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3. 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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5. 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

6. 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
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7. 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

12. Synthetic proteins for COVID-19 diagnostics

Author

Schein, Catherine H., primary, Levine, Corri B., additional, McLellan, Susan L.F., additional, Negi, Surendra S., additional, Braun, Werner, additional, Dreskin, Stephen C., additional, Anaya, Elizabeth S., additional, and Schmidt, Jurgen, additional

Published
2021
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16. Synthetic protein antigens for COVID-19 diagnostics

Author

Schein, Catherine H., primary, Levine, Corri B., additional, McLellan, Susan L F, additional, Negi, Surendra S., additional, Braun, Werner, additional, Dreskin, Stephen C., additional, Anaya, Elizabeth S., additional, and Schmidt, Jurgen, additional

Published
2021
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20. IgE binding to linear epitopes of Ara h 2 in peanut allergic preschool children undergoing oral Immunotherapy

Author

Dreskin, Stephen C., primary, Germinaro, Matthew, additional, Reinhold, Dominik, additional, Chen, Xueni, additional, Vickery, Brian P., additional, Kulis, Michael, additional, Burks, A. Wesley, additional, Negi, Surendra S., additional, Braun, Werner, additional, Chambliss, Jeffery M., additional, Eglite, Spodra, additional, and McNulty, Caitlin M. G., additional

Published
2019
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22. Prediction of homoprotein and heteroprotein complexes by protein docking and template-based modeling: A CASP-CAPRI experiment

Author

Lensink, Marc F., Velankar, Sameer, Kryshtafovych, Andriy, Huang, Shen You, Schneidman-Duhovny, Dina, Sali, Andrej, Segura, Joan, Fernandez-Fuentes, Narcis, Viswanath, Shruthi, Elber, Ron, Grudinin, Sergei, Popov, Petr, Neveu, Emilie, Lee, Hasup, Baek, Minkyung, Park, Sangwoo, Heo, Lim, Lee, Gyu Rie, Seok, Chaok, Qin, Sanbo, Zhou, Huan Xiang, Ritchie, David W., Maigret, Bernard, Devignes, Marie Dominique, Ghoorah, Anisah, Torchala, Mieczyslaw, Chaleil, Raphaël A.G., Bates, Paul A., Ben-Zeev, Efrat, Eisenstein, Miriam, Negi, Surendra S., Weng, Zhiping, Vreven, Thom, Pierce, Brian G., Borrman, Tyler M., Yu, Jinchao, Ochsenbein, Françoise, Guerois, Raphaël, Vangone, Anna, Rodrigues, João P.G.L.M., Van Zundert, Gydo, Nellen, Mehdi, Xue, Li, Karaca, Ezgi, Melquiond, Adrien S.J., Visscher, Koen, Kastritis, Panagiotis L., Bonvin, Alexandre M.J.J., Xu, Xianjin, Qiu, Liming, Yan, Chengfei, Li, Jilong, Ma, Zhiwei, Cheng, Jianlin, Zou, Xiaoqin, Shen, Yang, Peterson, Lenna X., Kim, Hyung Rae, Roy, Amit, Han, Xusi, Esquivel-Rodriguez, Juan, Kihara, Daisuke, Yu, Xiaofeng, Bruce, Neil J., Fuller, Jonathan C., Wade, Rebecca C., Anishchenko, Ivan, Kundrotas, Petras J., Vakser, Ilya A., Imai, Kenichiro, Yamada, Kazunori, Oda, Toshiyuki, Nakamura, Tsukasa, Tomii, Kentaro, Pallara, Chiara, Romero-Durana, Miguel, Jiménez-García, Brian, Moal, Iain H., Férnandez-Recio, Juan, Joung, Jong Young, Kim, Jong Yun, Joo, Keehyoung, Lee, Jooyoung, Kozakov, Dima, Vajda, Sandor, Mottarella, Scott, Hall, David R., Beglov, Dmitri, Mamonov, Artem, Xia, Bing, Bohnuud, Tanggis, Del Carpio, Carlos A., Ichiishi, Eichiro, Marze, Nicholas, Kuroda, Daisuke, Roy Burman, Shourya S., Gray, Jeffrey J., Chermak, Edrisse, Cavallo, Luigi, Oliva, Romina, Tovchigrechko, Andrey, Wodak, Shoshana J., Molecular and Computational Toxicology, and AIMMS

