Restricted N-glycan Conformational Space in the PDB and Its Implication in Glycan Structure Modeling

A grant from the One-University Open Access Fund at the University of Kansas was used to defray the author’s publication fees in this Open Access journal. The Open Access Fund, administered by librarians from the KU, KU Law, and KUMC libraries, is made possible by contributions from the offices of KU Provost, KU Vice Chancellor for Research & Graduate Studies, and KUMC Vice Chancellor for Research. For more information about the Open Access Fund, please see http://library.kumc.edu/authors-fund.xml.Understanding glycan structure and dynamics is central to understanding protein-carbohydrate recognition and its role in protein-protein interactions. Given the difficulties in obtaining the glycan's crystal structure in glycoconjugates due to its flexibility and heterogeneity, computational modeling could play an important role in providing glycosylated protein structure models. To address if glycan structures available in the PDB can be used as templates or fragments for glycan modeling, we present a survey of the N-glycan structures of 35 different sequences in the PDB. Our statistical analysis shows that the N-glycan structures found on homologous glycoproteins are significantly conserved compared to the random background, suggesting that N-glycan chains can be confidently modeled with template glycan structures whose parent glycoproteins share sequence similarity. On the other hand, N-glycan structures found on non-homologous glycoproteins do not show significant global structural similarity. Nonetheless, the internal substructures of these N-glycans, particularly, the substructures that are closer to the protein, show significantly similar structures, suggesting that such substructures can be used as fragments in glycan modeling. Increased interactions with protein might be responsible for the restricted conformational space of N-glycan chains. Our results suggest that structure prediction/modeling of N-glycans of glycoconjugates using structure database could be effective and different modeling approaches would be needed depending on the availability of template structures

Generalized biomolecular modeling and design with RoseTTAFold All-Atom

Deep learning methods have revolutionized protein structure prediction and design but are currently limited to protein-only systems. We describe RoseTTAFold All-Atom (RFAA) which combines a residue-based representation of amino acids and DNA bases with an atomic representation of all other groups to model assemblies containing proteins, nucleic acids, small molecules, metals, and covalent modifications given their sequences and chemical structures. By fine tuning on denoising tasks we obtain RFdiffusionAA, which builds protein structures around small molecules. Starting from random distributions of amino acid residues surrounding target small molecules, we design and experimentally validate, through crystallography and binding measurements, proteins that bind the cardiac disease therapeutic digoxigenin, the enzymatic cofactor heme, and the light harvesting molecule bilin

Emerging out of the “blur”: exploration, evaluation and significance of 3D N-glycans’ structure through molecular dynamic studies

Glycosylation is the most abundant and diverse post-translational modification of proteins, contributing to protein folding, trafficking, structural stability and dynamics, and function. Complex N-glycans are a class of glycans found in eukaryotes, sharing a common pentasaccharide core structure. The functionalization of the arms and the branching patterns are specific to different species, while the complexity of the cellular biosynthetic pathways contribute to the broad variety and to the heterogeneity of N-glycan sequences. By understanding at an atomistic level of detail the structural implications of glycan sequence, we can relate the glycan sequence to its function in a given glycoprotein environment. With this ultimate goal in mind I conducted, through conventional and enhanced molecular dynamics (MD) methods, a series of systematic studies of mammalian, plant and invertebrate glycosylation patterns, in order to characterize the intrinsic 3D architecture of different sets of commonly found and synthetic (non-natural) glycan structures. From these results, we were able to disentangle the complexity of N-glycans structure and dynamics through a new 3D representation, which describes N- glycans not only in terms of the monosaccharides sequence, but that also includes anomeric configurations and linkage specificity. Within this framework, we defined N-glycans as structured by specific groups of monosaccharide units, named “glycoblocks”. This formulation incorporates 3D structural information and uniquely dictates the overall conformational landscape of any given N - glycan. With this expanded viewpoint of sequence-to-structure dependencies in complex N-glycans, we applie d this knowledge to glycoproteins, where variation of glycan composition affects its functiona l capabilities. In the two cases presented in this thesis, we determined how changes in the sequence of the N-glycans in the Fc region of IgG1 antibodies affect its effector function, and discovered for the first time a unique functional role of the glycan shield in the SARS-CoV-2 spike protein. In both cases, we observed that the conformational equilibria of complex N-glycans change to promote conformers that can accommodate interactions with the glycoprotein environment, but this adaption does not interfere with the intrinsic 3D glycan architecture, shifting a paradigm commonly assumed in structural biology, where the protein dictates the glycan conformation by actively morphing it. The work presented in this thesis shows an alternative atomistic perspective of N-glycans structure and dynamics, where glycans play a starring role rather than a cameo as a simple protein “decoration”, while the knowledge and insight gained could inform the ad-hoc design and modulation of sequence-to- structure-to-function relationships of complex N-glycans, with applications in glycoengineering and therapeutic and diagnostic strategies

Towards prediction of N-glycan compositions from atomic structural data

Glycobiology, the study of saccharides and their biological significance, delves into understanding glycans, oligosaccharides that form essential structures in various living organisms. However, these glycans, covalently linked to proteins or lipids, possess a structural complexity that exceeds that of nucleic acids and proteins, attributed to their non-templated assembly. This complexity, characterised by diverse linkage positions, degrees of branching, and isomerism, facilitates glycans' multifaceted roles, including cell-cell recognition, immune response, and protein function optimization. Structural Biology is one of the fields concerned with the study of glycobiology, however current model-building software leans heavily towards proteins. A major hurdle is the absence of upfront knowledge of glycan compositions at glycosylation sites. While protein sequences are easily derived from DNA, glycan sequences are not directly encoded in genomes. As a result of these challenges, many modelled N-glycan chains in glycoproteins show errors as featured in numerous communications and remediation efforts. Therefore, part of the thesis was devoted to implementing a software solution that would enable scientists building atomic models of glycoproteins to easily access information retrieved from glycoproteomic studies. The new code, implemented as part of the Privateer carbohydrate model validation and analysis software, was demonstrated to be useful in validation of modelled N-glycan compositions during iterative model building. Following the successful bridging of atomic coordinates and glycoproteomic data, the research pivoted to assess the interplay between amino acid identities and N-glycan composition. Limited data indicated a potential relationship, especially with aromatic amino acids. Thankfully, the advent of AlphaFold motivated the implementation of a grafting algorithm in the Privateer software, responsible for transplanting N-glycan atomic coordinates, therefore enabling the expansion of N-glycan atomic structure data. The development of new software tools enabled the discovery of potentially meaningful discriminatory relationships in terms of neighbouring amino acid chemical properties and the N-glycan processing products