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3. De novo design of immunoglobulin-like domains

Author

Chidyausiku, Tamuka M., Mendes, Soraia R., Klima, Jason C., Nadal, Marta, Eckhard, Ulrich, Roel-Touris, Jorge, Houliston, Scott, Guevara, Tibisay, Haddox, Hugh K., Moyer, Adam, Arrowsmith, Cheryl H., Gomis-Rüth, F. Xavier, Baker, David, and Marcos, Enrique

Published
2022
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7. Impact of AlphaFold on structure prediction of protein complexes: The CASP15‐CAPRI experiment

Author

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
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10. 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, 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

11. 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

12. 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

13. Impact of AlphaFold on structure prediction of protein complexes: The CASP15-CAPRI experiment

Author

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

14. The LightDock Server: Artificial Intelligence-powered modeling of macromolecular interactions

Author

EMBO, European Commission, Generalitat de Catalunya, Jiménez-García, Brian, Roel-Touris, Jorge, Barradas-Bautista, Didier, EMBO, European Commission, Generalitat de Catalunya, Jiménez-García, Brian, Roel-Touris, Jorge, and Barradas-Bautista, Didier

Abstract

Computational docking is an instrumental method of the structural biology toolbox. Specifically, integrative modeling software, such as LightDock, arise as complementary and synergetic methods to experimental structural biology techniques. Ubiquitousness and accessibility are fundamental features to promote ease of use and to improve user experience. With this goal in mind, we have developed the LightDock Server, a web server for the integrative modeling of macromolecular interactions, along with several dedicated usage modes. The server builds upon the LightDock macromolecular docking framework, which has proved useful for modeling medium-to-high flexible complexes, antibody-antigen interactions, or membrane-associated protein assemblies. We believe that this free-to-use resource will be a valuable addition to the structural biology community and can be accessed online at: https://server.lightdock.org

Published
2023

15. Single-chain dimers from de novo immunoglobulins as robust scaffolds for multiple binding loops

Author

Ministerio de Economía y Competitividad (España), Ministerio de Ciencia, Innovación y Universidades (España), Agencia Estatal de Investigación (España), EMBO, Roel-Touris, Jorge, Nadal, Marta, Marcos, Enrique, Ministerio de Economía y Competitividad (España), Ministerio de Ciencia, Innovación y Universidades (España), Agencia Estatal de Investigación (España), EMBO, Roel-Touris, Jorge, Nadal, Marta, and Marcos, Enrique

Abstract

Antibody derivatives have sought to recapitulate the antigen binding properties of antibodies, but with improved biophysical attributes convenient for therapeutic, diagnostic and research applications. However, their success has been limited by the naturally occurring structure of the immunoglobulin dimer displaying hypervariable binding loops, which is hard to modify by traditional engineering approaches. Here, we devise geometrical principles for de novo designing single-chain immunoglobulin dimers, as a tunable two-domain architecture that optimizes biophysical properties through more favorable dimer interfaces. Guided by these principles, we computationally designed protein scaffolds that were hyperstable, structurally accurate and robust for accommodating multiple functional loops, both individually and in combination, as confirmed through biochemical assays and X-ray crystallography. We showcase the modularity of this architecture by deep-learning-based diversification, opening up the possibility for tailoring the number, positioning, and relative orientation of ligand-binding loops targeting one or two distal epitopes. Our results provide a route to custom-design robust protein scaffolds for harboring multiple functional loops.

Published
2023

19. De Novo Design of Immunoglobulin-like Domains

Author

Chidyausiku, Tamuka M., primary, Mendes, Soraia R., additional, Klima, Jason C., additional, Eckhard, Ulrich, additional, Houliston, Scott, additional, Nadal, Marta, additional, Roel-Touris, Jorge, additional, Guevara, Tibisay, additional, Haddox, Hugh K., additional, Moyer, Adam, additional, Arrowsmith, Cheryl H., additional, Gomis-Rüth, F. Xavier, additional, Baker, David, additional, and Marcos, Enrique, additional

Published
2021
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20. Prediction of protein assemblies, the next frontier: The CASP14‐CAPRI experiment

Author

Lensink, Marc F., primary, Brysbaert, Guillaume, additional, Mauri, Théo, additional, Nadzirin, Nurul, additional, Velankar, Sameer, additional, Chaleil, Raphael A. G., additional, Clarence, Tereza, additional, Bates, Paul A., additional, Kong, Ren, additional, Liu, Bin, additional, Yang, Guangbo, additional, Liu, Ming, additional, Shi, Hang, additional, Lu, Xufeng, additional, Chang, Shan, additional, Roy, Raj S., additional, Quadir, Farhan, additional, Liu, Jian, additional, Cheng, Jianlin, additional, Antoniak, Anna, additional, Czaplewski, Cezary, additional, Giełdoń, Artur, additional, Kogut, Mateusz, additional, Lipska, Agnieszka G., additional, Liwo, Adam, additional, Lubecka, Emilia A., additional, Maszota‐Zieleniak, Martyna, additional, Sieradzan, Adam K., additional, Ślusarz, Rafał, additional, Wesołowski, Patryk A., additional, Zięba, Karolina, additional, Del Carpio Muñoz, Carlos A., additional, Ichiishi, Eiichiro, additional, Harmalkar, Ameya, additional, Gray, Jeffrey J., additional, Bonvin, Alexandre M. J. J., additional, Ambrosetti, Francesco, additional, Vargas Honorato, Rodrigo, additional, Jandova, Zuzana, additional, Jiménez‐García, Brian, additional, Koukos, Panagiotis I., additional, Van Keulen, Siri, additional, Van Noort, Charlotte W., additional, Réau, Manon, additional, Roel‐Touris, Jorge, additional, Kotelnikov, Sergei, additional, Padhorny, Dzmitry, additional, Porter, Kathryn A., additional, Alekseenko, Andrey, additional, Ignatov, Mikhail, additional, Desta, Israel, additional, Ashizawa, Ryota, additional, Sun, Zhuyezi, additional, Ghani, Usman, additional, Hashemi, Nasser, additional, Vajda, Sandor, additional, Kozakov, Dima, additional, Rosell, Mireia, additional, Rodríguez‐Lumbreras, Luis A., additional, Fernandez‐Recio, Juan, additional, Karczynska, Agnieszka, additional, Grudinin, Sergei, additional, Yan, Yumeng, additional, Li, Hao, additional, Lin, Peicong, additional, Huang, Sheng‐You, additional, Christoffer, Charles, additional, Terashi, Genki, additional, Verburgt, Jacob, additional, Sarkar, Daipayan, additional, Aderinwale, Tunde, additional, Wang, Xiao, additional, Kihara, Daisuke, additional, Nakamura, Tsukasa, additional, Hanazono, Yuya, additional, Gowthaman, Ragul, additional, Guest, Johnathan D., additional, Yin, Rui, additional, Taherzadeh, Ghazaleh, additional, Pierce, Brian G., additional, Barradas‐Bautista, Didier, additional, Cao, Zhen, additional, Cavallo, Luigi, additional, Oliva, Romina, additional, Sun, Yuanfei, additional, Zhu, Shaowen, additional, Shen, Yang, additional, Park, Taeyong, additional, Woo, Hyeonuk, additional, Yang, Jinsol, additional, Kwon, Sohee, additional, Won, Jonghun, additional, Seok, Chaok, additional, Kiyota, Yasuomi, additional, Kobayashi, Shinpei, additional, Harada, Yoshiki, additional, Takeda‐Shitaka, Mayuko, additional, Kundrotas, Petras J., additional, Singh, Amar, additional, Vakser, Ilya A., additional, Dapkūnas, Justas, additional, Olechnovič, Kliment, additional, Venclovas, Česlovas, additional, Duan, Rui, additional, Qiu, Liming, additional, Xu, Xianjin, additional, Zhang, Shuang, additional, Zou, Xiaoqin, additional, and Wodak, Shoshana J., additional

Published
2021
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21. Prediction of protein assemblies, the next frontier: The CASP14-CAPRI experiment

