Critical assessment of methods of protein structure prediction ( CASP )—Round XV
Open accessPublished version of a preprint
Abstract
Computing protein structure from amino acid sequence information has been a long‐standing grand challenge. Critical assessment of structure prediction (CASP) conducts community experiments aimed at advancing solutions to this and related problems. Experiments are conducted every 2 years. The 2020 experiment (CASP14) saw major progress, with the second generation of deep learning methods delivering accuracy comparable with experiment for many single proteins. There is an expectation that these methods will have much wider application in computational structural biology. Here we summarize results from the most recent experiment, CASP15, in 2022, with an emphasis on new deep learning‐driven progress. Other papers in this special issue of proteins provide more detailed analysis. For single protein structures, the AlphaFold2 deep learning method is still superior to other approaches, but there are two points of note. First, although AlphaFold2 was the core of all the most successful methods, there was a wide variety of implementation and combination with other methods. Second, using the standard AlphaFold2 protocol and default parameters only produces the highest quality result for about two thirds of the targets, and more extensive sampling is required for the others. The major advance in this CASP is the enormous increase in the accuracy of computed protein complexes, achieved by the use of deep learning methods, although overall these do not fully match the performance for single proteins. Here too, AlphaFold2 based method perform best, and again more extensive sampling than the defaults is often required. Also of note are the encouraging early results on the use of deep learning to compute ensembles of macromolecular structures. Critically for the usability of computed structures, for both single proteins and protein complexes, deep learning derived estimates of both local and global accuracy are of high quality, however the estimates in interface regions are slightly less reliable. CASP15 also included computation of RNA structures for the first time. Here, the classical approaches produced better agreement with experiment than the new deep learning ones, and accuracy is limited. Also, for the first time, CASP included the computation of protein–ligand complexes, an area of special interest for drug design. Here too, classical methods were still superior to deep learning ones. Many new approaches were discussed at the CASP conference, and it is clear methods will continue to advance.
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Cites
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“But strikingly, two versions of standard AF2 procedures (dashed black lines), one models from the ColabFold server 22 and one using a local installation of AF2 with default parameters 23 have substantially worse performance, and performance does fall-off markedly as homology information weakens.”
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“9 The original framing of the protein folding problem was done 50 years ago, 1 when there were only experimental structures for a few small, simple, highly ordered proteins.”
BackgroundAccurate prediction of protein structures and interactions using a 3-track network
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“The next most successful method is RosettaFold, 24 though the difference in performance is substantial.”
MethodProtein complex prediction with AlphaFold-Multimer
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“As with the single protein structure category, the most effective methods in assembly modeling are based on AlphaFold2, usually the newer AlphaFold-multimer, 31 a version of AlphaFold where training included data for protein complexes.”
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“Many new approaches were discussed at the CASP conference, and it is clear methods will continue to advance.”
BackgroundSelf-citationCritical assessment of methods of protein structure prediction (CASP)—Round <scp>XIV</scp>
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“Many new approaches were discussed at the CASP conference, and it is clear methods will continue to advance.”
BackgroundSelf-citationGeometric deep learning of RNA structure
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“Many new approaches were discussed at the CASP conference, and it is clear methods will continue to advance.”
BackgroundMachine-learning methods for ligand–protein molecular docking
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“7,8 CASP also continued its long-standing category on methods to estimate the accuracy of models with new emphasis on the estimated accuracy of protein complexes, a critical factor for structure usefulness.”
BackgroundApplying and improving AlphaFold at CASP14
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“But strikingly, two versions of standard AF2 procedures (dashed black lines), one models from the ColabFold server 22 and one using a local installation of AF2 with default parameters 23 have substantially worse performance, and performance does fall-off markedly as homology information weakens.”
Benchmarking <scp>AlphaFold</scp> for protein complex modeling reveals accuracy determinants
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“Earlier benchmarking 29 had shown that immune complexes defeat at least the standard AlphaFold2-Multimer FIGURE 4 CASP15 protein assembly subunit interface agreement with experiment.”
MethodResultInfluentialBenchmarking AlphaFold for protein complex modeling reveals accuracy determinants
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Processing and evaluation of predictions in CASP4
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Self-citationEfficient and accurate prediction of protein structure using RoseTTAFold2Preprint
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“Many preprints are appearing on improved ensemble modeling methods, so that we expect substantial advances in the next CASP round.”
BackgroundMethodAssessment of three‐dimensional RNA structure prediction in CASP15
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“The highest agreement of any submitted model was to one of the secondary density maps (Figure 7), illustrating that for some RNA structures additional experimental work may be needed for a proper assessment of computational models.”
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“In order to be taken seriously, calculated structures must also provide this information.”
BackgroundImpact of AlphaFold on structure prediction of protein complexes: The CASP15‐CAPRI experiment
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“Agreement with experiment also tended to be lower for the few NMR targets.”
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“26 There were no detectable sequence relatives outside that family, and the family sequences were only available on a specialized web site.”
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“5 For the first time, this CASP also includes categories on calculating RNA structure from sequence (in collaboration with RNA Puzzles 6 ) and of calculating the structure of protein-small ligand complexes, particularly relevant to drug design.”
BackgroundCAMEO - Perspectives on the future of fully automated evaluation of structure prediction methods
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Self-citationNew prediction categories in <scp>CASP15</scp>
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“17 The first is for modeling multiple structures in ensembles, both protein and RNA.”
