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Carbohydrate chains are ubiquitous in the complex molecular processes of life. These highly diverse chains are recognized by a variety of protein receptors, enabling glycans to regulate many biological functions. High-resolution structures of protein-glycoligand complexes reveal the atomic details necessary to understand this level of molecular recognition and inform application-focused scientific and engineering pursuits. When experimental challenges hinder high-throughput determination of quality structures, computational tools can, in principle, fill the gap. In this work, we introduce GlycanDock, a residue-centric protein-glycoligand docking refinement algorithm developed within the Rosetta macromolecular modeling and design software suite. We performed a benchmark docking assessment using a set of 109 experimentally determined protein-glycoligand complexes as well as 62 unbound protein structures. The GlycanDock algorithm can sample and discriminate among protein-glycoligand models of native-like structural accuracy with statistical reliability from starting structures of up to 7 A root-mean-square deviation in the glycoligand ring atoms. We show that GlycanDock-refined models qualitatively replicated the known binding specificity of a bacterial carbohydrate-binding module. Finally, we present a protein-glycoligand docking pipeline for generating putative protein-glycoligand complexes when only the glycoligand sequence and unbound protein structure are known. In combination with other carbohydrate modeling tools, the GlycanDock docking refinement algorithm will accelerate research in the glycosciences.

Biomolecular structure drives function, and computational capabilities have progressed such that the prediction and computational design of biomolecular structures is increasingly feasible. Because computational biophysics attracts students from many different backgrounds and with different levels of resources, teaching the subject can be challenging. One strategy to teach diverse learners is with interactive multimedia material that promotes self-paced, active learning. We have created a hands-on education strategy with a set of 16 modules that teach topics in biomolecular structure and design, from fundamentals of conformational sampling and energy evaluation to applications, such as protein docking, antibody design, and RNA structure prediction. Our modules are based on PyRosetta, a Python library that encapsulates all computational modules and methods in the Rosetta software package. The workshop-style modules are implemented as Jupyter Notebooks that can be executed in the Google Colaboratory, allowing learners access with just a Web browser. The digital format of Jupyter Notebooks allows us to embed images, molecular visualization movies, and interactive coding exercises. This multimodal approach may better reach students from different disciplines and experience levels, as well as attract more researchers from smaller labs and cognate backgrounds to leverage PyRosetta in science and engineering research. All materials are freely available at https://github.com/RosettaCommons/PyRosetta.notebooks.

The polypeptide N-acetylgalactosaminyl transferase (GalNAc-T) enzyme family initiates O-linked mucin-type glycosylation. The family constitutes 20 isozymes in humans—an unusually large number—unique to O-glycosylation. GalNAc-Ts exhibit both redundancy and finely tuned specificity for a wide range of peptide substrates. In this work, we deciphered the sequence and structural motifs that determine the peptide substrate preferences for the GalNAc-T2 isoform. Our approach involved sampling and characterization of peptide–enzyme conformations obtained from Rosetta Monte Carlo-minimization–based flexible docking. We computationally scanned 19 amino acid residues at positions −1 and +1 of an eight-residue peptide substrate, which comprised a dataset of 361 (19×19) peptides with previously characterized experimental GalNAc-T2 glycosylation efficiencies. The calculations recapitulated experimental specificity data, successfully discriminating between glycosylatable and non-glycosylatable peptides with a probability of 96.5% (ROC-AUC score), a balanced accuracy of 85.5% and a false positive rate of 7.3%. The glycosylatable peptide substrates viz. peptides with proline, serine, threonine, and alanine at the −1 position of the peptide preferentially exhibited cognate sequon-like conformations. The preference for specific residues at the −1 position of the peptide was regulated by enzyme residues R362, K363, Q364, H365 and W331, which modulate the pocket size and specific enzyme-peptide interactions. For the +1 position of the peptide, enzyme residues K281 and K363 formed gating interactions with aromatics and glutamines at the +1 position of the peptide, leading to modes of peptide-binding sub-optimal for catalysis. Overall, our work revealed enzyme features that lead to the finely tuned specificity observed for a broad range of peptide substrates for the GalNAc-T2 enzyme. We anticipate that the key sequence and structural motifs can be extended to analyze specificities of other isoforms of the GalNAc-T family and can be used to guide design of variants with tailored specificity.

