44 results on '"Kagaya, Yuki"'
Search Results
2. A Critical Pair Criterion for Level-Commutation of Conditional Term Rewriting Systems
- Author
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Haga, Ryota, Kagaya, Yuki, Aoto, Takahito, Goos, Gerhard, Founding Editor, Hartmanis, Juris, Founding Editor, Bertino, Elisa, Editorial Board Member, Gao, Wen, Editorial Board Member, Steffen, Bernhard, Editorial Board Member, Yung, Moti, Editorial Board Member, Sattler, Uli, editor, and Suda, Martin, editor
- Published
- 2023
- Full Text
- View/download PDF
3. A Critical Pair Criterion for Level-Commutation of Conditional Term Rewriting Systems
- Author
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Haga, Ryota, primary, Kagaya, Yuki, additional, and Aoto, Takahito, additional
- Published
- 2023
- Full Text
- View/download PDF
4. Real-time structure search and structure classification for AlphaFold protein models
- Author
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Aderinwale, Tunde, Bharadwaj, Vijay, Christoffer, Charles, Terashi, Genki, Zhang, Zicong, Jahandideh, Rashidedin, Kagaya, Yuki, and Kihara, Daisuke
- Published
- 2022
- Full Text
- View/download PDF
5. The relationship between sarcopenic obesity and changes in quadriceps muscle thickness and echo intensity in patients with stroke
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Maruyama, Motoki, primary, Kagaya, Yuki, additional, Kajiwara, Sota, additional, Oikawa, Takuto, additional, Horikawa, Manabu, additional, Fujimoto, Mika, additional, and Sasaki, Masahiro, additional
- Published
- 2024
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- View/download PDF
6. The Validity of Quadriceps Muscle Thickness as a Nutritional Risk Indicator in Patients with Stroke
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Maruyama, Motoki, primary, Kagaya, Yuki, additional, Kajiwara, Sota, additional, Oikawa, Takuto, additional, Horikawa, Manabu, additional, Fujimoto, Mika, additional, and Sasaki, Masahiro, additional
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- 2024
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- View/download PDF
7. Shrec2024: Non-Rigid Complementary Shapes Retrieval in Protein-Protein Interactions
- Author
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Yacoub, Taher, primary, Zarubina, Nika, additional, Depenveiller, Camille, additional, Nguyen, Hoang-Phuc, additional, Vong, Vinh-Toan, additional, Tran, Minh-Triet, additional, Kagaya, Yuki, additional, Nakamura, Tsukasa, additional, Kihara, Daisuke, additional, Langenfeld, Florent, additional, and Montes, Matthieu, additional
- Published
- 2024
- Full Text
- View/download PDF
8. Distance-AF: Modifying Predicted Protein Structure Models by Alphafold2 with User-Specified Distance Constraints
- Author
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Zhang, Yuanyuan, primary, Zhang, Zicong, additional, Kagaya, Yuki, additional, Terashi, Genki, additional, Zhao, Bowen, additional, Xiong, Yi, additional, and Kihara, Daisuke, additional
- Published
- 2023
- Full Text
- View/download PDF
9. Improved Peptide Docking with Privileged Knowledge Distillation using Deep Learning
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Zhang, Zicong, primary, Verburgt, Jacob, additional, Kagaya, Yuki, additional, Christoffer, Charles, additional, and Kihara, Daisuke, additional
- Published
- 2023
- Full Text
- View/download PDF
10. A novel circular ssDNA virus of the phylum Cressdnaviricota discovered in metagenomic data from otter clams (Lutraria rhynchaena)
- Author
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Kim, Oanh T. P., Kagaya, Yuki, Tran, Hoang S., Minei, Ryuhei, Tran, Trang T. H., Duong, Ha T. T., Le, Binh T. N., Dang, Lua T., Kinoshita, Kengo, Ogura, Atsushi, and Yura, Kei
- Published
- 2020
- Full Text
- View/download PDF
11. Analyzing effect of quadruple multiple sequence alignments on deep learning based protein inter-residue distance prediction
- Author
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Jain, Aashish, Terashi, Genki, Kagaya, Yuki, Maddhuri Venkata Subramaniya, Sai Raghavendra, Christoffer, Charles, and Kihara, Daisuke
- Published
- 2021
- Full Text
- View/download PDF
12. Impact of AlphaFold on structure prediction of protein complexes: The CASP15‐CAPRI experiment
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Lensink, Marc F., primary, Brysbaert, Guillaume, additional, Raouraoua, Nessim, additional, Bates, Paul A., additional, Giulini, Marco, additional, Honorato, Rodrigo V., additional, van Noort, Charlotte, additional, Teixeira, Joao M. C., additional, Bonvin, Alexandre M. J. J., additional, Kong, Ren, additional, Shi, Hang, additional, Lu, Xufeng, additional, Chang, Shan, additional, Liu, Jian, additional, Guo, Zhiye, additional, Chen, Xiao, additional, Morehead, Alex, additional, Roy, Raj S., additional, Wu, Tianqi, additional, Giri, Nabin, additional, Quadir, Farhan, additional, Chen, Chen, additional, Cheng, Jianlin, additional, Del Carpio, Carlos A., additional, Ichiishi, Eichiro, additional, Rodriguez‐Lumbreras, Luis A., additional, Fernandez‐Recio, Juan, additional, Harmalkar, Ameya, additional, Chu, Lee‐Shin, additional, Canner, Sam, additional, Smanta, Rituparna, additional, Gray, Jeffrey J., additional, Li, Hao, additional, Lin, Peicong, additional, He, Jiahua, additional, Tao, Huanyu, additional, Huang, Sheng‐You, additional, Roel‐Touris, Jorge, additional, Jimenez‐Garcia, Brian, additional, Christoffer, Charles W., additional, Jain, Anika J., additional, Kagaya, Yuki, additional, Kannan, Harini, additional, Nakamura, Tsukasa, additional, Terashi, Genki, additional, Verburgt, Jacob C., additional, Zhang, Yuanyuan, additional, Zhang, Zicong, additional, Fujuta, Hayato, additional, Sekijima, Masakazu, additional, Kihara, Daisuke, additional, Khan, Omeir, additional, Kotelnikov, Sergei, additional, Ghani, Usman, additional, Padhorny, Dzmitry, additional, Beglov, Dmitri, additional, Vajda, Sandor, additional, Kozakov, Dima, additional, Negi, Surendra S., additional, Ricciardelli, Tiziana, additional, Barradas‐Bautista, Didier, additional, Cao, Zhen, additional, Chawla, Mohit, additional, Cavallo, Luigi, additional, Oliva, Romina, additional, Yin, Rui, additional, Cheung, Melyssa, additional, Guest, Johnathan D., additional, Lee, Jessica, additional, Pierce, Brian G., additional, Shor, Ben, additional, Cohen, Tomer, additional, Halfon, Matan, additional, Schneidman‐Duhovny, Dina, additional, Zhu, Shaowen, additional, Yin, Rujie, additional, Sun, Yuanfei, additional, Shen, Yang, additional, Maszota‐Zieleniak, Martyna, additional, Bojarski, Krzysztof K., additional, Lubecka, Emilia A., additional, Marcisz, Mateusz, additional, Danielsson, Annemarie, additional, Dziadek, Lukasz, additional, Gaardlos, Margrethe, additional, Gieldon, Artur, additional, Liwo, Adam, additional, Samsonov, Sergey A., additional, Slusarz, Rafal, additional, Zieba, Karolina, additional, Sieradzan, Adam K., additional, Czaplewski, Cezary, additional, Kobayashi, Shinpei, additional, Miyakawa, Yuta, additional, Kiyota, Yasuomi, additional, Takeda‐Shitaka, Mayuko, additional, Olechnovic, Kliment, additional, Valancauskas, Lukas, additional, Dapkunas, Justas, additional, Venclovas, Ceslovas, additional, Wallner, Bjorn, additional, Yang, Lin, additional, Hou, Chengyu, additional, He, Xiaodong, additional, Guo, Shuai, additional, Jiang, Shenda, additional, Ma, Xiaoliang, additional, Duan, Rui, additional, Qui, Liming, additional, Xu, Xianjin, additional, Zou, Xiaoqin, additional, Velankar, Sameer, additional, and Wodak, Shoshana J., additional