BackgroundSelf-citationSynthetic data could be better than real data
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“Figure S1 shows that in CASP15 there is a tendency for performance to fall-off with decreasing alignment depth, and this is much more pronounced when the ratio of alignment depth to target length is below 0.1 (less than 10 appropriately diverse sequences per 100 residues).”
BackgroundInfluentialOzden, B., Kryshtafovych, A., & Karaca, E. (2023) · Proteins: Structure, Function, and Bioinformatics · doi:10.1002/prot.26598 · Cited by 51 · cited 3× · see citing works
“Thus, this appears to be a clear example of single sequence being insufficient to produce a good model, a deficiency also seen in DeepMind's benchmarking 27 and in the previous CASP.”
BackgroundMethodSelf-citationAssessment of protein–ligand complexes in CASP15
Robin, X., Studer, G., Durairaj, J., Eberhardt, J., Schwede, T., & Walters, W. P. (2023) · Proteins: Structure, Function, and Bioinformatics · doi:10.1002/prot.26601 · Cited by 47 · cited 2× · see citing works
“Many new approaches were discussed at the CASP conference, and it is clear methods will continue to advance.”
BackgroundMethodSelf-citationTertiary structure assessment at CASP15
Simpkin, A. J., Mesdaghi, S., Rodríguez, F. S., Elliott, L. G., Murphy, D., Kryshtafovych, A., Keegan, R. M., & Rigden, D. J. (2023) · Proteins Structure Function and Bioinformatics · doi:10.1002/prot.26593 · Cited by 43 · see citing works
Self-citationPrediction of protein assemblies by structure sampling followed by interface‐focused scoring
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“But some publicly available methods requiring at most a few additional models rank highly and may be valuable for users, for example, 32,37,38 in this issue.”
MethodRNA target highlights in CASP15: Evaluation of predicted models by structure providers
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“15,16 The issue also contains papers by selected participating groups.”
BackgroundSelf-citationImproved protein structure prediction with trRosettaX2 , AlphaFold2 , and optimized MSAs in CASP15
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“As in the previous CASP, there are also targets with shallow alignments which nevertheless have high agreement with experiment.”
ResultAssessment of the assessment—All about complexes
Studer, G., Tauriello, G., & Schwede, T. F. (2023) · Proteins Structure Function and Bioinformatics · doi:10.1002/prot.26612 · Cited by 31 · cited 1× · see citing works
“12 For multimers, third-party selection of models appears impressive, with about 2/3 of targets having a loss of accuracy less than 0.1 in TM score 36 when models estimated to be most accurate are selected.”
BackgroundInfluentialSelf-citationBreaking the conformational ensemble barrier: Ensemble structure modeling challenges in CASP15
Kryshtafovych, A., Montelione, G. T., Rigden, D. J., Mesdaghi, S., Karaca, E., & Moult, J. (2023) · Proteins: Structure, Function, and Bioinformatics · doi:10.1002/prot.26584 · Cited by 31 · cited 3× · see citing works
“9 The original framing of the protein folding problem was done 50 years ago, 1 when there were only experimental structures for a few small, simple, highly ordered proteins.”
BackgroundSelf-citationBreaking the conformational ensemble barrier: Ensemble structure modeling challenges in CASP15
Kryshtafovych, A., Montelione, G., Rigden, D., Mesdaghi, S., Karaca, E., & Moult, J. (2023) · doi:10.22541/au.169199504.48580490/v1 · Cited by 31 · see citing works
Self-citationProtein target highlights in CASP15 : Analysis of models by structure providers
Alexander, L. T., Durairaj, J., Kryshtafovych, A., Abriata, L. A., Bayo, Y., Bhabha, G., Breyton, C., Caulton, S. G., Chen, J., Degroux, S., et al. (2023) · Proteins Structure Function and Bioinformatics · doi:10.1002/prot.26545 · Cited by 27 · cited 1× · see citing works
“15,16 The issue also contains papers by selected participating groups.”
BackgroundSelf-citationA novel family of secreted insect proteins linked to plant gall development
Korgaonkar, A. K., Han, C., Lemire, A. L., Siwanowicz, I., Bennouna, D., Kopec, R. E., Andolfatto, P., Shigenobu, S., & Stern, D. L. (2021) · Current Biology · doi:10.1016/j.cub.2021.03.001 · Cited by 25 · see citing works
Estimation of model accuracy in CASP15 using the M odFOLDdock server
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“But some publicly available methods requiring at most a few additional models rank highly and may be valuable for users, for example, 32,37,38 in this issue.”
MethodLiu, J., Liu, D., He, G., & Zhang, G.-J. (2023) · Proteins: Structure, Function, and Bioinformatics · doi:10.1002/prot.26564 · Cited by 14 · cited 1× · see citing works
“But some publicly available methods requiring at most a few additional models rank highly and may be valuable for users, for example, 32,37,38 in this issue.”
MethodKryshtafovych, A., & Rigden, D. J. (2023) · Proteins Structure Function and Bioinformatics · doi:10.1002/prot.26533 · Cited by 13 · cited 2× · see citing works
“…(40 assessed), 81 monomeric proteins or subunits of multimeric complexes in the Tertiary Structure category (77 assessed, divided into 112 Evaluation units 18 ), 12 RNA structures in the RNA category, 23 protein-ligand complexes in the Ligand binding category, and two protein-RNA complexes.”
