Exploring the repeat protein universe through computational protein design

Brunette, TJ; Parmeggiani, Fabio; Huang, Po-Ssu; Bhabha, Gira; Ekiert, D.C.; Tsutakawa, Susan E.; Hura, Greg L.; Tainer, John A.; Baker, David

doi:10.1038/nature16162

Cited by 243 publications

(267 citation statements)

References 49 publications

(45 reference statements)

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“…Internal repeats were constrained to adopt the same primary sequence, side-chain, and backbone conformations by using symmetric sequence design and conformation sampling during all modeling moves. Symmetric structure prediction [18] and design has been used extensively in Rosetta [11] yielding atomic-accuracy predictions for large homomeric oligomers, designed cage-like assemblies or repeat proteins [12][13][14][15][19][20][21]. Our calculations were restricted to three internal repeats and two capping repeats.…”

Section: In Silico Designmentioning

confidence: 99%

“…Using this template, the relative orientations between subsequent repeats were extracted and imposed as symmetric modeling constraints during backbone and side chain sampling simulations using the Rosetta software suite [11]. Similar design protocols have been used for the computational design of repeat proteins, first with sequence and structural information obtained from natural repeat protein families [12,13] and then for de novo designed repeat proteins with open [14] and closed [15] architectures. Using such approaches, typically >50% of the designed constructs can be expressed as soluble, folded, monomeric proteins and determined structures agree well with the design models (typical RMSD of Cα atoms 0.5 -2.5 Å).…”

Section: Introductionmentioning

confidence: 99%

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Computationally Designed Armadillo Repeat Proteins for Modular Peptide Recognition

Reichen

Hansen

Forzani

et al. 2016

Journal of Molecular Biology

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Armadillo repeat proteins (ArmRP) recognize their target peptide in extended conformation and bind, in a first approximation, two residues per repeat. They may thus form the basis for building a modular system, in which each repeat is complementary to a piece of the target peptide. Accordingly, preselected repeats could be assembled into specific binding proteins on demand and thereby avoid the traditional generation of every new binding molecule by an independent selection from a library.Stacked armadillo repeats, each consisting of 42 amino acids arranged in three α-helices, build an elongated superhelical structure. Here, we analyzed curvature variations in natural ArmRPs, and identified a repeat pair from yeast importin-α as having the optimal curvature geometry to be complementary to a peptide over its whole length. We employed a symmetric in silico design to obtain a uniform sequence for a stackable repeat while maintaining the desired curvature geometry.Computationally designed armadillo repeat proteins (dArmRPs) had to be stabilized by mutations to remove regions of higher flexibility, which were identified by molecular dynamics (MD) simulations in explicit solvent. Using an N-capping repeat from the consensus-design approach, two different crystal structures of dArmRP were determined. Although the experimental structures of dArmRP deviated from the designed curvature, the insertion of the most conserved binding pockets of natural ArmRPs onto the surface of dArmRPs resulted in binders against the expected peptide with low nanomolar affinities, similar to the binders from the consensus-design series.

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Section: In Silico Designmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Computationally Designed Armadillo Repeat Proteins for Modular Peptide Recognition

Reichen

Hansen

Forzani

et al. 2016

Journal of Molecular Biology

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“…[1][2][3] The formation of coiled coils from the assembly of α-helices was first predicted over 60 years ago for naturally occurring proteins and peptides. 4 De novo peptides that fold into coiled coils have proven to be a robust scaffold with interesting properties.…”

Section: Introductionmentioning

confidence: 99%

X-ray Crystallographic Structure and Solution Behavior of an Antiparallel Coiled-Coil Hexamer Formed by de Novo Peptides

Spencer

Hochbaum

2016

Biochemistry

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The self-assembly of peptides and proteins into higher-ordered structures is encoded in the amino acid sequence of each peptide or protein. Understanding the relationship among the amino acid sequence, the assembly dynamics, and the structure of well-defined peptide oligomers expands the synthetic toolbox for these structures. Here, we present the X-ray crystallographic structure and solution behavior of de novo peptides that form antiparallel coiled-coil hexamers (ACC-Hex) by an interaction motif neither found in nature nor predicted by existing peptide design software. The 1.70 Å X-ray crystallographic structure of peptide 1a shows six α-helices associating in an antiparallel arrangement around a central axis comprising hydrophobic and aromatic residues. Size-exclusion chromatography studies suggest that peptides 1 form stable oligomers in solution, and circular dichroism experiments show that peptides 1 are stable to relatively high temperatures. Small-angle X-ray scattering studies of the solution behavior of peptide 1a indicate an equilibrium of dimers, hexamers, and larger aggregates in solution. The structures presented here represent a new motif of biomolecular self-assembly not previously observed for de novo peptides and suggest supramolecular design principles for material scaffolds based on coiled-coil motifs containing aromatic residues.

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“…In particular, large proteins are involved in countless cellular processes, and the capacity to design proteins of any size and structure, and subsequently any function, will be crucial to synthetic biology. A major advance has been accomplished by Brunette et al (2015) and Koga et al (2012), who have determined some rules to design proteins and then successfully applied them to design large artificial proteins.…”

Section: Discussionmentioning

confidence: 99%

The unexpected structure of the designed protein Octarellin V.1 forms a challenge for protein structure prediction tools

Figueroa¹,

Sleutel²,

Vandevenne³

et al. 2016

Journal of Structural Biology

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a b s t r a c tDespite impressive successes in protein design, designing a well-folded protein of more 100 amino acids de novo remains a formidable challenge. Exploiting the promising biophysical features of the artificial protein Octarellin V, we improved this protein by directed evolution, thus creating a more stable and soluble protein: Octarellin V.1. Next, we obtained crystals of Octarellin V.1 in complex with crystallization chaperons and determined the tertiary structure. The experimental structure of Octarellin V.1 differs from its in silico design: the (aba) sandwich architecture bears some resemblance to a Rossman-like fold instead of the intended TIM-barrel fold. This surprising result gave us a unique and attractive opportunity to test the state of the art in protein structure prediction, using this artificial protein free of any natural selection. We tested 13 automated webservers for protein structure prediction and found none of them to predict the actual structure. More than 50% of them predicted a TIM-barrel fold, i.e. the structure we set out to design more than 10 years ago. In addition, local software runs that are human operated can sample a structure similar to the experimental one but fail in selecting it, suggesting that the scoring and ranking functions should be improved. We propose that artificial proteins could be used as tools to test the accuracy of protein structure prediction algorithms, because their lack of evolutionary pressure and unique sequences features.

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Exploring the repeat protein universe through computational protein design

Cited by 243 publications

References 49 publications

Computationally Designed Armadillo Repeat Proteins for Modular Peptide Recognition

Computationally Designed Armadillo Repeat Proteins for Modular Peptide Recognition

X-ray Crystallographic Structure and Solution Behavior of an Antiparallel Coiled-Coil Hexamer Formed by de Novo Peptides

The unexpected structure of the designed protein Octarellin V.1 forms a challenge for protein structure prediction tools

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