- Published
- 2023
- Full Text
- View/download PDF
13. NuFold: A Novel Tertiary RNA Structure Prediction Method Using Deep Learning with Flexible Nucleobase Center Representation
- Author
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Kagaya, Yuki, primary, Zhang, Zicong, additional, Ibtehaz, Nabil, additional, Wang, Xiao, additional, Nakamura, Tsukasa, additional, Huang, David, additional, and Kihara, Daisuke, additional
- Published
- 2023
- Full Text
- View/download PDF
14. Domain-PFP: Protein Function Prediction Using Function-Aware Domain Embedding Representations
- Author
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Ibtehaz, Nabil, primary, Kagaya, Yuki, additional, and Kihara, Daisuke, additional
- Published
- 2023
- Full Text
- View/download PDF
15. Impact of AlphaFold on Structure Prediction of Protein Complexes: The CASP15-CAPRI Experiment
- Author
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Lensink, Marc, primary, Brysbaert, Guillaume, additional, Raouraoua, Nessim, additional, Bates, Paul, additional, Giulini, Marco, additional, Honorato, Rodrigo Vargas, additional, Noort, Charlotte van, additional, Teixeira, João, additional, Bonvin, Alexandre M.J.J., additional, Kong, Ren, additional, Shi, Hang, additional, Lu, Xufeng, additional, Chang, Shan, additional, Liu, Jian, additional, Guo, Zhiye, additional, Chen, Xiao, additional, Morehead, Alex, additional, Roy, Raj, additional, Wu, Tianqi, additional, Giri, Nabin, additional, Quadir, Farhan, additional, Chen, Chen, additional, Cheng, Jianlin, additional, Carpio, Carlos Del, additional, Ichiishi, Eichiro, additional, A, Luis Rodriguez-Lumbreras, additional, Fernández-Recio, Juan, additional, Harmalkar, Ameya, additional, Chu, Lee-Shin, additional, Canner, Sam, additional, Smanta, Rituparna, additional, Gray, Jeffrey, additional, Li, Hao, additional, Lin, Peicong, additional, He, Jiahua, additional, Tao, Huanyu, additional, Huang, Shengyou, additional, Roel, Jorge, additional, Jimenez-Garcia, Brian, additional, Christoffer, Charles, additional, J, Anika Jain, additional, Kagaya, Yuki, additional, Kannan, Harini, additional, Nakamura, Tsukasa, additional, Terashi, Genki, additional, Verburgt, Jacob, additional, Zhang, Yuanyuan, additional, Zhang, Zicong, additional, Fujuta, Hayato, additional, Sekijima, Masakazu, additional, Kihara, Daisuke, additional, Khan, Omeir, additional, Kotelnikov, Sergei, additional, Ghani, Usman, additional, Padhorny, Dzmitry, additional, Beglov, Dmitri, additional, Vajda, Sandor, additional, Kozakov, Dima, additional, S, Surendra Negi, additional, Ricciardelli, Tiziana, additional, Barradas-Bautista, Didier, additional, Cao, Zhen, additional, Chawla, Mohit, additional, Cavallo, Luigi, additional, Oliva, Romina, additional, Yin, Rui, additional, Cheung, Melyssa, additional, Guest, Johnathan, additional, Lee, Jessica, additional, Pierce, Brian, additional, Shor, Ben, additional, Cohen, Tomer, additional, Halfon, Matan, additional, Schneidman-Duhovny, Dina, additional, Zhu, Shaowen, additional, Yin, Rujie, additional, Sun, Yuanfei, additional, Shen, Yang, additional, Maszota-Zieleniak, Martyna, additional, K, Krzysztof Bojarski, additional, Lubecka, Emilia, additional, Marcisz, Mateusz, additional, Danielsson, Annemarie, additional, Dziadek, Lukasz, additional, Gaardlos, Margrethe, additional, Giełdoń, Artur, additional, Liwo, Jozef, additional, Samsonov, Sergey, additional, Slusarz, Rafal, additional, Zieba, Karolina, additional, Sieradzan, Adam, additional, Czaplewski, Cezary, additional, Kobayashi, Shinpei, additional, Miyakawa, Yuta, additional, Kiyota, Yasuomi, additional, Takeda-Shitaka, Mayuko, additional, Olechnovič, Kliment, additional, Valančauskas, Lukas, additional, Dapkūnas, Justas, additional, Venclovas, Ceslovas, additional, Wallner, Björn, additional, Yang, Lin, additional, Hou, Chengyu, additional, He, Xiaodong, additional, Guo, Shuai, additional, Jiang, Shenda, additional, Ma, Xiaoliang, additional, Duan, Rui, additional, Qiu, Liming, additional, Xu, Xianjin, additional, Zou, Xiaoqin, additional, Velankar, Sameer, additional, and J, Shoshana Wodak, additional
- Published
- 2023
- Full Text
- View/download PDF
16. Impact of AlphaFold on Structure Prediction of Protein Complexes: The CASP15-CAPRI Experiment
- Author
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Lensink, Marc, Brysbaert, Guillaume, Raouraoua, Nessim, Bates, Paul, Giulini, Marco, Honorato, Rodrigo Vargas, Noort, Charlotte van, Teixeira, João, Bonvin, Alexandre M.J.J., Kong, Ren, Shi, Hang, Lu, Xufeng, Chang, Shan, Liu, Jian, Guo, Zhiye, Chen, Xiao, Morehead, Alex, Roy, Raj, Wu, Tianqi, Giri, Nabin, Quadir, Farhan, Chen, Chen, Cheng, Jianlin, Carpio, Carlos Del, Ichiishi, Eichiro, Luis, Rodriguez-Lumbreras A, Fernández-Recio, Juan, Harmalkar, Ameya, Chu, Lee-Shin, Canner, Sam, Smanta, Rituparna, Gray, Jeffrey, Li, Hao, Lin, Peicong, He, Jiahua, Tao, Huanyu, Huang, Shengyou, Roel, Jorge, Jimenez-Garcia, Brian, Christoffer, Charles, Anika, Jain J, Kagaya, Yuki, Kannan, Harini, Nakamura, Tsukasa, Terashi, Genki, Verburgt, Jacob, Zhang, Yuanyuan, Zhang, Zicong, Fujuta, Hayato, Sekijima, Masakazu, Kihara, Daisuke, Khan, Omeir, Kotelnikov, Sergei, Ghani, Usman, Padhorny, Dzmitry, Beglov, Dmitri, Vajda, Sandor, Kozakov, Dima, Surendra, Negi S, Ricciardelli, Tiziana, Barradas-Bautista, Didier, Cao, Zhen, Chawla, Mohit, Cavallo, Luigi, Oliva, Romina, Yin, Rujie, Cheung, Melyssa, Guest, Johnathan, Lee, Jessica, Pierce, Brian, Shor, Ben, Cohen, Tomer, Halfon, Matan, Schneidman-Duhovny, Dina, Zhu, Shaowen, Sun, Yuanfei, Shen, Yang, Maszota-Zieleniak, Martyna, Krzysztof, Bojarski K, Lubecka, Emilia, Marcisz, Mateusz, Danielsson, Annemarie, Dziadek, Lukasz, Gaardlos, Margrethe, Giełdoń, Artur, Liwo, Jozef, Samsonov, Sergey, Slusarz, Rafal, Zieba, Karolina, Sieradzan, Adam, Czaplewski, Cezary, Kobayashi, Shinpei, Miyakawa, Yuta, Kiyota, Yasuomi, Takeda-Shitaka, Mayuko, Olechnovič, Kliment, Valančauskas, Lukas, Dapkūnas, Justas, Venclovas, Ceslovas, Wallner, Björn, Yang, Lin, Hou, Chengyu, He, Xiaodong, Guo, Shuai, Jiang, Shenda, Ma, Xiaoliang, Duan, Rui, Qiu, Liming, Xu, Xianjin, Zou, Xiaoqin, Velankar, Sameer, Shoshana, Wodak