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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. 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)
4. 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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5. Modeling beta‐sheet peptide‐protein interactions: Rosetta FlexPepDock in CAPRI rounds 38‐45

   https://doi.org/10.1002/prot.25871  

   Khramushin, Alisa; Marcu, Orly; Alam, Nawsad; Shimony, Orly; Padhorny, Dzmitry; Brini, Emiliano; Dill, Ken A.; Vajda, Sandor; Kozakov, Dima; Schueler‐Furman, Ora (January 2020, Proteins: Structure, Function, and Bioinformatics)

   Abstract Peptide‐protein docking is challenging due to the considerable conformational freedom of the peptide. CAPRI rounds 38‐45 included two peptide‐protein interactions, both characterized by a peptide forming an additional beta strand of a beta sheet in the receptor. Using theRosetta FlexPepDockpeptide docking protocol we generated top‐performing, high‐accuracy models for targets 134 and 135, involving an interaction between a peptide derived from L‐MAG with DLC8. In addition, we were able to generate the only medium‐accuracy models for a particularly challenging target, T121. In contrast to the classical peptide‐mediated interaction, in which receptor side chains contact both peptide backbone and side chains, beta‐sheet complementation involves a major contribution to binding by hydrogen bonds between main chain atoms. To establish how binding affinity and specificity are established in this special class of peptide‐protein interactions, we extractedPeptiDBeta, a benchmark of solved structures of different protein domains that are bound by peptides via beta‐sheet complementation, and tested our protocol for global peptide‐dockingPIPER‐FlexPepDockon this dataset. We find that the beta‐strand part of the peptide is sufficient to generate approximate and even high resolution models of many interactions, but inclusion of adjacent motif residues often provides additional information necessary to achieve high resolution model quality. 

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