Residue-centric modeling and design of saccharide and glycoconjugate structures

Computational toolbox for the analysis of protein-glycan interactions

Protein-glycan interactions play pivotal roles in numerous biological processes, ranging from cellular recognition to immune response modulation. Understanding the intricate details of these interactions is crucial for deciphering the molecular mechanisms underlying various physiological and pathological conditions. Computational techniques have emerged as powerful tools that can help in drawing, building and visualising complex biomolecules and provide insights into their dynamic behaviour at atomic and molecular levels. This review provides an overview of the main computational tools useful for studying biomolecular systems, particularly glycans, both in free state and in complex with proteins, also with reference to the principles, methodologies, and applications of all-atom molecular dynamics simulations. Herein, we focused on the programs that are generally employed for preparing protein and glycan input files to execute molecular dynamics simulations and analyse the corresponding results. The presented computational toolbox represents a valuable resource for researchers studying protein-glycan interactions and incorporates advanced computational methods for building, visualising and predicting protein/glycan structures, modelling protein-ligand complexes, and analyse MD outcomes. Moreover, selected case studies have been reported to highlight the importance of computational tools in studying protein-glycan systems, revealing the capability of these tools to provide valuable insights into the binding kinetics, energetics, and structural determinants that govern specific molecular interactions.

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.

The Mutagenic Plasticity of the Cholera Toxin B-Subunit Surface Residues: Stability and Affinity

Mastering selective molecule trafficking across human cell membranes poses a formidable challenge in healthcare biotechnology while offering the prospect of breakthroughs in drug delivery, gene therapy, and diagnostic imaging. The cholera toxin B-subunit (CTB) has the potential to be a useful cargo transporter for these applications. CTB is a robust protein that is amenable to reengineering for diverse applications; however, protein redesign has mostly focused on modifications of the N- and C-termini of the protein. Exploiting the full power of rational redesign requires a detailed understanding of the contributions of the surface residues to protein stability and binding activity. Here, we employed Rosetta-based computational saturation scans on 58 surface residues of CTB, including the GM1 binding site, to analyze both ligand-bound and ligand-free structures to decipher mutational effects on protein stability and GM1 affinity. Complimentary experimental results from differential scanning fluorimetry and isothermal titration calorimetry provided melting temperatures and GM1 binding affinities for 40 alanine mutants among these positions. The results showed that CTB can accommodate diverse mutations while maintaining its stability and ligand binding affinity. These mutations could potentially allow modification of the oligosaccharide binding specificity to change its cellular targeting, alter the B-subunit intracellular routing, or impact its shelf-life and in vivo half-life through changes to protein stability. We anticipate that the mutational space maps presented here will serve as a cornerstone for future CTB redesigns, paving the way for the development of innovative biotechnological tools.

Combining machine learning with structure-based protein design to predict and engineer post-translational modifications of proteins

Post-translational modifications (PTMs) of proteins play a vital role in their function and stability. These modifications influence protein folding, signaling, protein-protein interactions, enzyme activity, binding affinity, aggregation, degradation, and much more. To date, over 400 types of PTMs have been described, representing chemical diversity well beyond the genetically encoded amino acids. Such modifications pose a challenge to the successful design of proteins, but also represent a major opportunity to diversify the protein engineering toolbox. To this end, we first trained artificial neural networks (ANNs) to predict eighteen of the most abundant PTMs, including protein glycosylation, phosphorylation, methylation, and deamidation. In a second step, these models were implemented inside the computational protein modeling suite Rosetta, which allows flexible combination with existing protocols to model the modified sites and understand their impact on protein stability as well as function. Lastly, we developed a new design protocol that either maximizes or minimizes the predicted probability of a particular site being modified. We find that this combination of ANN prediction and structure-based design can enable the modification of existing, as well as the introduction of novel, PTMs. The potential applications of our work include, but are not limited to, glycan masking of epitopes, strengthening protein-protein interactions through phosphorylation, as well as protecting proteins from deamidation liabilities. These applications are especially important for the design of new protein therapeutics where PTMs can drastically change the therapeutic properties of a protein. Our work adds novel tools to Rosetta's protein engineering toolbox that allow for the rational design of PTMs.

