Introduction Carbohydrates are some of the most stereochemic

Introduction Carbohydrates are some of the most stereochemically complex biological molecules found in nature. In addition to their energetic and structural roles in living systems, their role when covalently linked to proteins is critically important in a myriad of molecular recognition processes. Viruses frequently use a glycan shield as a tool to evade the immune system, making glycoproteins the focus of intense interest for vaccinology initiatives targeting these viruses (Astronomo and Burton, 2010). Glycoproteins have historically been difficult to study structurally due to difficulties in overexpressing properly glycosylated proteins as well as their innate flexibility. Indeed, GSK591 are frequently removed for crystallization studies (Derewenda, 2004). Despite these challenges, several thousand glycoprotein crystal structures have been determined. Recent advances in cryo-electron microscopy (cryoEM) have allowed structural studies on previously intractable glycoproteins (Walls et al., 2016a, Walls et al., 2016b; Lyumkis et al., 2013). However, the vast majority of glycoprotein structures suffer from the fact that the resolution of the electron density or electron potential maps for the carbohydrate chains is too limited to allow for accurate atomic modeling of individual glycan moieties. As a result, the implementation of prior knowledge is necessary to obtain reliable and stereochemically realistic structural models. In 2004 a study reported that 30% of all PDB entries with covalently linked carbohydrates contain errors in nomenclature and/or chemistry (Derewenda, 2004). The potential for widespread erroneous carbohydrate structural analysis is increasing with the rise of cryoEM as a near-atomic-resolution structural technique, as exemplified by the numerous recent structures solved with unrealistic high-energy ring conformations (Agirre et al., 2015a). These observations emphasize the inadequacy and underutilization of available tools and highlight the need for the development of dedicated algorithms for glycoprotein structural refinement. To address this issue, we aimed to create a tool for the automatic detection and refinement of glycan coordinates while resolving incorrect starting glycan conformations. We developed an approach to identify, correct, and refine glycoproteins guided by low-resolution crystallographic or cryoEM data to expedite structure determination and interpretation. The approach builds upon previous Rosetta-based structure determination tools guided by low-resolution experimental data (Frenz et al., 2017, Wang et al., 2015) and makes use of a previously developed framework for modeling of carbohydrates (Labonte et al., 2017). Compared with previous glycan refinement methods (Agirre, 2017b, Gristick et al., 2017), our approach (1) uses a physically realistic force field to ensure glycan geometry remains correct even under large conformational changes, and (2) adds the ability to change the anomer of the glycan. These protocols are publicly available with the latest Rosetta release and enable refinement of glycoprotein structures against cryoEM (Wang et al., 2016) and X-ray crystallography data, the latter taking advantage of the combined Phenix-Rosetta reciprocal-space refinement pipeline (Terwilliger et al., 2012).
Results We developed a refinement protocol to detect and correct poorly modeled glycan configurations and to refine glycan coordinates guided by either low-resolution crystallographic data, via Phenix-Rosetta integration (DiMaio et al., 2013), or cryoEM density data. Of particular interest were the sugar rings of glycans, which may adopt a variety of different conformations with a range of energies (Figure 1A); therefore, we designed our protocol to specifically address these ring conformations. For more on glycan ring conformations, see Agirre et al. (2017a). Several glycan refinement-specific methods have been developed, including tools for automatic detection and setup of glycan-containing structures for subsequent refinement, a score term that enables cartesian refinement of carbohydrate chains, a dedicated routine for handling sugar ring stereochemistry, and a protocol for correcting the geometry of incorrectly built glycans while optimizing the fit to density. The protocol has a large radius of convergence and is able to make substantial modifications to the input models. For example, the protocol can refine high-energy sugar ring conformers into low-energy conformers and correct erroneous anomeric configurations (see Figure 1). Runtime is about six times slower than Phenix refinement: a 300 residue protein was refined in ∼25 min compared with ∼4 min for Phenix refinement alone. The approach is fully described in STAR Methods.