Including undergraduate research in STEM education is a well-supported and growing high-impact practice that has been made much more scalable through integrating these experiences into the classroom. Here we describe a new biochemistry Course-based Undergraduate Research Experience (CURE) that follows a design-to-data workflow with a strong connection to a worldwide community of protein modeling software developers. Analysis of psychosocial developments in association with participating in this CURE from the first set of students formally participating in the course suggest a beneficial effect on attributes associated with STEM persistence. To increase successful propagation, the design of the CURE’s curriculum, supporting learning materials, and instructor resources are provided to make it facile for faculty at any institution to join this network and implement the CURE. With this foundation, we expect student participation and the data set to continue to grow.

Cyclic symmetry is frequent in protein and peptide homo‐oligomers, but extremely rare within a single chain, as it is not compatible with free N‐ and C‐termini. Here we describe the computational design of mixed‐chirality peptide macrocycles with rigid structures that feature internal cyclic symmetries or improper rotational symmetries inaccessible to natural proteins. Crystal structures of three C2‐ and C3‐symmetric macrocycles, and of six diverse S2‐symmetric macrocycles, match the computationally‐designed models with backbone heavy‐atom RMSD values of 1 Å or better. Crystal structures of an S4‐symmetric macrocycle (consisting of a sequence and structure segment mirrored at each of three successive repeats) designed to bind zinc reveal a large‐scale zinc‐driven conformational change from an S4‐symmetric apo‐state to a nearly inverted S4‐symmetric holo‐state almost identical to the design model. These symmetric structures provide promising starting points for applications ranging from design of cyclic peptide based metal organic frameworks to creation of high affinity binders of symmetric protein homo‐oligomers. More generally, this work demonstrates the power of computational design for exploring symmetries and structures not found in nature, and for creating synthetic switchable systems., PDB Code(s): 6UFU, 6UG2, 6UG3, 6UG6, 6UGB, 6UGC, 6UCX, 6UD9, 6UDR, 6UDW, 6UDZ, 6UF4, 6UF7, 6UF8, 6UFA and 6UF9

Carbohydrates and glycoproteins modulate key biological functions. Computational approaches inform function to aid in carbohydrate structure prediction, structure determination, and design. However, experimental structure determination of sugar polymers is notoriously difficult as glycans can sample a wide range of low energy conformations, thus limiting the study of glycan-mediated molecular interactions. In this work, we expanded theRosettaCarbohydrateframework, developed and benchmarked effective tools for glycan modeling and design, and extended the Rosetta software suite to better aid in structural analysis and benchmarking tasks through the SimpleMetrics framework. 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. After a rigorous optimization of kinematic and energetic considerations to improve near-native sampling enrichment and decoy discrimination,GlycanTreeModelerwas benchmarked on a test set of diverse glycan structures, or “trees”. Structures predicted byGlycanTreeModeleragreed with native structures at high accuracy for bothde novomodeling and experimental density-guided building.GlycanTreeModeleralgorithms and associated tools were employed to designde novoglycan trees into a protein nanoparticle vaccine that are able to direct the immune response by shielding regions of the scaffold from antibody recognition. This work will inform glycoprotein model prediction, aid in both X-ray and electron microscopy density solutions and refinement, and help lead the way towards a new era of computational glycobiology.

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.