J, Lensink, Marc, Brysbaert, Guillaume, Raouraoua, Nessim, Bates, Paul, Giulini, Marco, Honorato, Rodrigo Vargas, Noort, Charlotte van, Teixeira, João, Bonvin, Alexandre M.J.J., Kong, Ren, Shi, Hang, Lu, Xufeng, Chang, Shan, Liu, Jian, Guo, Zhiye, Chen, Xiao, Morehead, Alex, Roy, Raj, Wu, Tianqi, Giri, Nabin, Quadir, Farhan, Chen, Chen, Cheng, Jianlin, Carpio, Carlos Del, Ichiishi, Eichiro, Luis, Rodriguez-Lumbreras A, Fernández-Recio, Juan, Harmalkar, Ameya, Chu, Lee-Shin, Canner, Sam, Smanta, Rituparna, Gray, Jeffrey, Li, Hao, Lin, Peicong, He, Jiahua, Tao, Huanyu, Huang, Shengyou, Roel, Jorge, Jimenez-Garcia, Brian, Christoffer, Charles, Anika, Jain J, Kagaya, Yuki, Kannan, Harini, Nakamura, Tsukasa, Terashi, Genki, Verburgt, Jacob, Zhang, Yuanyuan, Zhang, Zicong, Fujuta, Hayato, Sekijima, Masakazu, Kihara, Daisuke, Khan, Omeir, Kotelnikov, Sergei, Ghani, Usman, Padhorny, Dzmitry, Beglov, Dmitri, Vajda, Sandor, Kozakov, Dima, Surendra, Negi S, Ricciardelli, Tiziana, Barradas-Bautista, Didier, Cao, Zhen, Chawla, Mohit, Cavallo, Luigi, Oliva, Romina, Yin, Rujie, Cheung, Melyssa, Guest, Johnathan, Lee, Jessica, Pierce, Brian, Shor, Ben, Cohen, Tomer, Halfon, Matan, Schneidman-Duhovny, Dina, Zhu, Shaowen, Sun, Yuanfei, Shen, Yang, Maszota-Zieleniak, Martyna, Krzysztof, Bojarski K, Lubecka, Emilia, Marcisz, Mateusz, Danielsson, Annemarie, Dziadek, Lukasz, Gaardlos, Margrethe, Giełdoń, Artur, Liwo, Jozef, Samsonov, Sergey, Slusarz, Rafal, Zieba, Karolina, Sieradzan, Adam, Czaplewski, Cezary, Kobayashi, Shinpei, Miyakawa, Yuta, Kiyota, Yasuomi, Takeda-Shitaka, Mayuko, Olechnovič, Kliment, Valančauskas, Lukas, Dapkūnas, Justas, Venclovas, Ceslovas, Wallner, Björn, Yang, Lin, Hou, Chengyu, He, Xiaodong, Guo, Shuai, Jiang, Shenda, Ma, Xiaoliang, Duan, Rui, Qiu, Liming, Xu, Xianjin, Zou, Xiaoqin, Velankar, Sameer, and Shoshana, Wodak J
- 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 homo-dimers, 3 homo-trimers, 13 hetero-dimers 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 21941 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 5 best models. Results show substantial progress achieved across a significant fraction of the 60+ participating groups. High-quality models were produced for about 40% for the targets compared to 8% two years earlier, a remarkable improvement resulting from the wide use of the AlphaFold2 and AlphaFold-Multimer software. Creative use was made of the deep learning inference engines affording the sampling of a much larger number of models and enriching the multiple sequence alignments with sequences from various sources. Wide use was also made of the AlphaFold confidence metrics to rank models, permitting top performing groups to exceed the results of the public AlphaFold-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.
- Published
- 2023
17. Impact of AlphaFold on structure prediction of protein complexes: The CASP15-CAPRI experiment
- Author
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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, Shi, Hang, Lu, Xufeng, Chang, Shan, Liu, Jian, Guo, Zhiye, Chen, Xiao, Morehead, Alex, Roy, Raj S, Wu, Tianqi, Giri, Nabin, Quadir, Farhan, Chen, Chen, Cheng, Jianlin, Del Carpio, Carlos A, Ichiishi, Eichiro, Rodriguez-Lumbreras, Luis A, Fernandez-Recio, Juan, Harmalkar, Ameya, Chu, Lee-Shin, Canner, Sam, Smanta, Rituparna, Gray, Jeffrey J, Li, Hao, Lin, Peicong, He, Jiahua, Tao, Huanyu, Huang, Sheng-You, Roel-Touris, Jorge, Jimenez-Garcia, Brian, Christoffer, Charles W, Jain, Anika J, Kagaya, Yuki, Kannan, Harini, Nakamura, Tsukasa, Terashi, Genki, Verburgt, Jacob C, Zhang, Yuanyuan, Zhang, Zicong, Fujuta, Hayato, Sekijima, Masakazu, Kihara, Daisuke, Khan, Omeir, Kotelnikov, Sergei, Ghani, Usman, Padhorny, Dzmitry, Beglov, Dmitri, Vajda, Sandor, Kozakov, Dima, Negi, Surendra S, Ricciardelli, Tiziana, Barradas-Bautista, Didier, Cao, Zhen, Chawla, Mohit, Cavallo, Luigi, Oliva, Romina, Yin, Rujie, Cheung, Melyssa, Guest, Johnathan D, Lee, Jessica, Pierce, Brian G, Shor, Ben, Cohen, Tomer, Halfon, Matan, Schneidman-Duhovny, Dina, Zhu, Shaowen, Sun, Yuanfei, Shen, Yang, Maszota-Zieleniak, Martyna, Bojarski, Krzysztof K, Lubecka, Emilia A, Marcisz, Mateusz, Danielsson, Annemarie, Dziadek, Lukasz, Gaardlos, Margrethe, Gieldon, Artur, Liwo, Adam, Samsonov, Sergey A, Slusarz, Rafal, Zieba, Karolina, Sieradzan, Adam K, Czaplewski, Cezary, Kobayashi, Shinpei, Miyakawa, Yuta, Kiyota, Yasuomi, Takeda-Shitaka, Mayuko, Olechnovic, Kliment, Valancauskas, Lukas, Dapkunas, Justas, Venclovas, Ceslovas, Wallner, Bjorn, Yang, Lin, Hou, Chengyu, He, Xiaodong, Guo, Shuai, Jiang, Shenda, Ma, Xiaoliang, Duan, Rui, Qui, Liming, Xu, Xianjin, Zou, Xiaoqin, Velankar, Sameer, Wodak, Shoshana J, 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, Shi, Hang, Lu, Xufeng, Chang, Shan, Liu, Jian, Guo, Zhiye, Chen, Xiao, Morehead, Alex, Roy, Raj S, Wu, Tianqi, Giri, Nabin, Quadir, Farhan, Chen, Chen, Cheng, Jianlin, Del Carpio, Carlos A, Ichiishi, Eichiro, Rodriguez-Lumbreras, Luis A, Fernandez-Recio, Juan, Harmalkar, Ameya, Chu, Lee-Shin, Canner, Sam, Smanta, Rituparna, Gray, Jeffrey J, Li, Hao, Lin, Peicong, He, Jiahua, Tao, Huanyu, Huang, Sheng-You, Roel-Touris, Jorge, Jimenez-Garcia, Brian, Christoffer, Charles W, Jain, Anika J, Kagaya, Yuki, Kannan, Harini, Nakamura, Tsukasa, Terashi, Genki, Verburgt, Jacob C, Zhang, Yuanyuan, Zhang, Zicong, Fujuta, Hayato, Sekijima, Masakazu, Kihara, Daisuke, Khan, Omeir, Kotelnikov, Sergei, Ghani, Usman, Padhorny, Dzmitry, Beglov, Dmitri, Vajda, Sandor, Kozakov, Dima, Negi, Surendra S, Ricciardelli, Tiziana, Barradas-Bautista, Didier, Cao, Zhen, Chawla, Mohit, Cavallo, Luigi, Oliva, Romina, Yin, Rujie, Cheung, Melyssa, Guest, Johnathan D, Lee, Jessica, Pierce, Brian G, Shor, Ben, Cohen, Tomer, Halfon, Matan, Schneidman-Duhovny, Dina, Zhu, Shaowen, Sun, Yuanfei, Shen, Yang, Maszota-Zieleniak, Martyna, Bojarski, Krzysztof K, Lubecka, Emilia A, Marcisz, Mateusz, Danielsson, Annemarie, Dziadek, Lukasz, Gaardlos, Margrethe, Gieldon, Artur, Liwo, Adam, Samsonov, Sergey A, Slusarz, Rafal, Zieba, Karolina, Sieradzan, Adam K, Czaplewski, Cezary, Kobayashi, Shinpei, Miyakawa, Yuta, Kiyota, Yasuomi, Takeda-Shitaka, Mayuko, Olechnovic, Kliment, Valancauskas, Lukas, Dapkunas, Justas, Venclovas, Ceslovas, Wallner, Bjorn, Yang, Lin, Hou, Chengyu, He, Xiaodong, Guo, Shuai, Jiang, Shenda, Ma, Xiaoliang, Duan, Rui, Qui, Liming, Xu, Xianjin, Zou, Xiaoqin, Velankar, Sameer, and Wodak, Shoshana J
- 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.
