Target Submission Instructions

General

Targets will be released continuously from the beginning of May throughout the end of July, as evenly spaced as possible. Each target will be available for prediction for a period of three weeks, although in some cases we request a longer period to run additional modeling experiments. It is of course important that there not be any kind of public release of the experimental structure (including things like bioRxiv preprints, pictures on web pages or abstracts) until after the prediction for the target is closed.

Just two things to bear in mind. First, because of the timing framework, there should be at least a month between the target submission date and any release of the structure. Second, we would need the experimental co-ordinates by mid-August, so that the predictions can be timely assessed. At that point, these can be kept confidential if necessary, though we would like to provide them to those who modeled your structure at the end of November, so that they can see how well they have done. Participants would also usually like to be able to show slides and discuss their models at the meeting at the beginning of December.

How to submit a target

• PREFERRED: You can submit a target using our Target Submission Form.

  If you have not registered with the Prediction Center yet, you would have to do so first. This is a one-time procedure, where you will be asked to provide your contact information. If you are registered with the Prediction Center, you will have to login and supply only the information about the target.

• Email.

  As alternative to the submission through the Target Submission Form, you can simply send an email to casp AT predictioncenter DOT org containing target's sequence and estimated date of its public release. Information on how the structure was solved (method), protein name, organism name, known ligands, etc. would also be helpful.

• Marking PDB deposition as "CASP target" in PDB's submission system.

  A special arrangement has been negotiated with the PDB to allow structure depositors to designate their structures as "CASP target" in the PDB's submission system. This way it is possible to submit a structure without any delays. It will not be released until after a certain period of time (currently 8 weeks). In this case there is no need to submit directly to CASP (using options 1 or 2) as in this case we will receive a notification from the PDB.

Call for targets

We have seen the beginning of what deep learning methods may achieve in structural biology. In addition to further increases in the accuracy of protein complexes, methods are being developed for RNA structures, organic ligand-protein complexes, and for moving beyond single macromolecular structures to compute conformational ensembles. Accurate computational methods together with experimental data also offer the prospect of probing previously inaccessible biological systems. CASP has expanded its scope to provide critical assessment in all these areas.

CASP is only possible with the generous participation of the experimental structural biology community in providing suitable targets: A total of over 1100 targets have been obtained over the previous CASP rounds. We are now requesting targets for the 2024 CASP16 experiment. We need challenge targets in the following areas:

• Single protein structures: The 2020 and 2022 CASPs showed that, so far, Alphafold2 and methods built around it are by far the most accurate4. But there are limitations, particularly for some proteins where only a shallow sequence alignment is available and for very large proteins (more than 1000 amino acids). The best results also require substantial amounts of computing resources, well beyond that of the AlphaFold2 default settings. Many new methods are continuing to appear and these may remove some of the remaining difficulties. All types of protein targets are needed, but especially those with shallow sequence alignments, without structural templates, and large proteins.
• Protein complexes: In the 2022 CASP15, advanced deep learning methods were applied to protein complexes for the first time5. The result was a huge improvement in accuracy compared with classical docking approaches. But overall, the results are still not at the level achieved for single proteins. So, in CASP16 we need all sorts of targets in this area so as to determine progress since then. We particularly need complexes where there is no evolutionary information across the protein-protein interfaces, for example, antibody-antigen complexes. (This CASP category is conducted in close collaboration with our colleagues at CAPRI - Critical Assessment of protein interactions6).
• Nucleic acid structures and complexes: In recognition of the major role nucleic acid structures and complexes play in biology, CASP now includes this class of target. A number of papers claiming successful RNA structure computation using deep learning methods have been published, but those participating in the 2022 CASP RNA category performed less well than classical approaches, and no methods were able to effectively address the two RNA protein-complexes included7. CASP needs a wide variety of RNA, DNA, and complexes as targets to see if this situation has changed. (This CASP category is conducted in close collaboration with RNApuzzles8).
• Organic ligand-protein complexes: This area is of major importance for computer-aided drug discovery. Earlier, there have been community experiments to assess the accuracy of methods, particularly SAMPL, CSAR, D3R, and a new one, CACHE, has recently started (cache-challenge.org). These challenges have drawn strong international participation from researchers in both academia and industry. Here too, a number of promising deep learning papers have appeared, but in the 2022 CASP15 pilot, classical methods were still superior9. So, we need appropriate targets to see if progress has been made since. Ideally, these should be sets of three-dimensional protein-ligand complexes from drug discovery projects, but single targets would also be appreciated. Additionally, where available, we will assess non-structural quantities such as affinities or affinity rankings and other properties of pharmaceutical interest when these are available (small molecule pKs, and DMPK related properties).
• Ensembles of macromolecule conformations: It is now widely recognized that proteins and nucleic acids often adopt multiple conformations that can underpin their functions. In these cases, considering only a single protein or RNA conformation may be a significant oversimplification. The 2022 CASP15 included a pilot experiment to assess methods for computing multiple conformations, with encouraging results10, but with limitations imposed by the available experimental data. For 2024, we seek not only cases of multiple experimental three-dimensional structures for the same macromolecule but also other types of data that might be used for assessment of computed conformation ensembles such as cryoEM, NMR, X-ray crystallography, SAXS, and/or cross-link data.
• Integrative modeling: The more powerful computational methods open up new possibilities for combination with sparse or low-resolution experimental data to investigate previously inaccessible biological structures and machines. CASP is interested in exploring these possibilities and so requests experimentally difficult targets where structure has nevertheless been obtained. In appropriate cases, we expect to be able to collaborate with other experimental groups to provide appropriate data from NMR, cross-linking or SAXS.

