Literature DB >> 27900782

Residue-centric modeling and design of saccharide and glycoconjugate structures.

Jason W Labonte1, Jared Adolf-Bryfogle2, William R Schief2,3, Jeffrey J Gray1.   

Abstract

The RosettaCarbohydrate framework is a new tool for modeling a wide variety of saccharide and glycoconjugate structures. This report describes the development of the framework and highlights its applications. The framework integrates with established protocols within the Rosetta modeling and design suite, and it handles the vast complexity and variety of carbohydrate molecules, including branching and sugar modifications. To address challenges of sampling and scoring, RosettaCarbohydrate can sample glycosidic bonds, side-chain conformations, and ring forms, and it utilizes a glycan-specific term within its scoring function. Rosetta can work with standard PDB, GLYCAM, and GlycoWorkbench (.gws) file formats. Saccharide residue-specific chemical information is stored internally, permitting glycoengineering and design. Carbohydrate-specific applications described herein include virtual glycosylation, loop-modeling of carbohydrates, and docking of glyco-ligands to antibodies. Benchmarking data are presented and compared to other studies, demonstrating Rosetta's ability to predict glyco-ligand binding. The framework expands the tools available to glycoscientists and engineers.
© 2016 Wiley Periodicals, Inc. © 2016 Wiley Periodicals, Inc.

Entities:  

Keywords:  Rosetta; carbohydrates; computation; docking; glycoconjugates; glycoproteins; modeling; oligosaccharides; structure

Mesh:

Substances:

Year:  2016        PMID: 27900782      PMCID: PMC5182120          DOI: 10.1002/jcc.24679

Source DB:  PubMed          Journal:  J Comput Chem        ISSN: 0192-8651            Impact factor:   3.376


  67 in total

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Journal:  Protein Sci       Date:  2003-05       Impact factor: 6.725

2.  A mathematical model of N-linked glycosylation.

Authors:  Frederick J Krambeck; Michael J Betenbaugh
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3.  Extra precision glide: docking and scoring incorporating a model of hydrophobic enclosure for protein-ligand complexes.

Authors:  Richard A Friesner; Robert B Murphy; Matthew P Repasky; Leah L Frye; Jeremy R Greenwood; Thomas A Halgren; Paul C Sanschagrin; Daniel T Mainz
Journal:  J Med Chem       Date:  2006-10-19       Impact factor: 7.446

4.  PyRosetta: a script-based interface for implementing molecular modeling algorithms using Rosetta.

Authors:  Sidhartha Chaudhury; Sergey Lyskov; Jeffrey J Gray
Journal:  Bioinformatics       Date:  2010-01-07       Impact factor: 6.937

5.  Nomenclature of carbohydrates (recommendations 1996).

Authors:  A D McNaught
Journal:  Adv Carbohydr Chem Biochem       Date:  1997       Impact factor: 12.200

6.  Structural insights into drug processing by human carboxylesterase 1: tamoxifen, mevastatin, and inhibition by benzil.

Authors:  Christopher D Fleming; Sompop Bencharit; Carol C Edwards; Janice L Hyatt; Lyudmila Tsurkan; Feng Bai; Charles Fraga; Christopher L Morton; Escher L Howard-Williams; Philip M Potter; Matthew R Redinbo
Journal:  J Mol Biol       Date:  2005-09-09       Impact factor: 5.469

7.  A new GROMOS force field for hexopyranose-based carbohydrates.

Authors:  Roberto D Lins; Philippe H Hünenberger
Journal:  J Comput Chem       Date:  2005-10       Impact factor: 3.376

8.  The FlexX database docking environment--rational extraction of receptor based pharmacophores.

Authors:  Holger Claussen; Marcus Gastreich; Volker Apelt; Jonathan Greene; Sally A Hindle; Christian Lemmen
Journal:  Curr Drug Discov Technol       Date:  2004-01

9.  Importance of ligand conformational energies in carbohydrate docking: Sorting the wheat from the chaff.

Authors:  Anita K Nivedha; Spandana Makeneni; Bethany Lachele Foley; Matthew B Tessier; Robert J Woods
Journal:  J Comput Chem       Date:  2013-12-29       Impact factor: 3.376

10.  CHARMM-GUI Input Generator for NAMD, GROMACS, AMBER, OpenMM, and CHARMM/OpenMM Simulations Using the CHARMM36 Additive Force Field.

