REFERENCES
1. Anfinsen CB. Principles that govern the folding of protein chains.
Science. 1973;181(4096):223-30.
2. Robin X, Haas J, Gumienny R, Smolinski A, Tauriello G, Schwede T.
Continuous Automated Model EvaluatiOn (CAMEO)-Perspectives on the future
of fully automated evaluation of structure prediction methods. Proteins.
2021;89(12):1977-86.
3. Kryshtafovych A, Schwede T, Topf M, Fidelis K, Moult J. Critical
assessment of methods of protein structure prediction (CASP)-Round XIII.
Proteins. 2019;87(12):1011-20.
4. Kryshtafovych A, Schwede T, Topf M, Fidelis K, Moult J. Critical
assessment of methods of protein structure prediction (CASP)-Round XIV.
Proteins. 2021;89(12):1607-17.
5. Wodak SJ, Velankar S, Sternberg MJE. Modeling protein interactions
and complexes in CAPRI: Seventh CAPRI evaluation meeting, April 3-5
EMBL-EBI, Hinxton, UK. Proteins. 2020;88(8):913-5.
6. Magnus M, Antczak M, Zok T, Wiedemann J, Lukasiak P, Cao Y, et al.
RNA-Puzzles toolkit: a computational resource of RNA 3D structure
benchmark datasets, structure manipulation, and evaluation tools.
Nucleic Acids Res. 2020;48(2):576-88.
7. Townshend RJL, Eismann S, Watkins AM, Rangan R, Karelina M, Das R, et
al. Geometric deep learning of RNA structure. Science.
2021;373(6558):1047-51.
8. Crampon K, Giorkallos A, Deldossi M, Baud S, Steffenel LA.
Machine-learning methods for ligand-protein molecular docking. Drug
Discov Today. 2022;27(1):151-64.
9. Kryshtafovych A, Montelione G, Rigden D, Mesdaghi S, Karaca E, Moult
J. Breaking the conformational ensemble barrier: Ensemble structure
modeling challenges in CASP15. Proteins. 2023 (this issue,
https://doi.org/10.1002/prot.26584).
10. Simpkin AJ, Mesdaghi S,
Sanchez Rodriguez F, Elliott L, Murphy DL, Kryshtafovych A, et al.
Tertiary structure assessment at CASP15. Proteins. 2023 (this issue.
https://doi.org/10.1002/prot.26593).
11. Ozden B, Kryshtafovych A, Karaca E. The Impact of AI-Based Modeling
on the Accuracy of Protein Assembly Prediction: Insights from CASP15.
Proteins. 2023 (this issue, in production).
12. Studer G, Tauriello G, Schwede T. Assessment of the assessment - All
about complexes. Proteins. 2023 (this issue, in production).
13. Das R, Kretsch RC, Simpkin A, Mulvaney T, Pham P, Rangan R, et al.
Assessment of three-dimensional RNA structure prediction in CASP15.
Proteins 2023 (this issue, in production).
14. Walters P, Robin X, Studer G, Durairaj J, Eberhardt J, Schwede T.
Assessment of Protein-Ligand Complexes in CASP15. Proteins. 2023 (this
issue, in production).
15. Alexander LT, Durairaj J, Kryshtafovych A, Abriata LA, Bayo Y,
Bhabha G, et al. Protein target highlights in CASP15: Analysis of models
by structure providers. Proteins. 2023 (this issue,
http://doi.org/10.1002/prot.26545).
16. Kretsch RC, Andersen ES, Bujnicki JM, Chiu W, Das R et al. RNA
target highlights in CASP15: Evaluation of predicted models by structure
providers. Proteins. 2023 (this issue,
https://doi.org/10.1002/prot.26550).
17. Kryshtafovych A, Antczak M, Szachniuk M, Zok T, Kretsch RC, Rangan
R, et al. New prediction categories in CASP15. Proteins. 2023 (this
issue, http://doi.org/10.1002/prot.26515).
18. Kryshtafovych A, Rigden DJ. To split or not to split: CASP15 targets
and their processing into tertiary structure evaluation units. Proteins.
2023 (this issue, http://doi.org/10.1002/prot.26533).
19. Zemla A, Venclovas, Moult J, Fidelis K. Processing and evaluation of
predictions in CASP4. Proteins. 2001;Suppl 5:13-21.