Author

Lensink, Marc F., Brysbaert, Guillaume, Mauri, Théo, Nadzirin, Nurul, Velankar, Sameer, Chaleil, Raphael A.G., Clarence, Tereza, Bates, Paul A., Kong, Ren, Liu, Bin, Yang, Guangbo, Liu, Ming, Shi, Hang, Lu, Xufeng, Chang, Shan, Roy, Raj S., Quadir, Farhan, Liu, Jian, Cheng, Jianlin, Antoniak, Anna, Czaplewski, Cezary, Giełdoń, Artur, Kogut, Mateusz, Lipska, Agnieszka G., Liwo, Adam, Lubecka, Emilia A., Maszota-Zieleniak, Martyna, Sieradzan, Adam K., Ślusarz, Rafał, Wesołowski, Patryk A., Zięba, Karolina, Del Carpio Muñoz, Carlos A., Ichiishi, Eiichiro, Harmalkar, Ameya, Gray, Jeffrey J., Bonvin, Alexandre M.J.J., Ambrosetti, Francesco, Vargas Honorato, Rodrigo, Jandova, Zuzana, Jiménez-García, Brian, Koukos, Panagiotis I., Van Keulen, Siri, Van Noort, Charlotte W., Réau, Manon, Roel-Touris, Jorge, Kotelnikov, Sergei, Padhorny, Dzmitry, Porter, Kathryn A., Alekseenko, Andrey, Ignatov, Mikhail, Desta, Israel, Ashizawa, Ryota, Sun, Zhuyezi, Ghani, Usman, Hashemi, Nasser, Vajda, Sandor, Kozakov, Dima, Rosell, Mireia, Rodríguez-Lumbreras, Luis A., Fernandez-Recio, Juan, Karczynska, Agnieszka, Grudinin, Sergei, Yan, Yumeng, Li, Hao, Lin, Peicong, Huang, Sheng You, Christoffer, Charles, Terashi, Genki, Verburgt, Jacob, Sarkar, Daipayan, Aderinwale, Tunde, Wang, Xiao, Kihara, Daisuke, Nakamura, Tsukasa, Hanazono, Yuya, Gowthaman, Ragul, Guest, Johnathan D., Yin, Rui, Taherzadeh, Ghazaleh, Pierce, Brian G., Barradas-Bautista, Didier, Cao, Zhen, Cavallo, Luigi, Oliva, Romina, Sun, Yuanfei, Zhu, Shaowen, Shen, Yang, Park, Taeyong, Woo, Hyeonuk, Yang, Jinsol, Kwon, Sohee, Won, Jonghun, Seok, Chaok, Kiyota, Yasuomi, Kobayashi, Shinpei, Harada, Yoshiki, Takeda-Shitaka, Mayuko, Kundrotas, Petras J., Singh, Amar, Vakser, Ilya A., Dapkūnas, Justas, Olechnovič, Kliment, Venclovas, Česlovas, Duan, Rui, Qiu, Liming, Xu, Xianjin, Zhang, Shuang, Zou, Xiaoqin, Wodak, Shoshana J., Lensink, Marc F., Brysbaert, Guillaume, Mauri, Théo, Nadzirin, Nurul, Velankar, Sameer, Chaleil, Raphael A.G., Clarence, Tereza, Bates, Paul A., Kong, Ren, Liu, Bin, Yang, Guangbo, Liu, Ming, Shi, Hang, Lu, Xufeng, Chang, Shan, Roy, Raj S., Quadir, Farhan, Liu, Jian, Cheng, Jianlin, Antoniak, Anna, Czaplewski, Cezary, Giełdoń, Artur, Kogut, Mateusz, Lipska, Agnieszka G., Liwo, Adam, Lubecka, Emilia A., Maszota-Zieleniak, Martyna, Sieradzan, Adam K., Ślusarz, Rafał, Wesołowski, Patryk A., Zięba, Karolina, Del Carpio Muñoz, Carlos A., Ichiishi, Eiichiro, Harmalkar, Ameya, Gray, Jeffrey J., Bonvin, Alexandre M.J.J., Ambrosetti, Francesco, Vargas Honorato, Rodrigo, Jandova, Zuzana, Jiménez-García, Brian, Koukos, Panagiotis I., Van Keulen, Siri, Van Noort, Charlotte W., Réau, Manon, Roel-Touris, Jorge, Kotelnikov, Sergei, Padhorny, Dzmitry, Porter, Kathryn A., Alekseenko, Andrey, Ignatov, Mikhail, Desta, Israel, Ashizawa, Ryota, Sun, Zhuyezi, Ghani, Usman, Hashemi, Nasser, Vajda, Sandor, Kozakov, Dima, Rosell, Mireia, Rodríguez-Lumbreras, Luis A., Fernandez-Recio, Juan, Karczynska, Agnieszka, Grudinin, Sergei, Yan, Yumeng, Li, Hao, Lin, Peicong, Huang, Sheng You, Christoffer, Charles, Terashi, Genki, Verburgt, Jacob, Sarkar, Daipayan, Aderinwale, Tunde, Wang, Xiao, Kihara, Daisuke, Nakamura, Tsukasa, Hanazono, Yuya, Gowthaman, Ragul, Guest, Johnathan D., Yin, Rui, Taherzadeh, Ghazaleh, Pierce, Brian G., Barradas-Bautista, Didier, Cao, Zhen, Cavallo, Luigi, Oliva, Romina, Sun, Yuanfei, Zhu, Shaowen, Shen, Yang, Park, Taeyong, Woo, Hyeonuk, Yang, Jinsol, Kwon, Sohee, Won, Jonghun, Seok, Chaok, Kiyota, Yasuomi, Kobayashi, Shinpei, Harada, Yoshiki, Takeda-Shitaka, Mayuko, Kundrotas, Petras J., Singh, Amar, Vakser, Ilya A., Dapkūnas, Justas, Olechnovič, Kliment, Venclovas, Česlovas, Duan, Rui, Qiu, Liming, Xu, Xianjin, Zhang, Shuang, Zou, Xiaoqin, and Wodak, Shoshana J.

Abstract

We present the results for CAPRI Round 50, the fourth joint CASP-CAPRI protein assembly prediction challenge. The Round comprised a total of twelve targets, including six dimers, three trimers, and three higher-order oligomers. Four of these were easy targets, for which good structural templates were available either for the full assembly, or for the main interfaces (of the higher-order oligomers). Eight were difficult targets for which only distantly related templates were found for the individual subunits. Twenty-five CAPRI groups including eight automatic servers submitted ~1250 models per target. Twenty groups including six servers participated in the CAPRI scoring challenge submitted ~190 models per target. The accuracy of the predicted models was evaluated using the classical CAPRI criteria. The prediction performance was measured by a weighted scoring scheme that takes into account the number of models of acceptable quality or higher submitted by each group as part of their five top-ranking models. Compared to the previous CASP-CAPRI challenge, top performing groups submitted such models for a larger fraction (70–75%) of the targets in this Round, but fewer of these models were of high accuracy. Scorer groups achieved stronger performance with more groups submitting correct models for 70–80% of the targets or achieving high accuracy predictions. Servers performed less well in general, except for the MDOCKPP and LZERD servers, who performed on par with human groups. In addition to these results, major advances in methodology are discussed, providing an informative overview of where the prediction of protein assemblies currently stands.