Subjects
Protein docking, CASP, Protein interaction, SDG 1 - No Poverty, Oligomer state, Blind prediction, CAPRI
Abstract

We present the results for CAPRI Round 30, the first joint CASP-CAPRI experiment, which brought together experts from the protein structure prediction and protein-protein docking communities. The Round comprised 25 targets from amongst those submitted for the CASP11 prediction experiment of 2014. The targets included mostly homodimers, a few homotetramers, and two heterodimers, and comprised protein chains that could readily be modeled using templates from the Protein Data Bank. On average 24 CAPRI groups and 7 CASP groups submitted docking predictions for each target, and 12 CAPRI groups per target participated in the CAPRI scoring experiment. In total more than 9500 models were assessed against the 3D structures of the corresponding target complexes. Results show that the prediction of homodimer assemblies by homology modeling techniques and docking calculations is quite successful for targets featuring large enough subunit interfaces to represent stable associations. Targets with ambiguous or inaccurate oligomeric state assignments, often featuring crystal contact-sized interfaces, represented a confounding factor. For those, a much poorer prediction performance was achieved, while nonetheless often providing helpful clues on the correct oligomeric state of the protein. The prediction performance was very poor for genuine tetrameric targets, where the inaccuracy of the homology-built subunit models and the smaller pair-wise interfaces severely limited the ability to derive the correct assembly mode. Our analysis also shows that docking procedures tend to perform better than standard homology modeling techniques and that highly accurate models of the protein components are not always required to identify their association modes with acceptable accuracy. Proteins 2016; 84(Suppl 1):323-348. © 2016 Wiley Periodicals, Inc.

Published
2016

23. Thermodynamics of phosphopeptide tethering to BRCT: the structural minima for inhibitor design

Author

Lokesh, G.L., Muralidhara, B.K., Negi, Surendra S., and Natarajan, Amarnath

Subjects
Protein-protein interactions -- Research, DNA damage -- Research, Volumetric analysis -- Usage, Chemistry
Abstract

Fluorescence polarization (FP) and isothermal titration calorimetry (ITC) methods are used to define a tetrapeptide as a lead for a peptidomimetic based inhibitor design. The studies have shown that the presence of intramolecular hydrogen bonding could bias the pSPTF peptide to adopt the bound conformation for efficient C-terminal domains of BRCA1 (BRCT) tethering.

Published
2007

24. Dissecting the thermodynamics and cooperativity of ligand binding in cytochrome P450eryF

Author

Muralidhara, B.K., Negi, Surendra S., and Halpert, James R.

Subjects
Cytochrome P-450 -- Research, Thermodynamics -- Analysis, Ligand binding (Biochemistry) -- Research, Chemistry
Abstract

The isothermal titration calorimetry (ITC) is used to study the CYP2B4 flexibility in ligand binding to dissect the ligand-binding allostery in the soluble bacterial P450eryF with sparingly soluble hydrophobic ligands. The competitive binding studies have indicated a decrease in the affinities for 9-aminophenanthrene at both the sites, with large entropy-enthalpy compensation, showing the ability of the binding pocket to accommodate two ligands of diverse chemistry and enable cooperativity.

Published
2007

25. Functional classification of protein toxins as a basis for bioinformatic screening

Author

Negi, Surendra S., primary, Schein, Catherine H., additional, Ladics, Gregory S., additional, Mirsky, Henry, additional, Chang, Peter, additional, Rascle, Jean-Baptiste, additional, Kough, John, additional, Sterck, Lieven, additional, Papineni, Sabitha, additional, Jez, Joseph M., additional, Pereira Mouriès, Lucilia, additional, and Braun, Werner, additional

Published
2017
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27. Prediction of homoprotein and heteroprotein complexes by protein docking and template-based modeling: A CASP-CAPRI experiment