Significance Asparagine-linked (N-linked) protein glycosylation—the covalent attachment of complex sugars to the nitrogen atom in asparagine side chains—is the most widespread posttranslational modification to proteins and also the most complex. N-glycosylation affects a significant number of cellular proteins and can have profound effects on their most important attributes such as biological activity, chemical solubility, folding and stability, immunogenicity, and serum half-life. Accordingly, the strategic installation of glycans at naïve sites has become an attractive means for endowing proteins with advantageous biological and/or biophysical properties. Here, we describe a glycoprotein engineering strategy that enables systematic investigation of the structural and functional consequences of glycan installation at every position along a protein backbone and provides a new route to bespoke glycoproteins., As a common protein modification, asparagine-linked (N-linked) glycosylation has the capacity to greatly influence the biological and biophysical properties of proteins. However, the routine use of glycosylation as a strategy for engineering proteins with advantageous properties is limited by our inability to construct and screen large collections of glycoproteins for cataloguing the consequences of glycan installation. To address this challenge, we describe a combinatorial strategy termed shotgun scanning glycomutagenesis in which DNA libraries encoding all possible glycosylation site variants of a given protein are constructed and subsequently expressed in glycosylation-competent bacteria, thereby enabling rapid determination of glycosylatable sites in the protein. The resulting neoglycoproteins can be readily subjected to available high-throughput assays, making it possible to systematically investigate the structural and functional consequences of glycan conjugation along a protein backbone. The utility of this approach was demonstrated with three different acceptor proteins, namely bacterial immunity protein Im7, bovine pancreatic ribonuclease A, and human anti-HER2 single-chain Fv antibody, all of which were found to tolerate N-glycan attachment at a large number of positions and with relatively high efficiency. The stability and activity of many glycovariants was measurably altered by N-linked glycans in a manner that critically depended on the precise location of the modification. Structural models suggested that affinity was improved by creating novel interfacial contacts with a glycan at the periphery of a protein–protein interface. Importantly, we anticipate that our glycomutagenesis workflow should provide access to unexplored regions of glycoprotein structural space and to custom-made neoglycoproteins with desirable properties.

Many scientific disciplines rely on computational methods for data analysis, model generation, and prediction. Implementing these methods is often accomplished by researchers with domain expertise but without formal training in software engineering or computer science. This arrangement has led to underappreciation of sustainability and maintainability of scientific software tools developed in academic environments. Some software tools have avoided this fate, including the scientific library Rosetta. We use this software and its community as a case study to show how modern software development can be accomplished successfully, irrespective of subject area. Rosetta is one of the largest software suites for macromolecular modeling, with 3.1 million lines of code and many state-of-the-art applications. Since the mid 1990s, the software has been developed collaboratively by the RosettaCommons, a community of academics from over 60 institutions worldwide with diverse backgrounds including chemistry, biology, physiology, physics, engineering, mathematics, and computer science. Developing this software suite has provided us with more than two decades of experience in how to effectively develop advanced scientific software in a global community with hundreds of contributors. Here we illustrate the functioning of this development community by addressing technical aspects (like version control, testing, and maintenance), community-building strategies, diversity efforts, software dissemination, and user support. We demonstrate how modern computational research can thrive in a distributed collaborative community. The practices described here are independent of subject area and can be readily adopted by other software development communities.

Biomolecular structure drives function, and computational capabilities have progressed such that the prediction and computational design of biomolecular structures is increasingly feasible. Because computational biophysics attracts students from many different backgrounds and with different levels of resources, teaching the subject can be challenging. One strategy to teach diverse learners is with interactive multimedia material that promotes self-paced, active learning. We have created a hands-on education strategy with a set of fifteen modules that teach topics in biomolecular structure and design, from fundamentals of conformational sampling and energy evaluation to applications like protein docking, antibody design, and RNA structure prediction. Our modules are based on PyRosetta, a Python library that encapsulates all computational modules and methods in the Rosetta software package. The workshop-style modules are implemented as Jupyter Notebooks that can be executed in the Google Colaboratory, allowing learners access with just a web browser. The digital format of Jupyter Notebooks allows us to embed images, molecular visualization movies, and interactive coding exercises. This multimodal approach may better reach students from different disciplines and experience levels as well as attract more researchers from smaller labs and cognate backgrounds to leverage PyRosetta in their science and engineering research. All materials are freely available at https://github.com/RosettaCommons/PyRosetta.notebooks.

