Growing Glycans in Rosetta: Accurate de novo glycan modeling, density fitting, and rational sequon design.

Carbohydrates and glycoproteins modulate key biological functions. However, experimental structure determination of sugar polymers is notoriously difficult. Computational approaches can aid in carbohydrate structure prediction, structure determination, and design. In this work, we developed a glycan-modeling algorithm, GlycanTreeModeler, that computationally builds glycans layer-by-layer, using adaptive kernel density estimates (KDE) of common glycan conformations derived from data in the Protein Data Bank (PDB) and from quantum mechanics (QM) calculations. GlycanTreeModeler was benchmarked on a test set of glycan structures of varying lengths, or "trees". Structures predicted by GlycanTreeModeler agreed with native structures at high accuracy for both de novo modeling and experimental density-guided building. We employed these tools to design de novo glycan trees into a protein nanoparticle vaccine to shield regions of the scaffold from antibody recognition, and experimentally verified shielding. This work will inform glycoprotein model prediction, glycan masking, and further aid computational methods in experimental structure determination and refinement.

Simultaneous enhancement of multiple functional properties using evolution-informed protein design.

A major challenge in protein design is to augment existing functional proteins with multiple property enhancements. Altering several properties likely necessitates numerous primary sequence changes, and novel methods are needed to accurately predict combinations of mutations that maintain or enhance function. Models of sequence co-variation (e.g., EVcouplings), which leverage extensive information about various protein properties and activities from homologous protein sequences, have proven effective for many applications including structure determination and mutation effect prediction. We apply EVcouplings to computationally design variants of the model protein TEM-1 ß-lactamase. Nearly all the 14 experimentally characterized designs were functional, including one with 84 mutations from the nearest natural homolog. The designs also had large increases in thermostability, increased activity on multiple substrates, and nearly identical structure to the wild type enzyme. This study highlights the efficacy of evolutionary models in guiding large sequence alterations to generate functional diversity for protein design applications.

Molecular basis for the transcriptional regulation of an epoxide-based virulence circuit in Pseudomonas aeruginosa.

The opportunistic pathogen Pseudomonas aeruginosa infects cystic fibrosis (CF) patient airways and produces a virulence factor Cif that is associated with worse outcomes. Cif is an epoxide hydrolase that reduces cell-surface abundance of the cystic fibrosis transmembrane conductance regulator (CFTR) and sabotages pro-resolving signals. Its expression is regulated by a divergently transcribed TetR family transcriptional repressor. CifR represents the first reported epoxide-sensing bacterial transcriptional regulator, but neither its interaction with cognate operator sequences nor the mechanism of activation has been investigated. Using biochemical and structural approaches, we uncovered the molecular mechanisms controlling this complex virulence operon. We present here the first molecular structures of CifR alone and in complex with operator DNA, resolved in a single crystal lattice. Significant conformational changes between these two structures suggest how CifR regulates the expression of the virulence gene cif. Interactions between the N-terminal extension of CifR with the DNA minor groove of the operator play a significant role in the operator recognition of CifR. We also determined that cysteine residue Cys107 is critical for epoxide sensing and DNA release. These results offer new insights into the stereochemical regulation of an epoxide-based virulence circuit in a critically important clinical pathogen.

Simultaneous enhancement of multiple functional properties using evolution-informed protein design.

Designing optimized proteins is important for a range of practical applications. Protein design is a rapidly developing field that would benefit from approaches that enable many changes in the amino acid primary sequence, rather than a small number of mutations, while maintaining structure and enhancing function. Homologous protein sequences contain extensive information about various protein properties and activities that have emerged over billions of years of evolution. Evolutionary models of sequence co-variation, derived from a set of homologous sequences, have proven effective in a range of applications including structure determination and mutation effect prediction. In this work we apply one of these models (EVcouplings) to computationally design highly divergent variants of the model protein TEM-1 ß-lactamase, and characterize these designs experimentally using multiple biochemical and biophysical assays. Nearly all designed variants were functional, including one with 84 mutations from the nearest natural homolog. Surprisingly, all functional designs had large increases in thermostability and most had a broadening of available substrates. These property enhancements occurred while maintaining a nearly identical structure to the wild type enzyme. Collectively, this work demonstrates that evolutionary models of sequence co-variation (1) are able to capture complex epistatic interactions that successfully guide large sequence departures from natural contexts, and (2) can be applied to generate functional diversity useful for many applications in protein design.

