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Biophys Chem. 2010 Mar;147(1-2):13-9. doi: 10.1016/j.bpc.2009.12.003. Epub 2010 Jan 19.

Using multi-objective computational design to extend protein promiscuity.

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  • 1Synth-Bio Group, Universite d'Evry Val d'Essonne-Genopole-CNRS UPS3201. Batiment Geneavenir 6. Genopole Campus 1. 5, rue Henri Desbruères. 91030 Evry Cedex, France.

Abstract

Many enzymes possess, besides their native function, additional promiscuous activities. Proteins with several activities (multipurpose catalysts) may have a wide range of biotechnological and biomedical applications. Natural promiscuity, however, appears to be of limited scope in this context, because the latent (promiscuous) function is often related to the evolved one (sharing the active site and even the chemical mechanism) and its enhancement upon suitable mutations usually brings about a decrease in the native activity. Here we explore the use of computational protein design to overcome these limitations. The high-plasticity positions close to the original ("native") active-site are the most promising candidates for mutations that create a second active-site associated to a new function. To avoid compromising protein folding and native activity, we propose a minimal-perturbation approach based on the combinatorial optimization of, both the de novo catalytic activity and the folding free-energy: essentially, we construct the Pareto Set of optimal stability/promiscuous-function solutions. We validate our approach by introducing a promiscuous esterase activity in E. coli thioredoxin on the basis of mutations at positions close to the native-active-site disulfide-bridge. Native oxidoreductase activity is not compromised and it is, in fact, found to be 1.5-fold enhanced, as determined by an insulin-reduction assay. This work provides general guidelines as to how computational design can be used to expand the scope and applications of protein promiscuity. From a more general viewpoint, it illustrates the potential of multi-objective optimization as the computational analogue of multi-feature natural selection.

Copyright 2009 Elsevier B.V. All rights reserved.

PMID:
20034725
[PubMed - indexed for MEDLINE]
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