Critical Assessment of PRediction of Interactions (CAPRI) rounds 37 through 45 introduced larger complexes, new macromolecules, and multistage assemblies. For these rounds, we used and expanded docking methods in Rosetta to model 23 target complexes. We successfully predicted 14 target complexes and recognized and refined near-native models generated by other groups for two further targets. Notably, for targets T110 and T136, we achieved the closest prediction of any CAPRI participant. We created several innovative approaches during these rounds. Since round 39 (target 122), we have used the new RosettaDock 4.0, which has a revamped coarse-grained energy function and the ability to perform conformer selection during docking with hundreds of pregenerated protein backbones. Ten of the complexes had some degree of symmetry in their interactions, so we tested Rosetta SymDock, realized its shortcomings, and developed the next-generation symmetric docking protocol, SymDock2, which includes docking of multiple backbones and induced-fit refinement. Since the last CAPRI assessment, we also developed methods for modeling and designing carbohydrates in Rosetta, and we used them to successfully model oligosaccharide-protein complexes in round 41. Although the results were broadly encouraging, they also highlighted the pressing need to invest in (a) flexible docking algorithms with the ability to model loop and linker motions and in (b) new sampling and scoring methods for oligosaccharide-protein interactions.

The Rosetta software for macromolecular modeling, docking and design is extensively used in laboratories worldwide. During two decades of development by a community of laboratories at more than 60 institutions, Rosetta has been continuously refactored and extended. Its advantages are its performance and interoperability between broad modeling capabilities. Here we review tools developed in the last 5 years, including over 80 methods. We discuss improvements to the score function, user interfaces and usability. Rosetta is available at ., This Perspective reviews tools developed over the past five years in the macromolecular modeling, docking and design software Rosetta.

The RosettaCarbohydrate framework is a new tool for modeling a wide variety of saccharide and glycoconjugate structures. This report describes the development of the framework and highlights its applications. The framework integrates with established protocols within the Rosetta modeling and design suite, and it handles the vast complexity and variety of carbohydrate molecules, including branching and sugar modifications. To address challenges of sampling and scoring, RosettaCarbohydrate can sample glycosidic bonds, side-chain conformations, and ring forms, and it utilizes a glycan-specific term within its scoring function. Rosetta can work with standard PDB, GLYCAM, and GlycoWorkbench (.gws) file formats. Saccharide residue-specific chemical information is stored internally, permitting glycoengineering and design. Carbohydrate-specific applications described herein include virtual glycosylation, loop-modeling of carbohydrates, and docking of glyco-ligands to antibodies. Benchmarking data are presented and compared to other studies, demonstrating Rosetta's ability to predict glyco-ligand binding. The framework expands the tools available to glycoscientists and engineers. © 2016 Wiley Periodicals, Inc.

Acinetobacter baumannii is a Gram-negative coccobacillus found primarily in hospital settings that has recently emerged as a source of hospital-acquired infections. A. baumannii expresses a variety of virulence factors, including type IV pili, bacterial extracellular appendages often essential for attachment to host cells. Here, we report the high resolution structures of the major pilin subunit, PilA, from three Acinetobacter strains, demonstrating that A. baumannii subsets produce morphologically distinct type IV pilin glycoproteins. We examine the consequences of this heterogeneity for protein folding and assembly as well as host-cell adhesion by Acinetobacter. Comparisons of genomic and structural data with pilin proteins from other species of soil gammaproteobacteria suggest that these structural differences stem from evolutionary pressure that has resulted in three distinct classes of type IVa pilins, each found in multiple species.

A universal method that improves protein stability and evolution has thus far eluded discovery. Recently, however, studies have shown that insertional fusion to a protein chaperone stabilized various target proteins with minimal negative effects. The improved stability was derived from insertion into a hyperthermophilic protein, Pyrococcus furiosus maltodextrin-binding protein (PfMBP), rather than from changes to the target protein sequence. In this report, by evaluating the thermodynamic and kinetic stability of various inserted β-lactamase (BLA) homologues, we were able to examine the molecular determinants of stability realized by insertional fusion to PfMBP. Results indicated that enhanced stability and suppressed aggregation of BLA stemmed from enthalpic and entropic mechanisms. This report also suggests that insertional fusion to a stable protein scaffold has the potential to be a useful method for improving protein stability, as well as functional protein evolution.