Published
2021

22. On the study of biomolecular interactions at different resolutions: Does size matter?

Author

Roel Touris, Jorge Luis and Roel Touris, Jorge Luis

Abstract

Biomolecular interactions are critical in cellular environments. Proteins, which are the workhorses of the cellular machinery, mediate by their interactions a wide range of cellular processes. Structural Biology is the scientific discipline concerned with revealing the molecular functions of these macromolecules through analysis of their three-dimensional structures. Classical structural biology techniques include X-ray crystallography, Nuclear Magnetic Resonance (NMR) and cryo-Electron Microscopy (cryo-EM). These experimental techniques have limitations that preclude their application to all biological systems. For example, large proteins (>50 kDa) are difficult to study by NMR spectroscopy and X-ray crystallography requires high quality crystals, which is not always trivial to achieve. For some systems, such as membrane proteins, their characterization by purely experimental techniques in their native environment is still challenging. Computational Structural Biology is a consolidated branch of science, whose goal is to understand the role that structure and dynamics play in the definition of the function of biomolecular systems. In particular, biomolecular interactions have been a major focus of this field over the past decades. For this purpose, various computational approaches have been designed and applied to the modelling of interactions, among which molecular dynamics- Monte Carlo-, docking- and, more recently, template modeling-based methods are the most widely used ones. Roughly, docking methods aim to build three-dimensional models of macromolecular structures by first, generating thousands of possible conformations (models), and then discriminating between biologically- and non-biologically-relevant models. Docking can be performed in the absence of any experimental information (ab initio) or by integrating information into the calculations (data-driven). In this thesis, several developments into the modeling of protein-protein and protein-nucleic aci

Published
2021

23. Prediction of protein assemblies, the next frontier: The CASP14-CAPRI experiment

Author

Sub NMR Spectroscopy, Sub Overig UiLOTS, Sub Mathematics Education, NMR Spectroscopy, Lensink, Marc F., Brysbaert, Guillaume, Mauri, Théo, Nadzirin, Nurul, Velankar, Sameer, Chaleil, Raphael A.G., Clarence, Tereza, Bates, Paul A., Kong, Ren, Liu, Bin, Yang, Guangbo, Liu, Ming, Shi, Hang, Lu, Xufeng, Chang, Shan, Roy, Raj S., Quadir, Farhan, Liu, Jian, Cheng, Jianlin, Antoniak, Anna, Czaplewski, Cezary, Giełdoń, Artur, Kogut, Mateusz, Lipska, Agnieszka G., Liwo, Adam, Lubecka, Emilia A., Maszota-Zieleniak, Martyna, Sieradzan, Adam K., Ślusarz, Rafał, Wesołowski, Patryk A., Zięba, Karolina, Del Carpio Muñoz, Carlos A., Ichiishi, Eiichiro, Harmalkar, Ameya, Gray, Jeffrey J., Bonvin, Alexandre M.J.J., Ambrosetti, Francesco, Vargas Honorato, Rodrigo, Jandova, Zuzana, Jiménez-García, Brian, Koukos, Panagiotis I., Van Keulen, Siri, Van Noort, Charlotte W., Réau, Manon, Roel-Touris, Jorge, Kotelnikov, Sergei, Padhorny, Dzmitry, Porter, Kathryn A., Alekseenko, Andrey, Ignatov, Mikhail, Desta, Israel, Ashizawa, Ryota, Sun, Zhuyezi, Ghani, Usman, Hashemi, Nasser, Vajda, Sandor, Kozakov, Dima, Rosell, Mireia, Rodríguez-Lumbreras, Luis A., Fernandez-Recio, Juan, Karczynska, Agnieszka, Grudinin, Sergei, Yan, Yumeng, Li, Hao, Lin, Peicong, Huang, Sheng You, Christoffer, Charles, Terashi, Genki, Verburgt, Jacob, Sarkar, Daipayan, Aderinwale, Tunde, Wang, Xiao, Kihara, Daisuke, Nakamura, Tsukasa, Hanazono, Yuya, Gowthaman, Ragul, Guest, Johnathan D., Yin, Rui, Taherzadeh, Ghazaleh, Pierce, Brian G., Barradas-Bautista, Didier, Cao, Zhen, Cavallo, Luigi, Oliva, Romina, Sun, Yuanfei, Zhu, Shaowen, Shen, Yang, Park, Taeyong, Woo, Hyeonuk, Yang, Jinsol, Kwon, Sohee, Won, Jonghun, Seok, Chaok, Kiyota, Yasuomi, Kobayashi, Shinpei, Harada, Yoshiki, Takeda-Shitaka, Mayuko, Kundrotas, Petras J., Singh, Amar, Vakser, Ilya A., Dapkūnas, Justas, Olechnovič, Kliment, Venclovas, Česlovas, Duan, Rui, Qiu, Liming, Xu, Xianjin, Zhang, Shuang, Zou, Xiaoqin, Wodak, Shoshana J., Sub NMR Spectroscopy, Sub Overig UiLOTS, Sub Mathematics Education, NMR Spectroscopy, Lensink, Marc F., Brysbaert, Guillaume, Mauri, Théo, Nadzirin, Nurul, Velankar, Sameer, Chaleil, Raphael A.G., Clarence, Tereza, Bates, Paul A., Kong, Ren, Liu, Bin, Yang, Guangbo, Liu, Ming, Shi, Hang, Lu, Xufeng, Chang, Shan, Roy, Raj S., Quadir, Farhan, Liu, Jian, Cheng, Jianlin, Antoniak, Anna, Czaplewski, Cezary, Giełdoń, Artur, Kogut, Mateusz, Lipska, Agnieszka G., Liwo, Adam, Lubecka, Emilia A., Maszota-Zieleniak, Martyna, Sieradzan, Adam K., Ślusarz, Rafał, Wesołowski, Patryk A., Zięba, Karolina, Del Carpio Muñoz, Carlos A., Ichiishi, Eiichiro, Harmalkar, Ameya, Gray, Jeffrey J., Bonvin, Alexandre M.J.J., Ambrosetti, Francesco, Vargas Honorato, Rodrigo, Jandova, Zuzana, Jiménez-García, Brian, Koukos, Panagiotis I., Van Keulen, Siri, Van Noort, Charlotte W., Réau, Manon, Roel-Touris, Jorge, Kotelnikov, Sergei, Padhorny, Dzmitry, Porter, Kathryn A., Alekseenko, Andrey, Ignatov, Mikhail, Desta, Israel, Ashizawa, Ryota, Sun, Zhuyezi, Ghani, Usman, Hashemi, Nasser, Vajda, Sandor, Kozakov, Dima, Rosell, Mireia, Rodríguez-Lumbreras, Luis A., Fernandez-Recio, Juan, Karczynska, Agnieszka, Grudinin, Sergei, Yan, Yumeng, Li, Hao, Lin, Peicong, Huang, Sheng You, Christoffer, Charles, Terashi, Genki, Verburgt, Jacob, Sarkar, Daipayan, Aderinwale, Tunde, Wang, Xiao, Kihara, Daisuke, Nakamura, Tsukasa, Hanazono, Yuya, Gowthaman, Ragul, Guest, Johnathan D., Yin, Rui, Taherzadeh, Ghazaleh, Pierce, Brian G., Barradas-Bautista, Didier, Cao, Zhen, Cavallo, Luigi, Oliva, Romina, Sun, Yuanfei, Zhu, Shaowen, Shen, Yang, Park, Taeyong, Woo, Hyeonuk, Yang, Jinsol, Kwon, Sohee, Won, Jonghun, Seok, Chaok, Kiyota, Yasuomi, Kobayashi, Shinpei, Harada, Yoshiki, Takeda-Shitaka, Mayuko, Kundrotas, Petras J., Singh, Amar, Vakser, Ilya A., Dapkūnas, Justas, Olechnovič, Kliment, Venclovas, Česlovas, Duan, Rui, Qiu, Liming, Xu, Xianjin, Zhang, Shuang, Zou, Xiaoqin, and Wodak, Shoshana J.

