Rational design of TNFα binding proteins based on the de novo designed protein DS119

De novo protein design offers templates for engineering tailor‐made protein functions and orthogonal protein interaction networks for synthetic biology research. Various computational methods have been developed to introduce functional sites in known protein structures. De novo designed protein scaffolds provide further opportunities for functional protein design. Here we demonstrate the rational design of novel tumor necrosis factor alpha (TNFα) binding proteins using a home‐made grafting program AutoMatch. We grafted three key residues from a virus 2L protein to a de novo designed small protein, DS119, with consideration of backbone flexibility. The designed proteins bind to TNFα with micromolar affinities. We further optimized the interface residues with RosettaDesign and significantly improved the binding capacity of one protein Tbab1‐4. These designed proteins inhibit the activity of TNFα in cellular luciferase assays. Our work illustrates the potential application of the de novo designed protein DS119 in protein engineering, biomedical research, and protein sequence‐structure‐function studies.     

Selection and structural analysis of de novo proteins from an α3β3 genetic library

The construction of novel functional proteins has been a key area of protein engineering. However, there are few reports of functional proteins constructed from artificial scaffolds. Here, we have constructed a genetic library encoding α3β3 de novo proteins to generate novel scaffolds in smaller size using a binary combination of simplified hydrophobic and hydrophilic amino acid sets. To screen for folded de novo proteins, we used a GFP‐based screening system and successfully obtained the proteins from the colonies emitting the very bright fluorescence as a similar intensity of GFP. Proteins isolated from the very bright colonies (vTAJ) and bright colonies (wTAJ) were analyzed by circular dichroism (CD), 8‐anilino‐1‐naphthalenesulfonate (ANS) binding assay, and analytical size‐exclusion chromatography (SEC). CD studies revealed that vTAJ and wTAJ proteins had both α‐helix and β‐sheet structures with thermal stabilities. Moreover, the selected proteins demonstrated a variety of association states existing as monomer, dimer, and oligomer formation. The SEC and ANS binding assays revealed that vTAJ proteins tend to be a characteristic of the folded protein, but not in a molten‐globule state. A vTAJ protein, vTAJ13, which has a packed globular structure and exists as a monomer, was further analyzed by nuclear magnetic resonance. NOE connectivities between backbone signals of vTAJ13 suggested that the protein contains three α‐helices and three β‐strands as intended by its design. Thus, it would appear that artificially generated α3β3 de novo proteins isolated from very bright colonies using the GFP fusion system exhibit excellent properties similar to folded proteins and would be available as artificial scaffolds to generate functional proteins with catalytic and ligand binding properties.     

Computational modeling of membrane proteins

The determination of membrane protein (MP) structures has always trailed that of soluble proteins due to difficulties in their overexpression, reconstitution into membrane mimetics, and subsequent structure determination. The percentage of MP structures in the protein databank (PDB) has been at a constant 1–2% for the last decade. In contrast, over half of all drugs target MPs, only highlighting how little we understand about drug‐specific effects in the human body. To reduce this gap, researchers have attempted to predict structural features of MPs even before the first structure was experimentally elucidated. In this review, we present current computational methods to predict MP structure, starting with secondary structure prediction, prediction of trans‐membrane spans, and topology. Even though these methods generate reliable predictions, challenges such as predicting kinks or precise beginnings and ends of secondary structure elements are still waiting to be addressed. We describe recent developments in the prediction of 3D structures of both α‐helical MPs as well as β‐barrels using comparative modeling techniques, de novo methods, and molecular dynamics (MD) simulations. The increase of MP structures has (1) facilitated comparative modeling due to availability of more and better templates, and (2) improved the statistics for knowledge‐based scoring functions. Moreover, de novo methods have benefited from the use of correlated mutations as restraints. Finally, we outline current advances that will likely shape the field in the forthcoming decade. Proteins 2015; 83:1–24. © 2014 Wiley Periodicals, Inc.     

Anchored design of protein-protein interfaces

Background

Few existing protein-protein interface design methods allow for extensive backbone rearrangements during the design process. There is also a dichotomy between redesign methods, which take advantage of the native interface, and de novo methods, which produce novel binders.