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“5 For the first time, this CASP also includes categories on calculating RNA structure from sequence (in collaboration with RNA Puzzles 6 ) and of calculating the structure of protein-small ligand complexes, particularly relevant to drug design.”
BackgroundCAMEO - Perspectives on the future of fully automated evaluation of structure prediction methodsPreprint
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Self-citationA novel family of secreted insect proteins linked to plant gall development
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“46 Figure 3A illustrates a number of criteria that seem to link to target difficulty measured as median GDT_TS for the top 10 groups overall (Figure 3A).”
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“…a long-standing community-wide blind prediction experiment, as it also incorporates three complementary metrics: DockQ, a continuous score combining the CAPRI model quality metrics, and the MM-score and l-DDT, two commonly used model quality metrics for evaluating predicted protein structures [35].”
DNAproDB: an updated database for the automated and interactive analysis of protein–DNA complexes
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“This is evidenced by the CASP16 challenge for predicting solvent shells around the Tetrahymena ribozyme structure ( 31 ).”
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Assessment of Nucleic Acid Structure Prediction in <scp>CASP16</scp>
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“The trend in RNA secondary structure performance was more reminiscent of the performance observed in current protein 3D structure prediction [78], suggesting that these prediction methods have reached sufficient accuracy in their prediction of secondary structure to be important and useful in…”
Self-citationProtein target highlights in CASP15 : Analysis of models by structure providers
Alexander, L. T., Durairaj, J., Kryshtafovych, A., Abriata, L. A., Bayo, Y., Bhabha, G., Breyton, C., Caulton, S. G., Chen, J., Degroux, S., et al. (2023) · Proteins Structure Function and Bioinformatics · doi:10.1002/prot.26545 · Cited by 27 · see citing works
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Assessment of Protein Complex Predictions in CASP16 : Are We Making Progress?
Zhang, J., Yuan, R., Kryshtafovych, A., Pei, J., Kretsch, R. C., Schaeffer, R. D., Zhou, J., Das, R., Grishin, N. V., & Cong, Q. (2025) · Proteins Structure Function and Bioinformatics · doi:10.1002/prot.70068 · Cited by 24 · see citing works
Self-citationComputational toolbox for the analysis of protein–glycan interactions
Nieto‐Fabregat, F., Lenza, M. P., Marseglia, A., Carluccio, C. D., Molinaro, A., Silipo, A., & Marchetti, R. (2024) · Beilstein Journal of Organic Chemistry · doi:10.3762/bjoc.20.180 · Cited by 23 · see citing works
CACHE Challenge #2: Targeting the RNA Site of the SARS-CoV-2 Helicase Nsp13
Herasymenko, O., Silva, M., Abu‐Saleh, A. A. A., Ahmad, A., Alvarado-Huayhuaz, J. A., Arce, O. E. A., Armstrong, R. J., Arrowsmith, C. H., Bachta, K. E. R., Beck, H., et al. (2025) · Journal of Chemical Information and Modeling · doi:10.1021/acs.jcim.5c00535 · Cited by 22 · see citing works
Jeyaraj, G., Rajendran, A. K., Sathishkumar, K., Almutairi, B. O., Vadivelu, A., Chokkakula, S., Tu, Y., & Xie, W. (2025) · Frontiers in Molecular Biosciences · doi:10.3389/fmolb.2025.1688455 · Cited by 23 · see citing works
“For example, results from CASP14 and CASP15 showed consistent declines in AlphaFold’s accuracy when modeling highly dynamic regions or novel folds not represented in the training data (Kryshtafovych et al., 2023).”
InfluentialDapkūnas, J., Timinskas, A., Olechnovič, K., Tomkuvienė, M., & Venclovas, Č. (2024) · Nucleic Acids Research · doi:10.1093/nar/gkae278 · Cited by 20 · see citing works
“However, predicting structures for protein–protein and protein–peptide complexes remains challenging ( 3 ,4 ), whereas prediction of protein-DNA or protein-RNA complexes is even harder ( 5 ).”
BackgroundZhang, Y., Zhang, Z., Kagaya, Y., Terashi, G., Zhao, B., Xiong, Y., & Kihara, D. (2025) · Communications Biology · doi:10.1038/s42003-025-08783-5 · Cited by 19 · see citing works
CASP16 Protein Monomer Structure Prediction Assessment
Yuan, R., Zhang, J., Kryshtafovych, A., Schaeffer, R. D., Zhou, J., Cong, Q., & Grishin, N. V. (2025) · Proteins Structure Function and Bioinformatics · doi:10.1002/prot.70031 · Cited by 19 · see citing works
“The results again demonstrate that protein monomer structure prediction is a largely solved problem [33], and improvement in the monomer prediction accuracy in CASP16 compared to CASP15 is only subtle.”
Self-citationA Computational Community Blind Challenge on Pan-Coronavirus Drug Discovery Data
MacDermott-Opeskin, H., Scheen, J., Wognum, C., Horton, J. T., West, D., Payne, A. M., Castellanos, M. A., Colby, S., Griffen, E., Cousins, D. L., et al. (2026) · Journal of Chemical Information and Modeling · doi:10.1021/acs.jcim.5c02106 · Cited by 24 · see citing works
Recent progress and future challenges in structure-based protein-protein interaction prediction
Yuan, R., Zhang, J., Zhou, J., & Cong, Q. (2025) · Molecular Therapy · doi:10.1016/j.ymthe.2025.04.003 · Cited by 20 · see citing works
“113,114 Encouraged by these successes, researchers started to build more advanced neural networks that can handle other molecules, such as nucleotides 115 and small molecules, 116 in addition to proteins.”
