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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- DOI:
- Digital Object Identifier (open in new tab) 10.1002/prot.26609
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- Category:
- Articles
- Type:
- artykuły w czasopismach
- Published in:
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PROTEINS-STRUCTURE FUNCTION AND BIOINFORMATICS
ISSN: 0887-3585 - Language:
- English
- Publication year:
- 2023
- Bibliographic description:
- Lensink M. F., Brysbaert G., Raouraoua N., Bates P. A., Giulini M., Honorato R. V., van Noort C., Teixeira J. S., Bonvin A. M. J. J., Kong R., Shi H., Lu X., Chang S., Liu J., Guo Z., Chen X., Morehead A., Roy R. S., Wu T., Giri N., Quadir F., Chen C., Cheng J., Del Carpio C. A., Ichiishi E., Rodriguez‐lumbreras L. A., Fernandez‐recio J., Harmalkar A., Chu L., Canner S., Smanta R., Gray J. J., Li H., Lin P., He J., Tao H., Huang S., Roel‐touris J., Jimenez‐garcia B., Christoffer C., Jain A. J., Kagaya Y., Kannan H., Nakamura T., Terashi G., Verburgt J., 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 S. S., Ricciardelli T., Barradas‐bautista D., Cao Z., Chawla M., Cavallo L., Oliva R., Yin R., Cheung M., Guest J. D., Lee J., Pierce B. G., Shor B., Cohen T., Halfon M., Schneidman‐duhovny D., Zhu S., Yin R., Sun Y., Shen Y., Maszota-Zieleniak M., Bojarski K., Lubecka E., Marcisz M., Danielsson A., Dziadek L., Gaardlos M., Gieldon A., Liwo A., Samsonov S. A., Ślusarz R., Zięba K., Sieradzan A., Czaplewski C., Kobayashi S., Miyakawa Y., Kiyota Y., Takeda-Shitaka M., Olechnovič K., Valancauskas L., Dapkunas J., Venclovas Č., 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 S. J.: Impact of AlphaFold on structure prediction of protein complexes: The CASP15‐CAPRI experiment// PROTEINS-STRUCTURE FUNCTION AND BIOINFORMATICS -, (2023),
- DOI:
- Digital Object Identifier (open in new tab) 10.1002/prot.26609
- Sources of funding:
-
- Francis Crick Institute; Cancer Research UK, Grant/Award Number: FC0001003; UK Medical Research Council, Grant/Award Number: FC001003; Wellcome Trust, Grant/Award Number: FC001003; European Union Horizon 2020, Grant/Award Number: 823830
- Verified by:
- Gdańsk University of Technology
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