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Tytuł:
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Improving homology modeling from low-sequence identity templates in Rosetta: A case study in GPCRs.
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Autorzy:
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Bender BJ; Department of Pharmacology, Department of Chemistry, and Center for Structural Biology, Vanderbilt University, Nashville, Tennessee, United States of America.
Marlow B; Department of Pharmacology, Department of Chemistry, and Center for Structural Biology, Vanderbilt University, Nashville, Tennessee, United States of America.
Meiler J; Department of Pharmacology, Department of Chemistry, and Center for Structural Biology, Vanderbilt University, Nashville, Tennessee, United States of America.; Institute for Drug Discovery, Leipzig University Medical School, Leipzig, SAC, Germany.
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Źródło:
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PLoS computational biology [PLoS Comput Biol] 2020 Oct 28; Vol. 16 (10), pp. e1007597. Date of Electronic Publication: 2020 Oct 28 (Print Publication: 2020).
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Typ publikacji:
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Journal Article; Research Support, N.I.H., Extramural; Research Support, Non-U.S. Gov't
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Język:
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English
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Imprint Name(s):
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Original Publication: San Francisco, CA : Public Library of Science, [2005]-
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MeSH Terms:
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Models, Molecular*
Receptors, G-Protein-Coupled*/chemistry
Receptors, G-Protein-Coupled*/genetics
Sequence Homology, Amino Acid*
Computational Biology/*methods
Humans ; Sequence Alignment ; Sequence Analysis, Protein ; Software
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References:
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Grant Information:
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R01 HL122010 United States HL NHLBI NIH HHS; P30 DK058404 United States DK NIDDK NIH HHS; R01 DA046138 United States DA NIDA NIH HHS; R01 GM129261 United States GM NIGMS NIH HHS; R01 GM080403 United States GM NIGMS NIH HHS
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Substance Nomenclature:
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0 (Receptors, G-Protein-Coupled)
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Entry Date(s):
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Date Created: 20201028 Date Completed: 20210127 Latest Revision: 20220329
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Update Code:
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20240104
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PubMed Central ID:
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PMC7652349
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DOI:
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10.1371/journal.pcbi.1007597
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PMID:
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33112852
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As sequencing methodologies continue to advance, the availability of protein sequences far outpaces the ability of structure determination. Homology modeling is used to bridge this gap but relies on high-identity templates for accurate model building. G-protein coupled receptors (GPCRs) represent a significant target class for pharmaceutical therapies in which homology modeling could fill the knowledge gap for structure-based drug design. To date, only about 17% of druggable GPCRs have had their structures characterized at atomic resolution. However, modeling of the remaining 83% is hindered by the low sequence identity between receptors. Here we test key inputs in the model building process using GPCRs as a focus to improve the pipeline in two critical ways: Firstly, we use a blended sequence- and structure-based alignment that accounts for structure conservation in loop regions. Secondly, by merging multiple template structures into one comparative model, the best possible template for every region of a target can be used expanding the conformational space sampled in a meaningful way. This optimization allows for accurate modeling of receptors using templates as low as 20% sequence identity, which accounts for nearly the entire druggable space of GPCRs. A model database of all non-odorant GPCRs is made available at www.rosettagpcr.org. Additionally, all protocols are made available with insights into modifications that may improve accuracy at new targets.
Competing Interests: The authors have declared that no competing interests exist.
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