Published
2021

25. Prediction of protein assemblies, the next frontier: The CASP14-CAPRI experiment

Author

Cancer Research UK, Department of Energy and Climate Change (UK), European Commission, Institut National de Recherche en Informatique et en Automatique (France), Medical Research Council (UK), Japan Society for the Promotion of Science, Ministerio de Ciencia, Innovación y Universidades (España), Agencia Estatal de Investigación (España), National Institute of General Medical Sciences (US), National Institutes of Health (US), National Natural Science Foundation of China, National Science Foundation (US), Lensink, Marc F., Brysbaert, Guillaume, Mauri, Théo, Nadzirin, Nurul, Velankar, Sameer, Chaleil, Raphaël A. G., Clarence, Tereza, Bates, Paul A., Kong, Ren, Liu, Bin, Yang, Guangbo, Liu, Ming, Shi, Hang, Lu, Xufeng, Chang, Xang, Roy, Raj S., Quadir, Farhan, Liu, Jian, Cheng, Jianlin, Antoniak, Anna, Czaplewski, Cezary, Giełdón, Artur, Kogut, Mateusz, Lipska, Agnieszka, Liwo, Adam, Lubecka, Emilia, Maszota-Zieleniak, Martyna, Sieradzan, Adam K., Ślusarz, Rafał, Wesołowski, Patryk A., Zięba, Karolina, Carpio Muñoz, Carlos A. del, Ichiishi, Eiichiro, Harmalkar, Ameya, Gray, Jeffrey J., Bonvin, Alexandre M. J. J., Ambrosetti, Francesco, Vargas Honorato, Rodrigo, Jandova, Zuzana, Jiménez-García, Brian, Koukos, Panagiotis I., Keulen, Siri van, Noort, Charlotte W. van, Réau, Manon, Roel-Touris, Jorge, Kotelnikov, Sergey, Padhorny, Dzmitry, Porter, Kathryn, Alekseenko, Andrey, Ignatov, Mikhail, Desta, Israel, Ashizawa, Ryota, Sun, Zhuyezi, Ghani, Usman, Hashemi, Nasser, Vajda, Sandor, Kozakov, Dima, Rosell, Mireia, Rodríguez-Lumbreras, Luis A., Fernández-Recio, Juan, Karczynska, Agnieszka, Grudinin, Sergei, Yan, Yumeng, Li, Hao, Lin, Peicong, Huang, Sheng-You, Christoffer, Charles, Terashi, Genki, Verburgt, Jacob, Sarkar, Daipayan, Aderinwale, Tunde, Wang, Xiao, Kihara, Daisuke, Nakamura, Tsukasa, Hanazono, Huya, Gowthaman, Ragul, Guest, Johnathan D., Yin, Rui, Taherzadeh, Ghazaleh, Pierce, Brian G., Barradas-Bautista, Didier, Cao, Zhen, Cavallo, Luigi, Oliva, Romina, Sun, Yuanfei, Zhu, Shaowen, Shen, Yang, Park, Taeyong, Woo, Hyeonuk, Yang, Jinsol, Kwon, Sohee, Won, Jonghun, Seok, Chaok, Kiyota, Yasuomi, Kobayashi, Shinpei, Harada, Yoshiki, Takeda-Shitaka, Mayuko, Kundrotas, Petras J., Singh, Amar, Vakser, Ilya A., Dapkunas, Justas, Olechnovic, Kliment, Venclovas, Česlovas, Duan, Rui, Qiu, Liming, Xu, Xianjin, Zhang, Shuang, Zou, Xiaoqin, Wodak, Shoshana J., Cancer Research UK, Department of Energy and Climate Change (UK), European Commission, Institut National de Recherche en Informatique et en Automatique (France), Medical Research Council (UK), Japan Society for the Promotion of Science, Ministerio de Ciencia, Innovación y Universidades (España), Agencia Estatal de Investigación (España), National Institute of General Medical Sciences (US), National Institutes of Health (US), National Natural Science Foundation of China, National Science Foundation (US), Lensink, Marc F., Brysbaert, Guillaume, Mauri, Théo, Nadzirin, Nurul, Velankar, Sameer, Chaleil, Raphaël A. G., Clarence, Tereza, Bates, Paul A., Kong, Ren, Liu, Bin, Yang, Guangbo, Liu, Ming, Shi, Hang, Lu, Xufeng, Chang, Xang, Roy, Raj S., Quadir, Farhan, Liu, Jian, Cheng, Jianlin, Antoniak, Anna, Czaplewski, Cezary, Giełdón, Artur, Kogut, Mateusz, Lipska, Agnieszka, Liwo, Adam, Lubecka, Emilia, Maszota-Zieleniak, Martyna, Sieradzan, Adam K., Ślusarz, Rafał, Wesołowski, Patryk A., Zięba, Karolina, Carpio Muñoz, Carlos A. del, Ichiishi, Eiichiro, Harmalkar, Ameya, Gray, Jeffrey J., Bonvin, Alexandre M. J. J., Ambrosetti, Francesco, Vargas Honorato, Rodrigo, Jandova, Zuzana, Jiménez-García, Brian, Koukos, Panagiotis I., Keulen, Siri van, Noort, Charlotte W. van, Réau, Manon, Roel-Touris, Jorge, Kotelnikov, Sergey, Padhorny, Dzmitry, Porter, Kathryn, Alekseenko, Andrey, Ignatov, Mikhail, Desta, Israel, Ashizawa, Ryota, Sun, Zhuyezi, Ghani, Usman, Hashemi, Nasser, Vajda, Sandor, Kozakov, Dima, Rosell, Mireia, Rodríguez-Lumbreras, Luis A., Fernández-Recio, Juan, Karczynska, Agnieszka, Grudinin, Sergei, Yan, Yumeng, Li, Hao, Lin, Peicong, Huang, Sheng-You, Christoffer, Charles, Terashi, Genki, Verburgt, Jacob, Sarkar, Daipayan, Aderinwale, Tunde, Wang, Xiao, Kihara, Daisuke, Nakamura, Tsukasa, Hanazono, Huya, Gowthaman, Ragul, Guest, Johnathan D., Yin, Rui, Taherzadeh, Ghazaleh, Pierce, Brian G., Barradas-Bautista, Didier, Cao, Zhen, Cavallo, Luigi, Oliva, Romina, Sun, Yuanfei, Zhu, Shaowen, Shen, Yang, Park, Taeyong, Woo, Hyeonuk, Yang, Jinsol, Kwon, Sohee, Won, Jonghun, Seok, Chaok, Kiyota, Yasuomi, Kobayashi, Shinpei, Harada, Yoshiki, Takeda-Shitaka, Mayuko, Kundrotas, Petras J., Singh, Amar, Vakser, Ilya A., Dapkunas, Justas, Olechnovic, Kliment, Venclovas, Česlovas, Duan, Rui, Qiu, Liming, Xu, Xianjin, Zhang, Shuang, Zou, Xiaoqin, and Wodak, Shoshana J.

Abstract

We present the results for CAPRI Round 50, the fourth joint CASP-CAPRI protein assembly prediction challenge. The Round comprised a total of twelve targets, including six dimers, three trimers, and three higher-order oligomers. Four of these were easy targets, for which good structural templates were available either for the full assembly, or for the main interfaces (of the higher-order oligomers). Eight were difficult targets for which only distantly related templates were found for the individual subunits. Twenty-five CAPRI groups including eight automatic servers submitted ~1250 models per target. Twenty groups including six servers participated in the CAPRI scoring challenge submitted ~190 models per target. The accuracy of the predicted models was evaluated using the classical CAPRI criteria. The prediction performance was measured by a weighted scoring scheme that takes into account the number of models of acceptable quality or higher submitted by each group as part of their five top-ranking models. Compared to the previous CASP-CAPRI challenge, top performing groups submitted such models for a larger fraction (70–75%) of the targets in this Round, but fewer of these models were of high accuracy. Scorer groups achieved stronger performance with more groups submitting correct models for 70–80% of the targets or achieving high accuracy predictions. Servers performed less well in general, except for the MDOCKPP and LZERD servers, who performed on par with human groups. In addition to these results, major advances in methodology are discussed, providing an informative overview of where the prediction of protein assemblies currently stands.

Published
2021

26. The gutSMASH web server : Automated identification of primary metabolic gene clusters from the gut microbiota

Author

Pascal Andreu, Victòria, Roel-Touris, Jorge, Dodd, Dylan, Fischbach, Michael A., Medema, Marnix H., Pascal Andreu, Victòria, Roel-Touris, Jorge, Dodd, Dylan, Fischbach, Michael A., and Medema, Marnix H.

