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Open Access Methodology article

TransCent: Computational enzyme design by transferring active sites and considering constraints relevant for catalysis

André Fischer1, Nils Enkler1, Gerd Neudert2, Marco Bocola1, Reinhard Sterner1 and Rainer Merkl1*

Author Affiliations

1 Institut für Biophysik und Physikalische Biochemie, Universität Regensburg, 93040 Regensburg, Germany

2 Institut für Pharmazeutische Chemie, Universität Marburg, 35032 Marburg, Germany

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BMC Bioinformatics 2009, 10:54  doi:10.1186/1471-2105-10-54

Published: 10 February 2009

Abstract

Background

Computational enzyme design is far from being applicable for the general case. Due to computational complexity and limited knowledge of the structure-function interplay, heuristic methods have to be used.

Results

We have developed TransCent, a computational enzyme design method supporting the transfer of active sites from one enzyme to an alternative scaffold. In an optimization process, it balances requirements originating from four constraints. These are 1) protein stability, 2) ligand binding, 3) pKa values of active site residues, and 4) structural features of the active site. Each constraint is handled by an individual software module. Modules processing the first three constraints are based on state-of-the-art concepts, i.e. RosettaDesign, DrugScore, and PROPKA. To account for the fourth constraint, knowledge-based potentials are utilized. The contribution of modules to the performance of TransCent was evaluated by means of a recapitulation test. The redesign of oxidoreductase cytochrome P450 was analyzed in detail. As a first application, we present and discuss models for the transfer of active sites in enzymes sharing the frequently encountered triosephosphate isomerase fold.

Conclusion

A recapitulation test on native enzymes showed that TransCent proposes active sites that resemble the native enzyme more than those generated by RosettaDesign alone. Additional tests demonstrated that each module contributes to the overall performance in a statistically significant manner.