Manfredi, M., Savojardo, C., Iardukhin, G., Salomoni, D., Costantini, A., Martelli, P. L., & Casadio, R. (2024) · Journal of Molecular Biology · doi:10.1016/j.jmb.2024.168593 · Cited by 17 · see citing works
Kryshtafovych, A., & Rigden, D. J. (2023) · Proteins Structure Function and Bioinformatics · doi:10.1002/prot.26533 · Cited by 13 · see citing works
“…were automatically assigned to four largely homology-based prediction classes (see Figure 1B and Table 1 As discussed in more detail elsewhere 21,29 it is clear that FM targets comprise the majority of those with which even the top predictive methods struggled, even though some FM targets were…”
BackgroundSelf-citationHigh-accuracy protein complex structure modeling based on sequence-derived structure complementarity
Hou, M., Xia, Y., Wang, P., Lv, Z., Hou, D., Zhou, X., Zeng, J., & Zhang, G. (2025) · Nature Communications · doi:10.1038/s41467-025-65090-7 · Cited by 13 · see citing works
Deep learning in GPCR drug discovery: benchmarking the path to accurate peptide binding
Dijkhof, L. R. H., Rönkkö, T., von Vegesack, H. C., Lenzing, J., & Hauser, A. S. (2025) · Briefings in Bioinformatics · doi:10.1093/bib/bbaf186 · Cited by 13 · see citing works
“In the CASP15 experiment [1,2] (2022),all the top-performing models were based on DL, akin to AlphaFold 2 (AF2) [3].”
Structure-guided drug discovery: back to the future
Arrowsmith, C. H. (2024) · Nature Structural & Molecular Biology · doi:10.1038/s41594-024-01244-3 · Cited by 13 · see citing works
Baumann, H. M., Horton, J. T., Henry, M. M., Travitz, A., Ries, B., Gowers, R. J., Swenson, D. A., Pulido, I., Rufa, D. A., Dotson, D., et al. (2026) · Journal of Chemical Information and Modeling · doi:10.1021/acs.jcim.6c00089 · Cited by 12 · see citing works
PUNCH2: Explore the strategy for intrinsically disordered protein predictor
Meng, D., & Pollastri, G. (2025) · PLoS ONE · doi:10.1371/journal.pone.0319208 · Cited by 13 · see citing works
A new age in structural S-layer biology: Experimental and in silico milestones
Grill-Walcher, S., & Schäffer, C. (2025) · Journal of Biological Chemistry · doi:10.1016/j.jbc.2025.110205 · Cited by 12 · see citing works
Advances and Challenges in Scoring Functions for RNA–Protein Complex Structure Prediction
Zeng, C., Chen, Z., Gao, J., Liu, H., & Zhao, Y. (2024) · Biomolecules · doi:10.3390/biom14101245 · Cited by 11 · see citing works
“A platform for assessing advancements in computational structure prediction is the biennial Critical Assessment of protein Structure Prediction (CASP) competition [24], complemented by CAPRI for protein complexes [25] and RNA-Puzzles for RNA structures [26].”
MethodEfficient protein structure generation with sparse denoising models
Jendrusch, M., & Korbel, J. O. (2025) · Nature Machine Intelligence · doi:10.1038/s42256-025-01100-z · Cited by 10 · see citing works
“Evaluating these models on the CASP14 and CASP15 monomer test sets 50,51 resulted in all models reaching 1 Å reconstruction accuracy after fewer than ten recycling iterations, regardless of equivariance and distogram-based neighbours (Fig.”
Toward a unified framework for determining conformational ensembles of disordered proteins
Ghafouri, H., Kadeřávek, P., Melo, A. M., Aspromonte, M. C., Bernadó, P., Cortés, J., Dosztányi, Z., Erdős, G., Feig, M., Janson, G., et al. (2026) · Nature Methods · doi:10.1038/s41592-026-03003-2 · Cited by 10 · see citing works
Manfredi, M., Vazzana, G., Savojardo, C., Martelli, P. L., & Casadio, R. (2025) · Computational and Structural Biotechnology Journal · doi:10.1016/j.csbj.2025.01.008 · Cited by 9 · see citing works
“AlphaFold2-based methods have been proven superior to ESMFold in the international benchmark of CASP15 [8], where however a limited number of structures were tested.”
Massive Sampling Strategy for Antibody–Antigen Targets in CAPRI Round 55 With MassiveFold
RAOURAOUA, N., Lensink, M. F., & Brysbaert, G. (2025) · Proteins Structure Function and Bioinformatics · doi:10.1002/prot.26802 · Cited by 10 · see citing works
“However, the evaluation of the models for the assembly prediction showed that the best approaches all used AF2, in various ways [5, 6].”