Complete Combinatorial Mutational Enumeration of a protein functional site enables sequence-landscape mapping and identifies highly-mutated variants that retain activity.

Understanding how proteins evolve under selective pressure is a longstanding challenge. The immensity of the search space has limited efforts to systematically evaluate the impact of multiple simultaneous mutations, so mutations have typically been assessed individually. However, epistasis, or the way in which mutations interact, prevents accurate prediction of combinatorial mutations based on measurements of individual mutations. Here, we use artificial intelligence to define the entire functional sequence landscape of a protein binding site in silico, and we call this approach Complete Combinatorial Mutational Enumeration (CCME). By leveraging CCME, we are able to construct a comprehensive map of the evolutionary connectivity within this functional sequence landscape. As a proof of concept, we applied CCME to the ACE2 binding site of the SARS-CoV-2 spike protein receptor binding domain. We selected representative variants from across the functional sequence landscape for testing in the laboratory. We identified variants that retained functionality to bind ACE2 despite changing over 40% of evaluated residue positions, and the variants now escape binding and neutralization by monoclonal antibodies. This work represents a crucial initial stride towards achieving precise predictions of pathogen evolution, opening avenues for proactive mitigation.

IgG targeting distinct seasonal coronavirus- conserved SARS-CoV-2 spike subdomains correlates with differential COVID-19 disease outcomes.

Despite SARS-CoV-2 being a "novel" virus, early detection of anti-spike IgG in severe COVID-19 patients may be caused by the amplification of humoral memory responses against seasonal coronaviruses. Here, we examine this phenomenon by characterizing anti-spike IgG responses in non-hospitalized convalescent individuals across a spectrum of COVID-19 severity. We observe that disease severity positively correlates with anti-spike IgG levels, IgG cross-reactivity against other betacoronaviruses (ß-CoVs), and FcγR activation. Analysis of IgG targeting ß-CoV-conserved and non-conserved immunodominant epitopes within the SARS-CoV-2 spike protein revealed epitope-specific relationships: IgG targeting the conserved heptad repeat (HR) 2 region significantly correlates with milder disease, while targeting the conserved S2'FP region correlates with more severe disease. Furthermore, a lower HR2-to-S2'FP IgG-binding ratio correlates with greater disease severity, with ICU-hospitalized COVID-19 patients showing the lowest HR2/S2'FP ratios. These findings suggest that HR2/S2'FP IgG profiles may predict disease severity and offer insight into protective versus deleterious humoral recall responses.

Analyses of the complex formation of staphylococcal enterotoxin A and the human gp130 cytokine receptor.

Superantigens (SAgs) are bacterial enterotoxins produced by Staphylococcus aureus. Staphylococcal enterotoxin type A (SEA), a staphylococcal superantigen, has been shown to bind to the cytokine signalling receptor glycoprotein 130 (gp130). The structural details, as well as the exact physiological role of this interaction, remain unclear. Here, we describe the structural details of the SEA-gp130 complex by combining crosslinking mass spectrometry and computational modelling. Interestingly, SEA is not able to bind gp130-homologues from rat and mouse. Our data suggest that SEA may interact with human gp130 in a different manner than other known gp130-ligands. Moreover, the fact that SEA does not bind mouse or rat gp130 suggests that SAgs have additional mechanisms of action in humans.

Ensuring scientific reproducibility in bio-macromolecular modeling via extensive, automated benchmarks.

Each year vast international resources are wasted on irreproducible research. The scientific community has been slow to adopt standard software engineering practices, despite the increases in high-dimensional data, complexities of workflows, and computational environments. Here we show how scientific software applications can be created in a reproducible manner when simple design goals for reproducibility are met. We describe the implementation of a test server framework and 40 scientific benchmarks, covering numerous applications in Rosetta bio-macromolecular modeling. High performance computing cluster integration allows these benchmarks to run continuously and automatically. Detailed protocol captures are useful for developers and users of Rosetta and other macromolecular modeling tools. The framework and design concepts presented here are valuable for developers and users of any type of scientific software and for the scientific community to create reproducible methods. Specific examples highlight the utility of this framework, and the comprehensive documentation illustrates the ease of adding new tests in a matter of hours.

Biochemical and structural characterization of two cif-like epoxide hydrolases from Burkholderia cenocepacia.

