CASP17 call for targets
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 and 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 materials) 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 Submission Form.
- Direct
Email: Contact us at casp@ucdavis.edu to suggest targets or
clarify questions.
- PDB
Submission: Designate your "on-hold" PDB entry as a CASP
target through the PDB 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
References
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