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@ARTICLE{Kryshtafovych:641980,
      author       = {Kryshtafovych, Andriy and Schwede, Torsten and Topf, Maya
                      and Fidelis, Krzysztof and Moult, John},
      title        = {{P}rogress and {B}ottlenecks for {D}eep {L}earning in
                      {C}omputational {S}tructure {B}iology: {CASP} {R}ound {XVI}},
      journal      = {Proteins},
      volume       = {94},
      number       = {1},
      issn         = {0887-3585},
      address      = {New York, NY},
      publisher    = {Wiley-Liss},
      reportid     = {PUBDB-2025-05267},
      pages        = {5 - 14},
      year         = {2025},
      abstract     = {CASP16 is the most recent in a series of community
                      experiments to rigorously assess the state of the art in
                      areas of computational structural biology. The field has
                      advanced enormously in recent years: in early CASPs, the
                      assessments centered around whether the methods were at all
                      useful. Now they mostly focus on how near we are to not
                      needing experiments. In most areas, deep learning methods
                      dominate, particularly AlphaFold variants and associated
                      technology. In this round, there is no significant change in
                      overall agreement between calculated monomer protein
                      structures and their experimental counterparts, not because
                      of method deficiencies but because, for most proteins,
                      agreement is likely as high as can be obtained given
                      experimental uncertainty. For protein complexes, huge gains
                      in accuracy were made in the previous CASP, but there still
                      appears to be room for further improvement. In contrast to
                      these encouraging results, for RNA structures, the deep
                      learning methods are notably unsuccessful at present and are
                      not superior to traditional approaches. Both approaches
                      still produce very poor results in the absence of structural
                      homology. For macromolecular ensembles, the small CASP
                      target set limits conclusions, but generally, in the absence
                      of structural templates, results tend to be poor and
                      detailed structures of alternative conformations are usually
                      of relatively low accuracy. For organic ligand–protein
                      structures and affinities (important for aspects of drug
                      design), deep learning methods are substantially more
                      successful than traditional ones on the relatively easy CASP
                      target set, though the results often fall short of
                      experimental accuracy. In the less glamorous but essential
                      area of methods for estimating the accuracy, previous
                      results found reliable accuracy estimates at the amino acid
                      level. The present CASP results show that the best methods
                      are also largely effective in selecting models of protein
                      complexes with high interface accuracy. Will upcoming method
                      improvements overcome the remaining barriers to reaching
                      experimental accuracy in all categories? We will have to
                      wait until the next CASP to find out, but there are two
                      promising trends. One is the combination of traditional
                      physics-inspired methods and deep learning, and the other is
                      the expected increase in training data, especially for
                      ligand–protein complexes.},
      cin          = {CSSB-LIV/UKE-MT},
      ddc          = {570},
      cid          = {$I:(DE-H253)CSSB-LIV_UKE-MT-20220525$},
      pnm          = {899 - ohne Topic (POF4-899)},
      pid          = {G:(DE-HGF)POF4-899},
      experiment   = {EXP:(DE-MLZ)NOSPEC-20140101},
      typ          = {PUB:(DE-HGF)16},
      doi          = {10.1002/prot.70076},
      url          = {https://bib-pubdb1.desy.de/record/641980},
}