The timeline for the 2024 CASP requires that targets are submitted starting now and until July 1. We would like to hear from you as soon as possible if you may have something suitable or have suggestions about other target sources. In order to maintain rigor, the experimental data for a target must not be publicly available until after computed structures have been collected. For assessment, CASP requires the experimental data by August 15, but the data can remain confidential after that. Target providers are invited to contribute to papers11-15 for a special CASP issue of the journal Proteins.

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. Simpkin AJ, Mesdaghi S, Sanchez Rodriguez F, Elliott L, Murphy DL, Kryshtafovych A, Keegan RM, Rigden DJ. Tertiary structure assessment at CASP15. Proteins 2023;91(12):1616-1635.
5. Ozden B, Kryshtafovych A, Karaca E. The impact of AI-based modeling on the accuracy of protein assembly prediction: Insights from CASP15. Proteins 2023;91(12):1636-1657.
6. Lensink MF, Brysbaert G, Raouraoua N, Bates PA, Giulini M, Honorato RV, van Noort C, Teixeira JMC, Bonvin A, Kong R, Shi H, Lu X, Chang S, Liu J, Guo Z, Chen X, Morehead A, Roy RS, Wu T, Giri N, Quadir F, Chen C, Cheng J, Del Carpio CA, Ichiishi E, Rodriguez-Lumbreras LA, Fernandez-Recio J, Harmalkar A, Chu LS, Canner S, Smanta R, Gray JJ, Li H, Lin P, He J, Tao H, Huang SY, Roel-Touris J, Jimenez-Garcia B, Christoffer CW, Jain AJ, Kagaya Y, Kannan H, Nakamura T, Terashi G, Verburgt JC, Zhang Y, Zhang Z, Fujuta H, Sekijima M, Kihara D, Khan O, Kotelnikov S, Ghani U, Padhorny D, Beglov D, Vajda S, Kozakov D, Negi SS, Ricciardelli T, Barradas-Bautista D, Cao Z, Chawla M, Cavallo L, Oliva R, Yin R, Cheung M, Guest JD, Lee J, Pierce BG, Shor B, Cohen T, Halfon M, Schneidman-Duhovny D, Zhu S, Yin R, Sun Y, Shen Y, Maszota-Zieleniak M, Bojarski KK, Lubecka EA, Marcisz M, Danielsson A, Dziadek L, Gaardlos M, Gieldon A, Liwo A, Samsonov SA, Slusarz R, Zieba K, Sieradzan AK, Czaplewski C, Kobayashi S, Miyakawa Y, Kiyota Y, Takeda-Shitaka M, Olechnovic K, Valancauskas L, Dapkunas J, Venclovas C, Wallner B, Yang L, Hou C, He X, Guo S, Jiang S, Ma X, Duan R, Qui L, Xu X, Zou X, Velankar S, Wodak SJ. Impact of AlphaFold on structure prediction of protein complexes: The CASP15-CAPRI experiment. Proteins 2023;91(12):1658-1683.
7. Das R, Kretsch RC, Simpkin AJ, Mulvaney T, Pham P, Rangan R, Bu F, Keegan RM, Topf M, Rigden DJ, Miao Z, Westhof E. Assessment of three-dimensional RNA structure prediction in CASP15. Proteins 2023;91(12):1747-1770.
8. Magnus M, Antczak M, Zok T, Wiedemann J, Lukasiak P, Cao Y, Bujnicki JM, Westhof E, Szachniuk M, Miao Z. RNA-Puzzles toolkit: a computational resource of RNA 3D structure benchmark datasets, structure manipulation, and evaluation tools. Nucleic Acids Res 2020;48(2):576-588.
9. Robin X, Studer G, Durairaj J, Eberhardt J, Schwede T, Walters WP. Assessment of protein-ligand complexes in CASP15. Proteins 2023;91(12):1811-1821.
10. Kryshtafovych A, Montelione GT, Rigden DJ, Mesdaghi S, Karaca E, Moult J. Breaking the conformational ensemble barrier: Ensemble structure modeling challenges in CASP15. Proteins 2023;91(12):1903-1911.
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, 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.
14. 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.
15. 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.