Epoxide hydrolases catalyze the conversion of epoxides to vicinal diols in a range of cellular processes such as signaling, detoxification, and virulence. These enzymes typically utilize a pair of tyrosine residues to orient the substrate epoxide ring in the active site and stabilize the hydrolysis intermediate. A new subclass of epoxide hydrolases that utilize a histidine in place of one of the tyrosines was established with the discovery of the CFTR Inhibitory Factor (Cif) from Pseudomonas aeruginosa. Although the presence of such Cif-like epoxide hydrolases was predicted in other opportunistic pathogens based on sequence analyses, only Cif and its homolog aCif from Acinetobacter nosocomialis have been characterized. Here we report the biochemical and structural characteristics of Cfl1 and Cfl2, two Cif-like epoxide hydrolases from Burkholderia cenocepacia. Cfl1 is able to hydrolyze xenobiotic as well as biological epoxides that might be encountered in the environment or during infection. In contrast, Cfl2 shows very low activity against a diverse set of epoxides. The crystal structures of the two proteins reveal quaternary structures that build on the well-known dimeric assembly of the α/ß hydrolase domain, but broaden our understanding of the structural diversity encoded in novel oligomer interfaces. Analysis of the interfaces reveals both similarities and key differences in sequence conservation between the two assemblies, and between the canonical dimer and the novel oligomer interfaces of each assembly. Finally, we discuss the effects of these higher-order assemblies on the intra-monomer flexibility of Cfl1 and Cfl2 and their possible roles in regulating enzymatic activity.

Breakthroughs in computational design methods open up new frontiers for de novo protein engineering.

Proteins catalyze the majority of chemical reactions in organisms, and harnessing this power has long been the focus of the protein engineering field. Computational protein design aims to create new proteins and functions in silico, and in doing so, accelerate the process, reduce costs and enable more sophisticated engineering goals to be accomplished. Challenges that very recently seemed impossible are now within reach thanks to several landmark advances in computational protein design methods. Here, we summarize these new methods, with a particular emphasis on de novo protein design advancements occurring within the past 5 years.

Toward complete rational control over protein structure and function through computational design.

The grand challenge of protein design is a general method for producing a polypeptide with arbitrary functionality, conformation, and biochemical properties. To that end, a wide variety of methods have been developed for the improvement of native proteins, the design of ideal proteins de novo, and the redesign of suboptimal proteins with better-performing substructures. These methods employ informatic comparisons of function-structure-sequence relationships as well as knowledge-based evaluation of protein properties to narrow the immense protein sequence search space down to an enumerable and often manually evaluable set of structures that meet specified criteria. While arbitrary manipulation of protein-protein interfaces and molecular catalysis remains an unsolved problem, and no protein shape or behavior manipulation algorithm is universally applicable, the promising results thus far are a strong indicator that a general approach to the arbitrary manipulation of polypeptides is within reach.

Structural characterization and computational analysis of PDZ domains in Monosiga brevicollis.

Identification of the molecular networks that facilitated the evolution of multicellular animals from their unicellular ancestors is a fundamental problem in evolutionary cellular biology. Choanoflagellates are recognized as the closest extant nonmetazoan ancestors to animals. These unicellular eukaryotes can adopt a multicellular-like "rosette" state. Therefore, they are compelling models for the study of early multicellularity. Comparative studies revealed that a number of putative human orthologs are present in choanoflagellate genomes, suggesting that a subset of these genes were necessary for the emergence of multicellularity. However, previous work is largely based on sequence alignments alone, which does not confirm structural nor functional similarity. Here, we focus on the PDZ domain, a peptide-binding domain which plays critical roles in myriad cellular signaling networks and which underwent a gene family expansion in metazoan lineages. Using a customized sequence similarity search algorithm, we identified 178 PDZ domains in the Monosiga brevicollis proteome. This includes 11 previously unidentified sequences, which we analyzed using Rosetta and homology modeling. To assess conservation of protein structure, we solved high-resolution crystal structures of representative M. brevicollis PDZ domains that are homologous to human Dlg1 PDZ2, Dlg1 PDZ3, GIPC, and SHANK1 PDZ domains. To assess functional conservation, we calculated binding affinities for mbGIPC, mbSHANK1, mbSNX27, and mbDLG-3 PDZ domains from M. brevicollis. Overall, we find that peptide selectivity is generally conserved between these two disparate organisms, with one possible exception, mbDLG-3. Overall, our results provide novel insight into signaling pathways in a choanoflagellate model of primitive multicellularity.