Methodology

Here, we propose a new method for designing novel protein reagents that combines advantages of redesign and de novo methods and allows for extensive backbone motion. This method requires a bound structure of a target and one of its natural binding partners. A key interaction in this interface, the anchor, is computationally grafted out of the partner and into a surface loop on the design scaffold. The design scaffold''s surface is then redesigned with backbone flexibility to create a new binding partner for the target. Careful choice of a scaffold will bring experimentally desirable characteristics into the new complex. The use of an anchor both expedites the design process and ensures that binding proceeds against a known location on the target. The use of surface loops on the scaffold allows for flexible-backbone redesign to properly search conformational space.

Conclusions and Significance

This protocol was implemented within the Rosetta3 software suite. To demonstrate and evaluate this protocol, we have developed a benchmarking set of structures from the PDB with loop-mediated interfaces. This protocol can recover the correct loop-mediated interface in 15 out of 16 tested structures, using only a single residue as an anchor.     

Computational protein design using flexible backbone remodeling and resurfacing: case studies in structure-based antigen design

Computational protein design has promise for vaccine design and other applications. We previously transplanted the HIV 4E10 epitope onto non-HIV protein scaffolds for structural stabilization and immune presentation. Here, we developed two methods to optimize the structure of an antigen, flexible backbone remodeling and resurfacing, and we applied these methods to a 4E10 scaffold. In flexible-backbone remodeling, an existing backbone segment is replaced by a de novo designed segment of prespecified length and secondary structure. With remodeling, we replaced a potentially immunodominant domain on the scaffold with a helix-loop segment that made intimate contact to the protein core. All three domain trim designs tested experimentally had improved thermal stability and similar binding affinity for the 4E10 antibody compared to the parent scaffold. A crystal structure of one design had a 0.8 Å backbone RMSD to the computational model in the rebuilt region. Comparison of parent and trimmed scaffold reactivity to anti-parent sera confirmed the deletion of an immunodominant domain. In resurfacing, the surface of an antigen outside a target epitope is redesigned to obtain variants that maintain only the target epitope. Resurfaced variants of two scaffolds were designed in which 50 positions amounting to 40% of the protein sequences were mutated. Surface-patch analyses indicated that most potential antibody footprints outside the 4E10 epitope were altered. The resurfaced variants maintained thermal stability and binding affinity. These results indicate that flexible-backbone remodeling and resurfacing are useful tools for antigen optimization and protein engineering generally.     

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.     

OptGraft: A computational procedure for transferring a binding site onto an existing protein scaffold

One of the many challenging tasks of protein design is the introduction of a completely new function into an existing protein scaffold. In this study, we introduce a new computational procedure OptGraft for placing a novel binding pocket onto a protein structure so as its geometry is minimally perturbed. This is accomplished by introducing a two‐level procedure where we first identify where are the most appropriate locations to graft the new binding pocket into the protein fold by minimizing the departure from a set of geometric restraints using mixed‐integer linear optimization. On identifying the suitable locations that can accommodate the new binding pocket, CHARMM energy calculations are employed to identify what mutations in the neighboring residues, if any, are needed to ensure that the minimum energy conformation of the binding pocket conserves the desired geometry. This computational framework is benchmarked against the results available in the literature for engineering a copper binding site into thioredoxin protein. Subsequently, OptGraft is used to guide the transfer of a calcium‐binding pocket from thermitase protein (PDB: 1thm) into the first domain of CD2 protein (PDB:1hng). Experimental characterization of three de novo redesigned proteins with grafted calcium‐binding centers demonstrated that they all exhibit high affinities for terbium (K_d ～ 22, 38, and 55 μM) and can selectively bind calcium over magnesium.     

Ig-VAE: Generative modeling of protein structure by direct 3D coordinate generation

While deep learning models have seen increasing applications in protein science, few have been implemented for protein backbone generation—an important task in structure-based problems such as active site and interface design. We present a new approach to building class-specific backbones, using a variational auto-encoder to directly generate the 3D coordinates of immunoglobulins. Our model is torsion- and distance-aware, learns a high-resolution embedding of the dataset, and generates novel, high-quality structures compatible with existing design tools. We show that the Ig-VAE can be used with Rosetta to create a computational model of a SARS-CoV2-RBD binder via latent space sampling. We further demonstrate that the model’s generative prior is a powerful tool for guiding computational protein design, motivating a new paradigm under which backbone design is solved as constrained optimization problem in the latent space of a generative model.     