Molecular correlates of vaccine-induced protection against typhoid fever

BACKGROUNDTyphoid fever is caused by the Gram-negative bacterium Salmonella enterica serovar Typhi and poses a substantial public health burden worldwide. Vaccines have been developed based on the surface Vi-capsular polysaccharide of S. Typhi; these include a plain-polysaccharide-based vaccine, ViPS, and a glycoconjugate vaccine, ViTT. To understand immune responses to these vaccines and their vaccine-induced immunological protection, molecular signatures were analyzed using bioinformatic approaches.METHODSBulk RNA-Seq data were generated from blood samples obtained from adult human volunteers enrolled in a vaccine trial, who were then challenged with S. Typhi in a controlled human infection model (CHIM). These data were used to conduct differential gene expression analyses, gene set and modular analyses, B cell repertoire analyses, and time-course analyses at various post-vaccination and post-challenge time points between participants receiving ViTT, ViPS, or a control meningococcal vaccine.RESULTSTranscriptomic responses revealed strong differential molecular signatures between the 2 typhoid vaccines, mostly driven by the upregulation in humoral immune signatures, including selective usage of immunoglobulin heavy chain variable region (IGHV) genes and more polarized clonal expansions. We describe several molecular correlates of protection against S. Typhi infection, including clusters of B cell receptor (BCR) clonotypes associated with protection, with known binders of Vi-polysaccharide among these.CONCLUSIONThe study reports a series of contemporary analyses that reveal the transcriptomic signatures after vaccination and infectious challenge, while identifying molecular correlates of protection that may inform future vaccine design and assessment.TRIAL REGISTRATIONClinicalTrials.gov NCT02324751.

Recent Advances in NMR Protein Structure Prediction with ROSETTA

Nuclear magnetic resonance (NMR) spectroscopy is a powerful method for studying the structure and dynamics of proteins in their native state. For high-resolution NMR structure determination, the collection of a rich restraint dataset is necessary. This can be difficult to achieve for proteins with high molecular weight or a complex architecture. Computational modeling techniques can complement sparse NMR datasets (<1 restraint per residue) with additional structural information to elucidate protein structures in these difficult cases. The Rosetta software for protein structure modeling and design is used by structural biologists for structure determination tasks in which limited experimental data is available. This review gives an overview of the computational protocols available in the Rosetta framework for modeling protein structures from NMR data. We explain the computational algorithms used for the integration of different NMR data types in Rosetta. We also highlight new developments, including modeling tools for data from paramagnetic NMR and hydrogen-deuterium exchange, as well as chemical shifts in CS-Rosetta. Furthermore, strategies are discussed to complement and improve structure predictions made by the current state-of-the-art AlphaFold2 program using NMR-guided Rosetta modeling.

Stabilizing proteins, simplified: A Rosetta-based webtool for predicting favorable mutations

Many proteins have low thermodynamic stability, which can lead to low expression yields and limit functionality in research, industrial and clinical settings. This article introduces two, web-based tools that use the high-resolution structure of a protein along with the Rosetta molecular modeling program to predict stabilizing mutations. The protocols were recently applied to three genetically and structurally distinct proteins and successfully predicted mutations that improved thermal stability and/or protein yield. In all three cases, combining the stabilizing mutations raised the protein unfolding temperatures by more than 20°C. The first protocol evaluates point mutations and can generate a site saturation mutagenesis heatmap. The second identifies mutation clusters around user-defined positions. Both applications only require a protein structure and are particularly valuable when a deep multiple sequence alignment is not available. These tools were created to simplify protein engineering and enable research that would otherwise be infeasible due to poor expression and stability of the native molecule.

Key aspects of the past 30 years of protein design

Proteins are the workhorse of life. They are the building infrastructure of living systems; they are the most efficient molecular machines known, and their enzymatic activity is still unmatched in versatility by any artificial system. Perhaps proteins' most remarkable feature is their modularity. The large amount of information required to specify each protein's function is analogically encoded with an alphabet of just ∼20 letters. The protein folding problem is how to encode all such information in a sequence of 20 letters. In this review, we go through the last 30 years of research to summarize the state of the art and highlight some applications related to fundamental problems of protein evolution.

