% 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{Faber:1048746,
      author       = {Faber, Christian and Upadhyay, Utkarsh and Taubert, Oskar
                      and Schug, Alexander},
      title        = {{I}nfluence of {C}ontact {M}ap {T}opology on {RNA}
                      {S}tructure {P}rediction},
      journal      = {Nucleic acids research},
      volume       = {53},
      issn         = {0305-1048},
      address      = {Oxford},
      publisher    = {Oxford Univ. Press},
      reportid     = {FZJ-2025-04861},
      pages        = {gkaf1370},
      year         = {2025},
      abstract     = {The available sequence data of RNA molecules have greatly
                      increased in the past years. Unfortunately, while
                      computational power is still under exponential growth, the
                      computer prediction quality from sequence to final structure
                      is still inferior to labour-intensive experimental work.
                      Although a reliable end-to-end procedure has already been
                      developed for proteins since Alphafold2, while its successor
                      AlphaFold3 can also predict RNA, its confidence, in
                      particular for novel sequences and folds, still appears
                      limited. Another strategy entails two steps: (i) predicting
                      potential contacts in the form of a contact map from
                      evolutionary data; and (ii) simulating the molecule with a
                      physical force field while using the contact map as
                      restraint. However, the quality of the structure prediction
                      crucially depends on the quality of the contact map. Until
                      now, only the proportion of true positive contacts was
                      considered as a quality characteristic. We propose to also
                      include the distribution of these contacts, and have done so
                      in our recent studies. We observed that the clustering of
                      contacts, as is common for many artificial intelligence
                      algorithms, has a negative impact on prediction quality. In
                      contrast, a more distributed topology is beneficial. We have
                      applied these findings from computer experiments to current
                      algorithms and introduced a measure of distribution, the
                      Gaussian score.},
      cin          = {JSC},
      ddc          = {570},
      cid          = {I:(DE-Juel1)JSC-20090406},
      pnm          = {5111 - Domain-Specific Simulation $\&$ Data Life Cycle Labs
                      (SDLs) and Research Groups (POF4-511)},
      pid          = {G:(DE-HGF)POF4-5111},
      typ          = {PUB:(DE-HGF)16},
      doi          = {10.1093/nar/gkaf1370},
      url          = {https://juser.fz-juelich.de/record/1048746},
}