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

Fischer A, Enkler N, Neudert G, Bocola M, Sterner R, Merkl R - BMC Bioinformatics (2009)

Bottom Line: The redesign of oxidoreductase cytochrome P450 was analyzed in detail.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.

View Article: PubMed Central - HTML - PubMed

Affiliation: Institut für Biophysik und Physikalische Biochemie, Universität Regensburg, Regensburg, Germany. andre.fischer@biologie.uni-regensburg.de

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.

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The performance of different module combinations of TransCent as judged by in silico recapitulation of active sites. Mean IDENT_RES values and standard deviations were determined for different combinations of modules. Abbreviations for modules are: ST (stability), LB (ligand binding), KP (knowledge-based potential), and PK (pKa values). For each combination of modules, 20 models were generated for each enzyme belonging to ENZ_TESThom. Values labeled with a # originate from a leave-one-out cross validation test.
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Figure 2: The performance of different module combinations of TransCent as judged by in silico recapitulation of active sites. Mean IDENT_RES values and standard deviations were determined for different combinations of modules. Abbreviations for modules are: ST (stability), LB (ligand binding), KP (knowledge-based potential), and PK (pKa values). For each combination of modules, 20 models were generated for each enzyme belonging to ENZ_TESThom. Values labeled with a # originate from a leave-one-out cross validation test.

Mentions: A central paradigm for evaluating the quality of a design program is the in silico recapitulation experiment introduced above. Due to the specific requirements of catalysis, active sites are generally highly conserved. Therefore, the comparison of modeled sites with the active site of the wild-type enzyme allows the evaluation of a program's performance. In order to assess the contribution of individual modules to the performance of TransCent, eight different combinations of TransCent's modules were tested. We generated 20 models for each enzyme belonging to ENZ_TESThom. Mean IDENT_RES values were determined and plotted. Results are summarized in Figure 2. The data clearly show that each module contributes significantly to the performance of TransCent. Compared to an exclusive usage of the ST-module, the combination of all four modules resulted in an increase of identical residues from 29.5% to 54.3%. A t-test based on IDENT_RES values showed that each addition of a module improved the performance in a statistically significant manner (p << 0.01).


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

Fischer A, Enkler N, Neudert G, Bocola M, Sterner R, Merkl R - BMC Bioinformatics (2009)

The performance of different module combinations of TransCent as judged by in silico recapitulation of active sites. Mean IDENT_RES values and standard deviations were determined for different combinations of modules. Abbreviations for modules are: ST (stability), LB (ligand binding), KP (knowledge-based potential), and PK (pKa values). For each combination of modules, 20 models were generated for each enzyme belonging to ENZ_TESThom. Values labeled with a # originate from a leave-one-out cross validation test.
© Copyright Policy - open-access
Related In: Results  -  Collection

License
Show All Figures
getmorefigures.php?uid=PMC2667513&req=5

Figure 2: The performance of different module combinations of TransCent as judged by in silico recapitulation of active sites. Mean IDENT_RES values and standard deviations were determined for different combinations of modules. Abbreviations for modules are: ST (stability), LB (ligand binding), KP (knowledge-based potential), and PK (pKa values). For each combination of modules, 20 models were generated for each enzyme belonging to ENZ_TESThom. Values labeled with a # originate from a leave-one-out cross validation test.
Mentions: A central paradigm for evaluating the quality of a design program is the in silico recapitulation experiment introduced above. Due to the specific requirements of catalysis, active sites are generally highly conserved. Therefore, the comparison of modeled sites with the active site of the wild-type enzyme allows the evaluation of a program's performance. In order to assess the contribution of individual modules to the performance of TransCent, eight different combinations of TransCent's modules were tested. We generated 20 models for each enzyme belonging to ENZ_TESThom. Mean IDENT_RES values were determined and plotted. Results are summarized in Figure 2. The data clearly show that each module contributes significantly to the performance of TransCent. Compared to an exclusive usage of the ST-module, the combination of all four modules resulted in an increase of identical residues from 29.5% to 54.3%. A t-test based on IDENT_RES values showed that each addition of a module improved the performance in a statistically significant manner (p << 0.01).

Bottom Line: The redesign of oxidoreductase cytochrome P450 was analyzed in detail.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.

View Article: PubMed Central - HTML - PubMed

Affiliation: Institut für Biophysik und Physikalische Biochemie, Universität Regensburg, Regensburg, Germany. andre.fischer@biologie.uni-regensburg.de

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.

Show MeSH