In silico prediction of variant effects: promises and limitations for precision plant breeding
Sendrowski, J., Bataillon, T. M., & Ramstein, G. P. (2025) · Theoretical and Applied Genetics · doi:10.1007/s00122-025-04973-1 · Cited by 10 · see citing works
“For protein structure prediction, standardized benchmark datasets, like CASP (Kryshtafovych et al. 2023), TS115 (Yang et al. 2018), and CB513 (Cuff and Barton 1999), enable independent evaluation of model performance, and demonstrate that predictive performance comparable to experimental evaluation could already be ac…”
InfluentialA fully automated benchmarking suite to compare macromolecular complexes
Studer, G., Robin, X., Bienert, S., Durairaj, J., Škrinjar, P., Tauriello, G., Waterhouse, A. M., & Schwede, T. (2026) · Nature Methods · doi:10.1038/s41592-025-02973-z · Cited by 10 · see citing works
Self-citationDesiRNA: structure-based design of RNA sequences with a replica exchange Monte Carlo approach
Wirecki, T. K., Łach, G., Badepally, N. G., Moafinejad, S. N., Jaryani, F., Klaudel, G., Nec, K., Baulin, E. F., & Bujnicki, J. M. (2024) · Nucleic Acids Research · doi:10.1093/nar/gkae1306 · Cited by 9 · see citing works
Modeling Alternative Conformational States in CASP16
Dube, N., Ramelot, T. A., Benavides, T. L., Huang, Y. J., Moult, J., Kryshtafovych, A., & Montelione, G. T. (2025) · Proteins Structure Function and Bioinformatics · doi:10.1002/prot.70065 · Cited by 10 · see citing works
Self-citationCaparotta, M., & Pérez, A. (2024) · Journal of Physical Chemistry B · doi:10.1021/acs.jpcb.3c04823 · Cited by 9 · see citing works
Wang, Y., Wang, Z., Sadeh, G., Zancato, L., Achille, A., Karypis, G., & Rangwala, H. (2024) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2024.10.29.620988 · Cited by 9 · see citing works
“Tasks Assessing Protein Embeddings (TAPE) TAPE (Rao et al., 2019) is a set of five biologically relevant semi-supervised learning tasks spread across different domains of protein biology.”
Haley, O. C., Tibbs‐Cortes, L. E., Hayford, R. K., Harding, S. F., Woodhouse, M. R., Cannon, E. K. S., Gardiner, J. M., Portwood, J. L., Sen, T. Z., Kim, H.-S., et al. (2025) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2025.07.05.663230 · Cited by 8 · see citing works
“The remarkable performance of AlphaFold 2 in the 14th Critical Assessment of Protein Structure Prediction (CASP14) experiments marked a substantial advancement in the field 2 .”
Leitão, A. L., & Enguita, F. J. (2025) · Non-Coding RNA · doi:10.3390/ncrna11020020 · Cited by 7 · see citing works
“Looking ahead, several key areas promise to drive further advancements in this field.”
Ille, A. M., Anas, E., Mathews, M. B., & Burley, S. K. (2025) · Structural Dynamics · doi:10.1063/4.0000765 · Cited by 7 · see citing works
Fischer, Y., Southiratn, T., Triki, D., & Cedeno, R. (2025) · Journal of Chemical Information and Modeling · doi:10.1021/acs.jcim.5c01982 · Cited by 7 · see citing works
Flow matching meets biology and life science: a survey
Li, Z., Zeng, Z., Lin, X., Fang, F., Qu, Y., Xu, Z., Liu, Z., Ning, X., Wei, T., Liu, G., et al. (2026) · npj Artificial Intelligence · doi:10.1038/s44387-025-00066-y · Cited by 12 · see citing works
LoCoHD: a metric for comparing local environments of proteins
Fazekas, Z., Menyhárd, D. K., & Perczel, A. (2024) · Nature Communications · doi:10.1038/s41467-024-48225-0 · Cited by 7 · see citing works
“InCASP 49 – 51 ,eachcontestantandtargetstructurehasanidenti fi ercode in the form of TS[contestant ID] and T[target ID], respectively.”
BackgroundFTDMP : A Framework for Protein–Protein, Protein– DNA, and Protein– RNA Docking and Scoring
Olechnovič, K., Banciul, R., Dapkūnas, J., & Venclovas, Č. (2025) · Proteins Structure Function and Bioinformatics · doi:10.1002/prot.26792 · Cited by 8 · see citing works
Apprehensions and emerging solutions in ML-based protein structure prediction
Dahlström, K. M., & Salminen, T. A. (2024) · Current Opinion in Structural Biology · doi:10.1016/j.sbi.2024.102819 · Cited by 8 · see citing works
E1: Retrieval-Augmented Protein Encoder ModelsPreprint
Jain, S., Beazer, J., Ruffolo, J. A., Bhatnagar, A., & Madani, A. (2025) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2025.11.12.688125 · Cited by 7 · see citing works
Üresin, D., Schulte, J., Morgner, N., & Soppa, J. (2024) · International Journal of Molecular Sciences · doi:10.3390/ijms25137166 · Cited by 6 · see citing works
“Taken together, all three programs, including the newest program from 2024, cannot correctly predict whether the structure formed by two C(P)XCG motifs contains zinc or is metal-free.”
BackgroundZhang, J., & Qian, J. (2024) · Biomolecules · doi:10.3390/biom14091083 · Cited by 6 · see citing works
“CASP (Critical Assessment of Structure Prediction) is a community that invites participants to provide their models to identify unknown protein structures [6].”
