CASP15 Call for Targets
CASP (Critical Assessment of protein Structure Prediction) is in search for targets for the upcoming CASP15 modeling experiment (starting in May 2022). CASP community experiments aim to advance the state of the art in protein structure modeling. Every other year since 1994, CASP collects information on soon-to-be released experimental structures, passes on sequence data to the structure modeling community, and collects blind predictions of structure for assessment. Typically, about 100 modeling groups from around the world participate. Results of CASP experiments are assessed by leaders in the field (Independent Assessors), and published in special issues of the journal PROTEINS.
Following the 2020 CASP14 experiment, it is hard to find a structural biologist who has not heard about the success of deep learning methods in modeling protein structures, particularly by the AlphaFold and more recently RosettaFold. As a result of these advances, computed protein structures are becoming much more widely used in a broadening range of applications. Since CASP14, the protein modeling community has intensified development of these methods and extended their application to include modeling of protein complexes and protein ensembles. CASP15 will provide definitive insight into how successful these new developments are.
CASP15’s success depends on generosity of the experimental community in providing targets as ground truth against which to assess the computation methods. Over the years more than 150 structure determination groups have provided over 1100 targets for CASP challenges. For CASP15, we are requesting submission of all types of experimental structures determined by X-ray crystallography, cryo-electron microscopy and NMR as potential targets, but are particularly interested in the following:
- High resolution structures of single proteins. Because of the high accuracy of the new computational methods, it is becoming difficult to distinguish experimental error from computational error in low resolution structures.
- Structures with few or no known sequence relatives. Consistently accurate computed structures for this class of target requires methods that do not depend on evolutionary relationships.
- Protein complexes. Deep learning methods already show increased performance in this area, and a range of complexes is needed to establish exactly how powerful these are. Assessment will be in partnership with CAPRI, as in other recent CASPs.
- RNA structures, RNA complexes, and protein RNA complexes. Many more RNA structures are now being determined experimentally, opening this area for more extensive rigorous assessment.
- Proteins with clearly determined alternative conformations. An obvious extension beyond single protein structures is the calculation of ensembles of conformations. The new computational methods are already being applied to this problem, but there is a paucity of definitive experimental data to assess these against, which may limit this category.
- Protein-organic ligand complexes. Deep learning methods are also being applied to these structures. We are exploring including this category in CASP15. A major challenge is obtaining suitable targets.
CASP also plans to include modeling assisted by sparse experimental data, in collaboration with experimental groups in NMR, SAXS, and crosslinking mass spectrometry. For that, protein material is needed (this is not expected for most targets, but if available, it would be much appreciated!).
So, if you have suitable targets in any if these areas, we would very much appreciate you getting in touch by replying to this email or writing to firstname.lastname@example.org or suggesting your target directly through the CASP15 target entry page.
Note that CASP target providers are regularly invited to contribute to CASP special journal issue papers (e.g. Computational models in the service of X-ray and cryo-electron microscopy structure determination (2021) Proteins 89: 1633-1646; Target highlights in CASP14: Analysis of models by structure providers (2021) Proteins 89: 1647-1672), and we plan to continue this practice in the future.