An accurate binding interaction model in de novo computational protein design of interactions: If you build it,they will bind

Computational protein design efforts aim to create novel proteins and functions in an automated manner and, in the process, these efforts shed light on the factors shaping natural proteins. The focus of these efforts has progressed from the interior of proteins to their surface and the design of functions, such as binding or catalysis. Here we examine progress in the development of robust methods for the computational design of non-natural interactions between proteins and molecular targets such as other proteins or small molecules. This problem is referred to as the de novo computational design of interactions. Recent successful efforts in de novo enzyme design and the de novo design of protein–protein interactions open a path towards solving this problem. We examine the common themes in these efforts, and review recent studies aimed at understanding the nature of successes and failures in the de novo computational design of interactions. While several approaches culminated in success, the use of a well-defined structural model for a specific binding interaction in particular has emerged as a key strategy for a successful design, and is therefore reviewed with special consideration.     

Principles of nanostructure design with protein building blocks

Currently there is increasing interest in nanostructures and their design. Nanostructure design involves the ability to predictably manipulate the properties of the self-assembly of autonomous units. Autonomous units have preferred conformational states. The units can be synthetic material science-based or derived from functional biological macromolecules. Autonomous biological building blocks with available structures provide an extremely rich and useful resource for design. For proteins, the structural databases contain large libraries of protein molecules and their building blocks with a range of shapes, surfaces, and chemical properties. The introduction of engineered synthetic residues or short peptides into these can expand the available chemical space and enhance the desired properties. Here we focus on the principles of nanostructure design with protein building blocks.     

Self-assembly of repeat proteins: Concepts and design of new interfaces

In nature, assembled protein structures offer the most complex functional structures. The understanding of the mechanisms ruling protein–protein interactions opens the door to manipulate protein assemblies in a rational way. Proteins are versatile scaffolds with great potential as tools in nanotechnology and biomedicine because of their chemical, structural, and functional versatility. Currently, bottom-up self-assembly based on biomolecular interactions of small and well-defined components, is an attractive approach to biomolecular engineering and biomaterial design. Specifically, repeat proteins are simplified systems for this purpose.In this work, we provide an overview of fundamental concepts of the design of new protein interfaces. We describe an experimental approach to form higher order architectures by a bottom-up assembly of repeated building blocks. For this purpose, we use designed consensus tetratricopeptide repeat proteins (CTPRs). CTPR arrays contain multiple identical repeats that interact through a single inter-repeat interface to form elongated superhelices. Introducing a novel interface along the CTPR superhelix allows two CTPR molecules to assemble into protein nanotubes. We apply three approaches to form protein nanotubes: electrostatic interactions, hydrophobic interactions, and π-π interactions. We isolate and characterize the stability and shape of the formed dimers and analyze the nanotube formation considering the energy of the interaction and the structure in the three different models. These studies provide insights into the design of novel protein interfaces for the control of the assembly into more complex structures, which will open the door to the rational design of nanostructures and ordered materials for many potential applications in nanotechnology.     

In silico protein design by combinatorial assembly of protein building blocks

Utilizing concepts of protein building blocks, we propose a de novo computational algorithm that is similar to combinatorial shuffling experiments. Our goal is to engineer new naturally occurring folds with low homology to existing proteins. A selected protein is first partitioned into its building blocks based on their compactness, degree of isolation from the rest of the structure, and hydrophobicity. Next, the protein building blocks are substituted by fragments taken from other proteins with overall low sequence identity, but with a similar hydrophobic/hydrophilic pattern and a high structural similarity. These criteria ensure that the designed protein has a similar fold, low sequence identity, and a good hydrophobic core compared with its native counterpart. Here, we have selected two proteins for engineering, protein G B1 domain and ubiquitin. The two engineered proteins share approximately 20% and approximately 25% amino acid sequence identities with their native counterparts, respectively. The stabilities of the engineered proteins are tested by explicit water molecular dynamics simulations. The algorithm implements a strategy of designing a protein using relatively stable fragments, with a high population time. Here, we have selected the fragments by searching for local minima along the polypeptide chain using the protein building block model. Such an approach provides a new method for engineering new proteins with similar folds and low homology.     