BackgroundEvaluation of the structural models of the human reference proteome: AlphaFold2 versus ESMFold
Manfredi, M., Savojardo, C., Martelli, P. L., & Casadio, R. (2025) · Current Research in Structural Biology · doi:10.1016/j.crstbi.2025.100167 · Cited by 6 · see citing works
Practical Outcomes From CASP16 for Users in Need of Biomolecular Structure Prediction
Abriata, L. A., & Peraro, M. D. (2025) · Proteins Structure Function and Bioinformatics · doi:10.1002/prot.70078 · Cited by 6 · see citing works
Open and sustainable AI: challenges, opportunities and the road ahead in the life sciences
Farrell, G., Adamidi, E., Buono, R. A., Anton, M., Attafi, O. A., Capella-Gutiérrez, S., Capriotti, E., Castro, L. J., Cirillo, D., Crossman, L. C., et al. (2025) · Nature Methods · doi:10.1038/s41592-026-03037-6 · Cited by 11 · see citing works
Abriata, L. A. (2026) · Communications Biology · doi:10.1038/s42003-026-10112-3 · Cited by 5 · see citing works
Disela, R., Neijenhuis, T., Bussy, O. L., Geldhof, G., Klijn, M. E., Pabst, M., & Ottens, M. (2024) · Biotechnology and Bioengineering · doi:10.1002/bit.28840 · Cited by 6 · see citing works
“Recent advances in protein structure prediction by tools like Alpha-Fold allow for construction of missing HCP structures (Kryshtafovych et al., 2023).”
MassiveFold Data for CASP16 ‐ CAPRI : A Systematic Massive Sampling Experiment
RAOURAOUA, N., Lensink, M. F., & Brysbaert, G. (2025) · Proteins Structure Function and Bioinformatics · doi:10.1002/prot.70040 · Cited by 6 · see citing works
Ali, M. A., & Caetano-Anollés, G. (2024) · Viruses · doi:10.3390/v16091358 · Cited by 6 · see citing works
“AlphaFold2 [31], the star of the last two biannual ab initio structure prediction experiments (CASP 14 and 15) [32,33], greatly broke records as its neural network (called the evoformer ) approach to modeling proteins without homologs was within the margin of error of experimental structure…”
BackgroundIlle, A. M., Markosian, C., Burley, S. K., Pasqualini, R., & Arap, W. (2025) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2025.07.03.663068 · Cited by 6 · see citing works
“…the relationship between sequence and 3D structure [30, 31], including those using multiple sequence alignments (MSAs) [24, 26, 28], have exhibited unprecedented accuracy [32, 33] for prediction of both individual structures and those of multiprotein complexes from amino acid sequence alone.”
Improving diffusion-based protein backbone generation with global-geometry-aware latent encoding
(2025) · doi:10.1038/s42256-025-01059-x · Cited by 7 · see citing works
“…symmetric with respect to the order of inputs, we define it based on the average of the query TM-score and the target TM-score: This distance definition is a natural choice as the TM-score is designed to measure the global structural similarity and has been widely used for structure evaluation 79 .”
Chatzimiltis, S., Agathocleous, M., Promponas, V. J., & Christodoulou, C. C. (2025) · Computational and Structural Biotechnology Journal · doi:10.1016/j.csbj.2024.12.022 · Cited by 5 · see citing works
“We selected 26 free modelling targets from the CASP15 community experiment [57], as “hard” prediction targets, with none of them baring significant sequence similarity to the dataset used to train the CNN-HFO-CB513 models.”
Verdonk, C. J., Gagalova, K., Raffaele, S., & Derbyshire, M. C. (2025) · Computational and Structural Biotechnology Journal · doi:10.1016/j.csbj.2025.06.048 · Cited by 5 · see citing works
“Furthermore, although PPI modelling is improving rapidly, AI techniques are not as accurate for protein complexes as for monomers [71].”
Swapna, G. V. T., Dube, N., Roth, M. J., & Montelione, G. T. (2025) · PLoS Computational Biology · doi:10.1371/journal.pcbi.1013590 · Cited by 5 · see citing works
“The ability of AF2 to model protein structures not included in its training has been demonstrated in various CASP blind assessments [66,67].”
Blind Prediction of Complex Water and Ion Ensembles Around RNA in CASP16
Kretsch, R. C., Posani, E., Baulin, E. F., Bujnicki, J. M., Bussi, G., Cheatham, T. E., Chen, S., Elofsson, A., Farsani, M. A., Fisher, O. N., et al. (2025) · Proteins Structure Function and Bioinformatics · doi:10.1002/prot.70079 · Cited by 6 · see citing works
“The Critical Assessment of Structure Prediction (CASP) has been an important venue for enabling the field of structure prediction by organizing a biannual blind structure prediction challenge where groups across the world predict the 3D structures of proteins, and, since CASP15, nucleic acids [1–5].”
Protein dynamics prediction by integrating biophysics and artificial intelligence
Huang, H., Guan, X., Li, W., Zhang, J., & Wang, W. (2026) · Current Opinion in Structural Biology · doi:10.1016/j.sbi.2026.103223 · Cited by 5 · see citing works
Niazi, S. K. (2026) · International Journal of Molecular Sciences · doi:10.3390/ijms27104349 · Cited by 7 · see citing works
“CASP and CAMEO assess structure prediction using GDT-TS and lDDT [76,80].”
Physical-aware model accuracy estimation for protein complex using deep learning method
Wang, H., Sun, M., Xie, L., Liu, D., & Zhang, G. (2025) · Computational and Structural Biotechnology Journal · doi:10.1016/j.csbj.2025.01.017 · Cited by 5 · see citing works
“The CASP15 results show that most successful prediction methods for protein multimers used modifications of the standard AlphaFold, including extensive sampling through variations on MSA construction, the use of multiple seeds, an increased number of recycles and extensive network dropout [7].”
