Computationally Designed Armadillo Repeat Proteins for Modular Peptide Recognition

Identification of a Polypeptide Inhibitor of O-GlcNAc Transferase with Picomolar Affinity

O-GlcNAc transferase (OGT) is an essential mammalian enzyme that binds thousands of different proteins, including substrates that it glycosylates and nonsubstrate interactors that regulate its biology. OGT also has one proteolytic substrate, the transcriptional coregulator host cell factor 1 (HCF-1), which it cleaves in a process initiated by glutamate side chain glycosylation at a series of central repeats. Although HCF-1 is OGT's most prominent binding partner, its affinity for the enzyme has not been quantified. Here, we report a time-resolved Förster resonance energy transfer assay to measure ligand binding to OGT and show that an HCF-1-derived polypeptide (HCF3R) binds with picomolar affinity to the enzyme (KD ≤ 85 pM). This high affinity is driven in large part by conserved asparagines in OGT's tetratricopeptide repeat domain, which form bidentate contacts to the HCF-1 peptide backbone; replacing any one of these asparagines with alanine reduces binding by more than 5 orders of magnitude. Because the HCF-1 polypeptide binds so tightly to OGT, we tested its ability to inhibit enzymatic function. We found that HCF3R potently inhibits OGT both in vitro and in cells and used this finding to develop a genetically encoded, inducible OGT inhibitor that can be degraded with a small molecule, allowing for reversible and tunable inhibition of OGT.

Unexpected dynamics in femtomolar complexes of binding proteins with peptides

Ultra-tight binding is usually observed for proteins associating with rigidified molecules. Previously, we demonstrated that femtomolar binders derived from the Armadillo repeat proteins (ArmRPs) can be designed to interact very tightly with fully flexible peptides. Here we show for ArmRPs with four and seven sequence-identical internal repeats that the peptide-ArmRP complexes display conformational dynamics. These dynamics stem from transient breakages of individual protein-residue contacts that are unrelated to overall unbinding. The labile contacts involve electrostatic interactions. We speculate that these dynamics allow attaining very high binding affinities, since they reduce entropic losses. Importantly, only NMR techniques can pick up these local events by directly detecting conformational exchange processes without complications from changes in solvent entropy. Furthermore, we demonstrate that the interaction surface of the repeat protein regularizes upon peptide binding to become more compatible with the peptide geometry. These results provide novel design principles for ultra-tight binders.

De novo design of modular peptide-binding proteins by superhelical matching

General approaches for designing sequence-specific peptide-binding proteins would have wide utility in proteomics and synthetic biology. However, designing peptide-binding proteins is challenging, as most peptides do not have defined structures in isolation, and hydrogen bonds must be made to the buried polar groups in the peptide backbone1-3. Here, inspired by natural and re-engineered protein-peptide systems4-11, we set out to design proteins made out of repeating units that bind peptides with repeating sequences, with a one-to-one correspondence between the repeat units of the protein and those of the peptide. We use geometric hashing to identify protein backbones and peptide-docking arrangements that are compatible with bidentate hydrogen bonds between the side chains of the protein and the peptide backbone12. The remainder of the protein sequence is then optimized for folding and peptide binding. We design repeat proteins to bind to six different tripeptide-repeat sequences in polyproline II conformations. The proteins are hyperstable and bind to four to six tandem repeats of their tripeptide targets with nanomolar to picomolar affinities in vitro and in living cells. Crystal structures reveal repeating interactions between protein and peptide interactions as designed, including ladders of hydrogen bonds from protein side chains to peptide backbones. By redesigning the binding interfaces of individual repeat units, specificity can be achieved for non-repeating peptide sequences and for disordered regions of native proteins.

De novo design of a polycarbonate hydrolase

Enzymatic degradation of plastics is currently limited to the use of engineered natural enzymes. As of yet, all engineering approaches applied to plastic degrading enzymes retain the natural $\alpha /\beta $-fold. While mutations can be used to increase thermostability, an inherent maximum likely exists for the $\alpha /\beta $-fold. It is thus of interest to introduce catalytic activity toward plastics in a different protein fold to escape the sequence space of plastic degrading enzymes. Here, a method for designing highly thermostable enzymes that can degrade plastics is described. With the help of Rosetta an active site catalysing the hydrolysis of polycarbonate is introduced into a set of thermostable scaffolds. Through computational evaluation, a potential PCase was selected and produced recombinantly in Escherichia coli. Thermal analysis suggests that the design has a melting temperature of >95$^{\circ }$C. Activity toward polycarbonate was confirmed using atomic force spectroscopy (AFM), proving the successful design of a PCase.