Abstract

Anaerobic bacteria from the human microbiome produce a wide array of molecules at high concentrations that can directly or indirectly affect the host. The production of these molecules, mostly derived from their primary metabolism, is frequently encoded in metabolic gene clusters (MGCs). However, despite the importance of microbiome-derived primary metabolites, no tool existed to predict the gene clusters responsible for their production. For this reason, we recently introduced gutSMASH. gutSMASH can predict 41 different known pathways, including MGCs involved in bioenergetics, but also putative ones that are candidates for novel pathway discovery. To make the tool more user-friendly and accessible, we here present the gutSMASH web server, hosted at https://gutsmash.bioinformatics.nl/. The user can either input the GenBank assembly accession or upload a genome file in FASTA or GenBank format. Optionally, the user can enable additional analyses to obtain further insights into the predicted MGCs. An interactive HTML output (viewable online or downloadable for offline use) provides a user-friendly way to browse functional gene annotations and sequence comparisons with reference gene clusters as well as gene clusters predicted in other genomes. Thus, this web server provides the community with a streamlined and user-friendly interface to analyze the metabolic potential of gut microbiomes.

Published
2021

29. An overview of data-driven HADDOCK strategies in CAPRI rounds 38-45

Author

Koukos, Panagiotis I., Roel-Touris, Jorge, Ambrosetti, Francesco, Geng, Cunliang, Schaarschmidt, Jorg, Trellet, Mikael E., Melquiond, Adrien S. J., Xue, Li C., Honorato, Rodrigo V., Moreira, Irina, Kurkcuoglu, Zeynep, Vangone, Anna, Bonvin, Alexandre M. J. J., NMR Spectroscopy, and Sub NMR Spectroscopy

Subjects
complexes, biomolecular interactions, scoring, integrative modeling, prediction
Abstract

Our information‐driven docking approach HADDOCK has demonstrated a sustained performance since the start of its participation to CAPRI. This is due, in part, to its ability to integrate data into the modeling process, and to the robustness of its scoring function. We participated in CAPRI both as server and manual predictors. In CAPRI rounds 38‐45, we have used various strategies depending on the available information. These ranged from imposing restraints to a few residues identified from literature as being important for the interaction, to binding pockets identified from homologous complexes or template‐based refinement/CA‐CA restraint‐guided docking from identified templates. When relevant, symmetry restraints were used to limit the conformational sampling. We also tested for a large decamer target a new implementation of the MARTINI coarse‐grained force field in HADDOCK. Overall, we obtained acceptable or better predictions for 13 and 11 server and manual submissions, respectively, out of the 22 interfaces. Our server performance (acceptable or higher‐quality models when considering the top 10) was better (59%) than the manual (50%) one, in which we typically experiment with various combinations of protocols and data sources. Again, our simple scoring function based on a linear combination of intermolecular van der Waals and electrostatic energies and an empirical desolvation term demonstrated a good performance in the scoring experiment with a 63% success rate across all 22 interfaces. An analysis of model quality indicates that, while we are consistently performing well in generating acceptable models, there is room for improvement for generating/identifying higher quality models

Published
2020

30. MARTINI-Based Protein-DNA Coarse-Grained HADDOCKing

Author

Honorato, Rodrigo V., Roel-Touris, Jorge, Bonvin, Alexandre M. J. J., Sub NMR Spectroscopy, NMR Spectroscopy, Sub NMR Spectroscopy, and NMR Spectroscopy

Subjects
0301 basic medicine, Computer science, biomolecular complexes, Network topology, Biochemistry, Genetics and Molecular Biology (miscellaneous), Biochemistry, Force field (chemistry), 03 medical and health sciences, 0302 clinical medicine, Molecular Biosciences, Technology and Code, lcsh:QH301-705.5, Molecular Biology, force field, Energy landscape, coarse-graining, Rigid body, Maxima and minima, nucleic acids, 030104 developmental biology, lcsh:Biology (General), Proof of concept, Docking (molecular), 030220 oncology & carcinogenesis, docking, Granularity, Algorithm
Abstract

Modeling biomolecular assemblies is an important field in computational structural biology. The inherent complexity of their energy landscape and the computational cost associated with modeling large and complex assemblies are major drawbacks for integrative modeling approaches. The so-called coarse-graining approaches, which reduce the degrees of freedom of the system by grouping several atoms into larger “pseudo-atoms,” have been shown to alleviate some of those limitations, facilitating the identification of the global energy minima assumed to correspond to the native state of the complex, while making the calculations more efficient. Here, we describe and assess the implementation of the MARTINI force field for DNA into HADDOCK, our integrative modeling platform. We combine it with our previous implementation for protein-protein coarse-grained docking, enabling coarse-grained modeling of protein-nucleic acid complexes. The system is modeled using MARTINI topologies and interaction parameters during the rigid body docking and semi-flexible refinement stages of HADDOCK, and the resulting models are then converted back to atomistic resolution by an atom-to-bead distance restraints-guided protocol. We first demonstrate the performance of this protocol using 44 complexes from the protein-DNA docking benchmark, which shows an overall ~6-fold speed increase and maintains similar accuracy as compared to standard atomistic calculations. As a proof of concept, we then model the interaction between the PRC1 and the nucleosome (a former CAPRI target in round 31), using the same information available at the time the target was offered, and compare all-atom and coarse-grained models.

Published
2019

31. Less is more: Coarse-grained integrative modeling of large biomolecular assemblies with HADDOCK

Author

Roel-Touris, Jorge, Don, Charleen G., V. Honorato, Rodrigo, Rodrigues, João P.G.L.M., Bonvin, Alexandre M.J.J., Sub NMR Spectroscopy, NMR Spectroscopy, Sub NMR Spectroscopy, and NMR Spectroscopy

Subjects
Global energy, Energy landscape, Computer science, Sampling (statistics), 010402 general chemistry, 01 natural sciences, Article, Force field (chemistry), Atomic physics, Protein–protein interaction, 03 medical and health sciences, Bacterial Proteins, Docking (dog), 0103 physical sciences, Genetics, Computational structural biology, Macromolecular docking, Physical and Theoretical Chemistry, Protein Structure, Quaternary, 030304 developmental biology, Morphing, 0303 health sciences, 010304 chemical physics, biology, Circadian Rhythm Signaling Peptides and Proteins, Cryoelectron Microscopy, Computational science, Mutagenesis, Complex energy, Proteins, Haddock, biology.organism_classification, 0104 chemical sciences, Computer Science Applications, Molecular Docking Simulation, Force field (physics), Chemistry, Docking (molecular), Biological system, Thermodynamics, Smoothing
Abstract

Predicting the 3D structure of protein interactions remains a challenge in the field of computational structural biology. This is in part due to difficulties in sampling the complex energy landscape of multiple interacting flexible polypeptide chains. Coarse-graining approaches, which reduce the number of degrees of freedom of the system, help address this limitation by smoothing the energy landscape, allowing an easier identification of the global energy minimum. They also accelerate the calculations, allowing to model larger assemblies. Here, we present the implementation of the MARTINI coarse-grained force field for proteins into HADDOCK, our integrative modelling platform. Docking and refinement are performed at the coarse-grained level and the resulting models are then converted back to atomistic resolution through a distance restraints-guided morphing procedure. Our protocol, tested on the largest complexes of the protein docking benchmark 5, shows an overall ~7-fold speed increase compared to standard all-atom calculations, while maintaining a similar accuracy and yielding substantially more near-native solutions. To showcase the potential of our method, we performed simultaneous 7 body docking to model the 1:6 KaiC-KaiB complex, integrating mutagenesis and hydrogen/deuterium exchange data from mass spectrometry with symmetry restraints, and validated the resulting models against a recently published cryo-EM structure.

