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CASP

January 01, 1970

For other uses, see CASP (disambiguation).

Critical Assessment of Structure Prediction (CASP),[1] sometimes called Critical Assessment of Protein Structure Prediction, is a community-wide, worldwide experiment for protein structure prediction taking place every two years since 1994.[2] CASP provides research groups with an opportunity to objectively test their structure prediction methods and delivers an independent assessment of the state of the art in protein structure modeling to the research community and software users. Even though the primary goal of CASP is to help advance the methods of identifying protein three-dimensional structure from its amino acid sequence many view the experiment more as a “world championship” in this field of science. More than 100 research groups from all over the world participate in CASP on a regular basis and it is not uncommon for entire groups to suspend their other research for months while they focus on getting their servers ready for the experiment and on performing the detailed predictions.

A target structure (ribbons) and 354 template-based predictions superimposed (gray Calpha backbones); from CASP8

Selection of target proteins edit

In order to ensure that no predictor can have prior information about a protein's structure that would put them at an advantage, it is important that the experiment be conducted in a double-blind fashion: Neither predictors nor the organizers and assessors know the structures of the target proteins at the time when predictions are made. Targets for structure prediction are either structures soon-to-be solved by X-ray crystallography or NMR spectroscopy, or structures that have just been solved (mainly by one of the structural genomics centers) and are kept on hold by the Protein Data Bank. If the given sequence is found to be related by common descent to a protein sequence of known structure (called a template), comparative protein modeling may be used to predict the tertiary structure. Templates can be found using sequence alignment methods (e.g. BLAST or HHsearch) or protein threading methods, which are better in finding distantly related templates. Otherwise, de novo protein structure prediction must be applied (e.g. Rosetta), which is much less reliable but can sometimes yield models with the correct fold (usually, for proteins less than 100-150 amino acids). Truly new folds are becoming quite rare among the targets,[3][4] making that category smaller than desirable.

Evaluation edit

The primary method of evaluation[5] is a comparison of the predicted model α-carbon positions with those in the target structure. The comparison is shown visually by cumulative plots of distances between pairs of equivalents α-carbon in the alignment of the model and the structure, such as shown in the figure (a perfect model would stay at zero all the way across), and is assigned a numerical score GDT-TS (Global Distance Test—Total Score) describing percentage of well-modeled residues in the model with respect to the target.[6] Free modeling (template-free, or de novo) is also evaluated visually by the assessors, since the numerical scores do not work as well for finding loose resemblances in the most difficult cases.[7] High-accuracy template-based predictions were evaluated in CASP7 by whether they worked for molecular-replacement phasing of the target crystal structure[8] with successes followed up later,[9] and by full-model (not just α-carbon) model quality and full-model match to the target in CASP8.[10]

Evaluation of the results is carried out in the following prediction categories:

Tertiary structure prediction category was further subdivided into:

  • homology modeling
  • fold recognition (also called protein threading; note that this naming is incorrect as threading is a method)
  • de novo structure prediction, now referred to as 'New Fold' as many methods apply evaluation, or scoring, functions that are biased by knowledge of native protein structures, such as an artificial neural network.

Starting with CASP7, categories have been redefined to reflect developments in methods. The 'Template based modeling' category includes all former comparative modeling, homologous fold based models and some analogous fold based models. The 'template free modeling (FM)' category includes models of proteins with previously unseen folds and hard analogous fold based models. Due to limited numbers of template free targets (they are quite rare), in 2011 so called CASP ROLL was introduced. This continuous (rolling) CASP experiment aims at more rigorous evaluation of template free prediction methods through assessment of a larger number of targets outside of the regular CASP prediction season. Unlike LiveBench and EVA, this experiment is in the blind-prediction spirit of CASP, i.e. all the predictions are made on yet unknown structures.[11]

The CASP results are published in special supplement issues of the scientific journal Proteins, all of which are accessible through the CASP website.[12] A lead article in each of these supplements describes specifics of the experiment[13][14] while a closing article evaluates progress in the field.[15][16]

AlphaFold edit

In December 2018, CASP13 made headlines when it was won by AlphaFold, an artificial intelligence program created by DeepMind.[17] In November 2020, an improved version 2 of AlphaFold won CASP14.[18] According to one of CASP co-founders John Moult, AlphaFold scored around 90 on a 100-point scale of prediction accuracy for moderately difficult protein targets.[19] AlphaFold was made open source in 2021, and in CASP15 in 2022, while DeepMind did not enter, virtually all of the high-ranking teams used AlphaFold or modifications of AlphaFold.[20]

