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Control of repeat-protein curvature by computational protein design.

Park K, Shen BW, Parmeggiani F, Huang PS, Stoddard BL, Baker D - Nat. Struct. Mol. Biol. (2015)

Bottom Line: Second, a set of junction modules that connect the different building blocks are designed.Finally, new proteins with custom-designed shapes are generated by appropriately combining building-block and junction modules.Crystal structures of the designs illustrate the power of the approach in controlling repeat-protein curvature.

View Article: PubMed Central - PubMed

Affiliation: 1] Department of Biochemistry, University of Washington, Seattle, Washington, USA. [2] Institute for Protein Design, University of Washington, Seattle, Washington, USA.

ABSTRACT
Shape complementarity is an important component of molecular recognition, and the ability to precisely adjust the shape of a binding scaffold to match a target of interest would greatly facilitate the creation of high-affinity protein reagents and therapeutics. Here we describe a general approach to control the shape of the binding surface on repeat-protein scaffolds and apply it to leucine-rich-repeat proteins. First, self-compatible building-block modules are designed that, when polymerized, generate surfaces with unique but constant curvatures. Second, a set of junction modules that connect the different building blocks are designed. Finally, new proteins with custom-designed shapes are generated by appropriately combining building-block and junction modules. Crystal structures of the designs illustrate the power of the approach in controlling repeat-protein curvature.

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Crystal structures of the idealized building block and junction module designs. The design models (green) for (a) L22, (b) L24, (c) L24→L22, (d) L24→L28 (DLRR_G3), and (e) L24→L32→L24 (DLRR_H2) superimposed on the crystal structures (magenta). Close-up views for the designed junction modules (dashed region) are shown for (d) and (e). Residues mutated from the original building block sequences are annotated and shown as sticks. Additional residues that vary within the designs are shown as lines. Except for that of DLRR_A, the crystal structures have missing electron density at the C-terminus (10–20 amino acids). PyMOL (http://www.pymol.org/) are used in all structural visualization.
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Figure 3: Crystal structures of the idealized building block and junction module designs. The design models (green) for (a) L22, (b) L24, (c) L24→L22, (d) L24→L28 (DLRR_G3), and (e) L24→L32→L24 (DLRR_H2) superimposed on the crystal structures (magenta). Close-up views for the designed junction modules (dashed region) are shown for (d) and (e). Residues mutated from the original building block sequences are annotated and shown as sticks. Additional residues that vary within the designs are shown as lines. Except for that of DLRR_A, the crystal structures have missing electron density at the C-terminus (10–20 amino acids). PyMOL (http://www.pymol.org/) are used in all structural visualization.

Mentions: We solved the crystal structures of DLRR_A (L226) and DLRR_B (L247) (Table 1) and found that they closely match the design models (DLRR_A at Cα root mean square deviation (RMSD) 1.4 Å; DLRR_B at Cα RMSD 1.7 Å, Fig. 3a-b). The crystal structures contain water-mediated networks localized to the convex side of the repeats; it may be possible to incorporate these in future design calculations (Supplementary Fig. 1a). Each of the idealized building block repeats has the expected overall curvature: repeats of the L22 and L24 building blocks generate solenoid-like structures, whereas repeats of the {L28→L29} building block are almost circular and have a more curved concave surface. Parametric descriptions of the global shapes generated by each building block repeat are provided in Supplementary Figure 1b and Supplementary Table 1.


Control of repeat-protein curvature by computational protein design.

Park K, Shen BW, Parmeggiani F, Huang PS, Stoddard BL, Baker D - Nat. Struct. Mol. Biol. (2015)

Crystal structures of the idealized building block and junction module designs. The design models (green) for (a) L22, (b) L24, (c) L24→L22, (d) L24→L28 (DLRR_G3), and (e) L24→L32→L24 (DLRR_H2) superimposed on the crystal structures (magenta). Close-up views for the designed junction modules (dashed region) are shown for (d) and (e). Residues mutated from the original building block sequences are annotated and shown as sticks. Additional residues that vary within the designs are shown as lines. Except for that of DLRR_A, the crystal structures have missing electron density at the C-terminus (10–20 amino acids). PyMOL (http://www.pymol.org/) are used in all structural visualization.
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Related In: Results  -  Collection

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Figure 3: Crystal structures of the idealized building block and junction module designs. The design models (green) for (a) L22, (b) L24, (c) L24→L22, (d) L24→L28 (DLRR_G3), and (e) L24→L32→L24 (DLRR_H2) superimposed on the crystal structures (magenta). Close-up views for the designed junction modules (dashed region) are shown for (d) and (e). Residues mutated from the original building block sequences are annotated and shown as sticks. Additional residues that vary within the designs are shown as lines. Except for that of DLRR_A, the crystal structures have missing electron density at the C-terminus (10–20 amino acids). PyMOL (http://www.pymol.org/) are used in all structural visualization.
Mentions: We solved the crystal structures of DLRR_A (L226) and DLRR_B (L247) (Table 1) and found that they closely match the design models (DLRR_A at Cα root mean square deviation (RMSD) 1.4 Å; DLRR_B at Cα RMSD 1.7 Å, Fig. 3a-b). The crystal structures contain water-mediated networks localized to the convex side of the repeats; it may be possible to incorporate these in future design calculations (Supplementary Fig. 1a). Each of the idealized building block repeats has the expected overall curvature: repeats of the L22 and L24 building blocks generate solenoid-like structures, whereas repeats of the {L28→L29} building block are almost circular and have a more curved concave surface. Parametric descriptions of the global shapes generated by each building block repeat are provided in Supplementary Figure 1b and Supplementary Table 1.

Bottom Line: Second, a set of junction modules that connect the different building blocks are designed.Finally, new proteins with custom-designed shapes are generated by appropriately combining building-block and junction modules.Crystal structures of the designs illustrate the power of the approach in controlling repeat-protein curvature.

View Article: PubMed Central - PubMed

Affiliation: 1] Department of Biochemistry, University of Washington, Seattle, Washington, USA. [2] Institute for Protein Design, University of Washington, Seattle, Washington, USA.

ABSTRACT
Shape complementarity is an important component of molecular recognition, and the ability to precisely adjust the shape of a binding scaffold to match a target of interest would greatly facilitate the creation of high-affinity protein reagents and therapeutics. Here we describe a general approach to control the shape of the binding surface on repeat-protein scaffolds and apply it to leucine-rich-repeat proteins. First, self-compatible building-block modules are designed that, when polymerized, generate surfaces with unique but constant curvatures. Second, a set of junction modules that connect the different building blocks are designed. Finally, new proteins with custom-designed shapes are generated by appropriately combining building-block and junction modules. Crystal structures of the designs illustrate the power of the approach in controlling repeat-protein curvature.

Show MeSH