Published
2019
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32. LightDock goes information-driven

Author

Roel-Touris, Jorge, Bonvin, Alexandre M.J.J., Jiménez-García, Brian, and Ponty, Yann

Subjects
Statistics and Probability, Biochemistry, Computer science, Computer Science Applications, Data science, Computational Mathematics, Complete information, Docking (dog), Computational Theory and Mathematics, Ab initio, Genetics, Macromolecular docking, Analysis data, Biology, Data mining, Molecular Biology, Software
Abstract

Motivation The use of experimental information has been demonstrated to increase the success rate of computational macromolecular docking. Many methods use information to post-filter the simulation output while others drive the simulation based on experimental restraints, which can become problematic for more complex scenarios such as multiple binding interfaces. Results We present a novel method for including interface information into protein docking simulations within the LightDock framework. Prior to the simulation, irrelevant regions from the receptor are excluded for sampling (filter of initial swarms) and initial ligand poses are pre-oriented based on ligand input information. We demonstrate the applicability of this approach on the new 55 cases of the Protein–Protein Docking Benchmark 5, using different amounts of information. Even with incomplete or incorrect information, a significant improvement in performance is obtained compared to blind ab initio docking. Availability and implementation The software is supported and freely available from https://github.com/brianjimenez/lightdock and analysis data from https://github.com/brianjimenez/lightdock_bm5. Supplementary information Supplementary data are available at Bioinformatics online.

Published
2019

33. LightDock goes information-driven

Author

Roel-Touris, Jorge, Bonvin, Alexandre M J J, Jiménez-García, Brian, Roel-Touris, Jorge, Bonvin, Alexandre M J J, and Jiménez-García, Brian

Abstract

The use of experimental information has been demonstrated to increase the success rate of computational macromolecular docking. Many methods use information to post-filter the simulation output while others drive the simulation based on experimental restraints, which can become problematic for more complex scenarios such as multiple binding interfaces. We present a novel two-step method for including interface information into protein docking simulations within the LightDock framework. Prior to the simulation, irrelevant regions from the receptor are excluded for sampling (filter of initial swarms) and initial ligand poses are pre-oriented based on ligand input information. We demonstrate the applicability of this approach on the new 55 cases of the Protein-Protein Docking Benchmark 5, using different amounts of information. Even with incomplete information, a significant improvement in performance is obtained compared to blind ab initio docking. The software is supported and freely available from and analysis data from .

Published
2020

34. Integrative modeling of membrane-associated protein assemblies

Author

Sub NMR Spectroscopy, NMR Spectroscopy, Roel-Touris, Jorge, Jiménez-García, Brian, Bonvin, Alexandre M.J.J., Sub NMR Spectroscopy, NMR Spectroscopy, Roel-Touris, Jorge, Jiménez-García, Brian, and Bonvin, Alexandre M.J.J.

Published
2020

36. LightDock goes information-driven

Author

Sub NMR Spectroscopy, NMR Spectroscopy, Roel-Touris, Jorge, Bonvin, Alexandre M J J, Jiménez-García, Brian, Sub NMR Spectroscopy, NMR Spectroscopy, Roel-Touris, Jorge, Bonvin, Alexandre M J J, and Jiménez-García, Brian

Published
2020

37. Modeling Antibody-Antigen Complexes by Information-Driven Docking

Author

NMR Spectroscopy, Sub NMR Spectroscopy, Ambrosetti, Francesco, Jiménez-García, Brian, Roel-Touris, Jorge, Bonvin, Alexandre M J J, NMR Spectroscopy, Sub NMR Spectroscopy, Ambrosetti, Francesco, Jiménez-García, Brian, Roel-Touris, Jorge, and Bonvin, Alexandre M J J

Published
2020

38. An overview of data-driven HADDOCK strategies in CAPRI rounds 38-45

Author

NMR Spectroscopy, Sub NMR Spectroscopy, Koukos, Panagiotis I., Roel-Touris, Jorge, Ambrosetti, Francesco, Geng, Cunliang, Schaarschmidt, Jorg, Trellet, Mikael E., Melquiond, Adrien S. J., Xue, Li C., Honorato, Rodrigo V., Moreira, Irina, Kurkcuoglu, Zeynep, Vangone, Anna, Bonvin, Alexandre M. J. J., NMR Spectroscopy, Sub NMR Spectroscopy, Koukos, Panagiotis I., Roel-Touris, Jorge, Ambrosetti, Francesco, Geng, Cunliang, Schaarschmidt, Jorg, Trellet, Mikael E., Melquiond, Adrien S. J., Xue, Li C., Honorato, Rodrigo V., Moreira, Irina, Kurkcuoglu, Zeynep, Vangone, Anna, and Bonvin, Alexandre M. J. J.

Published
2020

39. Finding the ΔΔ G spot: Are predictors of binding affinity changes upon mutations in protein-protein interactions ready for it?

Author

Geng, Cunliang, Xue, Li C., Roel-Touris, Jorge, Bonvin, Alexandre M.J.J., Sub NMR Spectroscopy, NMR Spectroscopy, Sub NMR Spectroscopy, and NMR Spectroscopy

Subjects
0303 health sciences, 010304 chemical physics, Chemistry, protein–protein interactions, mutations, 01 natural sciences, Biochemistry, Protein–protein interaction, Computer Science Applications, 03 medical and health sciences, Computational Mathematics, machine learning, 0103 physical sciences, binding affinity, scoring function, Materials Chemistry, Physical and Theoretical Chemistry, ΔΔG prediction, 030304 developmental biology
Abstract

Predicting the structure and thermodynamics of protein–protein interactions (PPIs) are key to a proper understanding and modulation of their function. Since experimental methods might not be able to catch up with the fast growth of genomic data, computational alternatives are therefore required. We present here a review dealing with various aspects of predicting binding affinity changes upon mutations (ΔΔG). We focus on predictors that consider three-dimensional structure information to estimate the impact of mutations on the binding affinity of a protein–protein complex, excluding the rigorous free energy perturbation methods. Training and evaluation, ΔΔG databases, data selection, and existing ΔΔG predictors are specially emphasized. We also establish the parallel with scoring functions used in docking since those share many similar PPI features with ΔΔG predictors. The field has seen a common evolution of ΔΔG predictors and scoring functions over time, transforming from purely energetic functions to statistical energy-based and further to machine learning-based functions. As machine learning has come to age, limitations in terms of quantity, quality and variety of the available data become the bottlenecks for the future development of these computational methods. This can be alleviated by building infrastructures for data generation, collection and sharing. Further developments can be catalyzed by conducting community-wide blind challenges for method assessment. This article is categorized under: Structure and Mechanism > Molecular Structures Structure and Mechanism > Computational Biochemistry and Biophysics Molecular and Statistical Mechanics > Molecular Interactions.

Published
2019
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40. An overview of data‐driven HADDOCK strategies in CAPRI rounds 38‐45

Author

Koukos, Panagiotis I., primary, Roel‐Touris, Jorge, additional, Ambrosetti, Francesco, additional, Geng, Cunliang, additional, Schaarschmidt, Jörg, additional, Trellet, Mikael E., additional, Melquiond, Adrien S. J., additional, Xue, Li C., additional, Honorato, Rodrigo V., additional, Moreira, Irina, additional, Kurkcuoglu, Zeynep, additional, Vangone, Anna, additional, and Bonvin, Alexandre M. J. J., additional

Published
2020
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41. Blind prediction of homo‐ and hetero‐protein complexes: The CASP13‐CAPRI experiment