Multifaceted Computational Modeling in Glycoscience

Glycoscience assembles all the scientific disciplines involved in studying various molecules and macromolecules containing carbohydrates and complex glycans. Such an ensemble involves one of the most extensive sets of molecules in quantity and occurrence since they occur in all microorganisms and higher organisms. Once the compositions and sequences of these molecules are established, the determination of their three-dimensional structural and dynamical features is a step toward understanding the molecular basis underlying their properties and functions. The range of the relevant computational methods capable of addressing such issues is anchored by the specificity of stereoelectronic effects from quantum chemistry to mesoscale modeling throughout molecular dynamics and mechanics and coarse-grained and docking calculations. The Review leads the reader through the detailed presentations of the applications of computational modeling. The illustrations cover carbohydrate-carbohydrate interactions, glycolipids, and N- and O-linked glycans, emphasizing their role in SARS-CoV-2. The presentation continues with the structure of polysaccharides in solution and solid-state and lipopolysaccharides in membranes. The full range of protein-carbohydrate interactions is presented, as exemplified by carbohydrate-active enzymes, transporters, lectins, antibodies, and glycosaminoglycan binding proteins. A final section features a list of 150 tools and databases to help address the many issues of structural glycobioinformatics.

prepareforleap: An automated tool for fast PDB-to-parameter generation

Setting up molecular dynamics simulations from experimentally determined structures is often complicated by a variety of factors, particularly the inclusion of carbohydrates, since these have several anomer types which can be linked in a variety of ways. Here we present a stand-alone tool implemented in the widely-used software CPPTRAJ that can be used to automate building structures and generating a "ready to run" parameter and coordinate file pair. This tool automatically identifies carbohydrate anomer type, configuration, linkage, and functional groups, and performs topology modifications (e.g., renaming residue/atom names) required to build the final system using state of the art GLYCAM force field parameters. It will also generate the necessary commands for bonding carbohydrates and creating any disulfide bonds.

Strategies for Glycoengineering Therapeutic Proteins

Almost all therapeutic proteins are glycosylated, with the carbohydrate component playing a long-established, substantial role in the safety and pharmacokinetic properties of this dominant category of drugs. In the past few years and moving forward, glycosylation is increasingly being implicated in the pharmacodynamics and therapeutic efficacy of therapeutic proteins. This article provides illustrative examples of drugs that have already been improved through glycoengineering including cytokines exemplified by erythropoietin (EPO), enzymes (ectonucleotide pyrophosphatase 1, ENPP1), and IgG antibodies (e.g., afucosylated Gazyva®, Poteligeo®, Fasenra™, and Uplizna®). In the future, the deliberate modification of therapeutic protein glycosylation will become more prevalent as glycoengineering strategies, including sophisticated computer-aided tools for "building in" glycans sites, acceptance of a broad range of production systems with various glycosylation capabilities, and supplementation methods for introducing non-natural metabolites into glycosylation pathways further develop and become more accessible.

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.

Development and Evaluation of GlycanDock: A Protein-Glycoligand Docking Refinement Algorithm in Rosetta

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 Å 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.

Modeling Immunity with Rosetta: Methods for Antibody and Antigen Design

Structure-based antibody and antigen design has advanced greatly in recent years, due not only to the increasing availability of experimentally determined structures but also to improved computational methods for both prediction and design. Constant improvements in performance within the Rosetta software suite for biomolecular modeling have given rise to a greater breadth of structure prediction, including docking and design application cases for antibody and antigen modeling. Here, we present an overview of current protocols for antibody and antigen modeling using Rosetta and exemplify those by detailed tutorials originally developed for a Rosetta workshop at Vanderbilt University. These tutorials cover antibody structure prediction, docking, and design and antigen design strategies, including the addition of glycans in Rosetta. We expect that these materials will allow novice users to apply Rosetta in their own projects for modeling antibodies and antigens.

An expanded benchmark for antibody-antigen docking and affinity prediction reveals insights into antibody recognition determinants

Accurate predictive modeling of antibody-antigen complex structures and structure-based antibody design remain major challenges in computational biology, with implications for biotherapeutics, immunity, and vaccines. Through a systematic search for high-resolution structures of antibody-antigen complexes and unbound antibody and antigen structures, in conjunction with identification of experimentally determined binding affinities, we have assembled a non-redundant set of test cases for antibody-antigen docking and affinity prediction. This benchmark more than doubles the number of antibody-antigen complexes and corresponding affinities available in our previous benchmarks, providing an unprecedented view of the determinants of antibody recognition and insights into molecular flexibility. Initial assessments of docking and affinity prediction tools highlight the challenges posed by this diverse set of cases, which includes camelid nanobodies, therapeutic monoclonal antibodies, and broadly neutralizing antibodies targeting viral glycoproteins. This dataset will enable development of advanced predictive modeling and design methods for this therapeutically relevant class of protein-protein interactions.

