% IMPORTANT: The following is UTF-8 encoded.  This means that in the presence
% of non-ASCII characters, it will not work with BibTeX 0.99 or older.
% Instead, you should use an up-to-date BibTeX implementation like “bibtex8” or
% “biber”.

@ARTICLE{Mulnaes:889849,
      author       = {Mulnaes, Daniel and Koenig, Filip and Gohlke, Holger},
      title        = {{T}op{S}uite {W}eb {S}erver: {A} {M}eta-{S}uite for
                      {D}eep-{L}earning-{B}ased {P}rotein {S}tructure and
                      {Q}uality {P}rediction},
      journal      = {Journal of chemical information and modeling},
      volume       = {61},
      number       = {2},
      issn         = {1549-960X},
      address      = {Washington, DC},
      publisher    = {American Chemical Society64160},
      reportid     = {FZJ-2021-00457},
      pages        = {548–553},
      year         = {2021},
      abstract     = {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/.},
      cin          = {JSC / NIC / IBI-7},
      ddc          = {540},
      cid          = {I:(DE-Juel1)JSC-20090406 / I:(DE-Juel1)NIC-20090406 /
                      I:(DE-Juel1)IBI-7-20200312},
      pnm          = {5111 - Domain-Specific Simulation Data Life Cycle Labs
                      (SDLs) and Research Groups (POF4-511) / Forschergruppe
                      Gohlke $(hkf7_20200501)$ / DFG project 267205415 - SFB 1208:
                      Identität und Dynamik von Membransystemen - von Molekülen
                      bis zu zellulären Funktionen},
      pid          = {G:(DE-HGF)POF4-5111 / $G:(DE-Juel1)hkf7_20200501$ /
                      G:(GEPRIS)267205415},
      typ          = {PUB:(DE-HGF)16},
      pubmed       = {33464891},
      UT           = {WOS:000621663600002},
      doi          = {10.1021/acs.jcim.0c01202},
      url          = {https://juser.fz-juelich.de/record/889849},
}