See also edit

References edit

  1. ^ "Home - CASP15". predictioncenter.org. Retrieved 2022-12-14.
  2. ^ Moult J, Pedersen JT, Judson R, Fidelis K (November 1995). "A large-scale experiment to assess protein structure prediction methods". Proteins. 23 (3): ii–v. doi:10.1002/prot.340230303. PMID 8710822. S2CID 11216440.
  3. ^ Tress ML, Ezkurdia I, Richardson JS (2009). "Target domain definition and classification in CASP8". Proteins. 77 Suppl 9 (Suppl 9): 10–7. doi:10.1002/prot.22497. PMC 2805415. PMID 19603487.
  4. ^ Zhang Y, Skolnick J (January 2005). "The protein structure prediction problem could be solved using the current PDB library". Proceedings of the National Academy of Sciences of the United States of America. 102 (4): 1029–34. Bibcode:2005PNAS..102.1029Z. doi:10.1073/pnas.0407152101. PMC 545829. PMID 15653774.
  5. ^ Cozzetto D, Kryshtafovych A, Fidelis K, Moult J, Rost B, Tramontano A (2009). "Evaluation of template-based models in CASP8 with standard measures". Proteins. 77 Suppl 9 (Suppl 9): 18–28. doi:10.1002/prot.22561. PMC 4589151. PMID 19731382.
  6. ^ Zemla A (July 2003). "LGA: A method for finding 3D similarities in protein structures". Nucleic Acids Research. 31 (13): 3370–4. doi:10.1093/nar/gkg571. PMC 168977. PMID 12824330.
  7. ^ Ben-David M, Noivirt-Brik O, Paz A, Prilusky J, Sussman JL, Levy Y (2009). "Assessment of CASP8 structure predictions for template free targets". Proteins. 77 Suppl 9 (Suppl 9): 50–65. doi:10.1002/prot.22591. PMID 19774550. S2CID 16517118.
  8. ^ Read RJ, Chavali G (2007). "Assessment of CASP7 predictions in the high accuracy template-based modeling category". Proteins. 69 Suppl 8 (Suppl 8): 27–37. doi:10.1002/prot.21662. PMID 17894351. S2CID 33172629.
  9. ^ Qian B, Raman S, Das R, Bradley P, McCoy AJ, Read RJ, Baker D (November 2007). "High-resolution structure prediction and the crystallographic phase problem". Nature. 450 (7167): 259–64. Bibcode:2007Natur.450..259Q. doi:10.1038/nature06249. PMC 2504711. PMID 17934447.
  10. ^ Keedy DA, Williams CJ, Headd JJ, Arendall WB, Chen VB, Kapral GJ, et al. (2009). "The other 90% of the protein: assessment beyond the Calphas for CASP8 template-based and high-accuracy models". Proteins. 77 Suppl 9 (Suppl 9): 29–49. doi:10.1002/prot.22551. PMC 2877634. PMID 19731372.
  11. ^ Kryshtafovych A, Monastyrskyy B, Fidelis K (February 2014). "CASP prediction center infrastructure and evaluation measures in CASP10 and CASP ROLL". Proteins. 82 Suppl 2 (2): 7–13. doi:10.1002/prot.24399. PMC 4396618. PMID 24038551.
  12. ^ "CASP Proceedings".
  13. ^ Moult J, Fidelis K, Kryshtafovych A, Rost B, Hubbard T, Tramontano A (2007). "Critical assessment of methods of protein structure prediction-Round VII". Proteins. 69 Suppl 8 (Suppl 8): 3–9. doi:10.1002/prot.21767. PMC 2653632. PMID 17918729.
  14. ^ Moult J, Fidelis K, Kryshtafovych A, Rost B, Tramontano A (2009). "Critical assessment of methods of protein structure prediction - Round VIII". Proteins. 77 Suppl 9 (Suppl 9): 1–4. doi:10.1002/prot.22589. PMID 19774620. S2CID 9704851.
  15. ^ Kryshtafovych A, Fidelis K, Moult J (2007). "Progress from CASP6 to CASP7". Proteins. 69 Suppl 8 (Suppl 8): 194–207. doi:10.1002/prot.21769. PMID 17918728. S2CID 40200832.
  16. ^ Kryshtafovych A, Fidelis K, Moult J (2009). "CASP8 results in context of previous experiments". Proteins. 77 Suppl 9 (Suppl 9): 217–28. doi:10.1002/prot.22562. PMC 5479686. PMID 19722266.
  17. ^ Sample, Ian (2 December 2018). "Google's DeepMind predicts 3D shapes of proteins". The Guardian. Retrieved 19 July 2019.
  18. ^ "DeepMind's protein-folding AI has solved a 50-year-old grand challenge of biology". MIT Technology Review. Retrieved 30 November 2020.
  19. ^ Callaway, Ewen (2020). "'It will change everything': DeepMind's AI makes gigantic leap in solving protein structures". Nature. 588 (7837): 203–204. doi:10.1038/d41586-020-03348-4. PMID 33257889. S2CID 227243204.
  20. ^ Schreiner, Maximilian (2022-12-14). "CASP15: AlphaFold's success spurs new challenges in protein-structure prediction". The Decoder. Retrieved 2023-02-13.