CoV3D: a database of high resolution coronavirus protein structures

SARS-CoV-2, the etiologic agent of COVID-19, exemplifies the general threat to global health posed by coronaviruses. The urgent need for effective vaccines and therapies is leading to a rapid rise in the number of high resolution structures of SARS-CoV-2 proteins that collectively reveal a map of virus vulnerabilities. To assist structure-based design of vaccines and therapeutics against SARS-CoV-2 and other coronaviruses, we have developed CoV3D, a database and resource for coronavirus protein structures, which is updated on a weekly basis. CoV3D provides users with comprehensive sets of structures of coronavirus proteins and their complexes with antibodies, receptors, and small molecules. Integrated molecular viewers allow users to visualize structures of the spike glycoprotein, which is the major target of neutralizing antibodies and vaccine design efforts, as well as sets of spike-antibody complexes, spike sequence variability, and known polymorphisms. In order to aid structure-based design and analysis of the spike glycoprotein, CoV3D permits visualization and download of spike structures with modeled N-glycosylation at known glycan sites, and contains structure-based classification of spike conformations, generated by unsupervised clustering. CoV3D can serve the research community as a centralized reference and resource for spike and other coronavirus protein structures, and is available at: https://cov3d.ibbr.umd.edu.

Recent advances in de novo protein design: Principles, methods, and applications

The computational de novo protein design is increasingly applied to address a number of key challenges in biomedicine and biological engineering. Successes in expanding applications are driven by advances in design principles and methods over several decades. Here, we review recent innovations in major aspects of the de novo protein design and include how these advances were informed by principles of protein architecture and interactions derived from the wealth of structures in the Protein Data Bank. We describe developments in de novo generation of designable backbone structures, optimization of sequences, design scoring functions, and the design of the function. The advances not only highlight design goals reachable now but also point to the challenges and opportunities for the future of the field.

Three-Dimensional Structures of Carbohydrates and Where to Find Them

Analysis and systematization of accumulated data on carbohydrate structural diversity is a subject of great interest for structural glycobiology. Despite being a challenging task, development of computational methods for efficient treatment and management of spatial (3D) structural features of carbohydrates breaks new ground in modern glycoscience. This review is dedicated to approaches of chemo- and glyco-informatics towards 3D structural data generation, deposition and processing in regard to carbohydrates and their derivatives. Databases, molecular modeling and experimental data validation services, and structure visualization facilities developed for last five years are reviewed.

Computational tools for drawing, building and displaying carbohydrates: a visual guide

Drawing and visualisation of molecular structures are some of the most common tasks carried out in structural glycobiology, typically using various software. In this perspective article, we outline developments in the computational tools for the sketching, visualisation and modelling of glycans. The article also provides details on the standard representation of glycans, and glycoconjugates, which helps the communication of structure details within the scientific community. We highlight the comparative analysis of the available tools which could help researchers to perform various tasks related to structure representation and model building of glycans. These tools can be useful for glycobiologists or any researcher looking for a ready to use, simple program for the sketching or building of glycans.

Novel sampling strategies and a coarse-grained score function for docking homomers, flexible heteromers, and oligosaccharides using Rosetta in CAPRI rounds 37-45

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.

Macromolecular modeling and design in Rosetta: recent methods and frameworks

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 http://www.rosettacommons.org.

CoV3D: A database and resource for high resolution coronavirus protein structures

SARS-CoV-2, the etiologic agent behind COVID-19, exemplifies the general threat to global health posed by coronaviruses. The urgent need for effective vaccines and therapies is leading to a rapid rise in the number of high resolution structures of SARS-CoV-2 proteins that collectively reveal a map of virus vulnerabilities. To assist structure-based design of vaccines and therapeutics against SARS-CoV-2 and other coronaviruses, we have developed CoV3D, a database and resource for coronavirus protein structures, which is updated on a weekly basis. CoV3D provides users with comprehensive sets of structures of coronavirus proteins and their complexes with antibodies, receptors, and small molecules. Integrated molecular viewers allow users to visualize structures of the spike glycoprotein, which is the major target of neutralizing antibodies and vaccine design efforts, as well as sets of spike-antibody complexes, spike sequence variability, and known polymorphisms. In order to aid structure-based design and analysis of the spike glycoprotein, CoV3D permits visualization and download of spike structures with modeled N-glycosylation at known glycan sites, and contains structure-based classification of spike conformations, generated by unsupervised clustering. CoV3D can serve the research community as a centralized reference and resource for spike and other coronavirus protein structures, and is available at: https://cov3d.ibbr.umd.edu.