Databases and web-based tools for studying structures of protein-nucleic acid complexes.
Dapkūnas, J., & Venclovas, Č. (2025) · Current Opinion in Structural Biology · doi:10.1016/j.sbi.2025.103079 · Cited by 4 · see citing works
From Dataset Curation to Unified Evaluation: Revisiting Structure Prediction Benchmarks with PXMeterPreprint
Ma, W., Liu, Z., Yang, J., Lu, C., Zhang, H., & Xiao, W. (2025) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2025.07.17.664878 · Cited by 5 · see citing works
The rise of AlphaFold in drug design
Stecuła, A., Paul, R., Litchfield, K., Dalton, S. E., Low, C. M. R., Reis, C. R., & Congreve, M. (2025) · Progress in medicinal chemistry · doi:10.1016/bs.pmch.2025.09.002 · Cited by 4 · see citing works
Critical evaluation of drug response prediction models with DrEval
Bernett, J., Iversen, P., Picciani, M., Wilhelm, M., Baum, K., & List, M. (2026) · Nature Communications · doi:10.1038/s41467-026-72903-w · Cited by 5 · see citing works
The euroSAMPL1 p K a blind prediction and reproducible research data management challenge
Tielker, N., Lim, M., Kibies, P., Gretz, J., Hein‐Janke, B., Chodun, C., Mata, R. A., Czodrowski, P., & Kast, S. M. (2025) · Physical Chemistry Chemical Physics · doi:10.1039/d5cp01448d · Cited by 4 · see citing works
Refinement of AlphaFold-Multimer structures with single sequence inputPreprint
Oda, T. (2022) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2022.12.27.521991 · Cited by 4 · see citing works
(2025) · doi:10.1093/nar/gkaf1099 · Cited by 4 · see citing works
“A central feature of QSProteome is its comprehensive, transparent framework for model validation, designed to bring CASP-like benchmarking [ 39 ] to scale.”
Influence of Stereochemistry in a Local Approach for Calculating Protein Conformations
da Rocha, W., Liberti, L., Mucherino, A., & Malliavin, T. E. (2024) · Journal of Chemical Information and Modeling · doi:10.1021/acs.jcim.4c01232 · Cited by 3 · see citing works
Protein design and optimization for synthetic cells
Frohn, B. P., Kohyama, S., & Schwille, P. (2025) · Nature Reviews Bioengineering · doi:10.1038/s44222-025-00318-1 · Cited by 3 · see citing works
“Similar to the critical assessment of structure prediction (CASP) competition 157 or the NanoCar race 158 , which have contributed to the definition of methodological standards and promotion of scientific interaction, a synthetic cell competition could specify a quantitatively measurable phenotype…”
Kennedy, L., Harper, M., & Čuperlović-Culf, M. (2025) · Briefings Bioinform. · doi:10.1093/bib/bbaf635 · Cited by 5 · see citing works
“The use of such computational tools to assist biological research is exemplified in the closely related problem of protein structure prediction, which has seen great success driven by the accuracy of methods like AlphaFold [18–20].”
Zhang, J., Wang, S., Gao, L., & Li, Q. (2026) · Journal of Biotechnology · doi:10.1016/j.jbiotec.2026.03.012 · Cited by 4 · see citing works
Sebastiano, M. R., Apprato, G., Francisco, S., Ermondi, G., & Caron, G. (2026) · Medicinal Research Reviews · doi:10.1002/med.70038 · Cited by 4 · see citing works
“Considering AF's performance in the latest Critical Assessment of Structure Prediction [99], similar or lower performances are expected from other AI‐based predictors (e.g., Meta platform ESMFold [100], see Table S1).”
Stefańczyk, E., Mitura, A., Utratna, M., & Staniszewska, M. M. (2025) · Computational and Structural Biotechnology Journal · doi:10.1016/j.csbj.2025.06.001 · Cited by 3 · see citing works
“Computational methods have shown steady progress in modelling protein structures and docking simulations, with antibody-antigen interactions becoming an increasingly active area of research [25].”
Swapna, G. V. T., Dube, N., Roth, M. J., & Montelione, G. T. (2024) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2024.07.15.603529 · Cited by 3 · see citing works
On inputs to deep learning for RNA 3D structure predictionPreprint
Szikszai, M., Magnus, M., Kadyan, S., & Rivas, E. (2025) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2025.02.14.638364 · Cited by 3 · see citing works
“The blind-assesment competition CASP15 (Kryshtafovych et al., 2023) joined RNA-Puzzles (Cruz et al., 2012; Miao et al., 2015; 2017; 2020) in 2022 to include RNA-only targets in the competition.”
From Hype to Health Check: Critical Evaluation of Drug Response Prediction Models with DrEvalPreprint
Bernett, J., Iversen, P., Picciani, M., Wilhelm, M., Baum, K., & List, M. (2025) · bioRxiv · doi:10.1101/2025.05.26.655288 · Cited by 9 · see citing works
“Benchmarks like ImageNet [Deng et al., 2009], GLUE [Wang et al., 2018], and CASP [Kryshtafovych et al., 2023] provided rigorous, community-accepted evaluation standards that enabled transparent comparisons and drove algorithmic innovation.”
Tying a true topological protein knot by cyclization
Sriramoju, M. K., Ko, K.-T., & Hsu, S. D. (2024) · Biochemical and Biophysical Research Communications · doi:10.1016/j.bbrc.2024.149470 · Cited by 2 · see citing works
Ciccarese, S., Lefranc, M., Perrone, G. C. M., D’Addabbo, P., & Pierri, C. L. (2025) · Antibodies · doi:10.3390/antib14020046 · Cited by 2 · see citing works
AI in drug design: evolution or revolution?
