi: 10.1128/9781555816827.ch31
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Image of FIGURE 1

Possible choices of rational design methods for a given level of screening capacity. The upper triangle gives approximate requirements for structural information in the form of percentage of sequence identity (seqid) of the homology model template to the enzyme sequence. The lower triangle specifies the corresponding screening requirements for each approach Npos, number of positrons targeted.

Citation: Clark L. 2010. Enzyme Engineering: Combining Computational Approaches with Directed Evolution, p 453-465. In Baltz R, Demain A, Davies J, Bull A, Junker B, Katz L, Lynd L, Masurekar P, Reeves C, Zhao H (ed), Manual of Industrial Microbiology and Biotechnology, Third Edition. ASM Press, Washington, DC. doi: 10.1128/9781555816827.ch31
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Image of FIGURE 2

Probability of mutating from a given residue type to another during a single-base change. Random mutations are inherently biased on a residue level, and 42% of all residue-to-residue type changes are impossible. Residues are ordered approximately by hydrophobicity. The figure is read from left to right (row index to column index). Example: the probability of mutating from M to I is higher than mutating from I to M.

Citation: Clark L. 2010. Enzyme Engineering: Combining Computational Approaches with Directed Evolution, p 453-465. In Baltz R, Demain A, Davies J, Bull A, Junker B, Katz L, Lynd L, Masurekar P, Reeves C, Zhao H (ed), Manual of Industrial Microbiology and Biotechnology, Third Edition. ASM Press, Washington, DC. doi: 10.1128/9781555816827.ch31
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Image of FIGURE 3

A hypothetical energy landscape for an enzyme-catalyzed reaction (black line) compared with the uncatalyzed reaction (gray dotted line). The overall reaction is thermodynamically favorable (ΔG < 0). Notice that, in this case, the catalyzed reaction has only a slightly more favorable activation energy (ΔG) than the uncatalyzed reaction (ΔG) due to strong binding to the substrate (ΔG). E, ezyme; S, substrate; P, product.

Citation: Clark L. 2010. Enzyme Engineering: Combining Computational Approaches with Directed Evolution, p 453-465. In Baltz R, Demain A, Davies J, Bull A, Junker B, Katz L, Lynd L, Masurekar P, Reeves C, Zhao H (ed), Manual of Industrial Microbiology and Biotechnology, Third Edition. ASM Press, Washington, DC. doi: 10.1128/9781555816827.ch31
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Image of FIGURE 4

Distribution of mutation effect on the ΔG of protein folding. Data are taken from the Protherm4.0 data set and restricted to single mutations where the protein structure is known ( ). Buried residues are <20% exposed relative to exposure in an alanine-X-alanine tripeptide. Surface residues are defined as having at least 60% relative exposure.

Citation: Clark L. 2010. Enzyme Engineering: Combining Computational Approaches with Directed Evolution, p 453-465. In Baltz R, Demain A, Davies J, Bull A, Junker B, Katz L, Lynd L, Masurekar P, Reeves C, Zhao H (ed), Manual of Industrial Microbiology and Biotechnology, Third Edition. ASM Press, Washington, DC. doi: 10.1128/9781555816827.ch31
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