Author

Lensink, Marc F., primary, Brysbaert, Guillaume, additional, Nadzirin, Nurul, additional, Velankar, Sameer, additional, Chaleil, Raphaël A. G., additional, Gerguri, Tereza, additional, Bates, Paul A., additional, Laine, Elodie, additional, Carbone, Alessandra, additional, Grudinin, Sergei, additional, Kong, Ren, additional, Liu, Ran‐Ran, additional, Xu, Xi‐Ming, additional, Shi, Hang, additional, Chang, Shan, additional, Eisenstein, Miriam, additional, Karczynska, Agnieszka, additional, Czaplewski, Cezary, additional, Lubecka, Emilia, additional, Lipska, Agnieszka, additional, Krupa, Paweł, additional, Mozolewska, Magdalena, additional, Golon, Łukasz, additional, Samsonov, Sergey, additional, Liwo, Adam, additional, Crivelli, Silvia, additional, Pagès, Guillaume, additional, Karasikov, Mikhail, additional, Kadukova, Maria, additional, Yan, Yumeng, additional, Huang, Sheng‐You, additional, Rosell, Mireia, additional, Rodríguez‐Lumbreras, Luis A., additional, Romero‐Durana, Miguel, additional, Díaz‐Bueno, Lucía, additional, Fernandez‐Recio, Juan, additional, Christoffer, Charles, additional, Terashi, Genki, additional, Shin, Woong‐Hee, additional, Aderinwale, Tunde, additional, Maddhuri Venkata Subraman, Sai Raghavendra, additional, Kihara, Daisuke, additional, Kozakov, Dima, additional, Vajda, Sandor, additional, Porter, Kathryn, additional, Padhorny, Dzmitry, additional, Desta, Israel, additional, Beglov, Dmitri, additional, Ignatov, Mikhail, additional, Kotelnikov, Sergey, additional, Moal, Iain H., additional, Ritchie, David W., additional, Chauvot de Beauchêne, Isaure, additional, Maigret, Bernard, additional, Devignes, Marie‐Dominique, additional, Ruiz Echartea, Maria E., additional, Barradas‐Bautista, Didier, additional, Cao, Zhen, additional, Cavallo, Luigi, additional, Oliva, Romina, additional, Cao, Yue, additional, Shen, Yang, additional, Baek, Minkyung, additional, Park, Taeyong, additional, Woo, Hyeonuk, additional, Seok, Chaok, additional, Braitbard, Merav, additional, Bitton, Lirane, additional, Scheidman‐Duhovny, Dina, additional, Dapkūnas, Justas, additional, Olechnovič, Kliment, additional, Venclovas, Česlovas, additional, Kundrotas, Petras J., additional, Belkin, Saveliy, additional, Chakravarty, Devlina, additional, Badal, Varsha D., additional, Vakser, Ilya A., additional, Vreven, Thom, additional, Vangaveti, Sweta, additional, Borrman, Tyler, additional, Weng, Zhiping, additional, Guest, Johnathan D., additional, Gowthaman, Ragul, additional, Pierce, Brian G., additional, Xu, Xianjin, additional, Duan, Rui, additional, Qiu, Liming, additional, Hou, Jie, additional, Ryan Merideth, Benjamin, additional, Ma, Zhiwei, additional, Cheng, Jianlin, additional, Zou, Xiaoqin, additional, Koukos, Panagiotis I., additional, Roel‐Touris, Jorge, additional, Ambrosetti, Francesco, additional, Geng, Cunliang, additional, Schaarschmidt, Jörg, additional, Trellet, Mikael E., additional, Melquiond, Adrien S. J., additional, Xue, Li, additional, Jiménez‐García, Brian, additional, van Noort, Charlotte W., additional, Honorato, Rodrigo V., additional, Bonvin, Alexandre M. J. J., additional, and Wodak, Shoshana J., additional

Published
2019
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44. MARTINI-Based Protein-DNA Coarse-Grained HADDOCKing

Author

Sub NMR Spectroscopy, NMR Spectroscopy, Honorato, Rodrigo V., Roel-Touris, Jorge, Bonvin, Alexandre M. J. J., Sub NMR Spectroscopy, NMR Spectroscopy, Honorato, Rodrigo V., Roel-Touris, Jorge, and Bonvin, Alexandre M. J. J.

Published
2019

46. Blind prediction of homo- and hetero-protein complexes: The CASP13-CAPRI experiment

Author

Agence Nationale de la Recherche (France), Cancer Research UK, European Commission, Medical Research Council (UK), National Institutes of Health (US), National Natural Science Foundation of China, National Research Foundation of Korea, National Science Foundation (US), Ministerio de Economía y Competitividad (España), Università degli Studi di Napoli PARTHENOPE, Wellcome Trust, Lensink, Marc F., Brysbaert, Guillaume, Nadzirin, Nurul, Velankar, Sameer, Chaleil, Raphaël A. G., Gerguri, Tereza, Bates, Paul A., Laine, Elodie, Carbone, Alessandra, Grudinin, Sergei, Kong, Ren, Weng, Zhiping, Guest, Johnathan D., Gowthaman, Ragul, Pierce, Brian G., Xu, Xianjin, Duan, Rui, Qiu, Liming, Hou, Jie, Merideth, Benjamin Ryan, Ma, Zhiwei, Cheng, Jianlin, Zou, Xiaoqin, Koukos, Panagiotis I., Roel-Touris, Jorge, Ambrosetti, Francesco, Geng, Cunliang, Schaarschmidt, Jörg, Trellet, Mikael E., Melquiond, Adrien S. J., Xue, Li, Jiménez-García, Brian, Noort, Charlotte W. van, Honorato, Rodrigo V., Bonvin, A. M. J. J., Wodak, Shoshana J., Liu, Ran-Ran, Xu, Xi-Ming, Shi, Hang, Chang, Shan, Eisenstein, Miriam, Karczynska, Agnieszka, Czaplewski, Cezary, Emilia Lubecka, Emilia, Lipska, Agnieszka, Krupa, Paweł, Mozolewska, Magdalena, Golon, Łukasz, Samsonov, Sergey, Liwo, Adam, Crivelli, Silvia, Pagès, Guillaume, Karasikov, Mikhaill, Kadukova, Maria, Yan, Yumeng, Huang, Sheng-You, Rosell, Mireia, Rodríguez-Lumbreras, Luis A., Romero-Durana, Miguel, Díaz-Bueno, Lucía, Fernández-Recio, Juan, Christoffer, Charles, Terashi, Genki, Shin, Woong-Hee, Aderinwale, Tunde, Venkata Subraman, Sai Raghavendra Maddhuri, Kihara, Daisuke, Kozakov, Dima, Vajda, Sandor, Porter, Kathryn, Padhorny, Dzmitry, Desta, Israel, Beglov, Dmitri, Ignatov, Mikhail, Kotelnikov, Sergey, Moal, Iain H., Ritchie, David W., Chauvot de Beauchêne, Isaure, Maigret, Bernard, Devignes, Marie-Dominique, Ruiz Echartea, Maria E., Barradas-Bautista, Didier, Cao, Zhen, Cavallo, Luigi, Oliva, Romina, Cao, Yue, Shen, Yang, Baek, Minkyung, Park, Taeyong, Woo, Hyeonuk, Seok, Chaok, Braitbard, Merav, Bitton, Lirane, Scheidman-Duhovny, Dina, Dapkunas, Justas, Olechnovic, Kliment, Venclovas, Česlovas, Kundrotas, Petras J., Belkin, Saveliy, Chakravarty, Devlina, Badal, Varsha D., Vakser, Ilya A., Vreven, Thom, Vangaveti, Sweta, Borrman, Tyler, Agence Nationale de la Recherche (France), Cancer Research UK, European Commission, Medical Research Council (UK), National Institutes of Health (US), National Natural Science Foundation of China, National Research Foundation of Korea, National Science Foundation (US), Ministerio de Economía y Competitividad (España), Università degli Studi di Napoli PARTHENOPE, Wellcome Trust, Lensink, Marc F., Brysbaert, Guillaume, Nadzirin, Nurul, Velankar, Sameer, Chaleil, Raphaël A. G., Gerguri, Tereza, Bates, Paul A., Laine, Elodie, Carbone, Alessandra, Grudinin, Sergei, Kong, Ren, Weng, Zhiping, Guest, Johnathan D., Gowthaman, Ragul, Pierce, Brian G., Xu, Xianjin, Duan, Rui, Qiu, Liming, Hou, Jie, Merideth, Benjamin Ryan, Ma, Zhiwei, Cheng, Jianlin, Zou, Xiaoqin, Koukos, Panagiotis I., Roel-Touris, Jorge, Ambrosetti, Francesco, Geng, Cunliang, Schaarschmidt, Jörg, Trellet, Mikael E., Melquiond, Adrien S. J., Xue, Li, Jiménez-García, Brian, Noort, Charlotte W. van, Honorato, Rodrigo V., Bonvin, A. M. J. J., Wodak, Shoshana J., Liu, Ran-Ran, Xu, Xi-Ming, Shi, Hang, Chang, Shan, Eisenstein, Miriam, Karczynska, Agnieszka, Czaplewski, Cezary, Emilia Lubecka, Emilia, Lipska, Agnieszka, Krupa, Paweł, Mozolewska, Magdalena, Golon, Łukasz, Samsonov, Sergey, Liwo, Adam, Crivelli, Silvia, Pagès, Guillaume, Karasikov, Mikhaill, Kadukova, Maria, Yan, Yumeng, Huang, Sheng-You, Rosell, Mireia, Rodríguez-Lumbreras, Luis A., Romero-Durana, Miguel, Díaz-Bueno, Lucía, Fernández-Recio, Juan, Christoffer, Charles, Terashi, Genki, Shin, Woong-Hee, Aderinwale, Tunde, Venkata Subraman, Sai Raghavendra Maddhuri, Kihara, Daisuke, Kozakov, Dima, Vajda, Sandor, Porter, Kathryn, Padhorny, Dzmitry, Desta, Israel, Beglov, Dmitri, Ignatov, Mikhail, Kotelnikov, Sergey, Moal, Iain H., Ritchie, David W., Chauvot de Beauchêne, Isaure, Maigret, Bernard, Devignes, Marie-Dominique, Ruiz Echartea, Maria E., Barradas-Bautista, Didier, Cao, Zhen, Cavallo, Luigi, Oliva, Romina, Cao, Yue, Shen, Yang, Baek, Minkyung, Park, Taeyong, Woo, Hyeonuk, Seok, Chaok, Braitbard, Merav, Bitton, Lirane, Scheidman-Duhovny, Dina, Dapkunas, Justas, Olechnovic, Kliment, Venclovas, Česlovas, Kundrotas, Petras J., Belkin, Saveliy, Chakravarty, Devlina, Badal, Varsha D., Vakser, Ilya A., Vreven, Thom, Vangaveti, Sweta, and Borrman, Tyler

