Glycan Conformation in the Heavily Glycosylated Protein, CEACAM1.

Glycans attached to glycoproteins can contribute to stability, mediate interactions with other proteins, and initiate signal transduction. Glycan conformation, which is critical to these processes, is highly variable and often depicted as sampling a multitude of conformers. These conformers can be generated by molecular dynamics simulations, and more inclusively by accelerated molecular dynamics, as well as other extended sampling methods. However, experimental assessments of the contribution that various conformers make to a native ensemble are rare. Here, we use long-range pseudo-contact shifts (PCSs) of NMR resonances from an isotopically labeled glycoprotein to identify preferred conformations of its glycans. The N-terminal domain from human Carcinoembryonic Antigen Cell Adhesion Molecule 1, hCEACAM1-Ig1, was used as the model glycoprotein in this study. It has been engineered to include a lanthanide-ion-binding loop that generates PCSs, as well as a homogeneous set of three 13C-labeled N-glycans. Analysis of the PCSs indicates that preferred glycan conformers have extensive contacts with the protein surface. Factors leading to this preference appear to include interactions between N-acetyl methyls of GlcNAc residues and hydrophobic surface pockets on the protein surface.

AssignSLP_GUI, a software tool exploiting AI for NMR resonance assignment of sparsely labeled proteins.

Not all proteins are amenable to uniform isotopic labeling with 13C and 15N, something needed for the widely used, and largely deductive, triple resonance assignment process. Among them are proteins expressed in mammalian cell culture where native glycosylation can be maintained, and proper formation of disulfide bonds facilitated. Uniform labeling in mammalian cells is prohibitively expensive, but sparse labeling with one or a few isotopically enriched amino acid types is an option for these proteins. However, assignment then relies on accessing the best match between a variety of measured NMR parameters and predictions based on 3D structure, often from X-ray crystallography. Finding this match is a challenging process that has benefitted from many computational tools, including trained neural nets for chemical shift prediction, genetic algorithms for searches through a myriad of assignment possibilities, and now AI-based prediction of high-quality structures for protein targets. AssignSLP_GUI, a new version of a software package for assignment of resonances from sparsely-labeled proteins, uses many of these tools. These tools and new additions to the package are highlighted in an application to a sparsely-labeled domain from a glycoprotein, CEACAM1.

Using molecular dynamics trajectories to predict nuclear spin relaxation behaviour in large spin systems.

Molecular dynamics (MD) trajectories provide useful insights into molecular structure and dynamics. However, questions persist about the quantitative accuracy of those insights. Experimental NMR spin relaxation rates can be used as tests, but only if relaxation superoperators can be efficiently computed from MD trajectories - no mean feat for the quantum Liouville space formalism where matrix dimensions quadruple with each added spin 1/2. Here we report a module for the Spinach software framework that computes Bloch-Redfield-Wangsness relaxation superoperators (including non-secular terms and cross-correlations) from MD trajectories. Predicted initial slopes of nuclear Overhauser effects for sucrose trajectories using advanced water models and a force field optimised for glycans are within 25% of experimental values.

NMR Resonance Assignment Methodology: Characterizing Large Sparsely Labeled Glycoproteins.

Characterization of proteins using NMR methods begins with assignment of resonances to specific residues. This is usually accomplished using sequential connectivities between nuclear pairs in proteins uniformly labeled with NMR active isotopes. This becomes impractical for larger proteins, and especially for proteins that are best expressed in mammalian cells, including glycoproteins. Here an alternate protocol for the assignment of NMR resonances of sparsely labeled proteins, namely, the ones labeled with a single amino acid type, or a limited subset of types, isotopically enriched with 15N or 13C, is described. The protocol is based on comparison of data collected using extensions of simple two-dimensional NMR experiments (correlated chemical shifts, nuclear Overhauser effects, residual dipolar couplings) to predictions from molecular dynamics trajectories that begin with known protein structures. Optimal pairing of predicted and experimental values is facilitated by a software package that employs a genetic algorithm, ASSIGN_SLP_MD. The approach is applied to the 36-kDa luminal domain of the sialyltransferase, rST6Gal1, in which all phenylalanines are labeled with 15N, and the results are validated by elimination of resonances via single-point mutations of selected phenylalanines to tyrosines. Assignment allows the use of previously published paramagnetic relaxation enhancements to evaluate placement of a substrate analog in the active site of this protein. The protocol will open the way to structural characterization of the many glycosylated and other proteins that are best expressed in mammalian cells.

Paramagnetic Tag for Glycosylation Sites in Glycoproteins: Structural Constraints on Heparan Sulfate Binding to Robo1.