External links edit

  • Official website
  • CASP ROLL
  • FORCASP Forum

Result ranking edit

Automated assessments for CASP15 (2022)

  • Official ranking for servers only
  • Official ranking for humans and servers

Automated assessments for CASP14 (2020)

  • Official ranking for servers only
  • Official ranking for humans and servers
  • Ranking by Zhang Lab

Automated assessments for CASP13 (2018)

  • Official ranking for servers only
  • Official ranking for humans and servers
  • Ranking by Zhang Lab

Automated assessments for CASP12 (2016)

  • Official ranking for servers only
  • Official ranking for humans and servers
  • Ranking by Zhang Lab

Automated assessments for CASP11 (2014)

  • Official ranking for servers only (126 targets)
  • Official ranking for humans and servers (78 targets)
  • Ranking by Zhang Lab

Automated assessments for CASP10 (2012)

  • Official ranking for servers only (127 targets)
  • Official ranking for humans and servers (71 targets)
  • Ranking by Zhang Lab

Automated assessments for CASP9 (2010)

  • Official ranking for servers only (147 targets)
  • Official ranking for humans and servers (78 targets)
  • Ranking by Grishin Lab (for server only)
  • Ranking by Grishin Lab (for human and servers)
  • Ranking by Zhang Lab

Automated assessments for CASP8 (2008)

  • Official ranking for servers only
  • Official ranking for humans and servers
  • Ranking by Zhang Lab
  • Ranking by Grishin Lab
  • Ranking McGuffin Lab

Automated assessments for CASP7 (2006)