A Brief History of De Novo Protein Design: Minimal,Rational, and Computational

Protein design has come of age, but how will it mature? In the 1980s and the 1990s, the primary motivation for de novo protein design was to test our understanding of the informational aspect of the protein-folding problem; i.e., how does protein sequence determine protein structure and function? This necessitated minimal and rational design approaches whereby the placement of each residue in a design was reasoned using chemical principles and/or biochemical knowledge. At that time, though with some notable exceptions, the use of computers to aid design was not widespread. Over the past two decades, the tables have turned and computational protein design is firmly established. Here, I illustrate this progress through a timeline of de novo protein structures that have been solved to atomic resolution and deposited in the Protein Data Bank. From this, it is clear that the impact of rational and computational design has been considerable: More-complex and more-sophisticated designs are being targeted with many being resolved to atomic resolution. Furthermore, our ability to generate and manipulate synthetic proteins has advanced to a point where they are providing realistic alternatives to natural protein functions for applications both in vitro and in cells. Also, and increasingly, computational protein design is becoming accessible to non-specialists. This all begs the questions: Is there still a place for minimal and rational design approaches? And, what challenges lie ahead for the burgeoning field of de novo protein design as a whole?     

Protein structure determination by conformational space annealing using NMR geometric restraints

We have carried out numerical experiments to investigate the applicability of the global optimization method of conformational space annealing (CSA) to the enhanced NMR protein structure determination over existing PDB structures. The NMR protein structure determination is driven by the optimization of collective multiple restraints arising from experimental data and the basic stereochemical properties of a protein‐like molecule. By rigorous and straightforward application of CSA to the identical NMR experimental data used to generate existing PDB structures, we redetermined 56 recent PDB protein structures starting from fully randomized structures. The quality of CSA‐generated structures and existing PDB structures were assessed by multiobjective functions in terms of their consistencies with experimental data and the requirements of protein‐like stereochemistry. In 54 out of 56 cases, CSA‐generated structures were better than existing PDB structures in the Pareto‐dominant manner, while in the remaining two cases, it was a tie with mixed results. As a whole, all structural features tested improved in a statistically meaningful manner. The most improved feature was the Ramachandran favored portion of backbone torsion angles with about 8.6% improvement from 88.9% to 97.5% (P‐value <10^?17). We show that by straightforward application of CSA to the efficient global optimization of an energy function, NMR structures will be of better quality than existing PDB structures. Proteins 2015; 83:2251–2262. © 2015 Wiley Periodicals, Inc.     

Automated scaffold selection for enzyme design

A major goal of computational protein design is the construction of novel functions on existing protein scaffolds. There the first question is which scaffold is suitable for a specific reaction. Given a set of catalytic residues and their spatial arrangement, one wants to identify a protein scaffold that can host this active site. Here, we present an algorithm called ScaffoldSelection that is able to rapidly search large sets of protein structures for potential attachment sites of an enzymatic motif. The method consists of two steps; it first identifies pairs of backbone positions in pocket‐like regions. Then, it combines these to complete attachment sites using a graph theoretical approach. Identified matches are assessed for their ability to accommodate the substrate or transition state. A representative set of structures from the Protein Data Bank (～3500) was searched for backbone geometries that support the catalytic residues for 12 chemical reactions. Recapitulation of native active site geometries is used as a benchmark for the performance of the program. The native motif is identified in all 12 test cases, ranking it in the top percentile in 5 out of 12. The algorithm is fast and efficient, although dependent on the complexity of the motif. Comparisons to other methods show that ScaffoldSelection performs equally well in terms of accuracy and far better in terms of speed. Thus, ScaffoldSelection will aid future computational protein design experiments by preselecting protein scaffolds that are suitable for a specific reaction type and the introduction of a predefined amino acid motif. Proteins 2009. © 2009 Wiley‐Liss, Inc.     

