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FLEXR: automated multi-conformer model building using electron-density map sampling
Protein conformational dynamics that may inform biology often lie dormant in high-resolution electron-density maps. While an estimated ∼18% of side chains in high-resolution models contain alternative conformations, these are underrepresented in current PDB models due to difficulties in manually det...
Autores principales: | , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
International Union of Crystallography
2023
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10167668/ https://www.ncbi.nlm.nih.gov/pubmed/37071395 http://dx.doi.org/10.1107/S2059798323002498 |
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author | Stachowski, Timothy R. Fischer, Marcus |
author_facet | Stachowski, Timothy R. Fischer, Marcus |
author_sort | Stachowski, Timothy R. |
collection | PubMed |
description | Protein conformational dynamics that may inform biology often lie dormant in high-resolution electron-density maps. While an estimated ∼18% of side chains in high-resolution models contain alternative conformations, these are underrepresented in current PDB models due to difficulties in manually detecting, building and inspecting alternative conformers. To overcome this challenge, we developed an automated multi-conformer modeling program, FLEXR. Using Ringer-based electron-density sampling, FLEXR builds explicit multi-conformer models for refinement. Thereby, it bridges the gap of detecting hidden alternate states in electron-density maps and including them in structural models for refinement, inspection and deposition. Using a series of high-quality crystal structures (0.8–1.85 Å resolution), we show that the multi-conformer models produced by FLEXR uncover new insights that are missing in models built either manually or using current tools. Specifically, FLEXR models revealed hidden side chains and backbone conformations in ligand-binding sites that may redefine protein–ligand binding mechanisms. Ultimately, the tool facilitates crystallographers with opportunities to include explicit multi-conformer states in their high-resolution crystallographic models. One key advantage is that such models may better reflect interesting higher energy features in electron-density maps that are rarely consulted by the community at large, which can then be productively used for ligand discovery downstream. FLEXR is open source and publicly available on GitHub at https://github.com/TheFischerLab/FLEXR. |
format | Online Article Text |
id | pubmed-10167668 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2023 |
publisher | International Union of Crystallography |
record_format | MEDLINE/PubMed |
spelling | pubmed-101676682023-05-10 FLEXR: automated multi-conformer model building using electron-density map sampling Stachowski, Timothy R. Fischer, Marcus Acta Crystallogr D Struct Biol Ccp4 Protein conformational dynamics that may inform biology often lie dormant in high-resolution electron-density maps. While an estimated ∼18% of side chains in high-resolution models contain alternative conformations, these are underrepresented in current PDB models due to difficulties in manually detecting, building and inspecting alternative conformers. To overcome this challenge, we developed an automated multi-conformer modeling program, FLEXR. Using Ringer-based electron-density sampling, FLEXR builds explicit multi-conformer models for refinement. Thereby, it bridges the gap of detecting hidden alternate states in electron-density maps and including them in structural models for refinement, inspection and deposition. Using a series of high-quality crystal structures (0.8–1.85 Å resolution), we show that the multi-conformer models produced by FLEXR uncover new insights that are missing in models built either manually or using current tools. Specifically, FLEXR models revealed hidden side chains and backbone conformations in ligand-binding sites that may redefine protein–ligand binding mechanisms. Ultimately, the tool facilitates crystallographers with opportunities to include explicit multi-conformer states in their high-resolution crystallographic models. One key advantage is that such models may better reflect interesting higher energy features in electron-density maps that are rarely consulted by the community at large, which can then be productively used for ligand discovery downstream. FLEXR is open source and publicly available on GitHub at https://github.com/TheFischerLab/FLEXR. International Union of Crystallography 2023-04-18 /pmc/articles/PMC10167668/ /pubmed/37071395 http://dx.doi.org/10.1107/S2059798323002498 Text en © Stachowski and Fischer 2023 https://creativecommons.org/licenses/by/4.0/This is an open-access article distributed under the terms of the Creative Commons Attribution (CC-BY) Licence, which permits unrestricted use, distribution, and reproduction in any medium, provided the original authors and source are cited. |
spellingShingle | Ccp4 Stachowski, Timothy R. Fischer, Marcus FLEXR: automated multi-conformer model building using electron-density map sampling |
title |
FLEXR: automated multi-conformer model building using electron-density map sampling |
title_full |
FLEXR: automated multi-conformer model building using electron-density map sampling |
title_fullStr |
FLEXR: automated multi-conformer model building using electron-density map sampling |
title_full_unstemmed |
FLEXR: automated multi-conformer model building using electron-density map sampling |
title_short |
FLEXR: automated multi-conformer model building using electron-density map sampling |
title_sort | flexr: automated multi-conformer model building using electron-density map sampling |
topic | Ccp4 |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10167668/ https://www.ncbi.nlm.nih.gov/pubmed/37071395 http://dx.doi.org/10.1107/S2059798323002498 |
work_keys_str_mv | AT stachowskitimothyr flexrautomatedmulticonformermodelbuildingusingelectrondensitymapsampling AT fischermarcus flexrautomatedmulticonformermodelbuildingusingelectrondensitymapsampling |