LZerD Protein-Protein Docking Webserver Enhanced With de novo Structure Prediction
Protein-protein docking is a useful tool for modeling the structures of protein complexes that have yet to be experimentally determined. Understanding the structures of protein complexes is a key component for formulating hypotheses in biophysics regarding the functional mechanisms of complexes. Pro...
Autores principales: | , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
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Frontiers Media S.A.
2021
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Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8403062/ https://www.ncbi.nlm.nih.gov/pubmed/34466411 http://dx.doi.org/10.3389/fmolb.2021.724947 |
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author | Christoffer, Charles Bharadwaj, Vijay Luu, Ryan Kihara, Daisuke |
author_facet | Christoffer, Charles Bharadwaj, Vijay Luu, Ryan Kihara, Daisuke |
author_sort | Christoffer, Charles |
collection | PubMed |
description | Protein-protein docking is a useful tool for modeling the structures of protein complexes that have yet to be experimentally determined. Understanding the structures of protein complexes is a key component for formulating hypotheses in biophysics regarding the functional mechanisms of complexes. Protein-protein docking is an established technique for cases where the structures of the subunits have been determined. While the number of known structures deposited in the Protein Data Bank is increasing, there are still many cases where the structures of individual proteins that users want to dock are not determined yet. Here, we have integrated the AttentiveDist method for protein structure prediction into our LZerD webserver for protein-protein docking, which enables users to simply submit protein sequences and obtain full-complex atomic models, without having to supply any structure themselves. We have further extended the LZerD docking interface with a symmetrical homodimer mode. The LZerD server is available at https://lzerd.kiharalab.org/. |
format | Online Article Text |
id | pubmed-8403062 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2021 |
publisher | Frontiers Media S.A. |
record_format | MEDLINE/PubMed |
spelling | pubmed-84030622021-08-30 LZerD Protein-Protein Docking Webserver Enhanced With de novo Structure Prediction Christoffer, Charles Bharadwaj, Vijay Luu, Ryan Kihara, Daisuke Front Mol Biosci Molecular Biosciences Protein-protein docking is a useful tool for modeling the structures of protein complexes that have yet to be experimentally determined. Understanding the structures of protein complexes is a key component for formulating hypotheses in biophysics regarding the functional mechanisms of complexes. Protein-protein docking is an established technique for cases where the structures of the subunits have been determined. While the number of known structures deposited in the Protein Data Bank is increasing, there are still many cases where the structures of individual proteins that users want to dock are not determined yet. Here, we have integrated the AttentiveDist method for protein structure prediction into our LZerD webserver for protein-protein docking, which enables users to simply submit protein sequences and obtain full-complex atomic models, without having to supply any structure themselves. We have further extended the LZerD docking interface with a symmetrical homodimer mode. The LZerD server is available at https://lzerd.kiharalab.org/. Frontiers Media S.A. 2021-08-12 /pmc/articles/PMC8403062/ /pubmed/34466411 http://dx.doi.org/10.3389/fmolb.2021.724947 Text en Copyright © 2021 Christoffer, Bharadwaj, Luu and Kihara. https://creativecommons.org/licenses/by/4.0/This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms. |
spellingShingle | Molecular Biosciences Christoffer, Charles Bharadwaj, Vijay Luu, Ryan Kihara, Daisuke LZerD Protein-Protein Docking Webserver Enhanced With de novo Structure Prediction |
title | LZerD Protein-Protein Docking Webserver Enhanced With de novo Structure Prediction |
title_full | LZerD Protein-Protein Docking Webserver Enhanced With de novo Structure Prediction |
title_fullStr | LZerD Protein-Protein Docking Webserver Enhanced With de novo Structure Prediction |
title_full_unstemmed | LZerD Protein-Protein Docking Webserver Enhanced With de novo Structure Prediction |
title_short | LZerD Protein-Protein Docking Webserver Enhanced With de novo Structure Prediction |
title_sort | lzerd protein-protein docking webserver enhanced with de novo structure prediction |
topic | Molecular Biosciences |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8403062/ https://www.ncbi.nlm.nih.gov/pubmed/34466411 http://dx.doi.org/10.3389/fmolb.2021.724947 |
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