What Have We Learned from Design of Function in Large Proteins?

The overarching goal of computational protein design is to gain complete control over protein structure and function. The majority of sophisticated binders and enzymes, however, are large and exhibit diverse and complex folds that defy atomistic design calculations. Encouragingly, recent strategies that combine evolutionary constraints from natural homologs with atomistic calculations have significantly improved design accuracy. In these approaches, evolutionary constraints mitigate the risk from misfolding and aggregation, focusing atomistic design calculations on a small but highly enriched sequence subspace. Such methods have dramatically optimized diverse proteins, including vaccine immunogens, enzymes for sustainable chemistry, and proteins with therapeutic potential. The new generation of deep learning-based ab initio structure predictors can be combined with these methods to extend the scope of protein design, in principle, to any natural protein of known sequence. We envision that protein engineering will come to rely on completely computational methods to efficiently discover and optimize biomolecular activities.

Modular peptide binders - development of a predictive technology as alternative for reagent antibodies

Current biomedical research and diagnostics critically depend on detection agents for specific recognition and quantification of protein molecules. Monoclonal antibodies have been used for this purpose over decades and facilitated numerous biological and biomedical investigations. Recently, however, it has become apparent that many commercial reagent antibodies lack specificity or do not recognize their target at all. Thus, synthetic alternatives are needed whose complex designs are facilitated by multidisciplinary approaches incorporating experimental protein engineering with computational modeling. Here, we review the status of such an engineering endeavor based on the modular armadillo repeat protein scaffold and discuss challenges in its implementation.

Improved RAD51 binders through motif shuffling based on the modularity of BRC repeats

Exchanges of protein sequence modules support leaps in function unavailable through point mutations during evolution. Here we study the role of the two RAD51-interacting modules within the eight binding BRC repeats of BRCA2. We created 64 chimeric repeats by shuffling these modules and measured their binding to RAD51. We found that certain shuffled module combinations were stronger binders than any of the module combinations in the natural repeats. Surprisingly, the contribution from the two modules was poorly correlated with affinities of natural repeats, with a weak BRC8 repeat containing the most effective N-terminal module. The binding of the strongest chimera, BRC8-2, to RAD51 was improved by -2.4 kCal/mol compared to the strongest natural repeat, BRC4. A crystal structure of RAD51:BRC8-2 complex shows an improved interface fit and an extended β-hairpin in this repeat. BRC8-2 was shown to function in human cells, preventing the formation of nuclear RAD51 foci after ionizing radiation.

An automated iterative approach for protein structure refinement using pseudocontact shifts

NMR structure calculation using NOE-derived distance restraints requires a considerable number of assignments of both backbone and sidechains resonances, often difficult or impossible to get for large or complex proteins. Pseudocontact shifts (PCSs) also play a well-established role in NMR protein structure calculation, usually to augment existing structural, mostly NOE-derived, information. Existing refinement protocols using PCSs usually either require a sizeable number of sidechain assignments or are complemented by other experimental restraints. Here, we present an automated iterative procedure to perform backbone protein structure refinements requiring only a limited amount of backbone amide PCSs. Already known structural features from a starting homology model, in this case modules of repeat proteins, are framed into a scaffold that is subsequently refined by experimental PCSs. The method produces reliable indicators that can be monitored to judge about the performance. We applied it to a system in which sidechain assignments are hardly possible, designed Armadillo repeat proteins (dArmRPs), and we calculated the solution NMR structure of YM4A, a dArmRP containing four sequence-identical internal modules, obtaining high convergence to a single structure. We suggest that this approach is particularly useful when approximate folds are known from other techniques, such as X-ray crystallography, while avoiding inherent artefacts due to, for instance, crystal packing.

Protlego: A Python package for the analysis and design of chimeric proteins

Motivation

Duplication and recombination of protein fragments have led to the highly diverse protein space that we observe today. By mimicking this natural process, the design of protein chimeras via fragment recombination has proven experimentally successful and has opened a new era for the design of customizable proteins. The in silico building of structural models for these chimeric proteins, however, remains a manual task that requires a considerable degree of expertise and is not amenable for high-throughput studies. Energetic and structural analysis of the designed proteins often require the use of several tools, each with their unique technical difficulties and available in different programming languages or web servers.