Green, D. V. S. (2025) · Emerging Topics in Life Sciences · doi:10.1042/etls20240005 · Cited by 2 · see citing works
“It was the CASP (Critical Assessment of Structure Prediction) [86] challenges that enabled the revolution in protein structure prediction.”
Zhang, L., Ding, R., Chen, X., Hou, J., Si, D., Wang, Y., Lin, K.-Y., & Cao, R.-Z. (2025) · bioRxiv · doi:10.1002/pro.70734 · Cited by 1 · see citing works
Predicting RNA Structure Utilizing Attention from Pretrained Language Models
Papazoglou, I., Chatzigoulas, A., Tsekenis, G., & Cournia, Z. (2025) · Journal of Chemical Information and Modeling · doi:10.1021/acs.jcim.4c02094 · Cited by 2 · see citing works
Computational methods for the analysis of long-read RNA-seq data
Santucci, K., Cheng, Y., Xu, S.-M., & Janitz, M. (2025) · Genomics · doi:10.1016/j.ygeno.2025.111144 · Cited by 2 · see citing works
The structural chemistry and biosynthesis of chlorophylls
Hunter, C. N., & Morey-Burrows, F. S. (2026) · RSC Chemical Biology · doi:10.1039/d6cb00082g · Cited by 2 · see citing works
Pradeep, B. K., Chen, S., Dima, R., Hassan, M., Joachimiak, A., Kihara, D., Kloczkowski, A., Law, J., Liwo, A., Meller, J., et al. (2026) · Journal of Molecular Biology · doi:10.1016/j.jmb.2026.169949 · Cited by 2 · see citing works
Expanding the scope of protein language modeling to protein-protein interactions with MSA Pairformer
Akiyama, Y., Zhang, Z., Tang, O., Kim, S., Mirdita, M., Steinegger, M., & Ovchinnikov, S. (2026) · Cell · doi:10.1016/j.cell.2026.06.029 · Cited by 3 · see citing works
Resurgence of magnetic resonance techniques in the era of AlphaFold
Skinner, C. E., Haynes, B. A., & Isaacson, R. L. (2025) · Biophysical Reviews · doi:10.1007/s12551-025-01329-3 · Cited by 2 · see citing works
“In the wake of AlphaFold, these could largely be regarded as solved, and the categories in CASP15 focused on specific areas of study, for example RNA structures and protein-small ligand complexes (Kryshtafovych et al. 2023).”
ProFam: Open-Source Protein Family Language Modelling for Fitness Prediction and DesignPreprint
Wells, J., Hooker, A., Livne, M., Lin, W., Miller, D., Dallago, C., Bordin, N., Paige, B., Rost, B., Orengo, C., et al. (2025) · bioRxiv · doi:10.64898/2025.12.19.695431 · Cited by 3 · see citing works
“Finally, we explore ProFam’s potential use in augmenting available evolutionary signal by generating synthetic MSAs for CASP targets [22], finding that they improve ColabFold-based structure prediction over single-sequence inputs [23].”
Structural highlights of macromolecular complexes and assemblies
Vallat, B. K., & Berman, H. M. (2024) · Current Opinion in Structural Biology · doi:10.1016/j.sbi.2023.102773 · Cited by 1 · see citing works
- Authors
- Kryshtafovych, A., Schwede, T., Topf, M., Fidelis, K., & Moult, J.
- Affiliations
- University of California, Davis (US)
- SIB Swiss Institute of Bioinformatics (CH)
- Leibniz Institute of Virology (LIV) (DE)
- Institute for Bioscience and Biotechnology Research (US)
- Corresponding author
- Moult, John
- Venue
- Proteins: Structure, Function, and Bioinformatics
- Publisher
- Wiley
- Published
- 2023-11-02
- Volume
- 91
- Issue
- 12
- Pages
- 1539-1549
- ISSN
- 0887-3585 (ISSN-L)
- 1097-0134
- PMC
- PMC10843301
- OpenAlex
- W4388302761
- Semantic Scholar
- 3ac7de3b8d9010974884ecc0c70c172cb0183188
- Cited by
- 148
- References
- 40
- Citations by year
- 2026: 45 · 2025: 65 · 2024: 32 · 2023: 4 · 2022: 2
- FWCI
- 12.98
- Type
- journal article
- Language
- en
- Open access
- hybrid
- doi.org
- Licence
- Locations
- Proteins Structure Function and Bioinformatics (publishedVersion, cc-by-nc-nd)
- PubMed (publishedVersion)
- PubMed Central (acceptedVersion)
- eScholarship (California Digital Library) (submittedVersion, cc-by)
- Funders
- National Institute of General Medical Sciences (R01GM100482)
- Topics
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Protein Structure and Dynamics
- Physical Sciences › Materials Science › Materials Chemistry › Enzyme Structure and Function
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Machine Learning in Bioinformatics
- Keywords
- protein structure prediction, CASP15, AlphaFold2, deep learning, protein complexes, protein-ligand complex, RNA structure prediction, structure ensemble, accuracy estimation, protein interfaces, drug design
- MeSH terms
- Amino Acid Sequence, Models, Molecular, Protein Conformation, Proteins, Computational Biology
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
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