Designing repeat proteins: modular leucine-rich repeat protein libraries based on the mammalian ribonuclease inhibitor family

We present a novel approach to design repeat proteins of the leucine-rich repeat (LRR) family for the generation of libraries of intracellular binding molecules. From an analysis of naturally occurring LRR proteins, we derived the concept to assemble repeat proteins with randomized surface positions from libraries of consensus repeat modules. As a guiding principle, we used the mammalian ribonuclease inhibitor (RI) family, which comprises cytosolic LRR proteins known for their extraordinary affinities to many RNases. By aligning the amino acid sequences of the internal repeats of human, pig, rat, and mouse RI, we derived a first consensus sequence for the characteristic alternating 28 and 29 amino acid residue A-type and B-type repeats. Structural considerations were used to replace all conserved cysteine residues, to define less conserved positions, and to decide where to introduce randomized amino acid residues. The so devised consensus RI repeat library was generated at the DNA level and assembled by stepwise ligation to give libraries of 2-12 repeats. Terminal capping repeats, known to shield the continuous hydrophobic core of the LRR domain from the surrounding solvent, were adapted from human RI. In this way, designed LRR protein libraries of 4-14 LRRs (equivalent to 130-415 amino acid residues) were obtained. The biophysical analysis of randomly chosen library members showed high levels of soluble expression in the Escherichia coli cytosol, monomeric behavior as characterized by gel-filtration, and alpha-helical CD spectra, confirming the success of our design approach.     

Tertiary motifs as building blocks for the design of protein‐binding peptides

Despite advances in protein engineering, the de novo design of small proteins or peptides that bind to a desired target remains a difficult task. Most computational methods search for binder structures in a library of candidate scaffolds, which can lead to designs with poor target complementarity and low success rates. Instead of choosing from pre‐defined scaffolds, we propose that custom peptide structures can be constructed to complement a target surface. Our method mines tertiary motifs (TERMs) from known structures to identify surface‐complementing fragments or “seeds.” We combine seeds that satisfy geometric overlap criteria to generate peptide backbones and score the backbones to identify the most likely binding structures. We found that TERM‐based seeds can describe known binding structures with high resolution: the vast majority of peptide binders from 486 peptide‐protein complexes can be covered by seeds generated from single‐chain structures. Furthermore, we demonstrate that known peptide structures can be reconstructed with high accuracy from peptide‐covering seeds. As a proof of concept, we used our method to design 100 peptide binders of TRAF6, seven of which were predicted by Rosetta to form higher‐quality interfaces than a native binder. The designed peptides interact with distinct sites on TRAF6, including the native peptide‐binding site. These results demonstrate that known peptide‐binding structures can be constructed from TERMs in single‐chain structures and suggest that TERM information can be applied to efficiently design novel target‐complementing binders.     

GENFOLD: a genetic algorithm for folding protein structures using NMR restraints.

We report the development and validation of the program GENFOLD, a genetic algorithm that calculates protein structures using restraints obtained from NMR, such as distances derived from nuclear Overhauser effects, and dihedral angles derived from coupling constants. The program has been tested on three proteins: the POU domain (a small three-helix DNA-binding protein), bovine pancreatic trypsin inhibitor (BPTI), and the starch-binding domain from Aspergillus niger glucoamylase I, a 108-residue beta-sheet protein. Structures were calculated for each protein using published NMR restraints. In addition, structures were calculated for BPTI using artificial restraints generated from a high-resolution crystal structure. In all cases the fittest calculated structures were close to the target structure, and could be refined to structures indistinguishable from the target structures by means of a low-temperature simulated annealing refinement. The effectiveness of the program is similar to that of distance geometry and simulated annealing methods, and it is capable of using a very wide range of restraints as input. It can thus be readily extended to the calculation of structures of large proteins, for which few NOE restraints may be available.     

