Advancing the Frontiers of Structural Biology

The Critical Assessment of Structure Prediction (CASP) experiments, held biennially, have recently witnessed a revolution in modeling accuracy driven by deep learning. In 2018, for the first time, the folds of most proteins were correctly computed [1]; in 2020, the accuracy of many computed protein structures rivaled that of experimental ones [2]; and in 2022, there was an enormous increase in the accuracy of computed protein complexes [3].

However, results from CASP16 (2024) suggest a performance plateau [4] in some key areas. As we look toward CASP17 in 2026, our primary goal is to catalyze breakthroughs in areas where deep learning has yet to deliver and where success has major practical implications.

We Need Your Targets

CASP is only possible through the generous participation of the experimental community. A total of 1,300 targets have been obtained over the previous sixteen CASP rounds. CASP is now more tightly focused on areas where deep learning methods are not yet adequate and where there is major applied significance to success. This tighter focus makes obtaining good target sets more challenging than in the past.

We are requesting targets for the 17th round in the following categories:

• Immune Complexes: A major failure area for current deep learning methods, and one with major applied importance. Two promising approaches for solving this problem have been seen in CASP. To find out whether these or others can succeed we need a rich and varied set of non-homologous targets: antibody-antigen, nanobody-antigen complexes, and T-cell receptor complexes in particular.
• Organic Ligand-Protein Complexes: These structures have obvious importance for the development of new small-molecule drugs. The most recent CASP experiment (2024) revealed that deep learning methods deliver results that often fall short of the experimental accuracy. We seek both: sets of 3D protein-ligand complexes for specific receptors and targets with novel ligand chemistries as well as data on affinity rankings.
• Nucleic Acids & Complexes: Despite claims that deep learning methods have solved the problem of computing nucleic acid structures, CASP16 [5] and a subsequent Kaggle challenge [6] showed that these methods are usually no better than classical approaches and that both fail badly in the absence of homologous structural information. But new deep learning methods are appearing now at a fast pace. Thus, for CASP17 we particularly need non-homologous RNA/DNA structures and protein-nucleic acid complexes.
• Conformational Ensembles: Testing methods for computing ensembles of structures, ranging from a few discrete conformations to semi-disordered states, is a major expansion area in CASP. We need targets with multiple high resolution conformations for assessment in the conventional way. We also seek targets where there are multiple lower resolution experimental datasets available, such as cryo-tomography; SAXS; NMR (RDC, chemical shifts and other data); FRET; and cross-linking.
• Difficult Protein Structures and Complexes: In many cases, current deep learning methods deliver high-accuracy structures for single proteins and complexes. But there are critical weaknesses. To help address these, we need targets in the following areas: Membrane proteins; Proteins and complexes with weak evolutionary information such as those with viral or parasite origin, "shallow" sequence alignments and recently evolved interfaces;
• Large proteins and complexes with complex stoichiometry arrangement of subunits (>1,000 amino acids).

Rule of Thumb: If AlphaFold3 can generate a high-quality model, it is likely not a CASP-grade challenge. If it struggles, we want it.

Submission Guidelines & Deadlines

To maintain the rigor of a "blind" test, experimental data must remain confidential (no papers, preprints, PDB releases, or conference presentations) until after the modeling phase.

• Submission window: Now through July 10, 2026.
• Data delivery: Experimental coordinates/data are required by September 1, 2026 (confidentiality can be maintained post-assessment).
• Incentives: Target providers are invited to co-author papers in the special CASP issue of a scientific journal [7-14].

How to Contribute:

• Web Portal (Preferred): Submit via the CASP17 Target Form.
• Direct Email: Contact us at casp@ucdavis.edu to suggest targets or clarify questions.
• PDB Submission: Designate your structure a CASP target with 8 weeks onhold at OneDep deposition interface.

We look forward to your contributions in making CASP17 a landmark event for structural biology.

The CASP Organizers: John Moult, Krzysztof Fidelis, Andriy Kryshtafovych, Torsten Schwede, Maya Topf

