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TMCrys: predict propensity of success for transmembrane protein crystallization

MOTIVATION: Transmembrane proteins (TMPs) are crucial in the life of the cells. As they have special properties, their structure is hard to determine––the PDB database consists of 2% TMPs, despite the fact that they are predicted to make up to 25% of the human proteome. Crystallization prediction me...

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Detalles Bibliográficos
Autores principales: Varga, Julia K, Tusnády, Gábor E
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Oxford University Press 2018
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6137969/
https://www.ncbi.nlm.nih.gov/pubmed/29718100
http://dx.doi.org/10.1093/bioinformatics/bty342
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author Varga, Julia K
Tusnády, Gábor E
author_facet Varga, Julia K
Tusnády, Gábor E
author_sort Varga, Julia K
collection PubMed
description MOTIVATION: Transmembrane proteins (TMPs) are crucial in the life of the cells. As they have special properties, their structure is hard to determine––the PDB database consists of 2% TMPs, despite the fact that they are predicted to make up to 25% of the human proteome. Crystallization prediction methods were developed to aid the target selection for structure determination, however, there is a need for a TMP specific service. RESULTS: Here, we present TMCrys, a crystallization prediction method that surpasses existing prediction methods in performance thanks to its specialization for TMPs. We expect TMCrys to improve target selection of TMPs. AVAILABILITY AND IMPLEMENTATION: https://github.com/brgenzim/tmcrys SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.
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spelling pubmed-61379692018-09-24 TMCrys: predict propensity of success for transmembrane protein crystallization Varga, Julia K Tusnády, Gábor E Bioinformatics Original Papers MOTIVATION: Transmembrane proteins (TMPs) are crucial in the life of the cells. As they have special properties, their structure is hard to determine––the PDB database consists of 2% TMPs, despite the fact that they are predicted to make up to 25% of the human proteome. Crystallization prediction methods were developed to aid the target selection for structure determination, however, there is a need for a TMP specific service. RESULTS: Here, we present TMCrys, a crystallization prediction method that surpasses existing prediction methods in performance thanks to its specialization for TMPs. We expect TMCrys to improve target selection of TMPs. AVAILABILITY AND IMPLEMENTATION: https://github.com/brgenzim/tmcrys SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online. Oxford University Press 2018-09-15 2018-04-26 /pmc/articles/PMC6137969/ /pubmed/29718100 http://dx.doi.org/10.1093/bioinformatics/bty342 Text en © The Author(s) 2018. Published by Oxford University Press. http://creativecommons.org/licenses/by-nc/4.0/ This is an Open Access article distributed under the terms of the Creative Commons Attribution Non-Commercial License (http://creativecommons.org/licenses/by-nc/4.0/), which permits non-commercial re-use, distribution, and reproduction in any medium, provided the original work is properly cited. For commercial re-use, please contact journals.permissions@oup.com
spellingShingle Original Papers
Varga, Julia K
Tusnády, Gábor E
TMCrys: predict propensity of success for transmembrane protein crystallization
title TMCrys: predict propensity of success for transmembrane protein crystallization
title_full TMCrys: predict propensity of success for transmembrane protein crystallization
title_fullStr TMCrys: predict propensity of success for transmembrane protein crystallization
title_full_unstemmed TMCrys: predict propensity of success for transmembrane protein crystallization
title_short TMCrys: predict propensity of success for transmembrane protein crystallization
title_sort tmcrys: predict propensity of success for transmembrane protein crystallization
topic Original Papers
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6137969/
https://www.ncbi.nlm.nih.gov/pubmed/29718100
http://dx.doi.org/10.1093/bioinformatics/bty342
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