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.     

Prediction of structures of zinc‐binding proteins through explicit modeling of metal coordination geometry

Metal ions play an essential role in stabilizing protein structures and contributing to protein function. Ions such as zinc have well‐defined coordination geometries, but it has not been easy to take advantage of this knowledge in protein structure prediction efforts. Here, we present a computational method to predict structures of zinc‐binding proteins given knowledge of the positions of zinc‐coordinating residues in the amino acid sequence. The method takes advantage of the “atom‐tree” representation of molecular systems and modular architecture of the Rosetta3 software suite to incorporate explicit metal ion coordination geometry into previously developed de novo prediction and loop modeling protocols. Zinc cofactors are tethered to their interacting residues based on coordination geometries observed in natural zinc‐binding proteins. The incorporation of explicit zinc atoms and their coordination geometry in both de novo structure prediction and loop modeling significantly improves sampling near the native conformation. The method can be readily extended to predict protein structures bound to other metal and/or small chemical cofactors with well‐defined coordination or ligation geometry.     

Recent advances in drug design methods: Where will they lead?

Drug design methods have made significant new advances over the last ten years, mainly in the areas of molecular modelling. In more recent times important developments in theory have led to a different type of modelling becoming possible, the so-called de novo or automated design algorithms. In this new method the programs perform much of the chemist's thinking, in finding appropriately sized chemical groups to fit into a target site. However this is a combinatoric problem which has no general analytical solution; it is ripe for optimization. Other advances, such as combinatorial chemical synthesis and screening, will dramatically influence the search for new lead structures for target sites, which at present are poorly understood. Already these methods are being applied to peptide libraries. Peptides do not make good drug compounds because of their poor bioavailability; further, their flexibility reduces their affinity. In some cases peptide backbones can be removed and replaced with rigid non-peptide scaffolds.     

Integration of the Rosetta suite with the python software stack via reproducible packaging and core programming interfaces for distributed simulation

The Rosetta software suite for macromolecular modeling is a powerful computational toolbox for protein design, structure prediction, and protein structure analysis. The development of novel Rosetta‐based scientific tools requires two orthogonal skill sets: deep domain‐specific expertise in protein biochemistry and technical expertise in development, deployment, and analysis of molecular simulations. Furthermore, the computational demands of molecular simulation necessitate large scale cluster‐based or distributed solutions for nearly all scientifically relevant tasks. To reduce the technical barriers to entry for new development, we integrated Rosetta with modern, widely adopted computational infrastructure. This allows simplified deployment in large‐scale cluster and cloud computing environments, and effective reuse of common libraries for simulation execution and data analysis. To achieve this, we integrated Rosetta with the Conda package manager; this simplifies installation into existing computational environments and packaging as docker images for cloud deployment. Then, we developed programming interfaces to integrate Rosetta with the PyData stack for analysis and distributed computing, including the popular tools Jupyter, Pandas, and Dask. We demonstrate the utility of these components by generating a library of a thousand de novo disulfide‐rich miniproteins in a hybrid simulation that included cluster‐based design and interactive notebook‐based analyses. Our new tools enable users, who would otherwise not have access to the necessary computational infrastructure, to perform state‐of‐the‐art molecular simulation and design with Rosetta.     

Computational design of protein antigens that interact with the CDR H3 loop of HIV broadly neutralizing antibody 2F5