1. 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):1011-1020.
2. 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):1607-1617.
3. Kryshtafovych A, Schwede T, Topf M, Fidelis K, Moult J. Critical assessment of methods of protein structure prediction (CASP) -Round XV. Proteins 2023;91(12):1539-1549.
4. Kryshtafovych A, Schwede T, Topf M, Fidelis K, Moult J. Progress and Bottlenecks for Deep Learning in Computational Structure Biology: CASP Round XVI. Proteins 2026; 94(1):5–14.
5. Kretsch RC, Hummer AM, He S, Yuan R, Zhang J, Karagianes T, Cong Q, Kryshtafovych A, Das R. Assessment of Nucleic Acid Structure Prediction in CASP16. Proteins. 2026 Jan;94(1):192-217.
6. Lee Y, He S, Oda T, Rao GJ, Kim Y, Kim R, Kim H, Heng CK, Kowerko D, Li H, Nguyen H, Sampathkumar A, Gómez RE, Chen M, Yoshizawa A, Kuraishi S, Ogawa K, Zou S, Paullier A, Zhao B, Chen HL, Hsu TA, Hirano T, Chiu W, Gezelle JG, Haack D, Hong Y, Jadhav S, Koirala D, Kretsch RC, Lewicka A, Li S, Marcia M, Piccirilli J, Rudolfs B, Srivastava Y, Steckelberg AL, Su Z, Toor N, Wang L, Yang Z, Zhang K, Zou J, Baker D, Chen SJ, Demkin M, Favor A, Hummer AM, Joshi CK, Kryshtafovych A, Küçükbenli E, Miao Z, Moult J, Munley C, Reade W, Viel T, Westhof E, Zhang S, Das R. Template-based RNA structure prediction advanced through a blind code competition.
7. bioRxiv [Preprint]. 2025 Dec 30:2025.12.30.696949. doi: 10.64898/2025.12.30.696949.
8. Kryshtafovych A, Albrecht R, Basle A, Bule P, Caputo AT, Carvalho AL, Chao KL, Diskin R, Fidelis K, Fontes C, Fredslund F, Gilbert HJ, Goulding CW, Hartmann MD, Hayes CS, Herzberg O, Hill JC, Joachimiak A, Kohring GW, Koning RI, Lo Leggio L, Mangiagalli M, Michalska K, Moult J, Najmudin S, Nardini M, Nardone V, Ndeh D, Nguyen TH, Pintacuda G, Postel S, van Raaij MJ, Roversi P, Shimon A, Singh AK, Sundberg EJ, Tars K, Zitzmann N, Schwede T. Target highlights from the first post-PSI CASP experiment (CASP12, May-August 2016). Proteins 2018;86 Suppl 1(Suppl 1):27-50.
9. Lepore R, Kryshtafovych A, Alahuhta M, Veraszto HA, Bomble YJ, Bufton JC, Bullock AN, Caba C, Cao H, Davies OR, Desfosses A, Dunne M, Fidelis K, Goulding CW, Gurusaran M, Gutsche I, Harding CJ, Hartmann MD, Hayes CS, Joachimiak A, Leiman PG, Loppnau P, Lovering AL, Lunin VV, Michalska K, Mir-Sanchis I, Mitra AK, Moult J, Phillips GN, Jr., Pinkas DM, Rice PA, Tong Y, Topf M, Walton JD, Schwede T. Target highlights in CASP13: Experimental target structures through the eyes of their authors. Proteins 2019;87(12):1037-1057.
10. Alexander LT, Lepore R, Kryshtafovych A, Adamopoulos A, Alahuhta M, Arvin AM, Bomble YJ, Bottcher B, Breyton C, Chiarini V, Chinnam NB, Chiu W, Fidelis K, Grinter R, Gupta GD, Hartmann MD, Hayes CS, Heidebrecht T, Ilari A, Joachimiak A, Kim Y, Linares R, Lovering AL, Lunin VV, Lupas AN, Makbul C, Michalska K, Moult J, Mukherjee PK, Nutt WS, Oliver SL, Perrakis A, Stols L, Tainer JA, Topf M, Tsutakawa SE, Valdivia-Delgado M, Schwede T. Target highlights in CASP14: Analysis of models by structure providers. Proteins 2021;89(12):1647-1672.
11. Kretsch RC, Andersen ES, Bujnicki JM, Chiu W, Das R, Luo B, Masquida B, McRae EKS, Schroeder GM, Su Z, Wedekind JE, Xu L, Zhang K, Zheludev IN, Moult J, Kryshtafovych A. RNA target highlights in CASP15: Evaluation of predicted models by structure providers. Proteins 2023;91(12):1600-1615.
12. Alexander LT, Durairaj J, Kryshtafovych A, Abriata LA, Bayo Y, Bhabha G, Breyton C, Caulton SG, Chen J, Degroux S, Ekiert DC, Erlandsen BS, Freddolino PL, Gilzer D, Greening C, Grimes JM, Grinter R, Gurusaran M, Hartmann MD, Hitchman CJ, Keown JR, Kropp A, Kursula P, Lovering AL, Lemaitre B, Lia A, Liu S, Logotheti M, Lu S, Markusson S, Miller MD, Minasov G, Niemann HH, Opazo F, Phillips GN, Jr., Davies OR, Rommelaere S, Rosas-Lemus M, Roversi P, Satchell K, Smith N, Wilson MA, Wu KL, Xia X, Xiao H, Zhang W, Zhou ZH, Fidelis K, Topf M, Moult J, Schwede T. Protein target highlights in CASP15: Analysis of models by structure providers. Proteins 2023;91(12):1571-1599.
13. Alexander LT, Follonier OM, Kryshtafovych A, Abesamis K, Bibi-Triki S, Box HG, Breyton C, Bringel F, Carrique L, d'Acapito A, Dong G, DuBois R, Fass D, Fiesco JM, Fox DR, Grimes JM, Grinter R, Jenkins M, Kamyshinsky R, Keown JR, Lackner G, Lammers M, Liu S, Lovering AL, Malinauskas T, Masquida B, Palm GJ, Siebold C, Su T, Zhang P, Zhou ZH, Fidelis K, Topf M, Moult J, Schwede T. Protein Target Highlights in CASP16: Insights From the Structure Providers. Proteins. 2026 Jan;94(1):25-50. doi: 10.1002/prot.70025.
14. Tosstorff A, Rudolph MG, Benz J, Kuhn B, Kramer C, Sharpe M, Huang CY, Metz A, Hazemann J, Ritz D, Sweeney AM, Gilson MK. The CASP 16 Experimental Protein-Ligand Datasets. Proteins. 2026 Jan;94(1):79-85. doi: 10.1002/prot.70053.
15. Kretsch RC, Albrecht R, Andersen ES, Chen HA, Chiu W, Das R, Gezelle JG, Hartmann MD, Höbartner C, Hu Y, Jadhav S, Johnson PE, Jones CP, Koirala D, Kristoffersen EL, Largy E, Lewicka A, Mackereth CD, Marcia M, Nigro M, Ojha M, Piccirilli JA, Rice PA, Shin H, Steckelberg AL, Su Z, Srivastava Y, Wang L, Wu Y, Xie J, Zwergius NH, Moult J, Kryshtafovych A. Functional Relevance of CASP16 Nucleic Acid Predictions as Evaluated by Structure Providers. Proteins. 2026 Jan;94(1):51-78. doi: 10.1002/prot.70043.

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