Increasing the Affinity of an O-Antigen Polysaccharide Binding Site in Shigella flexneri Bacteriophage Sf6 Tailspike Protein

Broad and unspecific use of antibiotics accelerates spread of resistances. Sensitive and robust pathogen detection is thus important for a more targeted application. Bacteriophages contain a large repertoire of pathogen-binding proteins. These tailspike proteins (TSP) often bind surface glycans and represent a promising design platform for specific pathogen sensors. We analysed bacteriophage Sf6 TSP that recognizes the O-polysaccharide of dysentery-causing Shigella flexneri to develop variants with increased sensitivity for sensor applications. Ligand polyrhamnose backbone conformations were obtained from 2D 1 H,1 H-trNOESY NMR utilizing methine-methine and methine-methyl correlations. They agreed well with conformations obtained from molecular dynamics (MD), validating the method for further predictions. In a set of mutants, MD predicted ligand flexibilities that were in good correlation with binding strength as confirmed on immobilized S. flexneri O-polysaccharide (PS) with surface plasmon resonance. In silico approaches combined with rapid screening on PS surfaces hence provide valuable strategies for TSP-based pathogen sensor design.

In Vivo Assembly of Nanoparticles Achieved through Synergy of Structure-Based Protein Engineering and Synthetic DNA Generates Enhanced Adaptive Immunity

Nanotechnologies are considered to be of growing importance to the vaccine field. Through decoration of immunogens on multivalent nanoparticles, designed nanovaccines can elicit improved humoral immunity. However, significant practical and monetary challenges in large-scale production of nanovaccines have impeded their widespread clinical translation. Here, an alternative approach is illustrated integrating computational protein modeling and adaptive electroporation-mediated synthetic DNA delivery, thus enabling direct in vivo production of nanovaccines. DNA-launched nanoparticles are demonstrated displaying an HIV immunogen spontaneously self-assembled in vivo. DNA-launched nanovaccines induce stronger humoral responses than their monomeric counterparts in both mice and guinea pigs, and uniquely elicit CD8+ effector T-cell immunity as compared to recombinant protein nanovaccines. Improvements in vaccine responses recapitulate when DNA-launched nanovaccines with alternative scaffolds and decorated antigen are designed and evaluated. Finally, evaluation of functional immune responses induced by DLnanovaccines demonstrates that, in comparison to control mice or mice immunized with DNA-encoded hemagglutinin monomer, mice immunized with a DNA-launched hemagglutinin nanoparticle vaccine fully survive a lethal influenza challenge, and have substantially lower viral load, weight loss, and influenza-induced lung pathology. Additional study of these next-generation in vivo-produced nanovaccines may offer advantages for immunization against multiple disease targets.

Neutralizing Antibody Induction by HIV-1 Envelope Glycoprotein SOSIP Trimers on Iron Oxide Nanoparticles May Be Impaired by Mannose Binding Lectin

We covalently attached human immunodeficiency virus type 1 (HIV-1) Env SOSIP trimers to iron oxide nanoparticles (IO-NPs) to create a particulate immunogen for neutralizing antibody (NAb) induction. The attached trimers, ∼20 per particle, retained native-like antigenicity, judged by reactivity with NAbs and non-NAbs. Bivalent (BG505 and B41) trimer IO-NPs were made, as were IO-NPs displaying B41 trimers carrying a PADRE T-cell helper epitope (TCHE). We immunized mice with B41 soluble or IO-NP trimers after PADRE peptide priming. After two immunizations, IO-NP presentation and the TCHE tag independently and substantially increased anti-trimer antibody responses, but titer differences waned after two further doses. Notable and unexpected findings were that autologous NAbs to the N289 glycan hole epitope were consistently induced in mice given soluble but not IO-NP trimers. Various recombinant mannose binding lectins (MBLs) and MBLs in sera of both murine and human origin bound to soluble and IO-NP trimers. MBL binding occluded the autologous NAb epitope on the B41 IO-NP trimers, which may contribute to its poor immunogenicity. The exposure of a subset of broadly active NAb epitopes was also impaired by MBL binding, which could have substantial implications for the utility of trimer-bearing nanoparticles in general and perhaps also for soluble Env proteins.IMPORTANCE Recombinant trimeric SOSIP proteins are vaccine components intended to induce neutralizing antibodies (NAbs) that prevent cells from infection by human immunodeficiency virus type 1 (HIV-1). A way to increase the strength of antibody responses to these proteins is to present them on the surface of nanoparticles (NPs). We chemically attached about 20 SOSIP trimers to NPs made of iron oxide (IO). The resulting IO-NP trimers had appropriate properties when we studied them in the laboratory but, unexpectedly, were less able to induce NAbs than nonattached trimers when used to immunize mice. We found that mannose binding lectins, proteins naturally present in the serum of mice and other animals, bound strongly to the soluble and IO-NP trimers, blocking access to antibody epitopes in a way that may impede the development of NAb responses. These findings should influence how trimer-bearing NPs of various designs are made and used.