Author

NMR Spectroscopy, Sub NMR Spectroscopy, Lensink, Marc F., Velankar, Sameer, Kryshtafovych, Andriy, Huang, Shen You, Schneidman-Duhovny, Dina, Sali, Andrej, Segura, Joan, Fernandez-Fuentes, Narcis, Viswanath, Shruthi, Elber, Ron, Grudinin, Sergei, Popov, Petr, Neveu, Emilie, Lee, Hasup, Baek, Minkyung, Park, Sangwoo, Heo, Lim, Rie Lee, Gyu, Seok, Chaok, Qin, Sanbo, Zhou, Huan Xiang, Ritchie, David W., Maigret, Bernard, Devignes, Marie Dominique, Ghoorah, Anisah, Torchala, Mieczyslaw, Chaleil, Raphaël A G, Bates, Paul A., Ben-Zeev, Efrat, Eisenstein, Miriam, Negi, Surendra S., Weng, Zhiping, Vreven, Thom, Pierce, Brian G., Borrman, Tyler M., Yu, Jinchao, Ochsenbein, Françoise, Guerois, Raphaël, Vangone, Anna, Garcia Lopes Maia Rodrigues, João, van Zundert, Gydo, Nellen, Mehdi, Xue, Li, Karaca, Ezgi, Melquiond, Adrien S J, Visscher, Koen, Kastritis, Panagiotis L., Bonvin, Alexandre M J J, Xu, Xianjin, Qiu, Liming, Yan, Chengfei, Li, Jilong, Ma, Zhiwei, Cheng, Jianlin, Zou, Xiaoqin, Shen, Yang, Peterson, Lenna X., Kim, Hyung Rae, Roy, Amit, Han, Xusi, Esquivel-Rodriguez, Juan, Kihara, Daisuke, Yu, Xiaofeng, Bruce, Neil J., Fuller, Jonathan C., Wade, Rebecca C., Anishchenko, Ivan, Kundrotas, Petras J., Vakser, Ilya A., Imai, Kenichiro, Yamada, Kazunori, Oda, Toshiyuki, Nakamura, Tsukasa, Tomii, Kentaro, Pallara, Chiara, Romero-Durana, Miguel, Jiménez-García, Brian, Moal, Iain H., Férnandez-Recio, Juan, Joung, Jong Young, Kim, Jong Yun, Joo, Keehyoung, Lee, Jooyoung, Kozakov, Dima, Vajda, Sandor, Mottarella, Scott, Hall, David R., Beglov, Dmitri, Mamonov, Artem, Xia, Bing, Bohnuud, Tanggis, Del Carpio, Carlos A., Ichiishi, Eichiro, Marze, Nicholas, Kuroda, Daisuke, Roy Burman, Shourya S., Gray, Jeffrey J., Chermak, Edrisse, Cavallo, Luigi, Oliva, Romina, Tovchigrechko, Andrey, Wodak, Shoshana J., NMR Spectroscopy, Sub NMR Spectroscopy, Lensink, Marc F., Velankar, Sameer, Kryshtafovych, Andriy, Huang, Shen You, Schneidman-Duhovny, Dina, Sali, Andrej, Segura, Joan, Fernandez-Fuentes, Narcis, Viswanath, Shruthi, Elber, Ron, Grudinin, Sergei, Popov, Petr, Neveu, Emilie, Lee, Hasup, Baek, Minkyung, Park, Sangwoo, Heo, Lim, Rie Lee, Gyu, Seok, Chaok, Qin, Sanbo, Zhou, Huan Xiang, Ritchie, David W., Maigret, Bernard, Devignes, Marie Dominique, Ghoorah, Anisah, Torchala, Mieczyslaw, Chaleil, Raphaël A G, Bates, Paul A., Ben-Zeev, Efrat, Eisenstein, Miriam, Negi, Surendra S., Weng, Zhiping, Vreven, Thom, Pierce, Brian G., Borrman, Tyler M., Yu, Jinchao, Ochsenbein, Françoise, Guerois, Raphaël, Vangone, Anna, Garcia Lopes Maia Rodrigues, João, van Zundert, Gydo, Nellen, Mehdi, Xue, Li, Karaca, Ezgi, Melquiond, Adrien S J, Visscher, Koen, Kastritis, Panagiotis L., Bonvin, Alexandre M J J, Xu, Xianjin, Qiu, Liming, Yan, Chengfei, Li, Jilong, Ma, Zhiwei, Cheng, Jianlin, Zou, Xiaoqin, Shen, Yang, Peterson, Lenna X., Kim, Hyung Rae, Roy, Amit, Han, Xusi, Esquivel-Rodriguez, Juan, Kihara, Daisuke, Yu, Xiaofeng, Bruce, Neil J., Fuller, Jonathan C., Wade, Rebecca C., Anishchenko, Ivan, Kundrotas, Petras J., Vakser, Ilya A., Imai, Kenichiro, Yamada, Kazunori, Oda, Toshiyuki, Nakamura, Tsukasa, Tomii, Kentaro, Pallara, Chiara, Romero-Durana, Miguel, Jiménez-García, Brian, Moal, Iain H., Férnandez-Recio, Juan, Joung, Jong Young, Kim, Jong Yun, Joo, Keehyoung, Lee, Jooyoung, Kozakov, Dima, Vajda, Sandor, Mottarella, Scott, Hall, David R., Beglov, Dmitri, Mamonov, Artem, Xia, Bing, Bohnuud, Tanggis, Del Carpio, Carlos A., Ichiishi, Eichiro, Marze, Nicholas, Kuroda, Daisuke, Roy Burman, Shourya S., Gray, Jeffrey J., Chermak, Edrisse, Cavallo, Luigi, Oliva, Romina, Tovchigrechko, Andrey, and Wodak, Shoshana J.