20. van Kempen M, Kim SS, Tumescheit C, Mirdita M, Lee J, Gilchrist CLM,
et al. Fast and accurate protein structure search with Foldseek. Nat
Biotechnol. 2023.
21. Zemla A. LGA: A method for finding 3D similarities in protein
structures. Nucleic Acids Res. 2003;31(13):3370-4.
22. Mirdita M, Schutze K, Moriwaki Y, Heo L, Ovchinnikov S, Steinegger
M. ColabFold: making protein folding accessible to all. Nat Methods.
2022;19(6):679-82.
23. Jumper J, Evans R, Pritzel A, Green T, Figurnov M, Ronneberger O, et
al. Applying and improving AlphaFold at CASP14. Proteins. 2021
Dec;89(12):1711-1721.
24. Baek M, DiMaio F, Anishchenko I, Dauparas J, Ovchinnikov S, Lee GR,
et al. Accurate prediction of protein structures and interactions using
a three-track neural network. Science. 2021;373(6557):871-6.
25. Baek M, Anishchenko I, Humphreys IR, Cong Q, Baker D, DiMaio F.
Efficient and accurate prediction of protein structure using
RoseTTAFold2. bioRxiv. 2023:2023.05.24.542179.
26. Korgaonkar A, Han C, Lemire AL, Siwanowicz I, Bennouna D, Kopec RE,
et al. A novel family of secreted insect proteins linked to plant gall
development. Curr Biol. 2021;31(9):2038.
27. Jumper J, Evans R, Pritzel A, Green T, Figurnov M, Ronneberger O, et
al. Highly accurate protein structure prediction with AlphaFold. Nature.
2021;596(7873):583-9.
28. Lin Z, Akin H, Rao R, Hie B, Zhu Z, Lu W, et al. Language models of
protein sequences at the scale of evolution enable accurate structure
prediction. bioRxiv. 2022:2022.07.20.500902.
29. Yin R, Feng BY, Varshney A, Pierce BG. Benchmarking AlphaFold for
protein complex modeling reveals accuracy determinants. Protein Sci.
2022;31(8):e4379.
30. Lensink MF, Brysbaert G, Raouraoua N, Bates P, al. E. Impact of
AlphaFold on Structure Prediction of Protein Complexes: The CASP15-CAPRI
Experiment. Proteins. 2023 (this issue, in press).
31. Evans R, O’Neill M, Pritzel A, Antropova N, Senior A, Green T, et
al. Protein complex prediction with AlphaFold-Multimer. bioRxiv.
2022:2021.10.04.463034.
32. Olechnovic K, Valancauskas L, Dapkunas J, Venclovas C. Prediction of
protein assemblies by structure sampling followed by interface-focused
scoring. Proteins. 2023 (this issue,
https://doi.org/10.1002/prot.26569).
33. Savage N. Synthetic data could be better than real data. Nature.
2023, Apr 27 (doi: 10.1038/d41586-023-01445-8).
34. Peng Z, Wang W, Wei H, Li X, Yang J. Improved protein structure
prediction with trRosettaX2, AlphaFold2, and optimized MSAs in CASP15.
Proteins. 2023 (this issue, https://doi.org/10.1002/prot.26570).
35. Mariani V, Biasini M, Barbato A, Schwede T. lDDT: a local
superposition-free score for comparing protein structures and models
using distance difference tests. Bioinformatics. 2013; 29(21):
2722-2728.
36. Zhang Y, Skolnick J. Scoring function for automated assessment of
protein structure template quality. Proteins. 2004;57(4):702-10.
37. Edmunds NS, Alharbi SMA, Genc AG, Adiyaman R, McGuffin LJ.
Estimation of model accuracy in CASP15 using the ModFOLDdock server.
Proteins. 2023 (this issue, https://doi.org/10.1002/prot.26532).
38. Liu J, Liu D, He G, Zhang G. Estimating protein complex model
accuracy based on ultrafast shape recognition and deep learning in
CASP15. Proteins. 2023 (this issue, https://doi.org/10.1002/prot.26564).
39. Parks CD, Gaieb Z, Chiu M, Yang H, Shao C, Walters WP, et al. D3R
grand challenge 4: blind prediction of protein-ligand poses, affinity
rankings, and relative binding free energies. J Comput Aided Mol Des.
2020;34(2):99-119.
40. Kwon S, Won J, Kryshtafovych A, Seok C. Assessment of protein model
structure accuracy estimation in CASP14: Old and new challenges.
Proteins. 2021;89(12):1940-1948.