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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

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.

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.

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.

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.

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.