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Prot2Prot: a deep learning model for rapid, photorealistic macromolecular visualization

Molecular visualization is a cornerstone of structural biology, providing insights into the form and function of biomolecules that are difficult to achieve any other way. Scientific analysis, publication, education, and outreach often benefit from photorealistic molecular depictions rendered using a...

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Autor principal: Durrant, Jacob D.
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Springer International Publishing 2022
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9512884/
https://www.ncbi.nlm.nih.gov/pubmed/36008698
http://dx.doi.org/10.1007/s10822-022-00471-4
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author Durrant, Jacob D.
author_facet Durrant, Jacob D.
author_sort Durrant, Jacob D.
collection PubMed
description Molecular visualization is a cornerstone of structural biology, providing insights into the form and function of biomolecules that are difficult to achieve any other way. Scientific analysis, publication, education, and outreach often benefit from photorealistic molecular depictions rendered using advanced computer-graphics programs such as Maya, 3ds Max, and Blender. However, setting up molecular scenes in these programs is laborious even for expert users, and rendering often requires substantial time and computer resources. We have created a deep-learning model called Prot2Prot that quickly imitates photorealistic visualization styles, given a much simpler, easy-to-generate molecular representation. The resulting images are often indistinguishable from images rendered using industry-standard 3D graphics programs, but they can be created in a fraction of the time, even when running in a web browser. To the best of our knowledge, Prot2Prot is the first example of image-to-image translation applied to macromolecular visualization. Prot2Prot is available free of charge, released under the terms of the Apache License, Version 2.0. Users can access a Prot2Prot-powered web app without registration at http://durrantlab.com/prot2prot. SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1007/s10822-022-00471-4.
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spelling pubmed-95128842022-09-28 Prot2Prot: a deep learning model for rapid, photorealistic macromolecular visualization Durrant, Jacob D. J Comput Aided Mol Des Article Molecular visualization is a cornerstone of structural biology, providing insights into the form and function of biomolecules that are difficult to achieve any other way. Scientific analysis, publication, education, and outreach often benefit from photorealistic molecular depictions rendered using advanced computer-graphics programs such as Maya, 3ds Max, and Blender. However, setting up molecular scenes in these programs is laborious even for expert users, and rendering often requires substantial time and computer resources. We have created a deep-learning model called Prot2Prot that quickly imitates photorealistic visualization styles, given a much simpler, easy-to-generate molecular representation. The resulting images are often indistinguishable from images rendered using industry-standard 3D graphics programs, but they can be created in a fraction of the time, even when running in a web browser. To the best of our knowledge, Prot2Prot is the first example of image-to-image translation applied to macromolecular visualization. Prot2Prot is available free of charge, released under the terms of the Apache License, Version 2.0. Users can access a Prot2Prot-powered web app without registration at http://durrantlab.com/prot2prot. SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1007/s10822-022-00471-4. Springer International Publishing 2022-08-26 2022 /pmc/articles/PMC9512884/ /pubmed/36008698 http://dx.doi.org/10.1007/s10822-022-00471-4 Text en © The Author(s) 2022 https://creativecommons.org/licenses/by/4.0/Open AccessThis article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/ (https://creativecommons.org/licenses/by/4.0/) .
spellingShingle Article
Durrant, Jacob D.
Prot2Prot: a deep learning model for rapid, photorealistic macromolecular visualization
title Prot2Prot: a deep learning model for rapid, photorealistic macromolecular visualization
title_full Prot2Prot: a deep learning model for rapid, photorealistic macromolecular visualization
title_fullStr Prot2Prot: a deep learning model for rapid, photorealistic macromolecular visualization
title_full_unstemmed Prot2Prot: a deep learning model for rapid, photorealistic macromolecular visualization
title_short Prot2Prot: a deep learning model for rapid, photorealistic macromolecular visualization
title_sort prot2prot: a deep learning model for rapid, photorealistic macromolecular visualization
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9512884/
https://www.ncbi.nlm.nih.gov/pubmed/36008698
http://dx.doi.org/10.1007/s10822-022-00471-4
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