A large scale test of computational protein design: folding and stability of nine completely redesigned globular proteins

A previously developed computer program for protein design, RosettaDesign, was used to predict low free energy sequences for nine naturally occurring protein backbones. RosettaDesign had no knowledge of the naturally occurring sequences and on average 65% of the residues in the designed sequences differ from wild-type. Synthetic genes for ten completely redesigned proteins were generated, and the proteins were expressed, purified, and then characterized using circular dichroism, chemical and temperature denaturation and NMR experiments. Although high-resolution structures have not yet been determined, eight of these proteins appear to be folded and their circular dichroism spectra are similar to those of their wild-type counterparts. Six of the proteins have stabilities equal to or up to 7kcal/mol greater than their wild-type counterparts, and four of the proteins have NMR spectra consistent with a well-packed, rigid structure. These encouraging results indicate that the computational protein design methods can, with significant reliability, identify amino acid sequences compatible with a target protein backbone.     

Nanoscale elongating control of the self‐assembled protein filament with the cysteine‐introduced building blocks

Self‐assembly of artificially designed proteins is extremely desirable for nanomaterials. Here we show a novel strategy for the creation of self‐assembling proteins, named “Nanolego.” Nanolego consists of “structural elements” of a structurally stable symmetrical homo‐oligomeric protein and “binding elements,” which are multiple heterointeraction proteins with relatively weak affinity. We have established two key technologies for Nanolego, a stabilization method and a method for terminating the self‐assembly process. The stabilization method is mediated by disulfide bonds between Cysteine‐residues incorporated into the binding elements, and the termination method uses “capping Nanolegos,” in which some of the binding elements in the Nanolego are absent for the self‐assembled ends. With these technologies, we successfully constructed timing‐controlled and size‐regulated filament‐shape complexes via Nanolego self‐assembly. The Nanolego concept and these technologies should pave the way for regulated nanoarchitecture using designed proteins.     

Crystal structures and increased stabilization of the protein G variants with switched folding pathways NuG1 and NuG2

We recently described two protein G variants (NuG1 and NuG2) with redesigned first hairpins that were almost twice as stable, folded 100-fold faster, and had a switched folding mechanism relative to the wild-type protein. To test the structural accuracy of our design algorithm and to provide insights to the dramatic changes in the kinetics and thermodynamics of folding, we have now determined the crystal structures of NuG1 and NuG2 to 1.8 A and 1.85 A, respectively. We find that they adopt hairpin structures that are closer to the computational models than to wild-type protein G; the RMSD of the NuG1 hairpin to the design model and the wild-type structure are 1.7 A and 5.1 A, respectively. The crystallographic B factor in the redesigned first hairpin of NuG1 is systematically higher than the second hairpin, suggesting that the redesigned region is somewhat less rigid. A second round of structure-based design yielded new variants of NuG1 and NuG2, which are further stabilized by 0.5 kcal/mole and 0.9 kcal/mole.     

Crystal structure of a consensus-designed ankyrin repeat protein: implications for stability

Consensus-designed ankyrin repeat (AR) proteins are thermodynamically very stable. The structural analysis of the designed AR protein E3_5 revealed that this stability is due to a regular fold with highly conserved structural motifs and H-bonding networks. However, the designed AR protein E3_19 exhibits a significantly lower stability than E3_5 (9.6 vs. 14.8 kcal/mol), despite 88% sequence identity. To investigate the structural correlations of this stability difference between E3_5 and E3_19, we determined the crystal structure of E3_19 at 1.9 A resolution. E3_19 as well has a regular AR domain fold with the characteristic H-bonding patterns. All structural features of the E3_5 and E3_19 molecules appear to be virtually identical (RMSD(Calpha) approximately 0.7 A). However, clear differences are observed in the surface charge distribution of the two AR proteins. E3_19 features clusters of charged residues and more exposed hydrophobic residues than E3_5. The atomic coordinates of E3_19 have been deposited in the Protein Data Bank. PDB ID: 2BKG.     

Peptidic modulators of protein‐protein interactions: Progress and challenges in computational design

With the decline in productivity of drug‐development efforts, novel approaches to rational drug design are being introduced and developed. Naturally occurring and synthetic peptides are emerging as novel promising compounds that can specifically and efficiently modulate signaling pathways in vitro and in vivo. We describe sequence‐based approaches that use peptides to mimic proteins in order to inhibit the interaction of the mimicked protein with its partners. We then discuss a structure‐based approach, in which protein‐peptide complex structures are used to rationally design and optimize peptidic inhibitors. We survey flexible peptide docking techniques and discuss current challenges and future directions in the rational design of peptidic inhibitors. © 2009 Wiley Periodicals, Inc. Biopolymers 91: 505–513, 2009. This article was originally published online as an accepted preprint. The “Published Online”date corresponds to the preprint version. You can request a copy of the preprint by emailing the Biopolymers editorial office at biopolymers@wiley.com     

