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

Nance, Morgan L.; Labonte, Jason W.; Adolf-Bryfogle, Jared; Gray, Jeffrey J.

doi:10.1021/acs.jpcb.1c00910

Cited by 16 publications

(22 citation statements)

References 96 publications

(170 reference statements)

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“… Test suite Tests Refs. Test author Quality measures Targets nstruct Runtime in CPUh Antibodies antibody_grafting 55 Jeliazko Jeliazkov Fraction residues within rmsd to native 48 1 3 antibody_h3_modeling 56 Score vs. rmsd 6 500 3000 antibody_snugdock 57 I_sc vs. I_rmsd 6 500 3000 Carbohydrates glycan_dock, (dock_glycans) * 58 , 59 Jason Labonte, Morgan Nance I_sc vs. L_rmsd 6 1000 1100 glycan_structure_prediction 60 Jared Adolf-Bryfogle Score vs. rmsd 4 500 950 Comparative modeling RosettaCM 61 Jason Fell GDT-MM 16 200 1800 Design ddg_alanine_scan 62 Ajasja Ljubetič R, MAE, fraction correctly classified 19: 381 1 3 Design SEWING 63 Frank Teets MotifScorer, InterModelMotifScorer 1 100 75 Design enzyme_design 64 Rocco Moretti Various sequence recoveries 50 1 50 Design …”

Section: Resultsmentioning

confidence: 99%

Ensuring scientific reproducibility in bio-macromolecular modeling via extensive, automated benchmarks

et al. 2021

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Each year vast international resources are wasted on irreproducible research. The scientific community has been slow to adopt standard software engineering practices, despite the increases in high-dimensional data, complexities of workflows, and computational environments. Here we show how scientific software applications can be created in a reproducible manner when simple design goals for reproducibility are met. We describe the implementation of a test server framework and 40 scientific benchmarks, covering numerous applications in Rosetta bio-macromolecular modeling. High performance computing cluster integration allows these benchmarks to run continuously and automatically. Detailed protocol captures are useful for developers and users of Rosetta and other macromolecular modeling tools. The framework and design concepts presented here are valuable for developers and users of any type of scientific software and for the scientific community to create reproducible methods. Specific examples highlight the utility of this framework, and the comprehensive documentation illustrates the ease of adding new tests in a matter of hours.

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Section: Resultsmentioning

confidence: 99%

Ensuring scientific reproducibility in bio-macromolecular modeling via extensive, automated benchmarks

et al. 2021

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“…Several docking programs are performing well in the docking of glycans to proteins. They are available as stand-alone software or/and as online services: BALLDock/SLICK , (), GlycoTorch Vina (); HADDOCK Modeling of biomolecular complexes with support of glycosylated proteins (), Cluspro Sulfated GAG docking , (), Vina-Carb (), and GlycanDock ().…”

Section: Computational Concepts and Toolsmentioning

confidence: 99%

“…The high conformational flexibility is one of the essential features of complex carbohydrates, along with CH−π interactions. Some programs have provisions for treating such features. ,− …”

Section: Computational Concepts and Toolsmentioning

confidence: 99%

“…Such variable performances could occur for different types of ligands. An accurate determination of carbohydrate–protein complexes remains a nontrivial issue. , The biased methods offer a tool to improve carbohydrate-binding mode prediction . The solvent-structure-biased docking considers the similarity in the location of water molecules in the binding sites and the interactions of carbohydrate’s OH-groups with the protein in the complex.…”

Section: Computational Concepts and Toolsmentioning

confidence: 99%

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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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“…Given the complex chemistry and conformational diversity involved, accurate modeling of glycans is currently a grand challenge in computational biology. Computational glycobiology tools and webapps have been developed for protein glycosylations 26 , validation of carbohydrate structural chemistry 27 , statistical analysis 28 , and docking 29,30 . Common methods in glycoprotein modeling typically involve molecular dynamics (MD) simulations 31 or adding glycans by manual placement and conformational tweaking into their density for structure determination 32 .…”

Section: Introductionmentioning

confidence: 99%

Growing Glycans in Rosetta: Accuratede novoglycan modeling, density fitting, and rational sequon design

Adolf-Bryfogle

Labonte

Kraft

et al. 2021

Preprint

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Carbohydrates and glycoproteins modulate key biological functions. Computational approaches inform function to aid in carbohydrate structure prediction, structure determination, and design. However, experimental structure determination of sugar polymers is notoriously difficult as glycans can sample a wide range of low energy conformations, thus limiting the study of glycan-mediated molecular interactions. In this work, we expanded the RosettaCarbohydrate framework, developed and benchmarked effective tools for glycan modeling and design, and extended the Rosetta software suite to better aid in structural analysis and benchmarking tasks through the SimpleMetrics framework. We developed a glycan-modeling algorithm, GlycanTreeModeler, that computationally builds glycans layer-by-layer, using adaptive kernel density estimates (KDE) of common glycan conformations derived from data in the Protein Data Bank (PDB) and from quantum mechanics (QM) calculations. After a rigorous optimization of kinematic and energetic considerations to improve near-native sampling enrichment and decoy discrimination, GlycanTreeModeler was benchmarked on a test set of diverse glycan structures, or "trees". Structures predicted by GlycanTreeModeler agreed with native structures at high accuracy for both de novo modeling and experimental density-guided building. GlycanTreeModeler algorithms and associated tools were employed to design de novo glycan trees into a protein nanoparticle vaccine that are able to direct the immune response by shielding regions of the scaffold from antibody recognition. This work will inform glycoprotein model prediction, aid in both X-ray and electron microscopy density solutions and refinement, and help lead the way towards a new era of computational glycobiology.

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

Cited by 16 publications

References 96 publications

Ensuring scientific reproducibility in bio-macromolecular modeling via extensive, automated benchmarks

Ensuring scientific reproducibility in bio-macromolecular modeling via extensive, automated benchmarks

Multifaceted Computational Modeling in Glycoscience

Growing Glycans in Rosetta: Accuratede novoglycan modeling, density fitting, and rational sequon design

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