Leveraging glycomics data in glycoprotein 3D structure validation with Privateer

Abstract

The heterogeneity, mobility and complexity of glycans in glycoproteins have been, and currently remain, significant challenges in structural biology. Those aspects present unique problems to the two most prolific techniques: X-ray crystallography and cryo-electron microscopy. At the same time, advances in mass spectrometry have made it possible to get deeper insights on precisely the information that is most difficult to recover by structure solution methods: full-length glycan composition, including linkage details for the glycosidic bonds. These developments have given rise to glycomics. Thankfully, several large scale glycomics initiatives have stored results in publicly-available databases, some of which can be accessed through API interfaces. In the present work, we will describe how the Privateer carbohydrate structure validation software has been extended to harness results from glycomics projects, and its use to greatly improve the validation of 3D glycoprotein structures.

Cryo‐EM reveals mechanisms of angiotensin I‐converting enzyme allostery and dimerization

“…A biantennary fucosylated complex‐type glycan structure was proposed by Yu et al and was therefore modeled into cryo‐EM maps in the present study. Glycans were built for each dimer N‐domain at N9, N25, N45, N82, N117, N289, N416, and N480, guided by difference maps from Privateer (Agirre et al , 2015; Bagdonas et al , 2020). For the monomer, glycans were built at these N‐domain residues and the C‐domain N648, N666, N685, N731, and N913.…”

“…Comprehensive validation and EM Ringer assessment (Barad et al , 2015) were performed in Phenix (Afonine et al , 2018) against the globally sharpened map. Glycan validation was performed using Privateer (Agirre et al , 2015; Bagdonas et al , 2020) with CCP4 (Winn et al , 2011) against the unsharpened maps with the difference maps used as a guide for glycan placement and branch length. To identify potential ligand densities, fractional difference maps (Joseph et al , 2020) were calculated with local scaling using the TEMPy:DiffMap tool in CCPEM version 1.5.0 (Burnley et al , 2017).…”

Three-Dimensional Structures of Carbohydrates and Where to Find Them

“…Vast variety of methods provide information about 3D structure of individual glycans and glycan moieties of glycoproteins and protein-carbohydrate complexes ( Figure 6 ) [ 285 , 286 ]. The following approaches are most utilized for 3D structural data validation [ 287 , 288 , 289 ]: Ccombination of carbohydrate simulated geometry data with X-ray crystallographic data analysis [ 225 , 290 ]; Analysis of inter-glycosidic NMR spin couplings, which depend on glycosidic bond torsions [ 114 , 291 , 292 ]; Deriving nuclear Overhauser effects (NOEs) from relative populations of the interatomic distances, with subsequent comparison to the experimental NOEs in solution [ 99 , 293 , 294 ]; Purely informatic detection of errors, such as incompatible atomic coordinates originating from incorrect processing or simulation [ 295 , 296 , 297 , 298 ]; Simulation by other computational methods at higher levels of theory [ 102 , 103 , 105 , 108 ]. …”

“…Purely informatic detection of errors, such as incompatible atomic coordinates originating from incorrect processing or simulation [ 295 , 296 , 297 , 298 ];…”