The current structural glycome landscape and emerging technologies

Carbohydrates represent one of the building blocks of life, along with nucleic acids, proteins and lipids. Although glycans are involved in a wide range of processes from embryogenesis to protein trafficking and pathogen infection, we are still a long way from deciphering the glycocode. In this review, we aim to present a few of the challenges that researchers working in the area of glycobiology can encounter and what strategies can be utilised to overcome them. Our goal is to paint a comprehensive picture of the current saccharide landscape available in the Protein Data Bank (PDB). We also review recently updated repositories relevant to the topic proposed, the impact of software development on strategies to structurally solve carbohydrate moieties, and state-of-the-art molecular and cellular biology methods that can shed some light on the function and structure of glycans.

The human naive B cell repertoire contains distinct subclasses for a germline-targeting HIV-1 vaccine immunogen

Traditional vaccine development to prevent some of the worst current pandemic diseases has been unsuccessful so far. Germline-targeting immunogens have potential to prime protective antibodies (Abs) via more targeted immune responses. Success of germline-targeting vaccines in humans will depend on the composition of the human naive B cell repertoire, including the frequencies and affinities of epitope-specific B cells. However, the human naive B cell repertoire remains largely undefined. Assessment of antigen-specific human naive B cells among hundreds of millions of B cells from multiple donors may be used as pre-phase 1 ex vivo human testing to potentially forecast B cell and Ab responses to new vaccine designs. VRC01 is an HIV broadly neutralizing Ab (bnAb) against the envelope CD4-binding site (CD4bs). We characterized naive human B cells recognizing eOD-GT8, a germline-targeting HIV-1 vaccine candidate immunogen designed to prime VRC01-class Abs. Several distinct subclasses of VRC01-class naive B cells were identified, sharing sequence characteristics with inferred precursors of known bnAbs VRC01, VRC23, PCIN63, and N6. Multiple naive B cell clones exactly matched mature VRC01-class bnAb L-CDR3 sequences. Non-VRC01-class B cells were also characterized, revealing recurrent public light chain sequences. Unexpectedly, we also identified naive B cells related to the IOMA-class CD4bs bnAb. These different subclasses within the human repertoire had strong initial affinities (K_D) to the immunogen, up to 13 nM, and represent encouraging indications that multiple independent pathways may exist for vaccine-elicited VRC01-class bnAb development in most individuals. The frequencies of these distinct eOD-GT8 B cell specificities give insights into antigen-specific compositional features of the human naive B cell repertoire and provide actionable information for vaccine design and advancement.

CHARMM-GUI Glycan Modeler for modeling and simulation of carbohydrates and glycoconjugates

Characterizing glycans and glycoconjugates in the context of three-dimensional structures is important in understanding their biological roles and developing efficient therapeutic agents. Computational modeling and molecular simulation have become an essential tool complementary to experimental methods. Here, we present a computational tool, Glycan Modeler for in silico N-/O-glycosylation of the target protein and generation of carbohydrate-only systems. In our previous study, we developed Glycan Reader, a web-based tool for detecting carbohydrate molecules from a PDB structure and generation of simulation system and input files. As integrated into Glycan Reader in CHARMM-GUI, Glycan Modeler (Glycan Reader & Modeler) enables to generate the structures of glycans and glycoconjugates for given glycan sequences and glycosylation sites using PDB glycan template structures from Glycan Fragment Database (http://glycanstructure.org/fragment-db). Our benchmark tests demonstrate the universal applicability of Glycan Reader & Modeler to various glycan sequences and target proteins. We also investigated the structural properties of modeled glycan structures by running 2-μs molecular dynamics simulations of HIV envelope protein. The simulations show that the modeled glycan structures built by Glycan Reader & Modeler have the similar structural features compared to the ones solved by X-ray crystallography. We also describe the representative examples of glycoconjugate modeling with video demos to illustrate the practical applications of Glycan Reader & Modeler. Glycan Reader & Modeler is freely available at http://charmm-gui.org/input/glycan.