X‐ray crystallographic validation of structure predictions used in computational design for protein stabilization

Protein engineering aimed at enhancing enzyme stability is increasingly supported by computational methods for calculation of mutant folding energies and for the design of disulfide bonds. To examine the accuracy of mutant structure predictions underlying these computational methods, crystal structures of thermostable limonene epoxide hydrolase variants obtained by computational library design were determined. Four different predicted effects indeed contributed to the obtained stabilization: (i) enhanced interactions between a flexible loop close to the N‐terminus and the rest of the protein; (ii) improved interactions at the dimer interface; (iii) removal of unsatisfied hydrogen bonding groups; and (iv) introduction of additional positively charged groups at the surface. The structures of an eightfold and an elevenfold mutant showed that most mutations introduced the intended stabilizing interactions, and side‐chain conformations were correctly predicted for 72–88% of the point mutations. However, mutations that introduced a disulfide bond in a flexible region had a larger influence on the backbone conformation than predicted. The enzyme active sites were unaltered, in agreement with the observed preservation of catalytic activities. The structures also revealed how a c‐Myc tag, which was introduced for facile detection and purification, can reduce access to the active site and thereby lower the catalytic activity. Finally, sequence analysis showed that comprehensive mutant energy calculations discovered stabilizing mutations that are not proposed by the consensus or B‐FIT methods. Proteins 2015; 83:940–951. © 2015 Wiley Periodicals, Inc.     

Structural context of exons in protein domains: implications for protein modelling and design

Intron boundaries were extracted from genomic data and mapped onto single-domain human and murine protein structures taken from the Protein Data Bank. A first analysis of this set of proteins shows that intron boundaries prefer to be in non-regular secondary structure elements, while avoiding alpha-helices and beta-strands. This fact alone suggests an evolutionary model in which introns are constrained by protein structure, particularly by tertiary structure contacts. In addition, in silico recombination experiments of a subset of these proteins together with their homologues, including those in different species, show that introns have a tendency to occur away from artificial crossover hot spots. Altogether, these findings support a model in which genes can preferentially harbour introns in less constrained regions of the protein fold they code for. In the light of these findings, we discuss some implications for protein modelling and design.     

Benchmarking a computational design method for the incorporation of metal ion‐binding sites at symmetric protein interfaces

The design of novel metal‐ion binding sites along symmetric axes in protein oligomers could provide new avenues for metalloenzyme design, construction of protein‐based nanomaterials and novel ion transport systems. Here, we describe a computational design method, symmetric protein recursive ion‐cofactor sampling (SyPRIS), for locating constellations of backbone positions within oligomeric protein structures that are capable of supporting desired symmetrically coordinated metal ion(s) chelated by sidechains (chelant model). Using SyPRIS on a curated benchmark set of protein structures with symmetric metal binding sites, we found high recovery of native metal coordinating rotamers: in 65 of the 67 (97.0%) cases, native rotamers featured in the best scoring model while in the remaining cases native rotamers were found within the top three scoring models. In a second test, chelant models were crossmatched against protein structures with identical cyclic symmetry. In addition to recovering all native placements, 10.4% (8939/86013) of the non‐native placements, had acceptable geometric compatibility scores. Discrimination between native and non‐native metal site placements was further enhanced upon constrained energy minimization using the Rosetta energy function. Upon sequence design of the surrounding first‐shell residues, we found further stabilization of native placements and a small but significant (1.7%) number of non‐native placement‐based sites with favorable Rosetta energies, indicating their designability in existing protein interfaces. The generality of the SyPRIS approach allows design of novel symmetric metal sites including with non‐natural amino acid sidechains, and should enable the predictive incorporation of a variety of metal‐containing cofactors at symmetric protein interfaces.     

