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1. Prediction of protein assemblies, the next frontier: The CASP14‐CAPRI experiment

   https://doi.org/10.1002/prot.26222  

   Lensink, Marc F.; Brysbaert, Guillaume; Mauri, Théo; Nadzirin, Nurul; Velankar, Sameer; Chaleil, Raphael A.; Clarence, Tereza; Bates, Paul A.; Kong, Ren; Liu, Bin; et al (December 2021, Proteins: Structure, Function, and Bioinformatics)
2. Blind prediction of homo‐ and hetero‐protein complexes: The CASP13‐CAPRI experiment

   https://doi.org/10.1002/prot.25838  

   Lensink, Marc F.; Brysbaert, Guillaume; Nadzirin, Nurul; Velankar, Sameer; Chaleil, Raphaël A.; Gerguri, Tereza; Bates, Paul A.; Laine, Elodie; Carbone, Alessandra; Grudinin, Sergei; et al (August 2019, Proteins: Structure, Function, and Bioinformatics)
3. Modeling CAPRI Targets of Round 55 by Combining AlphaFold and Docking

   https://doi.org/10.1002/prot.26853  

   Singh, Amar; Copeland, Matthew M; Kundrotas, Petras J; Vakser, Ilya A (June 2025, Proteins: Structure, Function, and Bioinformatics)

   In recent years, the field of structural biology has seen remarkable advancements, particularly in modeling of protein tertiary and quaternary structures. The AlphaFold deep learning approach revolutionized protein structure prediction by achieving near‐experimental accuracy on many targets. This paper presents a detailed account of structural modeling of oligomeric targets in Round 55 of CAPRI by combining deep learning‐based predictions (AlphaFold2 multimer pipeline) with traditional docking techniques in a hybrid approach to protein–protein docking. To complement the AlphaFold models generated for the given oligomeric state of the targets, we built docking predictions by combining models generated for lower‐oligomeric states—dimers for trimeric targets and trimers/dimers for tetrameric targets. In addition, we used a template‐based docking procedure applied to AlphaFold predicted structures of the monomers. We analyzed the clustering of the generated AlphaFold models, the confidence in the prediction of intra‐ and inter‐chain residue‐residue contacts, and the correlation of the AlphaFold predictions stability with the quality of the submitted models. 

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4. Capri: Compiler and Architecture Support for Whole-System Persistence

   https://doi.org/10.1145/3502181.3531474  

   Jeong, Jungi; Zeng, Jianping; Jung, Changhee (June 2022, Proceedings of the 31st International Symposium on High-Performance Parallel and Distributed Computing)
5. Impact of AlphaFold on structure prediction of protein complexes: The CASP15‐CAPRI experiment

   https://doi.org/10.1002/prot.26609  

   Lensink, Marc_F; Brysbaert, Guillaume; Raouraoua, Nessim; Bates, Paul_A; Giulini, Marco; Honorato, Rodrigo_V; van_Noort, Charlotte; Teixeira, Joao_M_C; Bonvin, Alexandre_M_J_J; Kong, Ren; et al (October 2023, Proteins: Structure, Function, and Bioinformatics)

   Abstract We present the results for CAPRI Round 54, the 5th joint CASP‐CAPRI protein assembly prediction challenge. The Round offered 37 targets, including 14 homodimers, 3 homo‐trimers, 13 heterodimers including 3 antibody–antigen complexes, and 7 large assemblies. On average ~70 CASP and CAPRI predictor groups, including more than 20 automatics servers, submitted models for each target. A total of 21 941 models submitted by these groups and by 15 CAPRI scorer groups were evaluated using the CAPRI model quality measures and the DockQ score consolidating these measures. The prediction performance was quantified by a weighted score based on the number of models of acceptable quality or higher submitted by each group among their five best models. Results show substantial progress achieved across a significant fraction of the 60+ participating groups. High‐quality models were produced for about 40% of the targets compared to 8% two years earlier. This remarkable improvement is due to the wide use of the AlphaFold2 and AlphaFold2‐Multimer software and the confidence metrics they provide. Notably, expanded sampling of candidate solutions by manipulating these deep learning inference engines, enriching multiple sequence alignments, or integration of advanced modeling tools, enabled top performing groups to exceed the performance of a standard AlphaFold2‐Multimer version used as a yard stick. This notwithstanding, performance remained poor for complexes with antibodies and nanobodies, where evolutionary relationships between the binding partners are lacking, and for complexes featuring conformational flexibility, clearly indicating that the prediction of protein complexes remains a challenging problem. 

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