- Published
- 2023
18. Impact of AlphaFold on structure prediction of protein complexes: The CASP15-CAPRI experiment
- Author
-
Sub NMR Spectroscopy, NMR Spectroscopy, 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, Shi, Hang, Lu, Xufeng, Chang, Shan, Liu, Jian, Guo, Zhiye, Chen, Xiao, Morehead, Alex, Roy, Raj S, Wu, Tianqi, Giri, Nabin, Quadir, Farhan, Chen, Chen, Cheng, Jianlin, Del Carpio, Carlos A, Ichiishi, Eichiro, Rodriguez-Lumbreras, Luis A, Fernandez-Recio, Juan, Harmalkar, Ameya, Chu, Lee-Shin, Canner, Sam, Smanta, Rituparna, Gray, Jeffrey J, Li, Hao, Lin, Peicong, He, Jiahua, Tao, Huanyu, Huang, Sheng-You, Roel-Touris, Jorge, Jimenez-Garcia, Brian, Christoffer, Charles W, Jain, Anika J, Kagaya, Yuki, Kannan, Harini, Nakamura, Tsukasa, Terashi, Genki, Verburgt, Jacob C, Zhang, Yuanyuan, Zhang, Zicong, Fujuta, Hayato, Sekijima, Masakazu, Kihara, Daisuke, Khan, Omeir, Kotelnikov, Sergei, Ghani, Usman, Padhorny, Dzmitry, Beglov, Dmitri, Vajda, Sandor, Kozakov, Dima, Negi, Surendra S, Ricciardelli, Tiziana, Barradas-Bautista, Didier, Cao, Zhen, Chawla, Mohit, Cavallo, Luigi, Oliva, Romina, Yin, Rujie, Cheung, Melyssa, Guest, Johnathan D, Lee, Jessica, Pierce, Brian G, Shor, Ben, Cohen, Tomer, Halfon, Matan, Schneidman-Duhovny, Dina, Zhu, Shaowen, Sun, Yuanfei, Shen, Yang, Maszota-Zieleniak, Martyna, Bojarski, Krzysztof K, Lubecka, Emilia A, Marcisz, Mateusz, Danielsson, Annemarie, Dziadek, Lukasz, Gaardlos, Margrethe, Gieldon, Artur, Liwo, Adam, Samsonov, Sergey A, Slusarz, Rafal, Zieba, Karolina, Sieradzan, Adam K, Czaplewski, Cezary, Kobayashi, Shinpei, Miyakawa, Yuta, Kiyota, Yasuomi, Takeda-Shitaka, Mayuko, Olechnovic, Kliment, Valancauskas, Lukas, Dapkunas, Justas, Venclovas, Ceslovas, Wallner, Bjorn, Yang, Lin, Hou, Chengyu, He, Xiaodong, Guo, Shuai, Jiang, Shenda, Ma, Xiaoliang, Duan, Rui, Qui, Liming, Xu, Xianjin, Zou, Xiaoqin, Velankar, Sameer, Wodak, Shoshana J, Sub NMR Spectroscopy, NMR Spectroscopy, 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, Shi, Hang, Lu, Xufeng, Chang, Shan, Liu, Jian, Guo, Zhiye, Chen, Xiao, Morehead, Alex, Roy, Raj S, Wu, Tianqi, Giri, Nabin, Quadir, Farhan, Chen, Chen, Cheng, Jianlin, Del Carpio, Carlos A, Ichiishi, Eichiro, Rodriguez-Lumbreras, Luis A, Fernandez-Recio, Juan, Harmalkar, Ameya, Chu, Lee-Shin, Canner, Sam, Smanta, Rituparna, Gray, Jeffrey J, Li, Hao, Lin, Peicong, He, Jiahua, Tao, Huanyu, Huang, Sheng-You, Roel-Touris, Jorge, Jimenez-Garcia, Brian, Christoffer, Charles W, Jain, Anika J, Kagaya, Yuki, Kannan, Harini, Nakamura, Tsukasa, Terashi, Genki, Verburgt, Jacob C, Zhang, Yuanyuan, Zhang, Zicong, Fujuta, Hayato, Sekijima, Masakazu, Kihara, Daisuke, Khan, Omeir, Kotelnikov, Sergei, Ghani, Usman, Padhorny, Dzmitry, Beglov, Dmitri, Vajda, Sandor, Kozakov, Dima, Negi, Surendra S, Ricciardelli, Tiziana, Barradas-Bautista, Didier, Cao, Zhen, Chawla, Mohit, Cavallo, Luigi, Oliva, Romina, Yin, Rujie, Cheung, Melyssa, Guest, Johnathan D, Lee, Jessica, Pierce, Brian G, Shor, Ben, Cohen, Tomer, Halfon, Matan, Schneidman-Duhovny, Dina, Zhu, Shaowen, Sun, Yuanfei, Shen, Yang, Maszota-Zieleniak, Martyna, Bojarski, Krzysztof K, Lubecka, Emilia A, Marcisz, Mateusz, Danielsson, Annemarie, Dziadek, Lukasz, Gaardlos, Margrethe, Gieldon, Artur, Liwo, Adam, Samsonov, Sergey A, Slusarz, Rafal, Zieba, Karolina, Sieradzan, Adam K, Czaplewski, Cezary, Kobayashi, Shinpei, Miyakawa, Yuta, Kiyota, Yasuomi, Takeda-Shitaka, Mayuko, Olechnovic, Kliment, Valancauskas, Lukas, Dapkunas, Justas, Venclovas, Ceslovas, Wallner, Bjorn, Yang, Lin, Hou, Chengyu, He, Xiaodong, Guo, Shuai, Jiang, Shenda, Ma, Xiaoliang, Duan, Rui, Qui, Liming, Xu, Xianjin, Zou, Xiaoqin, Velankar, Sameer, and Wodak, Shoshana J
- Published
- 2023
19. Impact of AlphaFold on structure prediction of protein complexes: The CASP15-CAPRI experiment
- Author
-
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, Shi, Hang, Lu, Xufeng, Chang, Shan, Liu, Jian, Guo, Zhiye, Chen, Xiao, Morehead, Alex, Roy, Raj S., Wu, Tianqi, Giri, Nabin, Quadir, Farhan, Chen, Chen, Cheng, Jianlin, Del Carpio, Carlos A., Ichiishi, Eichiro, Rodriguez-Lumbreras, Luis A., Fernandez-Recio, Juan, Harmalkar, Ameya, Chu, Lee-Shin, Canner, Sam, Smanta, Rituparna, Gray, Jeffrey J., Li, Hao, Lin, Peicong, He, Jiahua, Tao, Huanyu, Huang, Sheng-You, Roel-Touris, Jorge, Jimenez-Garcia, Brian, Christoffer, Charles W., Jain, Anika J., Kagaya, Yuki, Kannan, Harini, Nakamura, Tsukasa, Terashi, Genki, Verburgt, Jacob C., Zhang, Yuanyuan, Zhang, Zicong, Fujuta, Hayato, Sekijima, Masakazu, Kihara, Daisuke, Khan, Omeir, Kotelnikov, Sergei, Ghani, Usman, Padhorny, Dzmitry, Beglov, Dmitri, Vajda, Sandor, Kozakov, Dima, Negi, Surendra S., Ricciardelli, Tiziana, Barradas-Bautista, Didier, Cao, Zhen, Chawla, Mohit, Cavallo, Luigi, Oliva, Romina, Yin, Rui, Cheung, Melyssa, Guest, Johnathan D., Lee, Jessica, Pierce, Brian G., Shor, Ben, Cohen, Tomer, Halfon, Matan, Schneidman-Duhovny, Dina, Zhu, Shaowen, Yin, Rujie, Sun, Yuanfei, Shen, Yang, Maszota-Zieleniak, Martyna, Bojarski, Krzysztof K., Lubecka, Emilia A., Marcisz, Mateusz, Danielsson, Annemarie, Dziadek, Lukasz, Gaardlos, Margrethe, Gieldon, Artur, Liwo, Adam, Samsonov, Sergey A., Slusarz, Rafal, Zieba, Karolina, Sieradzan, Adam K., Czaplewski, Cezary, Kobayashi, Shinpei, Miyakawa, Yuta, Kiyota, Yasuomi, Takeda-Shitaka, Mayuko, Olechnovic, Kliment, Valancauskas, Lukas, Dapkunas, Justas, Venclovas, Ceslovas, Wallner, Björn, Yang, Lin, Hou, Chengyu, He, Xiaodong, Guo, Shuai, Jiang, Shenda, Ma, Xiaoliang, Duan, Rui, Qui, Liming, Xu, Xianjin, Zou, Xiaoqin, Velankar, Sameer, Wodak, Shoshana J., 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, Shi, Hang, Lu, Xufeng, Chang, Shan, Liu, Jian, Guo, Zhiye, Chen, Xiao, Morehead, Alex, Roy, Raj S., Wu, Tianqi, Giri, Nabin, Quadir, Farhan, Chen, Chen, Cheng, Jianlin, Del Carpio, Carlos A., Ichiishi, Eichiro, Rodriguez-Lumbreras, Luis A., Fernandez-Recio, Juan, Harmalkar, Ameya, Chu, Lee-Shin, Canner, Sam, Smanta, Rituparna, Gray, Jeffrey J., Li, Hao, Lin, Peicong, He, Jiahua, Tao, Huanyu, Huang, Sheng-You, Roel-Touris, Jorge, Jimenez-Garcia, Brian, Christoffer, Charles W., Jain, Anika J., Kagaya, Yuki, Kannan, Harini, Nakamura, Tsukasa, Terashi, Genki, Verburgt, Jacob C., Zhang, Yuanyuan, Zhang, Zicong, Fujuta, Hayato, Sekijima, Masakazu, Kihara, Daisuke, Khan, Omeir, Kotelnikov, Sergei, Ghani, Usman, Padhorny, Dzmitry, Beglov, Dmitri, Vajda, Sandor, Kozakov, Dima, Negi, Surendra