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Probing the mutational interplay between primary and promiscuous protein functions: a computational-experimental approach.

Garcia-Seisdedos H, Ibarra-Molero B, Sanchez-Ruiz JM - PLoS Comput. Biol. (2012)

Bottom Line: Application of the approach to the emergence of folding catalysis in thioredoxin scaffolds reveals an unanticipated scenario: diverse patterns of primary/promiscuous activity modulation are possible, including a moderate (but likely significant in a biological context) simultaneous enhancement of both activities.Overall, the results reported may help clarify the mechanisms of the evolution of new functions.From a different viewpoint, the partial-least-squares-reconstruction/Pareto-set-prediction approach we have introduced provides the computational basis for an efficient directed-evolution protocol aimed at the simultaneous enhancement of several protein features and should therefore open new possibilities in the engineering of multi-functional enzymes.

View Article: PubMed Central - PubMed

Affiliation: Facultad de Ciencias, Departamento de Quimica Fisica, Universidad de Granada, Granada, Spain.

ABSTRACT
Protein promiscuity is of considerable interest due its role in adaptive metabolic plasticity, its fundamental connection with molecular evolution and also because of its biotechnological applications. Current views on the relation between primary and promiscuous protein activities stem largely from laboratory evolution experiments aimed at increasing promiscuous activity levels. Here, on the other hand, we attempt to assess the main features of the simultaneous modulation of the primary and promiscuous functions during the course of natural evolution. The computational/experimental approach we propose for this task involves the following steps: a function-targeted, statistical coupling analysis of evolutionary data is used to determine a set of positions likely linked to the recruitment of a promiscuous activity for a new function; a combinatorial library of mutations on this set of positions is prepared and screened for both, the primary and the promiscuous activities; a partial-least-squares reconstruction of the full combinatorial space is carried out; finally, an approximation to the Pareto set of variants with optimal primary/promiscuous activities is derived. Application of the approach to the emergence of folding catalysis in thioredoxin scaffolds reveals an unanticipated scenario: diverse patterns of primary/promiscuous activity modulation are possible, including a moderate (but likely significant in a biological context) simultaneous enhancement of both activities. We show that this scenario can be most simply explained on the basis of the conformational diversity hypothesis, although alternative interpretations cannot be ruled out. Overall, the results reported may help clarify the mechanisms of the evolution of new functions. From a different viewpoint, the partial-least-squares-reconstruction/Pareto-set-prediction approach we have introduced provides the computational basis for an efficient directed-evolution protocol aimed at the simultaneous enhancement of several protein features and should therefore open new possibilities in the engineering of multi-functional enzymes.

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Use of the Pareto set to define the patterns of primary/promiscuous activity modulation.In all plots, each data point represents the primary and promiscuous activity data for a given protein variant. Different variants may be thought as corresponding to different combinations of mutations from a given set. (A) Illustration of Pareto set construction. Variant “b” is dominated by variant “a”, since the latter shows higher values of both activities. Variant “a” is not dominated by variant “c”, since promiscuous-activity(a)>promiscuous-activity(c). Variant “c” has the highest value for the primary activity and is not dominated neither by “a” nor “b”. The non-dominated variants “a” and “c” form the Pareto set (green data points) for this three-variant example. (B), (C) and (D) are illustrative examples of the relation between the Pareto set (green data points) and the primary/promiscuous trade-offs. In (B) and (C) the starting variant (marked with a red circle) belongs to the Pareto set and, therefore, increasing the promiscuous activity necessarily implies a decrease of the primary activity. The plot in (B) is meant to illustrate a weak trade-off along the Pareto set (a significant increase in promiscuous activity can be achieved with only a small decrease in primary activity) while (C) is meant to illustrate a strong trade-off. In (D) the starting variant does not belong to the Pareto set and, hence, the simultaneous enhancement of both activities is possible.
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pcbi-1002558-g001: Use of the Pareto set to define the patterns of primary/promiscuous activity modulation.In all plots, each data point represents the primary and promiscuous activity data for a given protein variant. Different variants may be thought as corresponding to different combinations of mutations from a given set. (A) Illustration of Pareto set construction. Variant “b” is dominated by variant “a”, since the latter shows higher values of both activities. Variant “a” is not dominated by variant “c”, since promiscuous-activity(a)>promiscuous-activity(c). Variant “c” has the highest value for the primary activity and is not dominated neither by “a” nor “b”. The non-dominated variants “a” and “c” form the Pareto set (green data points) for this three-variant example. (B), (C) and (D) are illustrative examples of the relation between the Pareto set (green data points) and the primary/promiscuous trade-offs. In (B) and (C) the starting variant (marked with a red circle) belongs to the Pareto set and, therefore, increasing the promiscuous activity necessarily implies a decrease of the primary activity. The plot in (B) is meant to illustrate a weak trade-off along the Pareto set (a significant increase in promiscuous activity can be achieved with only a small decrease in primary activity) while (C) is meant to illustrate a strong trade-off. In (D) the starting variant does not belong to the Pareto set and, hence, the simultaneous enhancement of both activities is possible.