An enzyme- and click chemistry-mediated methodology for the site-specific nitroxide spin labeling of glycoproteins has been developed and applied. The procedure relies on the presence of single N-glycosylation sites that are present natively in proteins or that can be engineered into glycoproteins by mutational elimination of all but one glycosylation site. Recombinantly expressing glycoproteins in HEK293S (GnT1-) cells results in N-glycans with high-mannose structures that can be processed to leave a single GlcNAc residue. This can in turn be modified by enzymatic addition of a GalNAz residue that is subject to reaction with an alkyne-carrying TEMPO moiety using copper(I)-catalyzed click chemistry. To illustrate the procedure, we have made an application to a two-domain construct of Robo1, a protein that carries a single N-glycosylation site in its N-terminal domains. The construct has also been labeled with 15N at amide nitrogens of lysine residues to provide a set of sites that are used to derive an effective location of the paramagnetic nitroxide moiety of the TEMPO group. This, in turn, allowed measurements of paramagnetic perturbations to the spectra of a new high affinity heparan sulfate ligand. Calculation of distance constraints from these data facilitated determination of an atomic level model for the docked complex.

Structural Aspects of Heparan Sulfate Binding to Robo1-Ig1-2.

Roundabout 1, or Robo1, is a cell surface signaling molecule important in axon guidance. Its interaction with heparan sulfate (HS) and members of the Slit protein family is essential to its activity, making characterization of these interactions by structural methods, such as NMR, highly desirable. However, the fact that Robo1 is a glycosylated protein prevents employment of commonly used bacterial hosts for expression of properly glycosylated forms with the uniform 15N, 13C, and 2H labeling needed for NMR studies. Here, we apply an alternative methodology, based on labeling with a single amino acid type and high structural content NMR data, to characterize a two-domain construct of glycosylated Robo1 (Robo1-Ig1-2) interacting with a synthetic HS tetramer (IdoA-GlcNS6S-IdoA2S-GlcNS6S-(CH2)5NH2). Significant chemical shift perturbations of the crosspeak from K81 on titration with the tetramer provide initial evidence for the location of a binding site and allow determination of a 255 µM disassociation constant. The binding epitopes, bound conformation, and binding site placement of the HS tetramer have been further characterized by saturation transfer difference (STD), transferred nuclear Overhauser effect (trNOE), and paramagnetic perturbation experiments. A model of the complex has been generated using constraints derived from the various NMR experiments. Postprocessing energetic analysis of this model provides a rationale for the role each glycan residue plays in the binding event, and examination of the binding site in the context of a previous Robo-Slit structure provides a rationale for modulation of Robo-Slit interactions by HS.

Sparse labeling of proteins: structural characterization from long range constraints.

Structural characterization of biologically important proteins faces many challenges associated with degradation of resolution as molecular size increases and loss of resolution improving tools such as perdeuteration when non-bacterial hosts must be used for expression. In these cases, sparse isotopic labeling (single or small subsets of amino acids) combined with long range paramagnetic constraints and improved computational modeling offer an alternative. This perspective provides a brief overview of this approach and two discussions of potential applications; one involving a very large system (an Hsp90 homolog) in which perdeuteration is possible and methyl-TROSY sequences can potentially be used to improve resolution, and one involving ligand placement in a glycosylated protein where resolution is achieved by single amino acid labeling (the sialyltransferase, ST6Gal1). This is not intended as a comprehensive review, but as a discussion of future prospects that promise impact on important questions in the structural biology area.

Chemical shift prediction for denatured proteins.

While chemical shift prediction has played an important role in aspects of protein NMR that include identification of secondary structure, generation of torsion angle constraints for structure determination, and assignment of resonances in spectra of intrinsically disordered proteins, interest has arisen more recently in using it in alternate assignment strategies for crosspeaks in (1)H-(15)N HSQC spectra of sparsely labeled proteins. One such approach involves correlation of crosspeaks in the spectrum of the native protein with those observed in the spectrum of the denatured protein, followed by assignment of the peaks in the latter spectrum. As in the case of disordered proteins, predicted chemical shifts can aid in these assignments. Some previously developed empirical formulas for chemical shift prediction have depended on basis data sets of 20 pentapeptides. In each case the central residue was varied among the 20 amino common acids, with the flanking residues held constant throughout the given series. However, previous choices of solvent conditions and flanking residues make the parameters in these formulas less than ideal for general application to denatured proteins. Here, we report (1)H and (15)N shifts for a set of alanine based pentapeptides under the low pH urea denaturing conditions that are more appropriate for sparse label assignments. New parameters have been derived and a Perl script was created to facilitate comparison with other parameter sets. A small, but significant, improvement in shift predictions for denatured ubiquitin is demonstrated.

Assignment of protein backbone resonances using connectivity, torsion angles and 13Calpha chemical shifts.

A program is presented which will return the most probable sequence location for a short connected set of residues in a protein given just (13)C(alpha) chemical shifts (delta((13)C(alpha))) and data restricting the phi and psi backbone angles. Data taken from both the BioMagResBank and the Protein Data Bank were used to create a probability density function (PDF) using a multivariate normal distribution in delta((13)C(alpha)), phi, and psi space for each amino acid residue. Extracting and combining probabilities for particular amino acid residues in a short proposed sequence yields a score indicative of the correctness of the proposed assignment. The program is illustrated using several proteins for which structure and (13)C(alpha) chemical shift data are available.