Abstract

We present the results for CAPRI Round 46, the third joint CASP‐CAPRI protein assembly prediction challenge. The Round comprised a total of 20 targets including 14 homo‐oligomers and 6 heterocomplexes. Eight of the homo‐oligomer targets and one heterodimer comprised proteins that could be readily modeled using templates from the Protein Data Bank, often available for the full assembly. The remaining 11 targets comprised 5 homodimers, 3 heterodimers, and two higher‐order assemblies. These were more difficult to model, as their prediction mainly involved “ab‐initio” docking of subunit models derived from distantly related templates. A total of ~30 CAPRI groups, including 9 automatic servers, submitted on average ~2000 models per target. About 17 groups participated in the CAPRI scoring rounds, offered for most targets, submitting ~170 models per target. The prediction performance, measured by the fraction of models of acceptable quality or higher submitted across all predictors groups, was very good to excellent for the nine easy targets. Poorer performance was achieved by predictors for the 11 difficult targets, with medium and high quality models submitted for only 3 of these targets. A similar performance “gap” was displayed by scorer groups, highlighting yet again the unmet challenge of modeling the conformational changes of the protein components that occur upon binding or that must be accounted for in template‐based modeling. Our analysis also indicates that residues in binding interfaces were less well predicted in this set of targets than in previous Rounds, providing useful insights for directions of future improvements.

Published
2019

48. Performance of HADDOCK and a simple contact-based protein–ligand binding affinity predictor in the D3R Grand Challenge 2

Author

Kurkcuoglu Soner, Zeynep, Koukos, Panos, Citro, Nevia, Trellet, Mikael E., Garcia Lopes Maia Rodrigues, Joao, de Sousa Moreira, Irina, Roel-touris, Jorge, Melquiond, Adrien S. J., Geng, Cunliang, Schaarschmidt, Jörg, Xue, Li C., Vangone, Anna, Bonvin, A. M. J. J., Kurkcuoglu Soner, Zeynep, Koukos, Panos, Citro, Nevia, Trellet, Mikael E., Garcia Lopes Maia Rodrigues, Joao, de Sousa Moreira, Irina, Roel-touris, Jorge, Melquiond, Adrien S. J., Geng, Cunliang, Schaarschmidt, Jörg, Xue, Li C., Vangone, Anna, and Bonvin, A. M. J. J.

Abstract

We present the performance of HADDOCK, our information-driven docking software, in the second edition of the D3R Grand Challenge. In this blind experiment, participants were requested to predict the structures and binding affinities of complexes between the Farnesoid X nuclear receptor and 102 different ligands. The models obtained in Stage1 with HADDOCK and ligand-specific protocol show an average ligand RMSD of 5.1 Å from the crystal structure. Only 6/35 targets were within 2.5 Å RMSD from the reference, which prompted us to investigate the limiting factors and revise our protocol for Stage2. The choice of the receptor conformation appeared to have the strongest influence on the results. Our Stage2 models were of higher quality (13 out of 35 were within 2.5 Å), with an average RMSD of 4.1 Å. The docking protocol was applied to all 102 ligands to generate poses for binding affinity prediction. We developed a modified version of our contact-based binding affinity predictor PRODIGY, using the number of interatomic contacts classified by their type and the intermolecular electrostatic energy. This simple structure-based binding affinity predictor shows a Kendall’s Tau correlation of 0.37 in ranking the ligands (7th best out of 77 methods, 5th/25 groups). Those results were obtained from the average prediction over the top10 poses, irrespective of their similarity/correctness, underscoring the robustness of our simple predictor. This results in an enrichment factor of 2.5 compared to a random predictor for ranking ligands within the top 25%, making it a promising approach to identify lead compounds in virtual screening.

Published
2018

49. Performance of HADDOCK and a simple contact-based protein–ligand binding affinity predictor in the D3R Grand Challenge 2

Author

NMR Spectroscopy, Sub NMR Spectroscopy, Kurkcuoglu Soner, Zeynep, Koukos, Panos, Citro, Nevia, Trellet, Mikael E., Garcia Lopes Maia Rodrigues, Joao, de Sousa Moreira, Irina, Roel-touris, Jorge, Melquiond, Adrien S. J., Geng, Cunliang, Schaarschmidt, Jörg, Xue, Li C., Vangone, Anna, Bonvin, A. M. J. J., NMR Spectroscopy, Sub NMR Spectroscopy, Kurkcuoglu Soner, Zeynep, Koukos, Panos, Citro, Nevia, Trellet, Mikael E., Garcia Lopes Maia Rodrigues, Joao, de Sousa Moreira, Irina, Roel-touris, Jorge, Melquiond, Adrien S. J., Geng, Cunliang, Schaarschmidt, Jörg, Xue, Li C., Vangone, Anna, and Bonvin, A. M. J. J.

Published
2018

50. LightDock: a new multi-scale approach to protein-protein docking

Author

Universitat Politècnica de Catalunya. Departament d'Arquitectura de Computadors, Universitat Politècnica de Catalunya. CAP - Grup de Computació d'Altes Prestacions, Jimenez Garcia, Brian, Roel Touris, Jorge, Romero Durana, Miguel, Vidal, Miquel, Jiménez González, Daniel, Fernández Recio, Juan, Universitat Politècnica de Catalunya. Departament d'Arquitectura de Computadors, Universitat Politècnica de Catalunya. CAP - Grup de Computació d'Altes Prestacions, Jimenez Garcia, Brian, Roel Touris, Jorge, Romero Durana, Miguel, Vidal, Miquel, Jiménez González, Daniel, and Fernández Recio, Juan

Abstract

Motivation: Computational prediction of protein-protein complex structure by docking can provide structural and mechanistic insights for protein interactions of biomedical interest. However, current methods struggle with difficult cases, such as those involving flexible proteins, low-affinity complexes or transient interactions. A major challenge is how to efficiently sample the structural and energetic landscape of the association at different resolution levels, given that each scoring function is often highly coupled to a specific type of search method. Thus, new methodologies capable of accommodating multi-scale conformational flexibility and scoring are strongly needed. Results: We describe here a new multi-scale protein-protein docking methodology, LightDock, capable of accommodating conformational flexibility and a variety of scoring functions at different resolution levels. Implicit use of normal modes during the search and atomic/coarse-grained combined scoring functions yielded improved predictive results with respect to state-of-the-art rigidbody docking, especially in flexible cases., Postprint (author's final draft)

Published
2018

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