This report describes the metabolic glycoengineering (MGE) of intracellular esterase activity in human colon cancer (LS174T) and Chinese hamster ovary (CHO) cells. In silico analysis of the carboxylesterases CES1 and CES2 suggested that these enzymes are modified with sialylated N-glycans, which are proposed to stabilize the active multimeric forms of these enzymes. This premise was supported by treating cells with butanolylated ManNAc to increase sialylation, which in turn increased esterase activity. By contrast, hexosamine analogues not targeted to sialic acid biosynthesis (e.g., butanoylated GlcNAc or GalNAc) had minimal impact. Measurement of mRNA and protein confirmed that esterase activity was controlled through glycosylation and not through transcription or translation. Azide-modified ManNAc analogues widely used in MGE also enhanced esterase activity and provided a way to enrich targeted “glycoengineered” proteins (such as CES2), thereby providing unambiguous evidence that the compounds were converted to sialosides and installed into the glycan structures of esterases as intended. Overall, this study provides a pioneering example of the modulation of intracellular enzyme activity through MGE, which expands the value of this technology from its current status as a labeling strategy and modulator of cell surface biological events.

Rounds 20-27 of the Critical Assessment of PRotein Interactions (CAPRI) provided a testing platform for computational methods designed to address a wide range of challenges. The diverse targets drove the creation of and new combinations of computational tools. In this study, RosettaDock and other novel Rosetta protocols were used to successfully predict four of the 10 blind targets. For example, for DNase domain of Colicin E2-Im2 immunity protein, RosettaDock and RosettaLigand were used to predict the positions of water molecules at the interface, recovering 46% of the native water-mediated contacts. For α-repeat Rep4-Rep2 and g-type lysozyme-PliG inhibitor complexes, homology models were built and standard and pH-sensitive docking algorithms were used to generate structures with interface RMSD values of 3.3 A and 2.0 A, respectively. A novel flexible sugar-protein docking protocol was also developed and used for structure prediction of the BT4661-heparin-like saccharide complex, recovering 71% of the native contacts. Challenges remain in the generation of accurate homology models for protein mutants and sampling during global docking. On proteins designed to bind influenza hemagglutinin, only about half of the mutations were identified that affect binding (T55: 54%; T56: 48%). The prediction of the structure of the xylanase complex involving homology modeling and multidomain docking pushed the limits of global conformational sampling and did not result in any successful prediction. The diversity of problems at hand requires computational algorithms to be versatile; the recent additions to the Rosetta suite expand the capabilities to encompass more biologically realistic docking problems.

Over the past decade, the Rosetta biomolecular modeling suite has informed diverse biological questions and engineering challenges ranging from interpretation of low-resolution structural data to design of nanomaterials, protein therapeutics, and vaccines. Central to Rosetta’s success is the energy function: amodel parameterized from small molecule and X-ray crystal structure data used to approximate the energy associated with each biomolecule conformation. This paper describes the mathematical models and physical concepts that underlie the latest Rosetta energy function, beta_nov15. Applying these concepts,we explain how to use Rosetta energies to identify and analyze the features of biomolecular models.Finally, we discuss the latest advances in the energy function that extend capabilities from soluble proteins to also include membrane proteins, peptides containing non-canonical amino acids, carbohydrates, nucleic acids, and other macromolecules.