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.

Cytosolic expression, solution structures, and molecular dynamics simulation of genetically encodable disulfide-rich de novo designed peptides.

Disulfide-rich peptides represent an important protein family with broad pharmacological potential. Recent advances in computational methods have made it possible to design new peptides which adopt a stable conformation de novo. Here, we describe a system to produce disulfide-rich de novo peptides using Escherichia coli as the expression host. The advantage of this system is that it enables production of uniformly 13 C- and 15 N-labeled peptides for solution nuclear magnetic resonance (NMR) studies. This expression system was used to isotopically label two previously reported de novo designed peptides, and to determine their solution structures using NMR. The ensemble of NMR structures calculated for both peptides agreed well with the design models, further confirming the accuracy of the design protocol. Collection of NMR data on the peptides under reducing conditions revealed a dependency on disulfide bonds to maintain stability. Furthermore, we performed long-time molecular dynamics (MD) simulations with tempering to assess the stability of two families of de novo designed peptides. Initial designs which exhibited a stable structure during simulations were more likely to adopt a stable structure in vitro, but attempts to utilize this method to redesign unstable peptides to fold into a stable state were unsuccessful. Further work is therefore needed to assess the utility of MD simulation techniques for de novo protein design.

Discovery and engineering of enhanced SUMO protease enzymes.

Small ubiquitin-like modifier (SUMO) is commonly used as a protein fusion domain to facilitate expression and purification of recombinant proteins, and a SUMO-specific protease is then used to remove SUMO from these proteins. Although this protease is highly specific, its limited solubility and stability hamper its utility as an in vitro reagent. Here, we report improved SUMO protease enzymes obtained via two approaches. First, we developed a computational method and used it to re-engineer WT Ulp1 from Saccharomyces cerevisiae to improve protein solubility. Second, we discovered an improved SUMO protease via genomic mining of the thermophilic fungus Chaetomium thermophilum, as proteins from thermophilic organisms are commonly employed as reagent enzymes. Following expression in Escherichia coli, we found that these re-engineered enzymes can be more thermostable and up to 12 times more soluble, all while retaining WT-or-better levels of SUMO protease activity. The computational method we developed to design solubility-enhancing substitutions is based on the RosettaScripts application for the macromolecular modeling suite Rosetta, and it is broadly applicable for the improvement of solution properties of other proteins. Moreover, we determined the X-ray crystal structure of a SUMO protease from C. thermophilum to 1.44 Å resolution. This structure revealed that this enzyme exhibits structural and functional conservation with the S. cerevisiae SUMO protease, despite exhibiting only 28% sequence identity. In summary, by re-engineering the Ulp1 protease and discovering a SUMO protease from C. thermophilum, we have obtained proteases that are more soluble, more thermostable, and more efficient than the current commercially available Ulp1 enzyme.

Screening, large-scale production and structure-based classification of cystine-dense peptides.

Peptides folded through interwoven disulfides display extreme biochemical properties and unique medicinal potential. However, their exploitation has been hampered by the limited amounts isolatable from natural sources and the expense of chemical synthesis. We developed reliable biological methods for high-throughput expression, screening and large-scale production of these peptides: 46 were successfully produced in multimilligram quantities, and >600 more were deemed expressible through stringent screening criteria. Many showed extreme resistance to temperature, proteolysis and/or reduction, and all displayed inhibitory activity against at least 1 of 20 ion channels tested, thus confirming their biological functionality. Crystal structures of 12 confirmed proper cystine topology and the utility of crystallography to study these molecules but also highlighted the need for rational classification. Previous categorization attempts have focused on limited subsets featuring distinct motifs. Here we present a global definition, classification and analysis of >700 structures of cystine-dense peptides, providing a unifying framework for these molecules.

An epoxide hydrolase secreted by Pseudomonas aeruginosa decreases mucociliary transport and hinders bacterial clearance from the lung.