Antigenicity and Immunogenicity of Differentially Glycosylated Hepatitis C Virus E2 Envelope Proteins Expressed in Mammalian and Insect Cells

The development of a prophylactic vaccine for hepatitis C virus (HCV) remains a global health challenge. Cumulative evidence supports the importance of antibodies targeting the HCV E2 envelope glycoprotein to facilitate viral clearance. However, a significant challenge for a B cell-based vaccine is focusing the immune response on conserved E2 epitopes capable of eliciting neutralizing antibodies not associated with viral escape. We hypothesized that glycosylation might influence the antigenicity and immunogenicity of E2. Accordingly, we performed head-to-head molecular, antigenic, and immunogenic comparisons of soluble E2 (sE2) produced in (i) mammalian (HEK293) cells, which confer mostly complex- and high-mannose-type glycans; and (ii) insect (Sf9) cells, which impart mainly paucimannose-type glycans. Mass spectrometry demonstrated that all 11 predicted N-glycosylation sites were utilized in both HEK293- and Sf9-derived sE2, but that N-glycans in insect sE2 were on average smaller and less complex. Both proteins bound CD81 and were recognized by conformation-dependent antibodies. Mouse immunogenicity studies revealed that similar polyclonal antibody responses were generated against antigenic domains A to E of E2. Although neutralizing antibody titers showed that Sf9-derived sE2 induced moderately stronger responses than did HEK293-derived sE2 against the homologous HCV H77c isolate, the two proteins elicited comparable neutralization titers against heterologous isolates. Given that global alteration of HCV E2 glycosylation by expression in different hosts did not appreciably affect antigenicity or overall immunogenicity, a more productive approach to increasing the antibody response to neutralizing epitopes may be complete deletion, rather than just modification, of specific N-glycans proximal to these epitopes.IMPORTANCE The development of a vaccine for hepatitis C virus (HCV) remains a global health challenge. A major challenge for vaccine development is focusing the immune response on conserved regions of the HCV envelope protein, E2, capable of eliciting neutralizing antibodies. Modification of E2 by glycosylation might influence the immunogenicity of E2. Accordingly, we performed molecular and immunogenic comparisons of E2 produced in mammalian and insect cells. Mass spectrometry demonstrated that the predicted glycosylation sites were utilized in both mammalian and insect cell E2, although the glycan types in insect cell E2 were smaller and less complex. Mouse immunogenicity studies revealed similar polyclonal antibody responses. However, insect cell E2 induced stronger neutralizing antibody responses against the homologous isolate used in the vaccine, albeit the two proteins elicited comparable neutralization titers against heterologous isolates. A more productive approach for vaccine development may be complete deletion of specific glycans in the E2 protein.

Automatically Fixing Errors in Glycoprotein Structures with Rosetta

Recent advances in single-particle cryo-electron microscopy (cryoEM) have resulted in determination of an increasing number of protein structures with resolved glycans. However, existing protocols for the refinement of glycoproteins at low resolution have failed to keep up with these advances. As a result, numerous deposited structures contain glycan stereochemical errors. Here, we describe a Rosetta-based approach for both cryoEM and X-ray crystallography refinement of glycoproteins that is capable of correcting conformational and configurational errors in carbohydrates. Building upon a previous Rosetta framework, we introduced additional features and score terms enabling automatic detection, setup, and refinement of glycan-containing structures. We benchmarked this approach using 12 crystal structures and showed that glycan geometries can be automatically improved while maintaining good fit to the crystallographic data. Finally, we used this method to refine carbohydrates of the human coronavirus NL63 spike glycoprotein and of an HIV envelope glycoprotein, demonstrating its usefulness for cryoEM refinement.