Mentions: A unique global optimum cannot be defined when dealing with a multi-objective optimization problem, such as, for instance, enhancing a promiscuous activity while keeping the level of the primary activity as high as possible. However, a set of several optimal solutions can be defined using the Pareto criterion: a solution (protein variant in this case) belongs to the set of optimal solutions (the so-called Pareto set) if it is not dominated by any other solution. The dominance relationship is defined as follows: a solution a dominates a solution b if it shows enhanced performance for all optimization objectives. In the specific case of interest here, variant a dominates variant b if primary-activity(a)>primary-activity(b) and simultaneously promiscuous-activity(a)>promiscuous-activity(b). The construction of the Pareto set of non-dominated solutions is illustrated with a simple example in Figure 1A. The Pareto set includes the solutions with optimal trade-offs between the different objectives and has been used extensively in economics, while its application to protein design has only been explored in recent years [29]–[31]. In the specific case of interest here, determination of the Pareto set should immediately clarify the main features of the modulation of the primary and promiscuous activities within a given mutational space. For instance, if the starting variant (the “wild-type” protein, for instance) already belongs to the Pareto set, enhancement of the promiscuous activity necessarily implies a decrease in primary function and the trade-off will be weak (Figure 1B) or strong (Figure 1C) depending of the general slope of the Pareto set in the plot of promiscuous activity versus primary activity. On the other hand, if the starting variant does not belong to the Pareto set, simultaneous optimization of both activities is in principle feasible (Figure 1D) and primary/promiscuous trade-offs can be avoided to some extent.


Probing the mutational interplay between primary and promiscuous protein functions: a computational-experimental approach.

Garcia-Seisdedos H, Ibarra-Molero B, Sanchez-Ruiz JM - PLoS Comput. Biol. (2012)

Use of the Pareto set to define the patterns of primary/promiscuous activity modulation.In all plots, each data point represents the primary and promiscuous activity data for a given protein variant. Different variants may be thought as corresponding to different combinations of mutations from a given set. (A) Illustration of Pareto set construction. Variant “b” is dominated by variant “a”, since the latter shows higher values of both activities. Variant “a” is not dominated by variant “c”, since promiscuous-activity(a)>promiscuous-activity(c). Variant “c” has the highest value for the primary activity and is not dominated neither by “a” nor “b”. The non-dominated variants “a” and “c” form the Pareto set (green data points) for this three-variant example. (B), (C) and (D) are illustrative examples of the relation between the Pareto set (green data points) and the primary/promiscuous trade-offs. In (B) and (C) the starting variant (marked with a red circle) belongs to the Pareto set and, therefore, increasing the promiscuous activity necessarily implies a decrease of the primary activity. The plot in (B) is meant to illustrate a weak trade-off along the Pareto set (a significant increase in promiscuous activity can be achieved with only a small decrease in primary activity) while (C) is meant to illustrate a strong trade-off. In (D) the starting variant does not belong to the Pareto set and, hence, the simultaneous enhancement of both activities is possible.
© Copyright Policy
Related In: Results  -  Collection

