Motif-directed redesign of enzyme specificity

Abstract

Computational protein design relies on several approximations, including the use of fixed backbones and rotamers, to reduce protein design to a computationally tractable problem. However, allowing backbone and off-rotamer flexibility leads to more accurate designs and greater conformational diversity. Exhaustive sampling of this additional conformational space is challenging, and often impossible. Here, we report a computational method that utilizes a preselected library of native interactions to direct backbone flexibility to accommodate placement of these functional contacts. Using these native interaction modules, termed motifs, improves the likelihood that the interaction can be realized, provided that suitable backbone perturbations can be identified. Furthermore, it allows a directed search of the conformational space, reducing the sampling needed to find low energy conformations. We implemented the motif-based design algorithm in Rosetta, and tested the efficacy of this method by redesigning the substrate specificity of methionine aminopeptidase. In summary, native enzymes have evolved to catalyze a wide range of chemical reactions with extraordinary specificity. Computational enzyme design seeks to generate novel chemical activities by altering the target substrates of these existing enzymes. We have implemented a novel approach to redesign the specificity of an enzyme and demonstrated its effectiveness on a model system.

Keywords: computational protein design; enzyme design; molecular specificity; mutagenesis.

Figure 1

Holo-eMAP (PDB ID 2MAT, white) shows minimal conformational changes (0.114 Å RMSD) when bound with methionine (PDB ID 1C21, purple). The residues that contact the substrate are shown in a stick representation.

Figure 2

Motif donors. The placed motifs are shown here in their final position in eLAP (A,C) and their native background (B,D). The native contact orientation is maintained throughout the design process by constraining the three atoms that define the motif coordinate system. Backbone atoms are allowed to move in discrete “inverse-rotameric” conformations to graft the motif into its acceptor position.

Figure 3

eLAP design. (A) Initial placement of the LL motif (cyan, sphere representation) pulls the remodeled loop (cyan, cartoon) inwards towards the substrate (orange). This step repositions the loop from its native conformation (green). (B) A second motif, the LI motif (purple, right) is placed adjacent to the substrate, and the loop is again remodeled (purple, cartoon) to accommodate the new motif. The LL motif is constrained in this step. (C) Residues surrounding both motifs in the loop region (black) are mutated to support the dual-motif placement, and relaxed in 10 iterations. Resulting loop movement is minimal, and the native interactions are maintained during remodeling.

Figure 4

Alignment of methionine aminopeptidase with the final design for a leucine aminopeptidase. The flexible loop region encompasses residues 56–70 with the two motif placements shown in bright red. Additional mutations made to accommodate a greater range of loop conformations are shown in bold letters.

Figure 5

Changes in specificity in designed eLAP. On, off and dissociation rates for eMAP and eLAP show similar specificities (∼20-fold affinity preference) for their target substrates (A), indicating that motif-based design successfully changes the specificity of eMAP. A comparison of binding to each substrate (B), shows that the primary increase in affinity is for the positive design state (ie eLAP for leucine) rather than against methionine. A raw sensogram used to derive the specificity comparisons is shown in (C).

Figure 6

Enzyme kinetics data for (A) eMAP cleaving methionine (eMAP-met), (B) eMAP-leu, (C) eLAP-met, and (D) eLAP-leu. Best-fit curves using the Michaelis-Menten model are overlaid along with error bars for reaction velocities measured in triplicate. Parameters are listed in Table II.