Carbohydrate – Protein aromatic ring interactions beyond CH/π interactions: A Protein Data Bank survey and quantum chemical calculations

Stanković, Ivana; Filipović, Jelena P. Blagojević; Zarić, Snežana D.

doi:10.1016/j.ijbiomac.2020.03.251

Cited by 15 publications

(9 citation statements)

References 45 publications

(80 reference statements)

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“…The crystal structure 5T05 of HS6ST containing a 2-O-sulfate at position +4 (IdoA2S +4 ) shows the acceptor N-sulfo moiety GlcNS +1 within hydrogen-bonding distance of the amide backbone of W210. The side chain of W210 points toward the GlcNS +1 acceptor in an unusual perpendicular orientation that may allow CH−π interactions such as typical aromatic–sugar interactions found in many glycan binding proteins. , In order to probe the presence of CH−π contacts between W210 and the acceptor glucosamine GlcNS +1 of both HS6ST:GlcA +4 and HS6ST:Ido2S +4 , we analyzed the distance between GlcNS +1 and the center of the indole phenyl ( d (CH−π)) and the angle between the CH vector and the phenyl ring plane normal vector (ω(CH−π)). − Aside from the differences between HS6ST:GlcA +4 and HS6ST:IdoA2S +4 , our analysis (see Discussion S3) does not meet the criteria for a classical or “T-shaped” CH−π interaction. Instead, W210 and the acceptor glucosamine interact via H-bonds in the HS6ST:Ido2S +4 complex (Figure S11A,B).…”

Section: Results and Discussionmentioning

confidence: 99%

Structural Determinants of Substrate Recognition and Catalysis by Heparan Sulfate Sulfotransferases

et al. 2021

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Heparan sulfate (HS) and heparin contain imprinted "sulfation codes", which dictate their diverse physiological and pathological functions. A group of orchestrated biosynthetic enzymes cooperate in polymerizing and modifying HS chains. The biotechnological development of enzymes that can recreate this sulfation pattern on synthetic heparin is challenging, primarily due to the paucity of quantitative data for sulfotransferase enzymes. Herein, we identified critical structural characteristics that determine substrate specificity and shed light on the catalytic mechanism of sugar sulfation of two HS sulfotransferases, 2-Osulfotransferase (HS2ST) and 6-O-sulfotransferase (HS6ST). Two sets of molecular clamps in HS2ST recognize appropriate substrates; these clamps flank the acceptor binding site on opposite sides. The hexuronic epimers, and not their puckers, have a critical influence on HS2ST selectivity. In contrast, HS6ST recognizes a broader range of substrates. This promiscuity is granted by a conserved tryptophan residue, W210, that positions the acceptor within the active site for catalysis by means of strong electrostatic interactions. Lysines K131 and K132 act in concert with a second tryptophan, W153, shedding water molecules from within the active site, thus providing HS6ST with a binding preference toward 2-O-sulfated substrates. QM/MM calculations provided valuable mechanistic insights into the catalytic process, identifying that the sulfation of both HS2ST and HS6ST follows a SN2-like mechanism. When they are taken together, our findings reveal the molecular basis of how these enzymes recognize different substrates and catalyze sugar sulfation, enabling the generation of enzymes that could create specific heparin epitopes.

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Section: Results and Discussionmentioning

confidence: 99%

Structural Determinants of Substrate Recognition and Catalysis by Heparan Sulfate Sulfotransferases

et al. 2021

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“…Sampling these exact interactions can be challenging, as evidenced by the relatively poor interfacial hydrogen bonding recovery of top-scoring GlycanDock models (Figure C,D). Future work on the RosettaCarbohydrate framework will address additional carbohydrate modeling considerations such as CH−π “stacking” interactions ,− and water-mediated hydrogen bonding, − which may improve GlycanDock sampling of native-like biophysical features at protein–glycoligand interfaces.…”

Section: Discussionmentioning

confidence: 99%

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

et al. 2021

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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.

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“…The CH–π stacking contribution to the overall binding energy ranges from −4 kcal/mol to −8 kcal/mol; currently, stacking CH–π interactions are considered driving forces of protein–carbohydrate complexation [ 18 ]. However, the calculated energies in the systems without CH–π interactions are in the range from −0.2 to −3.2 kcal/mol; hence, they can also be important for aromatic amino acid and carbohydrate binding processes [ 19 ]. In the enzymes used for glycan synthesis/transformations, weaker interactions that enable the release of small carbohydrate fragments after an enzymatic reaction are possibly preferred [ 19 ].…”

Section: Discussionmentioning

confidence: 99%

“…However, the calculated energies in the systems without CH–π interactions are in the range from −0.2 to −3.2 kcal/mol; hence, they can also be important for aromatic amino acid and carbohydrate binding processes [ 19 ]. In the enzymes used for glycan synthesis/transformations, weaker interactions that enable the release of small carbohydrate fragments after an enzymatic reaction are possibly preferred [ 19 ]. To illustrate this, H and Y residues are preferred in GO and only W426 is close to the catalytic area ( Figure 1 A).…”

Section: Discussionmentioning

confidence: 99%

Prebiotic Peptides Based on the Glycocodon Theory Analyzed with FRET

Nahálka

Hrabárová

2021

Life

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In modern protein–carbohydrate interactions, carbohydrate–aromatic contact with CH–π interactions are used. Currently, they are considered driving forces of this complexation. In these contacts, tryptophan, tyrosine, and histidine are preferred. In this study, we focus on primary prebiotic chemistry when only glycine, alanine, aspartic acid, and valine are available in polypeptides. In this situation, when the aromatic acids are not available, hydrogen-bonding aspartic acid must be used for monosaccharide complexation. It is shown here that (DAA)n polypeptides play important roles in primary “protein”–glucose recognition, that (DGG)n plays an important role in “protein”–ribose recognition, and that (DGA)n plays an important role in “protein”–galactose recognition. Glucose oxidase from Aspergillus niger, which still has some ancient prebiotic sequences, is chosen here as an example for discussion.

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Carbohydrate – Protein aromatic ring interactions beyond CH/π interactions: A Protein Data Bank survey and quantum chemical calculations

Cited by 15 publications

References 45 publications

Structural Determinants of Substrate Recognition and Catalysis by Heparan Sulfate Sulfotransferases

Structural Determinants of Substrate Recognition and Catalysis by Heparan Sulfate Sulfotransferases

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

Prebiotic Peptides Based on the Glycocodon Theory Analyzed with FRET

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