TY  - JOUR
AU  - Mulnaes, Daniel
AU  - Koenig, Filip
AU  - Gohlke, Holger
TI  - TopSuite Web Server: A Meta-Suite for Deep-Learning-Based Protein Structure and Quality Prediction
JO  - Journal of chemical information and modeling
VL  - 61
IS  - 2
SN  - 1549-960X
CY  - Washington, DC
PB  - American Chemical Society64160
M1  - FZJ-2021-00457
SP  - 548–553
PY  - 2021
AB  - Proteins carry out the most fundamental processes of life such as cellular metabolism, regulation, and communication. Understanding these processes at a molecular level requires knowledge of their three-dimensional structures. Experimental techniques such as X-ray crystallography, NMR spectroscopy, and cryogenic electron microscopy can resolve protein structures but are costly and time-consuming and do not work for all proteins. Computational protein structure prediction tries to overcome these problems by predicting the structure of a new protein using existing protein structures as a resource. Here we present TopSuite, a web server for protein model quality assessment (TopScore) and template-based protein structure prediction (TopModel). TopScore provides meta-predictions for global and residue-wise model quality estimation using deep neural networks. TopModel predicts protein structures using a top-down consensus approach to aid the template selection and subsequently uses TopScore to refine and assess the predicted structures. The TopSuite Web server is freely available at https://cpclab.uni-duesseldorf.de/topsuite/.
LB  - PUB:(DE-HGF)16
C6  - 33464891
UR  - <Go to ISI:>//WOS:000621663600002
DO  - DOI:10.1021/acs.jcim.0c01202
UR  - https://juser.fz-juelich.de/record/889849
ER  -