Authors:  Jumin Lee; Xi Cheng; Jason M Swails; Min Sun Yeom; Peter K Eastman; Justin A Lemkul; Shuai Wei; Joshua Buckner; Jong Cheol Jeong; Yifei Qi; Sunhwan Jo; Vijay S Pande; David A Case; Charles L Brooks; Alexander D MacKerell; Jeffery B Klauda; Wonpil Im
Journal:  J Chem Theory Comput       Date:  2015-12-03       Impact factor: 6.006

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  20 in total

Review 1.  Predicting the Structures of Glycans, Glycoproteins, and Their Complexes.

Authors:  Robert J Woods
Journal:  Chem Rev       Date:  2018-08-09       Impact factor: 60.622

2.  The Sulfur-Linked Analogue of O-GlcNAc (S-GlcNAc) Is an Enzymatically Stable and Reasonable Structural Surrogate for O-GlcNAc at the Peptide and Protein Levels.

Authors:  Cesar A De Leon; Paul M Levine; Timothy W Craven; Matthew R Pratt
Journal:  Biochemistry       Date:  2017-06-30       Impact factor: 3.162

3.  The human naive B cell repertoire contains distinct subclasses for a germline-targeting HIV-1 vaccine immunogen.

Authors:  Colin Havenar-Daughton; Anita Sarkar; Daniel W Kulp; Laura Toy; Xiaozhen Hu; Isaiah Deresa; Oleksandr Kalyuzhniy; Kirti Kaushik; Amit A Upadhyay; Sergey Menis; Elise Landais; Liwei Cao; Jolene K Diedrich; Sonu Kumar; Torben Schiffner; Samantha M Reiss; Grégory Seumois; John R Yates; James C Paulson; Steven E Bosinger; Ian A Wilson; William R Schief; Shane Crotty
Journal:  Sci Transl Med       Date:  2018-07-04       Impact factor: 17.956

4.  Stabilizing proteins, simplified: A Rosetta-based webtool for predicting favorable mutations.

Authors:  David F Thieker; Jack B Maguire; Stephan T Kudlacek; Andrew Leaver-Fay; Sergey Lyskov; Brian Kuhlman
Journal:  Protein Sci       Date:  2022-10       Impact factor: 6.993

5.  CHARMM-GUI Glycan Modeler for modeling and simulation of carbohydrates and glycoconjugates.

Authors:  Sang-Jun Park; Jumin Lee; Yifei Qi; Nathan R Kern; Hui Sun Lee; Sunhwan Jo; InSuk Joung; Keehyung Joo; Jooyoung Lee; Wonpil Im
Journal:  Glycobiology       Date:  2019-04-01       Impact factor: 4.313

6.  Development and Evaluation of GlycanDock: A Protein-Glycoligand Docking Refinement Algorithm in Rosetta.

Authors:  Morgan L Nance; Jason W Labonte; Jared Adolf-Bryfogle; Jeffrey J Gray
Journal:  J Phys Chem B       Date:  2021-06-16       Impact factor: 2.991

7.  Modeling Immunity with Rosetta: Methods for Antibody and Antigen Design.

Authors:  Clara T Schoeder; Samuel Schmitz; Jared Adolf-Bryfogle; Alexander M Sevy; Jessica A Finn; Marion F Sauer; Nina G Bozhanova; Benjamin K Mueller; Amandeep K Sangha; Jaume Bonet; Jonathan H Sheehan; Georg Kuenze; Brennica Marlow; Shannon T Smith; Hope Woods; Brian J Bender; Cristina E Martina; Diego Del Alamo; Pranav Kodali; Alican Gulsevin; William R Schief; Bruno E Correia; James E Crowe; Jens Meiler; Rocco Moretti
Journal:  Biochemistry       Date:  2021-03-11       Impact factor: 3.162

8.  An expanded benchmark for antibody-antigen docking and affinity prediction reveals insights into antibody recognition determinants.