Show All Figures
getmorefigures.php?uid=PMC3375227&req=5

pcbi-1002558-g001: Use of the Pareto set to define the patterns of primary/promiscuous activity modulation.In all plots, each data point represents the primary and promiscuous activity data for a given protein variant. Different variants may be thought as corresponding to different combinations of mutations from a given set. (A) Illustration of Pareto set construction. Variant “b” is dominated by variant “a”, since the latter shows higher values of both activities. Variant “a” is not dominated by variant “c”, since promiscuous-activity(a)>promiscuous-activity(c). Variant “c” has the highest value for the primary activity and is not dominated neither by “a” nor “b”. The non-dominated variants “a” and “c” form the Pareto set (green data points) for this three-variant example. (B), (C) and (D) are illustrative examples of the relation between the Pareto set (green data points) and the primary/promiscuous trade-offs. In (B) and (C) the starting variant (marked with a red circle) belongs to the Pareto set and, therefore, increasing the promiscuous activity necessarily implies a decrease of the primary activity. The plot in (B) is meant to illustrate a weak trade-off along the Pareto set (a significant increase in promiscuous activity can be achieved with only a small decrease in primary activity) while (C) is meant to illustrate a strong trade-off. In (D) the starting variant does not belong to the Pareto set and, hence, the simultaneous enhancement of both activities is possible.
Mentions: A unique global optimum cannot be defined when dealing with a multi-objective optimization problem, such as, for instance, enhancing a promiscuous activity while keeping the level of the primary activity as high as possible. However, a set of several optimal solutions can be defined using the Pareto criterion: a solution (protein variant in this case) belongs to the set of optimal solutions (the so-called Pareto set) if it is not dominated by any other solution. The dominance relationship is defined as follows: a solution a dominates a solution b if it shows enhanced performance for all optimization objectives. In the specific case of interest here, variant a dominates variant b if primary-activity(a)>primary-activity(b) and simultaneously promiscuous-activity(a)>promiscuous-activity(b). The construction of the Pareto set of non-dominated solutions is illustrated with a simple example in Figure 1A. The Pareto set includes the solutions with optimal trade-offs between the different objectives and has been used extensively in economics, while its application to protein design has only been explored in recent years [29]–[31]. In the specific case of interest here, determination of the Pareto set should immediately clarify the main features of the modulation of the primary and promiscuous activities within a given mutational space. For instance, if the starting variant (the “wild-type” protein, for instance) already belongs to the Pareto set, enhancement of the promiscuous activity necessarily implies a decrease in primary function and the trade-off will be weak (Figure 1B) or strong (Figure 1C) depending of the general slope of the Pareto set in the plot of promiscuous activity versus primary activity. On the other hand, if the starting variant does not belong to the Pareto set, simultaneous optimization of both activities is in principle feasible (Figure 1D) and primary/promiscuous trade-offs can be avoided to some extent.

Bottom Line: Application of the approach to the emergence of folding catalysis in thioredoxin scaffolds reveals an unanticipated scenario: diverse patterns of primary/promiscuous activity modulation are possible, including a moderate (but likely significant in a biological context) simultaneous enhancement of both activities.Overall, the results reported may help clarify the mechanisms of the evolution of new functions.From a different viewpoint, the partial-least-squares-reconstruction/Pareto-set-prediction approach we have introduced provides the computational basis for an efficient directed-evolution protocol aimed at the simultaneous enhancement of several protein features and should therefore open new possibilities in the engineering of multi-functional enzymes.

View Article: PubMed Central - PubMed

Affiliation: Facultad de Ciencias, Departamento de Quimica Fisica, Universidad de Granada, Granada, Spain.

ABSTRACT
Protein promiscuity is of considerable interest due its role in adaptive metabolic plasticity, its fundamental connection with molecular evolution and also because of its biotechnological applications. Current views on the relation between primary and promiscuous protein activities stem largely from laboratory evolution experiments aimed at increasing promiscuous activity levels. Here, on the other hand, we attempt to assess the main features of the simultaneous modulation of the primary and promiscuous functions during the course of natural evolution. The computational/experimental approach we propose for this task involves the following steps: a function-targeted, statistical coupling analysis of evolutionary data is used to determine a set of positions likely linked to the recruitment of a promiscuous activity for a new function; a combinatorial library of mutations on this set of positions is prepared and screened for both, the primary and the promiscuous activities; a partial-least-squares reconstruction of the full combinatorial space is carried out; finally, an approximation to the Pareto set of variants with optimal primary/promiscuous activities is derived. Application of the approach to the emergence of folding catalysis in thioredoxin scaffolds reveals an unanticipated scenario: diverse patterns of primary/promiscuous activity modulation are possible, including a moderate (but likely significant in a biological context) simultaneous enhancement of both activities. We show that this scenario can be most simply explained on the basis of the conformational diversity hypothesis, although alternative interpretations cannot be ruled out. Overall, the results reported may help clarify the mechanisms of the evolution of new functions. From a different viewpoint, the partial-least-squares-reconstruction/Pareto-set-prediction approach we have introduced provides the computational basis for an efficient directed-evolution protocol aimed at the simultaneous enhancement of several protein features and should therefore open new possibilities in the engineering of multi-functional enzymes.

Show MeSH