TY  - JOUR
AU  - Mulnaes, Daniel
AU  - Schott, Stephan
AU  - Koenig, Filip
AU  - Gohlke, Holger
TI  - TopProperty: Robust Metaprediction of Transmembrane and Globular Protein Features Using Deep Neural Networks
JO  - Journal of chemical theory and computation
VL  - 17
IS  - 11
SN  - 1549-9618
CY  - Washington, DC
M1  - FZJ-2021-04291
SP  - 7281 - 7289
PY  - 2021
AB  - 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/.
LB  - PUB:(DE-HGF)16
C6  - 34663069
UR  - <Go to ISI:>//WOS:000718183600049
DO  - DOI:10.1021/acs.jctc.1c00685
UR  - https://juser.fz-juelich.de/record/902472
ER  -