Authors:  Johnathan D Guest; Thom Vreven; Jing Zhou; Iain Moal; Jeliazko R Jeliazkov; Jeffrey J Gray; Zhiping Weng; Brian G Pierce
Journal:  Structure       Date:  2021-02-03       Impact factor: 5.871

9.  The Rosetta All-Atom Energy Function for Macromolecular Modeling and Design.

Authors:  Rebecca F Alford; Andrew Leaver-Fay; Jeliazko R Jeliazkov; Matthew J O'Meara; Frank P DiMaio; Hahnbeom Park; Maxim V Shapovalov; P Douglas Renfrew; Vikram K Mulligan; Kalli Kappel; Jason W Labonte; Michael S Pacella; Richard Bonneau; Philip Bradley; Roland L Dunbrack; Rhiju Das; David Baker; Brian Kuhlman; Tanja Kortemme; Jeffrey J Gray
Journal:  J Chem Theory Comput       Date:  2017-05-12       Impact factor: 6.006

Review 10.  Macromolecular modeling and design in Rosetta: recent methods and frameworks.

Authors:  Julia Koehler Leman; Brian D Weitzner; Steven M Lewis; Jared Adolf-Bryfogle; Nawsad Alam; Rebecca F Alford; Melanie Aprahamian; David Baker; Kyle A Barlow; Patrick Barth; Benjamin Basanta; Brian J Bender; Kristin Blacklock; Jaume Bonet; Scott E Boyken; Phil Bradley; Chris Bystroff; Patrick Conway; Seth Cooper; Bruno E Correia; Brian Coventry; Rhiju Das; René M De Jong; Frank DiMaio; Lorna Dsilva; Roland Dunbrack; Alexander S Ford; Brandon Frenz; Darwin Y Fu; Caleb Geniesse; Lukasz Goldschmidt; Ragul Gowthaman; Jeffrey J Gray; Dominik Gront; Sharon Guffy; Scott Horowitz; Po-Ssu Huang; Thomas Huber; Tim M Jacobs; Jeliazko R Jeliazkov; David K Johnson; Kalli Kappel; John Karanicolas; Hamed Khakzad; Karen R Khar; Sagar D Khare; Firas Khatib; Alisa Khramushin; Indigo C King; Robert Kleffner; Brian Koepnick; Tanja Kortemme; Georg Kuenze; Brian Kuhlman; Daisuke Kuroda; Jason W Labonte; Jason K Lai; Gideon Lapidoth; Andrew Leaver-Fay; Steffen Lindert; Thomas Linsky; Nir London; Joseph H Lubin; Sergey Lyskov; Jack Maguire; Lars Malmström; Enrique Marcos; Orly Marcu; Nicholas A Marze; Jens Meiler; Rocco Moretti; Vikram Khipple Mulligan; Santrupti Nerli; Christoffer Norn; Shane Ó'Conchúir; Noah Ollikainen; Sergey Ovchinnikov; Michael S Pacella; Xingjie Pan; Hahnbeom Park; Ryan E Pavlovicz; Manasi Pethe; Brian G Pierce; Kala Bharath Pilla; Barak Raveh; P Douglas Renfrew; Shourya S Roy Burman; Aliza Rubenstein; Marion F Sauer; Andreas Scheck; William Schief; Ora Schueler-Furman; Yuval Sedan; Alexander M Sevy; Nikolaos G Sgourakis; Lei Shi; Justin B Siegel; Daniel-Adriano Silva; Shannon Smith; Yifan Song; Amelie Stein; Maria Szegedy; Frank D Teets; Summer B Thyme; Ray Yu-Ruei Wang; Andrew Watkins; Lior Zimmerman; Richard Bonneau
Journal:  Nat Methods       Date:  2020-06-01       Impact factor: 28.547

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