{
 "id": "multimolecule/casp-rna",
 "owner": "multimolecule",
 "name": "casp-rna",
 "downloads": 24,
 "likes": 0,
 "last_modified": "2025-07-22T09:27:13.000Z",
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  {
   "id": "multimolecule/bprna",
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 "citation": "@ARTICLE{Kryshtafovych2023-je,\n  title     = \"Critical assessment of methods of protein structure prediction ({CASP)-Round} {XV}\",\n  author    = \"Kryshtafovych, Andriy and Schwede, Torsten and Topf, Maya and Fidelis, Krzysztof and Moult, John\",\n  abstract  = \"Computing protein structure from amino acid sequence information has been a long-standing grand challenge. Critical assessment of structure prediction (CASP) conducts community experiments aimed at advancing solutions to this and related problems. Experiments are conducted every 2 years. The 2020 experiment (CASP14) saw major progress, with the second generation of deep learning methods delivering accuracy comparable with experiment for many single proteins. There is an expectation that these methods will have much wider application in computational structural biology. Here we summarize results from the most recent experiment, CASP15, in 2022, with an emphasis on new deep learning-driven progress. Other papers in this special issue of proteins provide more detailed analysis. For single protein structures, the AlphaFold2 deep learning method is still superior to other approaches, but there are two points of note. First, although AlphaFold2 was the core of all the most successful methods, there was a wide variety of implementation and combination with other methods. Second, using the standard AlphaFold2 protocol and default parameters only produces the highest quality result for about two thirds of the targets, and more ex",
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 "reason": "The checks could not identify a recognised licence ID for at least one declared source dataset.",
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}