Rational design of proteins with novel binding specificities and increased affinity is one of the major goals of computational protein design. Epitope‐scaffolds are a new class of antigens engineered by transplanting viral epitopes of predefined structure to protein scaffolds, or by building protein scaffolds around such epitopes. Epitope‐scaffolds are of interest as vaccine components to attempt to elicit neutralizing antibodies targeting the specified epitope. In this study we developed a new computational protocol, MultiGraft Interface, that transplants epitopes but also designs additional scaffold features outside the epitope to enhance antibody‐binding specificity and potentially influence the specificity of elicited antibodies. We employed MultiGraft Interface to engineer novel epitope‐scaffolds that display the known epitope of human immunodeficiency virus 1 (HIV‐1) neutralizing antibody 2F5 and that also interact with the functionally important CDR H3 antibody loop. MultiGraft Interface generated an epitope‐scaffold that bound 2F5 with subnanomolar affinity (K_D = 400 pM) and that interacted with the antibody CDR H3 loop through computationally designed contacts. Substantial structural modifications were necessary to engineer this antigen, with the 2F5 epitope replacing a helix in the native scaffold and with 15% of the native scaffold sequence being modified in the design stage. This epitope‐scaffold represents a successful example of rational protein backbone engineering and protein–protein interface design and could prove useful in the field of HIV vaccine design. MultiGraft Interface can be generally applied to engineer novel binding partners with altered specificity and optimized affinity. Proteins 2014; 82:2770–2782. © 2014 Wiley Periodicals, Inc.     

Computational design of novel protein–protein interactions – An overview on methodological approaches and applications

Protein–protein interactions (PPIs) govern numerous cellular functions in terms of signaling, transport, defense and many others. Designing novel PPIs poses a fundamental challenge to our understanding of molecular interactions. The capability to robustly engineer PPIs has immense potential for the development of novel synthetic biology tools and protein-based therapeutics. Over the last decades, many efforts in this area have relied purely on experimental approaches, but more recently, computational protein design has made important contributions. Template-based approaches utilize known PPIs and transplant the critical residues onto heterologous scaffolds. De novo design instead uses computational methods to generate novel binding motifs, allowing for a broader scope of the sites engaged in protein targets. Here, we review successful design cases, giving an overview of the methodological approaches used for templated and de novo PPI design.     

Exploring alternate states and oligomerization preferences of coiled‐coils by de novo structure modeling

Homomeric coiled‐coils can self‐assemble into a wide range of structural states with different helix topologies and oligomeric states. In this study, we have combined de novo structure modeling with stability calculations to simultaneously predict structure and oligomeric states of homomeric coiled‐coils. For dimers an asymmetric modeling protocol was developed. Modeling without symmetry constraints showed that backbone asymmetry is important for the formation of parallel dimeric coiled‐coils. Collectively, our results demonstrate that high‐resolution structure of coiled‐coils, as well as parallel and antiparallel orientations of dimers and tetramers, can be accurately predicted from sequence. De novo modeling was also used to generate models of competing oligomeric states, which were used to compare stabilities and thus predict the native stoichiometry from sequence. In a benchmark set of 33 coiled‐coil sequences, forming dimers to pentamers, up to 70% of the oligomeric states could be correctly predicted. The calculations demonstrated that the free energy of helix folding could be an important factor for determining stability and oligomeric state of homomeric coiled‐coils. The computational methods developed here should be broadly applicable to studies of sequence‐structure relationships in coiled‐coils and the design of higher order assemblies with improved oligomerization specificity. Proteins 2015; 83:235–247. © 2014 Wiley Periodicals, Inc.     

A comparison of successful and failed protein interface designs highlights the challenges of designing buried hydrogen bonds

The accurate design of new protein–protein interactions is a longstanding goal of computational protein design. However, most computationally designed interfaces fail to form experimentally. This investigation compares five previously described successful de novo interface designs with 158 failures. Both sets of proteins were designed with the molecular modeling program Rosetta. Designs were considered a success if a high‐resolution crystal structure of the complex closely matched the design model and the equilibrium dissociation constant for binding was less than 10 μM. The successes and failures represent a wide variety of interface types and design goals including heterodimers, homodimers, peptide‐protein interactions, one‐sided designs (i.e., where only one of the proteins was mutated) and two‐sided designs. The most striking feature of the successful designs is that they have fewer polar atoms at their interfaces than many of the failed designs. Designs that attempted to create extensive sets of interface‐spanning hydrogen bonds resulted in no detectable binding. In contrast, polar atoms make up more than 40% of the interface area of many natural dimers, and native interfaces often contain extensive hydrogen bonding networks. These results suggest that Rosetta may not be accurately balancing hydrogen bonding and electrostatic energies against desolvation penalties and that design processes may not include sufficient sampling to identify side chains in preordered conformations that can fully satisfy the hydrogen bonding potential of the interface.