S., Ricciardelli, Tiziana, Barradas-Bautista, Didier, Cao, Zhen, Chawla, Mohit, Cavallo, Luigi, Oliva, Romina, Yin, Rui, Cheung, Melyssa, Guest, Johnathan D., Lee, Jessica, Pierce, Brian G., Shor, Ben, Cohen, Tomer, Halfon, Matan, Schneidman-Duhovny, Dina, Zhu, Shaowen, Yin, Rujie, Sun, Yuanfei, Shen, Yang, Maszota-Zieleniak, Martyna, Bojarski, Krzysztof K., Lubecka, Emilia A., Marcisz, Mateusz, Danielsson, Annemarie, Dziadek, Lukasz, Gaardlos, Margrethe, Gieldon, Artur, Liwo, Adam, Samsonov, Sergey A., Slusarz, Rafal, Zieba, Karolina, Sieradzan, Adam K., Czaplewski, Cezary, Kobayashi, Shinpei, Miyakawa, Yuta, Kiyota, Yasuomi, Takeda-Shitaka, Mayuko, Olechnovic, Kliment, Valancauskas, Lukas, Dapkunas, Justas, Venclovas, Ceslovas, Wallner, Björn, Yang, Lin, Hou, Chengyu, He, Xiaodong, Guo, Shuai, Jiang, Shenda, Ma, Xiaoliang, Duan, Rui, Qui, Liming, Xu, Xianjin, Zou, Xiaoqin, Velankar, Sameer, and Wodak, Shoshana J.
- 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 similar to 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., Funding Agencies|Francis Crick Institute; Cancer Research UK [FC0001003]; UK Medical Research Council [FC001003]; Wellcome Trust [FC001003]; European Union Horizon 2020 [823830]; Netherlands e-Science Center [027.020.G13]; US National Institutes of Health [R01GM146340, R01GM093123]; Spanish Ministry of Science [501100011033, AEI/10.13039, PID2019-110167RB-I00]; National Institute of Health [R35 GM144083, RM1135136, R35GM118078, R01GM140098, R01GM123055, R01GM133840, R35-GM141881]; Advanced Research Computing at Hopkins (ARCH) core facility; National Natural Science Foundation of China [32161133002, 62072199]; European Molecular Biology Organization (EMBO) [ALTF 145-2021]; Government of Catalonia's Agency for Business Competitiveness (ACCIO); National Science Foundation [DMS 2054251, DBI2003635, IIS2211598, DBI2146026, MCB1925643, CMMI1825941, IIS1763246, DBI1759934, CCF-1943008, OAC1920103]; National Institute of General Medical Sciences [T32 GM132024]; NIH/NIGMS [R35GM136409, R35GM124952]; National Science Center of Poland (Narodowe Centrum Nauki) (NCN) [UMO2017/27/B/ST4/00926, UMO-2017/26/M/ ST4/00044, UMO2017/25/B/ST4/01026]; Research Council of Lithuania [: S-MIP-21-25]; Wallenberg AI, Autonomous System and Software Program (WASP); Knut and Alice Wallenberg Foundation (KAW); Swedish Research Council; Science Foundation of the National Key Laboratory of Science and Technology; Fundamental Research Funds for the Central Universities of China; [801342]
- Published
- 2023
- Full Text
- View/download PDF
20. Impact of AlphaFold on structure prediction of protein complexes: The CASP15-CAPRI experiment
- Author
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Francis Crick Institute, Cancer Research UK, Medical Research Council (UK), Wellcome Trust, European Commission, National Science Foundation (US), National Institutes of Health (US), Agencia Estatal de Investigación (España), Ministerio de Ciencia, Innovación y Universidades (España), Johns Hopkins University, National Natural Science Foundation of China, EMBO, Generalitat de Catalunya, Purdue University, National Science Centre (Poland), University of Warsaw, Research Council of Lithuania, Knut and Alice Wallenberg Foundation, Swedish Research Council, 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, Shi, Hang, Samsonov, Sergey A., Slusarz, Rafal, Zieba, Karolina, Sieradzan, Adam K., Czaplewski, Cezary, Kobayashi, Shinpei, Miyakawa, Yuta, Kiyota, Yasuomi, Takeda-Shitaka, Mayuko, Olechnovic, Kliment, Wallner, Bjorn, Valancauskas, Lukas, Dapkunas, Justas, Venclovas, Ceslovas, Yang, Lin, Hou, Chengyu, He, Xiaodong, Guo, Shuai, Jiang, Shenda, Ma, Xiaoliang, Duan, Rui, Qui, Liming, Xu, Xianjin, Lu, Xufeng, Zou, Xiaoqin, Velankar, Sameer, Wodak, Shoshana J., Chang, Shan, Liu, Jian, Guo, Zhiye, Chen, Xiao, Morehead, Alex, Roy, Raj S., Wu, Tianqi, Giri, Nabin, Quadir, Farhan, Chen, Chen, Cheng, Jianlin, Del Carpio, Carlos A., Ichiishi, Eichiro, Rodríguez-Lumbreras, Luis A., Fernández-Recio, Juan, Harmalkar, Ameya, Chu, Lee-Shin, Canner, Sam, Smanta, Rituparna, Gray, Jeffrey J., Li, Hao, Lin, Peicong, He, Jiahua, Tao, Huanyu, Huang, Sheng-You, Roel-Touris, Jorge, Jimenez-Garcia, Brian, Christoffer, Charles W., Jain, Anika J., Kagaya, Yuki, Kannan, Harini, Nakamura, Tsukasa, Terashi, Genki, Verburgt, Jacob C., Zhang, Yuanyuan, Zhang, Zicong, Fujuta, Hayato, Sekijima, Masakazu, Kihara, Daisuke, Khan, Omeir, Kotelnikov, Sergei, Ghani, Usman, Padhorny, Dzmitry, Beglov, Dmitri, Vajda, Sandor, Kozakov, Dima, Negi, Surendra S., Ricciardelli, Tiziana, Barradas-Bautista, Didier, Cao, Zhen, Chawla, Mohit, Cavallo, Luigi, Oliva, Romina, Yin, Rui, Cheung, Melyssa, Guest, Johnathan D., Lee, Jessica, Pierce, Brian G., Shor, Ben, Cohen, Tomer, Halfon, Matan, Schneidman-Duhovny, Dina, Zhu, Shaowen, Yin, Rujie, Sun, Yuanfei, Shen, Yang, Maszota-Zieleniak, Martyna, Bojarski, Krzysztof K., Lubecka, Emilia A., Marcisz, Mateusz, Danielsson, Annemarie, Dziadek, Lukasz, Gaardlos, Margrethe, Gieldon, Artur, Liwo, Adam, Francis Crick Institute, Cancer Research UK, Medical Research Council (UK), Wellcome Trust, European Commission, National Science Foundation (US), National Institutes of Health (US), Agencia Estatal de Investigación (España), Ministerio de Ciencia, Innovación y Universidades (España), Johns Hopkins University, National Natural Science Foundation of China, EMBO, Generalitat de Catalunya, Purdue University, National Science Centre (Poland), University of Warsaw, Research Council of Lithuania, Knut and Alice Wallenberg Foundation, Swedish Research Council, 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, Shi, Hang, Samsonov, Sergey A., Slusarz, Rafal, Zieba, Karolina, Sieradzan, Adam K., Czaplewski, Cezary, Kobayashi, Shinpei, Miyakawa, Yuta, Kiyota, Yasuomi, Takeda-Shitaka, Mayuko, Olechnovic, Kliment, Wallner, Bjorn, Valancauskas, Lukas, Dapkunas, Justas, Venclovas, Ceslovas, Yang, Lin, Hou, Chengyu, He, Xiaodong, Guo, Shuai, Jiang, Shenda, Ma, Xiaoliang, Duan, Rui, Qui, Liming, Xu, Xianjin, Lu, Xufeng, Zou, Xiaoqin, Velankar, Sameer, Wodak, Shoshana J., Chang, Shan, Liu, Jian, Guo, Zhiye, Chen, Xiao, Morehead, Alex, Roy, Raj