  • Ranking by Livebench
  • Ranking by Zhang Lab

January 01, 1970
casp, other, uses, disambiguation, critical, assessment, structure, prediction, sometimes, called, critical, assessment, protein, structure, prediction, community, wide, worldwide, experiment, protein, structure, prediction, taking, place, every, years, since,. For other uses see CASP disambiguation Critical Assessment of Structure Prediction CASP 1 sometimes called Critical Assessment of Protein Structure Prediction is a community wide worldwide experiment for protein structure prediction taking place every two years since 1994 2 CASP provides research groups with an opportunity to objectively test their structure prediction methods and delivers an independent assessment of the state of the art in protein structure modeling to the research community and software users Even though the primary goal of CASP is to help advance the methods of identifying protein three dimensional structure from its amino acid sequence many view the experiment more as a world championship in this field of science More than 100 research groups from all over the world participate in CASP on a regular basis and it is not uncommon for entire groups to suspend their other research for months while they focus on getting their servers ready for the experiment and on performing the detailed predictions A target structure ribbons and 354 template based predictions superimposed gray Calpha backbones from CASP8 Contents 1 Selection of target proteins 2 Evaluation 3 AlphaFold 4 See also 5 References 6 External links 6 1 Result rankingSelection of target proteins editIn order to ensure that no predictor can have prior information about a protein s structure that would put them at an advantage it is important that the experiment be conducted in a double blind fashion Neither predictors nor the organizers and assessors know the structures of the target proteins at the time when predictions are made Targets for structure prediction are either structures soon to be solved by X ray crystallography or NMR spectroscopy or structures that have just been solved mainly by one of the structural genomics centers and are kept on hold by the Protein Data Bank If the given sequence is found to be related by common descent to a protein sequence of known structure called a template comparative protein modeling may be used to predict the tertiary structure Templates can be found using sequence alignment methods e g BLAST or HHsearch or protein threading methods which are better in finding distantly related templates Otherwise de novo protein structure prediction must be applied e g Rosetta which is much less reliable but can sometimes yield models with the correct fold usually for proteins less than 100 150 amino acids Truly new folds are becoming quite rare among the targets 3 4 making that category smaller than desirable Evaluation editThe primary method of evaluation 5 is a comparison of the predicted model a carbon positions with those in the target structure The comparison is shown visually by cumulative plots of distances between pairs of equivalents a carbon in the alignment of the model and the structure such as shown in the figure a perfect model would stay at zero all the way across and is assigned a numerical score GDT TS Global Distance Test Total Score describing percentage of well modeled residues in the model with respect to the target 6 Free modeling template free or de novo is also evaluated visually by the assessors since the numerical scores do not work as well for finding loose resemblances in the most difficult cases 7 High accuracy template based predictions were evaluated in CASP7 by whether they worked for molecular replacement phasing of the target crystal structure 8 with successes followed up later 9 and by full model not just a carbon model quality and full model match to the target in CASP8 10 Evaluation of the results is carried out in the following prediction categories tertiary structure prediction all CASPs secondary structure prediction dropped after CASP5 prediction of structure complexes CASP2 only a separate experiment CAPRI carries on this subject residue residue contact prediction starting CASP4 disordered regions prediction starting CASP5 domain boundary prediction CASP6 CASP8 function prediction starting CASP6 model quality assessment starting CASP7 model refinement starting CASP7 high accuracy template based prediction starting CASP7 Tertiary structure prediction category was further subdivided into homology modeling fold recognition also called protein threading note that this naming is incorrect as threading is a method de novo structure prediction now referred to as New Fold as many methods apply evaluation or scoring functions that are biased by knowledge of native protein structures such as an artificial neural network Starting with CASP7 categories have been redefined to reflect developments in methods The Template based modeling category includes all former comparative modeling homologous fold based models and some analogous fold based models The template free modeling FM category includes models of proteins with previously unseen folds and hard analogous fold based models Due to limited numbers of template free targets they are quite rare in 2011 so called CASP ROLL was introduced This continuous rolling CASP experiment aims at more rigorous evaluation of template free prediction methods through assessment of a larger number of targets outside of the regular CASP prediction season Unlike LiveBench and EVA this experiment is in the blind prediction spirit of CASP i e all the predictions are made on yet unknown structures 11 The CASP results are published in special supplement issues of the scientific journal Proteins all of which are accessible through the CASP website 12 A lead article in each of these supplements describes specifics of the experiment 13 14 while a closing article evaluates progress