The opportunistic pathogen Pseudomonas aeruginosa colonizes the lungs of susceptible individuals by deploying virulence factors targeting host defenses. The secreted factor Cif (cystic fibrosis transmembrane conductance regulator inhibitory factor) dysregulates the endocytic recycling of CFTR and thus reduces CFTR abundance in host epithelial membranes. We have postulated that the decrease in ion secretion mediated by Cif would slow mucociliary transport and decrease bacterial clearance from the lungs. To test this hypothesis, we explored the effects of Cif in cultured epithelia and in the lungs of mice. We developed a strategy to interpret the "hurricane-like" motions observed in reconstituted cultures and identified a Cif-mediated decrease in the velocity of mucus transport in vitro. Presence of Cif also increased the number of bacteria recovered at two time points in an acute mouse model of pneumonia caused by P. aeruginosa. Furthermore, recent work has demonstrated an inverse correlation between the airway concentrations of Cif and 15-epi-lipoxin A4, a proresolving lipid mediator important in host defense and the resolution of pathogen-initiated inflammation. Here, we observe elevated levels of 15-epi-lipoxin A4 in the lungs of mice infected with a strain of P. aeruginosa that expresses only an inactive form of cif compared with those mice infected with wild-type P. aeruginosa. Together these data support the inclusion of Cif on the list of virulence factors that assist P. aeruginosa in colonizing and damaging the airways of compromised patients. Furthermore, this study establishes techniques that enable our groups to explore the underlying mechanisms of Cif effects during respiratory infection.

Massively parallel de novo protein design for targeted therapeutics.

De novo protein design holds promise for creating small stable proteins with shapes customized to bind therapeutic targets. We describe a massively parallel approach for designing, manufacturing and screening mini-protein binders, integrating large-scale computational design, oligonucleotide synthesis, yeast display screening and next-generation sequencing. We designed and tested 22,660 mini-proteins of 37-43 residues that target influenza haemagglutinin and botulinum neurotoxin B, along with 6,286 control sequences to probe contributions to folding and binding, and identified 2,618 high-affinity binders. Comparison of the binding and non-binding design sets, which are two orders of magnitude larger than any previously investigated, enabled the evaluation and improvement of the computational model. Biophysical characterization of a subset of the binder designs showed that they are extremely stable and, unlike antibodies, do not lose activity after exposure to high temperatures. The designs elicit little or no immune response and provide potent prophylactic and therapeutic protection against influenza, even after extensive repeated dosing.

Active-Site Flexibility and Substrate Specificity in a Bacterial Virulence Factor: Crystallographic Snapshots of an Epoxide Hydrolase.

Pseudomonas aeruginosa secretes an epoxide hydrolase with catalytic activity that triggers degradation of the cystic fibrosis transmembrane conductance regulator (CFTR) and perturbs other host defense networks. Targets of this CFTR inhibitory factor (Cif) are largely unknown, but include an epoxy-fatty acid. In this class of signaling molecules, chirality can be an important determinant of physiological output and potency. Here we explore the active-site chemistry of this two-step α/ß-hydrolase and its implications for an emerging class of virulence enzymes. In combination with hydrolysis data, crystal structures of 15 trapped hydroxyalkyl-enzyme intermediates reveal the stereochemical basis of Cif's substrate specificity, as well as its regioisomeric and enantiomeric preferences. The structures also reveal distinct sets of conformational changes that enable the active site to expand dramatically in two directions, accommodating a surprising array of potential physiological epoxide targets. These new substrates may contribute to Cif's diverse effects in vivo, and thus to the success of P. aeruginosa and other pathogens during infection.

The cif Virulence Factor Gene Is Present in Isolates From Patients With Pseudomonas aeruginosa Keratitis.

PURPOSE: To determine whether the cif gene is present in pathogenic Pseudomonas aeruginosa isolates from patients with bacterial keratitis at Aravind Eye Hospital, a referral eye care center in southern India, and from corresponding environmental isolates. METHODS: Polymerase chain reaction amplification was performed on strains of P. aeruginosa isolated from ocular infections and environmental soil samples were collected from the area surrounding Aravind Eye Hospital. DNA sequencing of 16S ribosomal DNA amplicons was performed to verify strain identity. RESULTS: We determined that 45 of 48 patient isolates carry a genomic copy of cif. Analysis of a catalog of environmental strains previously isolated from the surrounding area revealed that only 4 of 10 P. aeruginosa strains and 1 of 14 strains of related species carry the cif gene. CONCLUSIONS: This is the first study to show that P. aeruginosa strains with ocular pathogenicity carry the cif gene and that the presence of this gene may be enriched over its prevalence in the environment. Taken together, these results suggest a potential role for Cif in acute bacterial keratitis.