Results

We implemented a Python package that enables automated, high-throughput design of chimeras and their structural analysis. First, it fetches evolutionarily conserved fragments from a built-in database (also available at fuzzle.uni-bayreuth.de). These relationships can then be represented via networks or further selected for chimera construction via recombination. Designed chimeras or natural proteins are then scored and minimized with the Charmm and Amber forcefields and their diverse structural features can be analyzed at ease. Here, we showcase Protlego’s pipeline by exploring the relationships between the P-loop and Rossmann superfolds, building and characterizing their offspring chimeras. We believe that Protlego provides a powerful new tool for the protein design community.

Availability and implementation

Protlego runs on the Linux platform and is freely available at (https://hoecker-lab.github.io/protlego/) with tutorials and documentation.

Supplementary information

Supplementary data are available at Bioinformatics online.

Structure-Guided Design of a Peptide Lock for Modular Peptide Binders

Peptides play an important role in intermolecular interactions and are frequent analytes in diagnostic assays, also as unstructured, linear epitopes in whole proteins. Yet, due to the many different sequence possibilities even for short peptides, classical selection of binding proteins from a library, one at a time, is not scalable to proteomes. However, moving away from selection to a rational assembly of preselected modules binding to predefined linear epitopes would split the problem into smaller parts. These modules could then be reassembled in any desired order to bind to, in principle, arbitrary sequences, thereby circumventing any new rounds of selection. Designed Armadillo repeat proteins (dArmRPs) are modular, and they do bind elongated peptides in a modular way. Their consensus sequence carries pockets that prefer arginine and lysine. In our quest to select pockets for all amino acid side chains, we had discovered that repetitive sequences can lead to register shifts and peptide flipping during selections from libraries, hindering the selection of new binding specificities. To solve this problem, we now created an orthogonal binding specificity by a combination of grafting from β-catenin, computational design and mutual optimization of the pocket and the bound peptide. We have confirmed the design and the desired interactions by X-ray structure determination. Furthermore, we could confirm the absence of sliding in solution by a single-molecule Förster resonance energy transfer. The new pocket could be moved from the N-terminus of the protein to the middle, retaining its properties, further underlining the modularity of the system.

Peptide binding affinity redistributes preassembled repeat protein fragments

Designed armadillo repeat proteins (dArmRPs) are modular peptide binders composed of N- and C-terminal capping repeats Y and A and a variable number of internal modules M that each specifically recognize two amino acids of the target peptide. Complementary fragments of dArmRPs obtained by splitting the protein between helices H1 and H2 of an internal module show conditional and specific assembly only in the presence of a target peptide (Michel, E., Plückthun, A., and Zerbe, O. (2018). Peptide-guided assembly of repeat protein fragments. Angew. Chem. Int. Ed. 57, 4576–4579). Here, we investigate dArmRP fragments that already spontaneously assemble with high affinity, e.g. those obtained from splits between entire modules or between helices H2 and H3. We find that the interaction of the peptide with the assembled fragments induces distal conformational rearrangements that suggest an induced fit on a global protein level. A population analysis of an equimolar mixture of an N-terminal and three C-terminal fragments with various affinities for the target peptide revealed predominant assembly of the weakest peptide binder. However, adding a target peptide to this mixture altered the population of the protein complexes such that the combination with the highest affinity for the peptide increased and becomes predominant when adding excess of peptide, highlighting the feasibility of peptide-induced enrichment of best binders from inter-modular fragment mixtures.

Hierarchical design of artificial proteins and complexes toward synthetic structural biology

In multiscale structural biology, synthetic approaches are important to demonstrate biophysical principles and mechanisms underlying the structure, function, and action of bio-nanomachines. A central goal of "synthetic structural biology" is the design and construction of artificial proteins and protein complexes as desired. In this paper, I review recent remarkable progress of an array of approaches for hierarchical design of artificial proteins and complexes that signpost the path forward toward synthetic structural biology as an emerging interdisciplinary field. Topics covered include combinatorial and protein-engineering approaches for directed evolution of artificial binding proteins and membrane proteins, binary code strategy for structural and functional de novo proteins, protein nanobuilding block strategy for constructing nano-architectures, protein-metal-organic frameworks for 3D protein complex crystals, and rational and computational approaches for design/creation of artificial proteins and complexes, novel protein folds, ideal/optimized protein structures, novel binding proteins for targeted therapeutics, and self-assembling nanomaterials. Protein designers and engineers look toward a bright future in synthetic structural biology for the next generation of biophysics and biotechnology.