cis-Autoproteolysis is a post-translational modification necessary for the function of ThnT, an enzyme involved in the biosynthesis of the β-lactam antibiotic thienamycin. This modification generates an N-terminal threonine nucleophile that is used to hydrolyze the pantetheinyl moiety of its natural substrate. We determined the crystal structure of autoactivated ThnT to 1.8Å through X-ray crystallography. Comparison to a mutationally inactivated precursor structure revealed several large conformational rearrangements near the active site. To probe the relevance of these transitions, we designed a pantetheine-like chloromethyl ketone inactivator and co-crystallized it with ThnT. Although this class of inhibitor has been in use for several decades, the mode of inactivation had not been determined for an enzyme that uses an N-terminal nucleophile. The co-crystal structure revealed the chloromethyl ketone bound to the N-terminal nucleophile of ThnT through an ether linkage, and analysis suggests inactivation through a direct displacement mechanism. More importantly, this inactivated complex shows that three regions of ThnT that are critical to the formation of the substrate binding pocket undergo rearrangement upon autoproteolysis. Comparison of ThnT with other autoproteolytic enzymes of disparate evolutionary lineage revealed a high degree of similarity within the proenzyme active site, reflecting shared chemical constraints. However, after autoproteolysis, many enzymes, like ThnT, are observed to rearrange in order to accommodate their specific substrate. We propose that this is a general phenomenon, whereby autoprocessing systems with shared chemistry may possess similar structural features that dissipate upon rearrangement into a mature state.

Rounds 20–27 of the Critical Assessment of PRotein Interactions (CAPRI) provided a testing platform for computational methods designed to address a wide range of challenges. The diverse targets drove the creation of and new combinations of computational tools. In this study, RosettaDock and other novel Rosetta protocols were used to successfully predict four of the 10 blind targets. For example, for DNase domain of Colicin E2–Im2 immunity protein, RosettaDock and RosettaLigand were used to predict the positions of water molecules at the interface, recovering 46% of the native water-mediated contacts. For α-repeat Rep4–Rep2 and g-type lysozyme–PliG inhibitor complexes, homology models were built and standard and pH-sensitive docking algorithms were used to generate structures with interface RMSD values of 3.3 Å and 2.0 Å, respectively. A novel flexible sugar–protein docking protocol was also developed and used for structure prediction of the BT4661–heparin-like saccharide complex, recovering 71% of the native contacts. Challenges remain in the generation of accurate homology models for protein mutants and sampling during global docking. On proteins designed to bind influenza hemagglutinin, only about half of the mutations were identified that affect binding (T55: 54%; T56: 48%). The prediction of the structure of the xylanase complex involving homology modeling and multidomain docking pushed the limits of global conformational sampling and did not result in any successful prediction. The diversity of problems at hand requires computational algorithms to be versatile; the recent additions to the Rosetta suite expand the capabilities to encompass more biologically realistic docking problems.

The 2-azetidinone ring of the Class A and D β-lactamase inhibitor clavulanic acid (1) is synthesized by the ATP-utilizing enzyme β-lactam synthetase (βLS). A hydroxyethyl group attached to C-6 of 1 in the (S) configuration markedly enhances the efficacy of this compound against Class C β-lactamases. Guided by a series of X-ray structures of βLS, we have engineered this enzyme to act upon a methylated substrate analogue to give selectively the (3S)-methyl β-lactam core, which, upon closure of the second ring of the bicyclic system of 1, would lead to the (6S)-methylated clavulanic acid derivative.

Mutations are central to evolution, providing the genetic variation upon which selection acts. A mutation's effect on the suitability of a gene to perform a particular function (gene fitness) can be positive, negative, or neutral. Knowledge of the distribution of fitness effects (DFE) of mutations is fundamental for understanding evolutionary dynamics, molecular-level genetic variation, complex genetic disease, the accumulation of deleterious mutations, and the molecular clock. We present comprehensive DFEs for point and codon mutants of the Escherichia coli TEM-1 β-lactamase gene and missense mutations in the TEM-1 protein. These DFEs provide insight into the inherent benefits of the genetic code's architecture, support for the hypothesis that mRNA stability dictates codon usage at the beginning of genes, an extensive framework for understanding protein mutational tolerance, and evidence that mutational effects on protein thermodynamic stability shape the DFE. Contrary to prevailing expectations, we find that deleterious effects of mutation primarily arise from a decrease in specific protein activity and not cellular protein levels.

