TopProperty dataset
| dc.contributor.author | Mulnaes, Daniel | |
| dc.contributor.author | Schott-Verdugo, Stephan | |
| dc.contributor.author | Koenig, Filip | |
| dc.contributor.author | Gohlke, Holger | |
| dc.date.accessioned | 2021-07-07T07:06:06Z | |
| dc.date.available | 2021-07-07T07:06:06Z | |
| dc.date.issued | 2021-07-07 | |
| dc.description.abstract | Transmembrane proteins (TMPs) are critical components of cellular life. However, due to experimental challenges, the number of experimentally resolved TMP structures is severely underrepresented in databases compared to their cellular abundance. Prediction of (per-residue) features such as transmembrane topology, membrane exposure, secondary structure, and solvent accessibility can be a useful starting point for experimental design or protein structure prediction, but often requires different computational tools for different features or types of proteins. We present TopProperty, a meta-predictor that predicts all of these features for TMPs or globular proteins. TopProperty predictions are robust, especially for proteins with few sequence homologs, and significantly better than the evaluated state-of-the-art primary predictors on all quality metrics. TopProperty eliminates the need for protein type- or feature-tailored tools, specifically for TMPs. TopProperty is freely available as web server and standalone at https://cpclab.uni-duesseldorf.de/topsuite/. | en |
| dc.identifier.uri | https://researchdata.hhu.de/handle/entry/90 | |
| dc.identifier.uri | http://dx.doi.org/10.25838/d5p-20 | |
| dc.language.iso | en | en |
| dc.publisher | N/A | en |
| dc.rights | Attribution-NonCommercial-NoDerivs 3.0 United States | * |
| dc.rights.uri | http://creativecommons.org/licenses/by-nc-nd/3.0/us/ | * |
| dc.subject | Protein structure | en |
| dc.subject | Sequence | en |
| dc.subject | Secondary structure | en |
| dc.subject | Transmembrane topology | en |
| dc.title | TopProperty dataset | en |
| dc.type | Dataset | en |
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