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New method for refinement of carbohydrates with low-resolution electron density

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Improved physical geometry of glycans in protein structures

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Compatible with cryoEM and X-ray crystallography data

Predicting the Structures of Glycans, Glycoproteins, and Their Complexes

Complex carbohydrates are ubiquitous in nature, and together with proteins and nucleic acids they comprise the building blocks of life. But unlike proteins and nucleic acids, carbohydrates form nonlinear polymers, and they are not characterized by robust secondary or tertiary structures but rather by distributions of well-defined conformational states. Their molecular flexibility means that oligosaccharides are often refractory to crystallization, and nuclear magnetic resonance (NMR) spectroscopy augmented by molecular dynamics (MD) simulation is the leading method for their characterization in solution. The biological importance of carbohydrate-protein interactions, in organismal development as well as in disease, places urgency on the creation of innovative experimental and theoretical methods that can predict the specificity of such interactions and quantify their strengths. Additionally, the emerging realization that protein glycosylation impacts protein function and immunogenicity places the ability to define the mechanisms by which glycosylation impacts these features at the forefront of carbohydrate modeling. This review will discuss the relevant theoretical approaches to studying the three-dimensional structures of this fascinating class of molecules and interactions, with reference to the relevant experimental data and techniques that are key for validation of the theoretical predictions.

A cyber-linked undergraduate research experience in computational biomolecular structure prediction and design

Computational biology is an interdisciplinary field, and many computational biology research projects involve distributed teams of scientists. To accomplish their work, these teams must overcome both disciplinary and geographic barriers. Introducing new training paradigms is one way to facilitate research progress in computational biology. Here, we describe a new undergraduate program in biomolecular structure prediction and design in which students conduct research at labs located at geographically-distributed institutions while remaining connected through an online community. This 10-week summer program begins with one week of training on computational biology methods development, transitions to eight weeks of research, and culminates in one week at the Rosetta annual conference. To date, two cohorts of students have participated, tackling research topics including vaccine design, enzyme design, protein-based materials, glycoprotein modeling, crowd-sourced science, RNA processing, hydrogen bond networks, and amyloid formation. Students in the program report outcomes comparable to students who participate in similar in-person programs. These outcomes include the development of a sense of community and increases in their scientific self-efficacy, scientific identity, and science values, all predictors of continuing in a science research career. Furthermore, the program attracted students from diverse backgrounds, which demonstrates the potential of this approach to broaden the participation of young scientists from backgrounds traditionally underrepresented in computational biology.

The Sulfur-Linked Analogue of O-GlcNAc (S-GlcNAc) Is an Enzymatically Stable and Reasonable Structural Surrogate for O-GlcNAc at the Peptide and Protein Levels

Synthetic proteins bearing site-specific posttranslational modifications have revolutionized our understanding of their biological functions in vitro and in vivo. One such modification, O-GlcNAcylation, is the dynamic addition of β-N-acetyl glucosamine to the side chains of serine and threonine residues of proteins, yet our understanding of the site-specific impact of O-GlcNAcylation remains difficult to evaluate in vivo because of the potential for enzymatic removal by endogenous O-GlcNAcase (OGA). Thioglycosides are generally perceived to be enzymatically stable structural mimics of O-GlcNAc; however, in vitro experiments with small-molecule GlcNAc thioglycosides have demonstrated that OGA can hydrolyze these linkages, indicating that S-linked β-N-acetyl glucosamine (S-GlcNAc) on peptides or proteins may not be completely stable. Here, we first develop a robust synthetic route to access an S-GlcNAcylated cysteine building block for peptide and protein synthesis. Using this modified amino acid, we establish that S-GlcNAc is an enzymatically stable surrogate for O-GlcNAcylation in its native protein setting. We also applied nuclear magnetic resonance spectroscopy and computational modeling to find that S-GlcNAc is an good structural mimic of O-GlcNAc. Finally, we demonstrate that site-specific S-GlcNAcylation results in biophysical characteristics that are the same as those of O-GlcNAc within the context of the protein α-synuclein. While this study is limited in focus to two model systems, these data suggest that S-GlcNAc broadly resembles O-GlcNAc and that it is indeed a stable analogue in the context of peptides and proteins.

The Rosetta All-Atom Energy Function for Macromolecular Modeling and Design

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: a model parametrized 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, called the Rosetta Energy Function 2015 (REF15). 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 its capabilities from soluble proteins to also include membrane proteins, peptides containing noncanonical amino acids, small molecules, carbohydrates, nucleic acids, and other macromolecules.