In 1996, Leesong et al. published the structure of FabA (PDB ID:1MKA), the dehydratase–isomerase enzyme involved in the bacterial type II fatty acid synthase (FAS) system from E. coli.1 The authors described a central, long α-helix “hot dog” surrounded by a bent β-sheet “bun” and so dubbed this the “hot dog fold” (Figure 1). Since that time, roughly 60 proteins have been crystallized and found to have the hot dog fold.2 The core fold topology can be described as an antiparallel β-sheet ordered 1-3-4-5-2. The long hot dog helix is located between strands β1 and β2 (Figure 2D). Some enzymes contain additional β-strands in their “bun”. Figure 1 The Hot Dog Fold Figure 2 Three Types of Hot Dog Folds This enzyme fold serves as a scaffold to execute a variety of reactions involving fatty acid or polyketide thioesters and is found in enzymes of both fatty acid catabolism and anabolism as well as in enzymes of natural product biosynthetic pathways. Every enzyme in this family containing a single hot dog (SHD) fold dimerizes (Figure 2A and D), and two identical active sites are found at the dimeric interface, with key residues in each active site residing on both monomers. Depending on the enzyme and species, these homodimers (protomers) themselves often associate into dimers of dimers or trimers of dimers. (For an excellent review of the various oligomeric states of hot dog enzymes, see Pidugu et al.2) Some hot dog enzymes have two hot dog motifs — thought to arise from gene duplication — and these double hot dog (DHD) enzymes (or domains) have two SHD subdomains that are fused together. In DHD structures, one of the active sites, which both reside at the pseudodimeric interface, has become inactive (Figure 2B and E). The hot dog helix of the C-terminal domain is often kinked; the one in the N-terminal domain remains linear. The oligomeric states of these pseudodimeric enzymes also vary. There are even instances of triple hot dog (THD) enzymes; fungal FAS dehydratase domains, for example, contain such a fold.3,4 In a THD enzyme, the third hot dog region is a domain insertion between the first and second hot dog motifs, which associate with each other in similar manner to the two motifs of a DHD enzyme, including the bent, C-terminal helix. The added domain is highly distorted, such that the hot dog helix is much shorter. It no longer locates within the β-sheet bun and is instead found alongside the β-sheet (Figure 2C and F). THD enzymes also only have a single active site at the pseudodimeric interface of the “normal” hot dog regions. The significance of only one hot dog helix remaining linear in both DHD and THD folds will be discussed later in this review. The secondary structural arrangements of idealized SHD, DHD, and THD folds are compared in the topology diagrams depicted in Figure 2D, E, and F. Because different enzymes have extra helices and strands in addition to the core hot dog and bun shown in the topology diagrams, for the purposes of comparing hot dog folds, we have formulated the following system: Core β-strands are labeled with an Arabic numeral in the same order as they occur in the sequence. Strands found in the second subdomain of DHD folds are treated as if they reside on an SHD with the further designation of a prime symbol. The strands of the inserted third hot dog motif of the THDs are designated with a double prime. Any extra strands in addition to these may be designated with an “x” followed by an Arabic numeral. The hot dog helices of the first, second, or third motifs are labeled αHD, αHD′, αHD″, respectively; additional helices are named by “x” followed by a capital letter. This review will use this notation throughout. The hot dog superfamily includes over a thousand enzymes or domains and has been divided into at least 17 subfamilies based on primary sequence analysis — although this analysis did not capture the majority of DHD and THD enzymes, including animal and fungal FAS domains.5 Hot dog folds are found or predicted in archaea, bacteria, and eukaryotes. Functionally, however, the majority of the superfamily can be primarily divided into two groups: those enzymes performing either dehydration or hydration of thioester substrates and those performing thioester hydrolysis.