Computational protein design with electrostatic focusing: Experimental characterization of a conditionally folded helical domain with a reduced amino acid alphabet

Automated methodologies to design synthetic proteins from first principles use energy computations to estimate the ability of the sequences to adopt a targeted structure. This approach is still far from systematically producing native-like sequences, due, most likely, to inaccuracies when modeling the interactions between the protein and its aqueous environment. This is particularly challenging when engineering small protein domains (with less polar pair interactions than with the solvent). We have re-designed a three-helix bundle, domain B, using a fixed backbone and a four amino acid alphabet. We have enlarged the rotamer library with conformers that increase the weight of electrostatic interactions within the design process without altering the energy function used to compute the folding free energy. Our synthetic sequences show less than 15% similarity to any Swissprot sequence. We have characterized our sequences in different solvents using circular dichroism and nuclear magnetic resonance. The targeted structure achieved is dependent on the solvent used. This method can be readily extended to larger domains. Our method will be useful for the engineering of proteins that become active only in a given solvent and for designing proteins in the context of hydrophobic solvents, an important fraction of the situations in the cell.     

Towards novel biocatalysts via protein design: the case of lipases

Abstract: During the past 3 years, the tertiary structures of several lipases have been solved by X-ray analysis. The structures revealed unique features such as hydrophobic 'patches' on the surface, presumably involved in lipid supersubstrate binding, and a lid structure which covers the active site in the absence of substrate. Only very recently the first X-ray structure of a bacterial lipase has been solved, and further structural features different from lipases of eukaryotic origin became apparent. Many lipase genes have been cloned and sequenced recently, and expression systems for the preparation of recombinant enzymes in good yields are available. As an example, the lipase from Rhizopus oryzae has been successfully expressed by us in Escherichia coli , and the resulting inclusion bodies were renatured in high yields. Consequently, the mechanism of action of lipases is now being studied via site-directed mutagenesis, and the rational design of lipases for the selective transformation of substrates is presently addressed in several laboratories.     

Structural test of the parameterized-backbone method for protein design

Designing new protein folds requires a method for simultaneously optimizing the conformation of the backbone and the side-chains. One approach to this problem is the use of a parameterized backbone, which allows the systematic exploration of families of structures. We report the crystal structure of RH3, a right-handed, three-helix coiled coil that was designed using a parameterized backbone and detailed modeling of core packing. This crystal structure was determined using another rationally designed feature, a metal-binding site that permitted experimental phasing of the X-ray data. RH3 adopted the intended fold, which has not been observed previously in biological proteins. Unanticipated structural asymmetry in the trimer was a principal source of variation within the RH3 structure. The sequence of RH3 differs from that of a previously characterized right-handed tetramer, RH4, at only one position in each 11 amino acid sequence repeat. This close similarity indicates that the design method is sensitive to the core packing interactions that specify the protein structure. Comparison of the structures of RH3 and RH4 indicates that both steric overlap and cavity formation provide strong driving forces for oligomer specificity.     

AbDesign: An algorithm for combinatorial backbone design guided by natural conformations and sequences

Computational design of protein function has made substantial progress, generating new enzymes, binders, inhibitors, and nanomaterials not previously seen in nature. However, the ability to design new protein backbones for function—essential to exert control over all polypeptide degrees of freedom—remains a critical challenge. Most previous attempts to design new backbones computed the mainchain from scratch. Here, instead, we describe a combinatorial backbone and sequence optimization algorithm called AbDesign, which leverages the large number of sequences and experimentally determined molecular structures of antibodies to construct new antibody models, dock them against target surfaces and optimize their sequence and backbone conformation for high stability and binding affinity. We used the algorithm to produce antibody designs that target the same molecular surfaces as nine natural, high‐affinity antibodies; in five cases interface sequence identity is above 30%, and in four of those the backbone conformation at the core of the antibody binding surface is within 1 Å root‐mean square deviation from the natural antibodies. Designs recapitulate polar interaction networks observed in natural complexes, and amino acid sidechain rigidity at the designed binding surface, which is likely important for affinity and specificity, is high compared to previous design studies. In designed anti‐lysozyme antibodies, complementarity‐determining regions (CDRs) at the periphery of the interface, such as L1 and H2, show greater backbone conformation diversity than the CDRs at the core of the interface, and increase the binding surface area compared to the natural antibody, potentially enhancing affinity and specificity. Proteins 2015; 83:1385–1406. © 2015 Wiley Periodicals, Inc.