in the field 15 16 AlphaFold editIn December 2018 CASP13 made headlines when it was won by AlphaFold an artificial intelligence program created by DeepMind 17 In November 2020 an improved version 2 of AlphaFold won CASP14 18 According to one of CASP co founders John Moult AlphaFold scored around 90 on a 100 point scale of prediction accuracy for moderately difficult protein targets 19 AlphaFold was made open source in 2021 and in CASP15 in 2022 while DeepMind did not enter virtually all of the high ranking teams used AlphaFold or modifications of AlphaFold 20 See also editCritical Assessment of Prediction of Interactions CAPRI Critical Assessment of Function Annotation CAFA Critical Assessment of Genome Interpretation CAGI References edit Home CASP15 predictioncenter org Retrieved 2022 12 14 Moult J Pedersen JT Judson R Fidelis K November 1995 A large scale experiment to assess protein structure prediction methods Proteins 23 3 ii v doi 10 1002 prot 340230303 PMID 8710822 S2CID 11216440 Tress ML Ezkurdia I Richardson JS 2009 Target domain definition and classification in CASP8 Proteins 77 Suppl 9 Suppl 9 10 7 doi 10 1002 prot 22497 PMC 2805415 PMID 19603487 Zhang Y Skolnick J January 2005 The protein structure prediction problem could be solved using the current PDB library Proceedings of the National Academy of Sciences of the United States of America 102 4 1029 34 Bibcode 2005PNAS 102 1029Z doi 10 1073 pnas 0407152101 PMC 545829 PMID 15653774 Cozzetto D Kryshtafovych A Fidelis K Moult J Rost B Tramontano A 2009 Evaluation of template based models in CASP8 with standard measures Proteins 77 Suppl 9 Suppl 9 18 28 doi 10 1002 prot 22561 PMC 4589151 PMID 19731382 Zemla A July 2003 LGA A method for finding 3D similarities in protein structures Nucleic Acids Research 31 13 3370 4 doi 10 1093 nar gkg571 PMC 168977 PMID 12824330 Ben David M Noivirt Brik O Paz A Prilusky J Sussman JL Levy Y 2009 Assessment of CASP8 structure predictions for template free targets Proteins 77 Suppl 9 Suppl 9 50 65 doi 10 1002 prot 22591 PMID 19774550 S2CID 16517118 Read RJ Chavali G 2007 Assessment of CASP7 predictions in the high accuracy template based modeling category Proteins 69 Suppl 8 Suppl 8 27 37 doi 10 1002 prot 21662 PMID 17894351 S2CID 33172629 Qian B Raman S Das R Bradley P McCoy AJ Read RJ Baker D November 2007 High resolution structure prediction and the crystallographic phase problem Nature 450 7167 259 64 Bibcode 2007Natur 450 259Q doi 10 1038 nature06249 PMC 2504711 PMID 17934447 Keedy DA Williams CJ Headd JJ Arendall WB Chen VB Kapral GJ et al 2009 The other 90 of the protein assessment beyond the Calphas for CASP8 template based and high accuracy models Proteins 77 Suppl 9 Suppl 9 29 49 doi 10 1002 prot 22551 PMC 2877634 PMID 19731372 Kryshtafovych A Monastyrskyy B Fidelis K February 2014 CASP prediction center infrastructure and evaluation measures in CASP10 and CASP ROLL Proteins 82 Suppl 2 2 7 13 doi 10 1002 prot 24399 PMC 4396618 PMID 24038551 CASP Proceedings Moult J Fidelis K Kryshtafovych A Rost B Hubbard T Tramontano A 2007 Critical assessment of methods of protein structure prediction Round VII Proteins 69 Suppl 8 Suppl 8 3 9 doi 10 1002 prot 21767 PMC 2653632 PMID 17918729 Moult J Fidelis K Kryshtafovych A Rost B Tramontano A 2009 Critical assessment of methods of protein structure prediction Round VIII Proteins 77 Suppl 9 Suppl 9 1 4 doi 10 1002 prot 22589 PMID 19774620 S2CID 9704851 Kryshtafovych A Fidelis K Moult J 2007 Progress from CASP6 to CASP7 Proteins 69 Suppl 8 Suppl 8 194 207 doi 10 1002 prot 21769 PMID 17918728 S2CID 40200832 Kryshtafovych A Fidelis K Moult J 2009 CASP8 results in context of previous experiments Proteins 77 Suppl 9 Suppl 9 217 28 doi 10 1002 prot 22562 PMC 5479686 PMID 19722266 Sample Ian 2 December 2018 Google s DeepMind predicts 3D shapes of proteins The Guardian Retrieved 19 July 2019 DeepMind s protein folding AI has solved a 50 year old grand challenge of biology MIT Technology Review Retrieved 30 November 2020 Callaway Ewen 2020 It will change everything DeepMind s AI makes gigantic leap in solving protein structures Nature 588 7837 203 204 doi 10 1038 d41586 020 03348 4 PMID 33257889 S2CID 227243204 Schreiner Maximilian 2022 12 14 CASP15 AlphaFold s success spurs new challenges in protein structure prediction The Decoder Retrieved 2023 02 13 External links editOfficial website CASP ROLL FORCASP Forum Result ranking edit Automated assessments for CASP15 2022 Official ranking for servers only Official ranking for humans and servers Automated assessments for CASP14 2020 Official ranking for servers only Official ranking for humans and servers Ranking by Zhang Lab Automated assessments for CASP13 2018 Official ranking for servers only Official ranking for humans and servers Ranking by Zhang Lab Automated assessments for CASP12 2016 Official ranking for servers only Official ranking for humans and servers Ranking by Zhang Lab Automated assessments for CASP11 2014 Official ranking for servers only 126 targets Official ranking for humans and servers 78 targets Ranking by Zhang Lab Automated assessments for CASP10 2012 Official ranking for servers only 127 targets Official ranking for humans and servers 71 targets Ranking by Zhang Lab Automated assessments for CASP9 2010 Official ranking for servers only 147 targets Official ranking for humans and servers 78 targets Ranking by Grishin Lab for server only Ranking by Grishin Lab for human and servers Ranking by Zhang Lab Ranking by Cheng Lab Automated assessments for CASP8 2008 Official ranking for servers only Official ranking for humans and servers Ranking by Zhang Lab Ranking by Grishin Lab Ranking McGuffin Lab Ranking by Cheng Lab Automated assessments for CASP7 2006 Ranking by Livebench Ranking by Zhang Lab Retrieved from https en wikipedia org w index php title CASP amp oldid 1189418602, wikipedia, wiki, book, books, library,

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