Polyketide natural products possess diverse architectures and biological functions and share a subset of biosynthetic steps with fatty acid synthesis. The final transformation catalyzed by both polyketide synthases (PKSs) and fatty acid synthases is most often carried out by a thioesterase (TE). The synthetic versatility of TE domains in fungal nonreducing, iterative PKSs (NR-PKSs) has been shown to extend to Claisen cyclase (CLC) chemistry by catalyzing C–C ring closure reactions as opposed to thioester hydrolysis or O–C/N–C macrocyclization observed in previously reported TE structures. Catalysis of C–C bond formation as a product release mechanism dramatically expands the synthetic potential of PKSs, but how this activity was acquired has remained a mystery. We report the biochemical and structural analyses of the TE/CLC domain in polyketide synthase A, the multidomain PKS central to the biosynthesis of aflatoxin B 1 , a potent environmental carcinogen. Mutagenesis experiments confirm the predicted identity of the catalytic triad and its role in catalyzing the final Claisen-type cyclization to the aflatoxin precursor, norsolorinic acid anthrone. The 1.7 Å crystal structure displays an α/β-hydrolase fold in the catalytic closed form with a distinct hydrophobic substrate-binding chamber. We propose that a key rotation of the substrate side chain coupled to a protein conformational change from the open to closed form spatially governs substrate positioning and C–C cyclization. The biochemical studies, the 1.7 Å crystal structure of the TE/CLC domain, and intermediate modeling afford the first mechanistic insights into this widely distributed C–C bond-forming class of TEs.

Polyketides are a class of natural products with diverse structures and biological activities. The structural variability of aromatic products of fungal nonreducing, multidomain iterative polyketide synthases (NR-PKS group of IPKSs) results from regiospecific cyclizations of reactive poly-beta-keto intermediates. How poly-beta-keto species are synthesized and stabilized, how their chain lengths are determined, and, in particular, how specific cyclization patterns are controlled have been largely inaccessible and functionally unknown until recently. A product template (PT) domain is responsible for controlling specific aldol cyclization and aromatization of these mature polyketide precursors, but the mechanistic basis is unknown. Here we present the 1.8 A crystal structure and mutational studies of a dissected PT monodomain from PksA, the NR-PKS that initiates the biosynthesis of the potent hepatocarcinogen aflatoxin B(1) in Aspergillus parasiticus. Despite having minimal sequence similarity to known enzymes, the structure displays a distinct 'double hot dog' (DHD) fold. Co-crystal structures with palmitate or a bicyclic substrate mimic illustrate that PT can bind both linear and bicyclic polyketides. Docking and mutagenesis studies reveal residues important for substrate binding and catalysis, and identify a phosphopantetheine localization channel and a deep two-part interior binding pocket and reaction chamber. Sequence similarity and extensive conservation of active site residues in PT domains suggest that the mechanistic insights gleaned from these studies will prove general for this class of IPKSs, and lay a foundation for defining the molecular rules controlling NR-PKS cyclization specificity.

Carbohydrates are infamously challenging to model, yet they affect protein structure, stability, and activity. We are developing accurate and fast methods of modeling and designing carbohydrates for applications in glycobiology. We have built a framework within the Rosetta structure and design suite for modeling saccharide ligands and complex glycoconjugates. Our intuitive and efficient data structures allow access to all torsion angles (φ, ψ, ω, and χ) and Cremer–Pople parameters for sampling ring forms and capture the high degree of flexibility, stereochemistry, and branching in carbohydrates. Rosetta's flexibility and speed enable modeling of any glycan-containing molecule in docking and refinement protocols through exploration of this vast torsional and ring-conformational diversity.Rosetta's residue-centric approach, coupled with combinatorial “patching” of standard residues with specific functional groups, allows for design algorithms that sample alternative saccharide units, enabling high-throughput screening of thousands of protein variants and glycoforms in a search for stable or functional molecules.Here, we will report three studies benchmarking monosaccharide ring conformations, oligosaccharide structure prediction, and bound–bound protein–oligosaccharide docking. We explore the relative energy surfaces of the ring forms of all D-aldohexopyranoses; we examine predicted structures of two LewisX oligosaccharides; and we compare the docking predictions of eleven antibody–glycoantigen pairs with known structures. These studies will allow us to rigorously test the Rosetta scoring (energy) function, to adapt it to the unique chemical effects of sugars.We will also present preliminary real-world applications in antibody accessibility for glycosylation enzymes, x-ray crystal refinement of an extensively glycosylated HIV-1 envelope protein trimer, and the activity of glycosylated carboxylesterases.These new approaches will provide glycobiologists and glycoengineers a new computational toolbox, further the understanding of the biomolecular mechanisms of disease, and create opportunities for a wide range of previously intractable studies.