S., Wu, Tianqi, Giri, Nabin, Quadir, Farhan, Chen, Chen, Cheng, Jianlin, Del Carpio, Carlos A., Ichiishi, Eichiro, Rodríguez-Lumbreras, Luis A., Fernández-Recio, Juan, Harmalkar, Ameya, Chu, Lee-Shin, Canner, Sam, Smanta, Rituparna, Gray, Jeffrey J., Li, Hao, Lin, Peicong, He, Jiahua, Tao, Huanyu, Huang, Sheng-You, Roel-Touris, Jorge, Jimenez-Garcia, Brian, Christoffer, Charles W., Jain, Anika J., Kagaya, Yuki, Kannan, Harini, Nakamura, Tsukasa, Terashi, Genki, Verburgt, Jacob C., Zhang, Yuanyuan, Zhang, Zicong, Fujuta, Hayato, Sekijima, Masakazu, Kihara, Daisuke, Khan, Omeir, Kotelnikov, Sergei, Ghani, Usman, Padhorny, Dzmitry, Beglov, Dmitri, Vajda, Sandor, Kozakov, Dima, Negi, Surendra S., Ricciardelli, Tiziana, Barradas-Bautista, Didier, Cao, Zhen, Chawla, Mohit, Cavallo, Luigi, Oliva, Romina, Yin, Rui, Cheung, Melyssa, Guest, Johnathan D., Lee, Jessica, Pierce, Brian G., Shor, Ben, Cohen, Tomer, Halfon, Matan, Schneidman-Duhovny, Dina, Zhu, Shaowen, Yin, Rujie, Sun, Yuanfei, Shen, Yang, Maszota-Zieleniak, Martyna, Bojarski, Krzysztof K., Lubecka, Emilia A., Marcisz, Mateusz, Danielsson, Annemarie, Dziadek, Lukasz, Gaardlos, Margrethe, Gieldon, Artur, and Liwo, Adam
- 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.
- Published
- 2023
21. Impact of AlphaFold on Structure Prediction of Protein Complexes: The CASP15-CAPRI Experiment
- Author
-
NMR Spectroscopy, Sub NMR Spectroscopy, Lensink, Marc, Brysbaert, Guillaume, Raouraoua, Nessim, Bates, Paul, Giulini, Marco, Honorato, Rodrigo Vargas, Noort, Charlotte van, Teixeira, João, Bonvin, Alexandre M.J.J., Kong, Ren, Shi, Hang, Lu, Xufeng, Chang, Shan, Liu, Jian, Guo, Zhiye, Chen, Xiao, Morehead, Alex, Roy, Raj, Wu, Tianqi, Giri, Nabin, Quadir, Farhan, Chen, Chen, Cheng, Jianlin, Carpio, Carlos Del, Ichiishi, Eichiro, Luis, Rodriguez-Lumbreras A, Fernández-Recio, Juan, Harmalkar, Ameya, Chu, Lee-Shin, Canner, Sam, Smanta, Rituparna, Gray, Jeffrey, Li, Hao, Lin, Peicong, He, Jiahua, Tao, Huanyu, Huang, Shengyou, Roel, Jorge, Jimenez-Garcia, Brian, Christoffer, Charles, Anika, Jain J, Kagaya, Yuki, Kannan, Harini, Nakamura, Tsukasa, Terashi, Genki, Verburgt, Jacob, Zhang, Yuanyuan, Zhang, Zicong, Fujuta, Hayato, Sekijima, Masakazu, Kihara, Daisuke, Khan, Omeir, Kotelnikov, Sergei, Ghani, Usman, Padhorny, Dzmitry, Beglov, Dmitri, Vajda, Sandor, Kozakov, Dima, Surendra, Negi S, Ricciardelli, Tiziana, Barradas-Bautista, Didier, Cao, Zhen, Chawla, Mohit, Cavallo, Luigi, Oliva, Romina, Yin, Rujie, Cheung, Melyssa, Guest, Johnathan, Lee, Jessica, Pierce, Brian, Shor, Ben, Cohen, Tomer, Halfon, Matan, Schneidman-Duhovny, Dina, Zhu, Shaowen, Sun, Yuanfei, Shen, Yang, Maszota-Zieleniak, Martyna, Krzysztof, Bojarski K, Lubecka, Emilia, Marcisz, Mateusz, Danielsson, Annemarie, Dziadek, Lukasz, Gaardlos, Margrethe, Giełdoń, Artur, Liwo, Jozef, Samsonov, Sergey, Slusarz, Rafal, Zieba, Karolina, Sieradzan, Adam, Czaplewski, Cezary, Kobayashi, Shinpei, Miyakawa, Yuta, Kiyota, Yasuomi, Takeda-Shitaka, Mayuko, Olechnovič, Kliment, Valančauskas, Lukas, Dapkūnas, Justas, Venclovas, Ceslovas, Wallner, Björn, Yang, Lin, Hou, Chengyu, He, Xiaodong, Guo, Shuai, Jiang, Shenda, Ma, Xiaoliang, Duan, Rui, Qiu, Liming, Xu, Xianjin, Zou, Xiaoqin, Velankar, Sameer, Shoshana, Wodak J, NMR Spectroscopy, Sub NMR Spectroscopy, Lensink, Marc, Brysbaert, Guillaume, Raouraoua, Nessim, Bates, Paul, Giulini, Marco, Honorato, Rodrigo Vargas, Noort, Charlotte van, Teixeira, João, Bonvin, Alexandre M.J.J., Kong, Ren, Shi, Hang, Lu, Xufeng, Chang, Shan, Liu, Jian, Guo, Zhiye, Chen, Xiao, Morehead, Alex, Roy, Raj, Wu, Tianqi, Giri, Nabin, Quadir, Farhan, Chen, Chen, Cheng, Jianlin, Carpio, Carlos Del, Ichiishi, Eichiro, Luis, Rodriguez-Lumbreras A, Fernández-Recio, Juan, Harmalkar, Ameya, Chu, Lee-Shin, Canner, Sam, Smanta, Rituparna, Gray, Jeffrey, Li, Hao, Lin, Peicong, He, Jiahua, Tao, Huanyu, Huang, Shengyou, Roel, Jorge, Jimenez-Garcia, Brian, Christoffer, Charles, Anika, Jain J, Kagaya, Yuki, Kannan, Harini, Nakamura, Tsukasa, Terashi, Genki, Verburgt, Jacob, Zhang, Yuanyuan, Zhang, Zicong, Fujuta, Hayato, Sekijima, Masakazu, Kihara, Daisuke, Khan, Omeir, Kotelnikov, Sergei, Ghani, Usman, Padhorny, Dzmitry, Beglov, Dmitri, Vajda, Sandor, Kozakov, Dima, Surendra, Negi S, Ricciardelli, Tiziana, Barradas-Bautista, Didier, Cao, Zhen, Chawla, Mohit, Cavallo, Luigi, Oliva, Romina, Yin, Rujie, Cheung, Melyssa, Guest, Johnathan, Lee, Jessica, Pierce, Brian, Shor, Ben, Cohen, Tomer, Halfon, Matan, Schneidman-Duhovny, Dina, Zhu, Shaowen, Sun, Yuanfei, Shen, Yang, Maszota-Zieleniak, Martyna, Krzysztof, Bojarski K, Lubecka, Emilia, Marcisz, Mateusz, Danielsson, Annemarie, Dziadek, Lukasz, Gaardlos, Margrethe, Giełdoń, Artur, Liwo, Jozef, Samsonov, Sergey, Slusarz, Rafal, Zieba, Karolina, Sieradzan, Adam, Czaplewski, Cezary, Kobayashi, Shinpei, Miyakawa, Yuta, Kiyota, Yasuomi, Takeda-Shitaka, Mayuko, Olechnovič, Kliment, Valančauskas, Lukas, Dapkūnas, Justas, Venclovas, Ceslovas, Wallner, Björn, Yang, Lin, Hou, Chengyu, He, Xiaodong, Guo, Shuai, Jiang, Shenda, Ma, Xiaoliang, Duan, Rui, Qiu, Liming, Xu, Xianjin, Zou, Xiaoqin, Velankar, Sameer, and Shoshana, Wodak J
- Published
- 2023
22. COXPRESdb v8: an animal gene coexpression database navigating from a global view to detailed investigations
- Author
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Obayashi, Takeshi, primary, Kodate, Shun, additional, Hibara, Himiko, additional, Kagaya, Yuki, additional, and Kinoshita, Kengo, additional
- Published
- 2022
- Full Text
- View/download PDF
23. ATTED-II in 2018: A Plant Coexpression Database Based on Investigation of the Statistical Property of the Mutual Rank Index
- Author
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Obayashi, Takeshi, Aoki, Yuichi, Tadaka, Shu, Kagaya, Yuki, and Kinoshita, Kengo
- Published
- 2018
- Full Text
- View/download PDF
24. ContactPFP: Protein Function Prediction Using Predicted Contact Information
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Kagaya, Yuki, primary, Flannery, Sean T., additional, Jain, Aashish, additional, and Kihara, Daisuke, additional
- Published
- 2022
- Full Text
- View/download PDF
25. ATTED-II v11: A Plant Gene Coexpression Database Using a Sample Balancing Technique by Subagging of Principal Components
- Author
