Growing Glycans in Rosetta: Accurate de novo glycan modeling, density fitting, and rational sequon design.

Autor: Adolf-Bryfogle J; Department of Immunology and Microbiology, The Scripps Research Institute, La Jolla, California, United States of America.; IAVI Neutralizing Antibody Center, The Scripps Research Institute, La Jolla, California, United States of America.; Consortium for HIV/AIDS Vaccine Development, The Scripps Research Institute, La Jolla, California, United States of America.; Institute for Protein Innovation, Boston, Massachusetts, United States of America.; Division of Hematology-Oncology, Boston Children's Hospital, Harvard Medical School, Boston, Massachusetts, United States of America., Labonte JW; Department of Chemistry & Biomolecular Engineering, Johns Hopkins University, Baltimore, Maryland, United States of America., Kraft JC; Department of Biochemistry, University of Washington, Seattle, Washington, United States of America.; Institute for Protein Design, University of Washington, Seattle, Washington, United States of America., Shapovalov M; Fox Chase Cancer Center, Philadelphia, Pennsylvania, United States of America., Raemisch S; Department of Immunology and Microbiology, The Scripps Research Institute, La Jolla, California, United States of America.; IAVI Neutralizing Antibody Center, The Scripps Research Institute, La Jolla, California, United States of America.; Consortium for HIV/AIDS Vaccine Development, The Scripps Research Institute, La Jolla, California, United States of America., Lütteke T; Institute of Veterinary Physiology and Biochemistry, Justus-Liebig-University Giessen, Giessen, Germany., DiMaio F; Department of Biochemistry, University of Washington, Seattle, Washington, United States of America.; Institute for Protein Design, University of Washington, Seattle, Washington, United States of America., Bahl CD; Institute for Protein Innovation, Boston, Massachusetts, United States of America.; Division of Hematology-Oncology, Boston Children's Hospital, Harvard Medical School, Boston, Massachusetts, United States of America., Pallesen J; Department of Molecular and Cellular Biochemistry, Indiana University, Bloomington, Indiana, United States of America.; Vaccine and Immunotherapy Center, The Wistar Institute, Philadelphia, Pennsylvania, United States of America., King NP; Department of Biochemistry, University of Washington, Seattle, Washington, United States of America.; Institute for Protein Design, University of Washington, Seattle, Washington, United States of America., Gray JJ; Department of Chemistry & Biomolecular Engineering, Johns Hopkins University, Baltimore, Maryland, United States of America., Kulp DW; Vaccine and Immunotherapy Center, The Wistar Institute, Philadelphia, Pennsylvania, United States of America., Schief WR; Department of Immunology and Microbiology, The Scripps Research Institute, La Jolla, California, United States of America.; IAVI Neutralizing Antibody Center, The Scripps Research Institute, La Jolla, California, United States of America.; Consortium for HIV/AIDS Vaccine Development, The Scripps Research Institute, La Jolla, California, United States of America.
Jazyk: angličtina
Zdroj: PLoS computational biology [PLoS Comput Biol] 2024 Jun 24; Vol. 20 (6), pp. e1011895. Date of Electronic Publication: 2024 Jun 24 (Print Publication: 2024).
DOI: 10.1371/journal.pcbi.1011895
Abstrakt: Carbohydrates and glycoproteins modulate key biological functions. However, experimental structure determination of sugar polymers is notoriously difficult. Computational approaches can aid in carbohydrate structure prediction, structure determination, and design. In this work, 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. GlycanTreeModeler was benchmarked on a test set of glycan structures of varying lengths, or "trees". Structures predicted by GlycanTreeModeler agreed with native structures at high accuracy for both de novo modeling and experimental density-guided building. We employed these tools to design de novo glycan trees into a protein nanoparticle vaccine to shield regions of the scaffold from antibody recognition, and experimentally verified shielding. This work will inform glycoprotein model prediction, glycan masking, and further aid computational methods in experimental structure determination and refinement.
Competing Interests: I have read the journal’s policy and the authors of this manuscript have the following competing interests: Dr. JJG is an unpaid board member of the Rosetta Commons. Under institutional participation agreements between the University of Washington, acting on behalf of the Rosetta Commons, Johns Hopkins University may be entitled to a portion of revenue received on licensing Rosetta software including methods discussed/developed in this study. As a member of the Scientific Advisory Board, JJG has a financial interest in Cyrus Biotechnology. Cyrus Biotechnology distributes the Rosetta software, which may include methods developed in this study. These arrangements have been reviewed and approved by the Johns Hopkins University in accordance with its conflict-of-interest policies. WRS is an employee of Moderna, Inc., but his contributions to this work were all conducted prior to his employment at Moderna. JAB is an employee of Johnson and Johnson Innovative Medicine, Inc., but his contributions to this work were all conducted prior to his current employment.
(Copyright: © 2024 Adolf-Bryfogle et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.)
Databáze: MEDLINE
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