Computational Prediction of Carbohydrate‐Binding Proteins and Binding Sites

Zhao, Huiying; Taherzadeh, Ghazaleh; Zhou, Yaoqi; Yang, Yuedong

doi:10.1002/cpps.75

Cited by 7 publications

(4 citation statements)

References 56 publications

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“…While various ligand binding site prediction software programs exist, including some specific to carbohydrate ligands, , we chose FTMap for its speed and ease of use via its online web server . FTMap predicts ligand binding “hot spots” by extensively sampling the surface of a macromolecular receptor using various small organic molecules as probes.…”

Section: Resultsmentioning

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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Section: Resultsmentioning

confidence: 99%

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

et al. 2021

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“…Other important events are mediated by or involved recognition, such as transporters and an essential group of carbohydrate-binding proteins: lectin, antibodies, carbohydrate-binding modules, and glycosaminoglycan binding proteins. Prediction experimentally based on the availability of elucidation of protein–carbohydrate crystalline complexes − or protein sequences Figure is a schematic description of the events involving protein–carbohydrate interactions.…”

Section: Protein Carbohydrate Interactionsmentioning

confidence: 99%

Multifaceted Computational Modeling in Glycoscience

Pérez

Makshakova

2022

Chem. Rev.

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Glycoscience assembles all the scientific disciplines involved in studying various molecules and macromolecules containing carbohydrates and complex glycans. Such an ensemble involves one of the most extensive sets of molecules in quantity and occurrence since they occur in all microorganisms and higher organisms. Once the compositions and sequences of these molecules are established, the determination of their three-dimensional structural and dynamical features is a step toward understanding the molecular basis underlying their properties and functions. The range of the relevant computational methods capable of addressing such issues is anchored by the specificity of stereoelectronic effects from quantum chemistry to mesoscale modeling throughout molecular dynamics and mechanics and coarse-grained and docking calculations. The Review leads the reader through the detailed presentations of the applications of computational modeling. The illustrations cover carbohydrate−carbohydrate interactions, glycolipids, and N-and O-linked glycans, emphasizing their role in SARS-CoV-2. The presentation continues with the structure of polysaccharides in solution and solid-state and lipopolysaccharides in membranes. The full range of protein-carbohydrate interactions is presented, as exemplified by carbohydrate-active enzymes, transporters, lectins, antibodies, and glycosaminoglycan binding proteins. A final section features a list of 150 tools and databases to help address the many issues of structural glycobioinformatics. CONTENTS 5.1. N-and O-Linked Glycans 5.2. Structure and Dynamics of SARS-CoV-2 5.3. Structure, Function, and Dynamics of Glycolipids 6. Polysaccharides 6.1. Polysaccharides in Solution 6.2. Polysaccharides in the Solid-State 6.3. Lipolysaccharides in Membranes 7. Protein Carbohydrate Interactions 7.1. Presentation: Synopsis of the Protein Families 7.2. Insights into Glycosyltransferases 7.3. Insights into Glycosyl Hydrolases 7.4. Enzymatic Degradation of Polysaccharides in the Solid-State

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“…In recent years, researchers have exploited various computational methods to study protein–carbohydrate interactions. These methods have mainly used structure-based, sequence-based, and homology-based approaches to predict protein–carbohydrate binding sites . Taroni et al made the first effort in this direction, which predicted the binding sites through structural analysis of protein–carbohydrate complexes in the Protein Data Bank .…”

Section: Introductionmentioning

confidence: 99%

“…These methods have mainly used structure-based, sequence-based, and homology-based approaches to predict protein–carbohydrate binding sites. 5 Taroni et al made the first effort in this direction, which predicted the binding sites through structural analysis of protein–carbohydrate complexes in the Protein Data Bank. 106 Later iterations of structure-based approaches involved various methods like docking, 7 empirical analysis, 8 and energy-based analysis.…”

Section: Introductionmentioning

confidence: 99%

PeSTo-Carbs: Geometric Deep Learning for Prediction of Protein–Carbohydrate Binding Interfaces

Bibekar,

Krapp,

Peraro

2024

J. Chem. Theory Comput.

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The Protein Structure Transformer (PeSTo), a geometric transformer, has exhibited exceptional performance in predicting protein−protein binding interfaces and distinguishing interfaces with nucleic acids, lipids, small molecules, and ions. In this study, we introduce PeSTo-Carbs, an extension of PeSTo specifically engineered to predict protein−carbohydrate binding interfaces. We evaluate the performance of this approach using independent test sets and compare them with those of previous methods. Furthermore, we highlight the model's capability to specialize in predicting interfaces involving cyclodextrins, a biologically and pharmaceutically significant class of carbohydrates. Our method consistently achieves remarkable accuracy despite the scarcity of available structural data for cyclodextrins.

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Computational Prediction of Carbohydrate‐Binding Proteins and Binding Sites

Cited by 7 publications

References 56 publications

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

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

Multifaceted Computational Modeling in Glycoscience

PeSTo-Carbs: Geometric Deep Learning for Prediction of Protein–Carbohydrate Binding Interfaces

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