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Obayashi, Takeshi, primary, Hibara, Himiko, additional, Kagaya, Yuki, additional, Aoki, Yuichi, additional, and Kinoshita, Kengo, additional
- Published
- 2022
- Full Text
- View/download PDF
26. COXPRESdb v8: an animal gene coexpression database navigating from a global view to detailed investigations.
- Author
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Obayashi, Takeshi, Kodate, Shun, Hibara, Himiko, Kagaya, Yuki, and Kinoshita, Kengo
- Published
- 2023
- Full Text
- View/download PDF
27. Real-Time Structure Search and Structure Classification for AlphaFold Protein Models
- Author
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Aderinwale, Tunde, primary, Bharadwaj, Vijay, additional, Christoffer, Charles, additional, Terashi, Genki, additional, Zhang, Zicong, additional, Jahandideh, Rashidedin, additional, Kagaya, Yuki, additional, and Kihara, Daisuke, additional
- Published
- 2021
- Full Text
- View/download PDF
28. Protein contact map refinement for improving structure prediction using generative adversarial networks
- Author
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Maddhuri Venkata Subramaniya, Sai Raghavendra, primary, Terashi, Genki, additional, Jain, Aashish, additional, Kagaya, Yuki, additional, and Kihara, Daisuke, additional
- Published
- 2021
- Full Text
- View/download PDF
29. AttentiveDist: Protein Inter-Residue Distance Prediction Using Deep Learning with Attention on Quadruple Multiple Sequence Alignments
- Author
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Jain, Aashish, primary, Terashi, Genki, additional, Kagaya, Yuki, additional, Maddhuri Venkata Subramaniya, Sai Raghavendra, additional, Christoffer, Charles, additional, and Kihara, Daisuke, additional
- Published
- 2020
- Full Text
- View/download PDF
30. Protein Contact Map Denoising Using Generative Adversarial Networks
- Author
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Venkata Subramaniya, Sai Raghavendra Maddhuri, primary, Terashi, Genki, additional, Jain, Aashish, additional, Kagaya, Yuki, additional, and Kihara, Daisuke, additional
- Published
- 2020
- Full Text
- View/download PDF
31. MAINMASTseg: Automated Map Segmentation Method for Cryo-EM Density Maps with Symmetry
- Author
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Terashi, Genki, primary, Kagaya, Yuki, additional, and Kihara, Daisuke, additional
- Published
- 2020
- Full Text
- View/download PDF
32. Metagenome Sequences from the Environment of Diseased Otter Clams, Lutraria rhynchaena, from a Farm in Vietnam
- Author
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Kagaya, Yuki, primary, Minei, Ryuhei, additional, Duong, Ha T. T., additional, Le, Binh T. N., additional, Dang, Lua T., additional, Tran, Trang T. H., additional, Nguyen, Hoa T., additional, Kinoshita, Kengo, additional, Yura, Kei, additional, Ogura, Atsushi, additional, and Kim, Oanh T. P., additional
- Published
- 2020
- Full Text
- View/download PDF
33. Protein contact map refinement for improving structure prediction using generative adversarial networks.
- Author
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Subramaniya, Sai Raghavendra Maddhuri Venkata, Terashi, Genki, Jain, Aashish, Kagaya, Yuki, and Kihara, Daisuke
- Subjects
GENERATIVE adversarial networks ,PROBABILISTIC generative models ,PROTEIN structure prediction ,PROTEIN structure ,COMPUTATIONAL biology ,TERTIARY structure - Abstract
Motivation Protein structure prediction remains as one of the most important problems in computational biology and biophysics. In the past few years, protein residue–residue contact prediction has undergone substantial improvement, which has made it a critical driving force for successful protein structure prediction. Boosting the accuracy of contact predictions has, therefore, become the forefront of protein structure prediction. Results We show a novel contact map refinement method, ContactGAN, which uses Generative Adversarial Networks (GAN). ContactGAN was able to make a significant improvement over predictions made by recent contact prediction methods when tested on three datasets including protein structure modeling targets in CASP13 and CASP14. We show improvement of precision in contact prediction, which translated into improvement in the accuracy of protein tertiary structure models. On the other hand, observed improvement over trRosetta was relatively small, reasons for which are discussed. ContactGAN will be a valuable addition in the structure prediction pipeline to achieve an extra gain in contact prediction accuracy. Availability and implementation https://github.com/kiharalab/ContactGAN. Supplementary information Supplementary data are available at Bioinformatics online. [ABSTRACT FROM AUTHOR]
- Published
- 2021
- Full Text
- View/download PDF
34. Short-term Inspiratory Muscle Training in hospitalized Heart Failure patients is safe and effective for improving inspiratory muscle strength and dyspnea on exertion
- Author
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Kagaya, Yuki, Okura, Kazuki, and Takanobu Shioya
- Published
- 2018
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- View/download PDF
35. Inspiratory Muscle Weakness Is Associated With Exercise Intolerance in Elderly Patients With Heart Failure
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Kagaya, Yuki, Okura, Kazuki, and Takanobu Shioya
- Published
- 2017
- Full Text
- View/download PDF
36. COXPRESdb v7: a gene coexpression database for 11 animal species supported by 23 coexpression platforms for technical evaluation and evolutionary inference
- Author
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Obayashi, Takeshi, primary, Kagaya, Yuki, additional, Aoki, Yuichi, additional, Tadaka, Shu, additional, and Kinoshita, Kengo, additional
- Published
- 2018
- Full Text
- View/download PDF
37. ATTED-II in 2018: A Plant Coexpression Database Based on Investigation of the Statistical Property of the Mutual Rank Index
- Author
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Obayashi, Takeshi, primary, Aoki, Yuichi, additional, Tadaka, Shu, additional, Kagaya, Yuki, additional, and Kinoshita, Kengo, additional
- Published
- 2017
- Full Text
- View/download PDF
38. COXPRESdb v7: a gene coexpression database for 11 animal species supported by 23 coexpression platforms for technical evaluation and evolutionary inference.
