%0 Journal Article
%A Mulnaes, Daniel
%A Koenig, Filip
%A Gohlke, Holger
%T TopSuite Web Server: A Meta-Suite for Deep-Learning-Based Protein Structure and Quality Prediction
%J Journal of chemical information and modeling
%V 61
%N 2
%@ 1549-960X
%C Washington, DC
%I American Chemical Society64160
%M FZJ-2021-00457
%P 548–553
%D 2021
%X 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/.
%F PUB:(DE-HGF)16
%9 Journal Article
%$ 33464891
%U <Go to ISI:>//WOS:000621663600002
%R 10.1021/acs.jcim.0c01202
%U https://juser.fz-juelich.de/record/889849