This title appears in the Scientific Report :
2021
Please use the identifier:
http://dx.doi.org/10.1021/acs.jctc.1c00685 in citations.
Please use the identifier: http://hdl.handle.net/2128/29043 in citations.
TopProperty: Robust Metaprediction of Transmembrane and Globular Protein Features Using Deep Neural Networks
TopProperty: Robust Metaprediction of Transmembrane and Globular Protein Features Using Deep Neural Networks
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 transmemb...
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Personal Name(s): | Mulnaes, Daniel |
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Schott, Stephan / Koenig, Filip / Gohlke, Holger (Corresponding author) | |
Contributing Institute: |
Strukturbiochemie; IBI-7 John von Neumann - Institut für Computing; NIC Jülich Supercomputing Center; JSC Bioinformatik; IBG-4 |
Published in: | Journal of chemical theory and computation, 17 (2021) 11, S. 7281 - 7289 |
Imprint: |
Washington, DC
2021
|
DOI: |
10.1021/acs.jctc.1c00685 |
Document Type: |
Journal Article |
Research Program: |
SFB 1208: Identität und Dynamik von Membransystemen - von Molekülen bis zu zellulären Funktionen Forschergruppe Gohlke Domain-Specific Simulation & Data Life Cycle Labs (SDLs) and Research Groups Utilization of renewable carbon and energy sources and engineering of ecosystem functions Biological and environmental resources for sustainable use |
Link: |
Get full text Published on 2021-10-18. Available in OpenAccess from 2022-10-18. |
Publikationsportal JuSER |
Please use the identifier: http://hdl.handle.net/2128/29043 in citations.
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 metapredictor that predicts all of these features for TMPs or globular proteins. TopProperty is trained on datasets without bias toward a high number of sequence homologs, and the predictions are 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 a web server and standalone at https://cpclab.uni-duesseldorf.de/topsuite/. |