- Author
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Obayashi, Takeshi, Kagaya, Yuki, Aoki, Yuichi, Tadaka, Shu, and Kinoshita, Kengo
- Published
- 2019
- Full Text
- View/download PDF
39. Genetic adaptation despite high gene flow in a range‐expanding population.
- Author
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Lee, Andy, Daniels, Benjamin N., Hemstrom, William, López, Cataixa, Kagaya, Yuki, Kihara, Daisuke, Davidson, Jean M., Toonen, Robert J., White, Crow, and Christie, Mark R.
- Subjects
- *
NATURAL selection , *GENE flow , *TRIOSE-phosphate isomerase , *GENETIC testing , *TRANSCRIPTOMES , *LARVAL dispersal - Abstract
Signals of natural selection can be quickly eroded in high gene flow systems, curtailing efforts to understand how and when genetic adaptation occurs in the ocean. This long‐standing, unresolved topic in ecology and evolution has renewed importance because changing environmental conditions are driving range expansions that may necessitate rapid evolutionary responses. One example occurs in Kellet's whelk (Kelletia kelletii), a common subtidal gastropod with an ~40‐ to 60‐day pelagic larval duration that expanded their biogeographic range northwards in the 1970s by over 300 km. To test for genetic adaptation, we performed a series of experimental crosses with Kellet's whelk adults collected from their historical (HxH) and recently expanded range (ExE), and conducted RNA‐Seq on offspring that we reared in a common garden environment. We identified 2770 differentially expressed genes (DEGs) between 54 offspring samples with either only historical range (HxH offspring) or expanded range (ExE offspring) ancestry. Using SNPs called directly from the DEGs, we assigned samples of known origin back to their range of origin with unprecedented accuracy for a marine species (92.6% and 94.5% for HxH and ExE offspring, respectively). The SNP with the highest predictive importance occurred on triosephosphate isomerase (TPI), an essential metabolic enzyme involved in cold stress response. TPI was significantly upregulated and contained a non‐synonymous mutation in the expanded range. Our findings pave the way for accurately identifying patterns of dispersal, gene flow and population connectivity in the ocean by demonstrating that experimental transcriptomics can reveal mechanisms for how marine organisms respond to changing environmental conditions. [ABSTRACT FROM AUTHOR]
- Published
- 2024
- Full Text
- View/download PDF
40. Erratum: ATTED-II in 2018: A Plant Coexpression Database Based on Investigation of the Statistical Property of the Mutual Rank Index.
- Author
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Obayashi, Takeshi, Aoki, Yuichi, Tadaka, Shu, Kagaya, Yuki, and Kinoshita, Kengo
- Subjects
GENE expression in plants ,DATABASE management - Published
- 2018
- Full Text
- View/download PDF
41. Distance-AF: Modifying Predicted Protein Structure Models by Alphafold2 with User-Specified Distance Constraints.
- Author
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Zhang Y, Zhang Z, Kagaya Y, Terashi G, Zhao B, Xiong Y, and Kihara D
- Abstract
The three-dimensional structure of a protein plays a fundamental role in determining its function and has an essential impact on understanding biological processes. Despite significant progress in protein structure prediction, such as AlphaFold2, challenges remain on those hard targets that Alphafold2 does not often perform well due to the complex folding of protein and a large number of possible conformations. Here we present a modified version of the AlphaFold2, called Distance-AF, which aims to improve the performance of AlphaFold2 by including distance constraints as input information. Distance-AF uses AlphaFold2's predicted structure as a starting point and incorporates distance constraints between amino acids to adjust folding of the protein structure until it meets the constraints. Distance-AF can correct the domain orientation on challenging targets, leading to more accurate structures with a lower root mean square deviation (RMSD). The ability of Distance-AF is also useful in fitting protein structures into cryo-electron microscopy maps.
- Published
- 2023
- Full Text
- View/download PDF
42. Improved Peptide Docking with Privileged Knowledge Distillation using Deep Learning.
- Author
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Zhang Z, Verburgt J, Kagaya Y, Christoffer C, and Kihara D
- Abstract
Protein-peptide interactions play a key role in biological processes. Understanding the interactions that occur within a receptor-peptide complex can help in discovering and altering their biological functions. Various computational methods for modeling the structures of receptor-peptide complexes have been developed. Recently, accurate structure prediction enabled by deep learning methods has significantly advanced the field of structural biology. AlphaFold (AF) is among the top-performing structure prediction methods and has highly accurate structure modeling performance on single-chain targets. Shortly after the release of AlphaFold, AlphaFold-Multimer (AFM) was developed in a similar fashion as AF for prediction of protein complex structures. AFM has achieved competitive performance in modeling protein-peptide interactions compared to previous computational methods; however, still further improvement is needed. Here, we present DistPepFold, which improves protein-peptide complex docking using an AFM-based architecture through a privileged knowledge distillation approach. DistPepFold leverages a teacher model that uses native interaction information during training and transfers its knowledge to a student model through a teacher-student distillation process. We evaluated DistPepFold's docking performance on two protein-peptide complex datasets and showed that DistPepFold outperforms AFM. Furthermore, we demonstrate that the student model was able to learn from the teacher model to make structural improvements based on AFM predictions.
- Published
- 2023
- Full Text
- View/download PDF
43. NuFold: A Novel Tertiary RNA Structure Prediction Method Using Deep Learning with Flexible Nucleobase Center Representation.
- Author
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Kagaya Y, Zhang Z, Ibtehaz N, Wang X, Nakamura T, Huang D, and Kihara D
- Abstract
RNA is not only playing a core role in the central dogma as mRNA between DNA and protein, but also many non-coding RNAs have been discovered to have unique and diverse biological functions. As genome sequences become increasingly available and our knowledge of RNA sequences grows, the study of RNA's structure and function has become more demanding. However, experimental determination of three-dimensional RNA structures is both costly and time-consuming, resulting in a substantial disparity between RNA sequence data and structural insights. In response to this challenge, we propose a novel computational approach that harnesses state-of-the-art deep learning architecture NuFold to accurately predict RNA tertiary structures. This approach aims to offer a cost-effective and efficient means of bridging the gap between RNA sequence information and structural comprehension. NuFold implements a nucleobase center representation, which allows it to reproduce all possible nucleotide conformations accurately.
- Published
- 2023
- Full Text
- View/download PDF
44. Domain-PFP: Protein Function Prediction Using Function-Aware Domain Embedding Representations.
- Author
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Ibtehaz N, Kagaya Y, and Kihara D
- Abstract
Domains are functional and structural units of proteins that govern various biological functions performed by the proteins. Therefore, the characterization of domains in a protein can serve as a proper functional representation of proteins. Here, we employ a self-supervised protocol to derive functionally consistent representations for domains by learning domain-Gene Ontology (GO) co-occurrences and associations. The domain embeddings we constructed turned out to be effective in performing actual function prediction tasks. Extensive evaluations showed that protein representations using the domain embeddings are superior to those of large-scale protein language models in GO prediction tasks. Moreover, the new function prediction method built on the domain embeddings, named Domain-PFP, significantly outperformed the state-of-the-art function predictors. Additionally, Domain-PFP demonstrated competitive performance in the CAFA3 evaluation, achieving overall the best performance among the top teams that participated in the assessment.
- Published
- 2023
- Full Text
- View/download PDF
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