Published
2016

28. Prediction of homoprotein and heteroprotein complexes by protein docking and template-based modeling: A CASP-CAPRI experiment

Author

Barcelona Supercomputing Center, Lesink, Marc F., Velankar, Sameer, Kryshtafovych, Andriy, Huang, Shen-You, Schneidman-Duhovny, Dina, Sali, Andrej, Segura, Joan, Fernandez-Fuentes, Narcis, Shruthi, Viswanath, Elber, Ron, Grudinin, Sergei, Popov, Petr, Neveu, Emilie, Lee, Hasup, Baek, Minkyung, Park, Sangwoo, Heo, Lim, Lee, Gyu R., Seok, Chaok, Qin, Sanbo, Zhou, Huan-Xiang, Ritchie, David W., Maigret, Bernard, Devignes, Marie-Dominique, Ghoorah, Anisah, Torchala, Mieczyslaw, Chaleil, Raphaël A.G., Bates, Paul A., Ben-Zeev, Efrat, Eisenstein, Miriam, Negi, Surendra S., Weng, Zhiping, Vreven, Thom, Pierce, Brian G., Borrman, Tyler M., Yu, Jinchao, Ochsenbein, Françoise, Guerois, Raphaël, Vangone, Anna, Rodrigues, Joao P.G.L.M., Zundert, Gydo van, Nellen, Mehdi, Xue, Li, Karaca, Ezgi, Melquiond, Adrien S.J., Visscher, Koen, Kastritis, Panagiotis L., Bonvin, Alexandre M.J.J., Xianjin, Xu, Qiu, Liming, Yan, Chengfei, Li, Jilong, Ma, Zhiwei, Cheng, Jianlin, Zou, Xiaoqin, Shen, Yang, Peterson, Lenna X., Kim, Hyung-Rae, Roy, Amit, Han, Xusi, Esquivel-Rodriguez, Juan, Kihara, Daisuke, Yu, Xiaofeng, Bruce, Neil J., Fuller, Jonathan C., Wade, Rebecca C., Anishchenko, Ivan, Kundrotas, Petras J., Vakser, Ilya A., Imai, Kenichiro, Yamada, Kazunori, Oda, Toshiyuki, Nakamura, Tsukasa, Tomii, Kentaro, Pallara, Chiara, Romero-Durana, Miguel, Jiménez-García, Brian, Moal, Iain H., Fernández-Recio, Juan, Joung, Jong Y., Kim, Jong Y., Joo, Keehyoung, Lee, Jooyoung, Kozakov, Dima, Vajda, Sandor, Mottarella, Scott, Hall, David R., Beglov, Dmitri, Mamonov, Artem, Xia, Bing, Bohnuud, Tanggis, del Carpio, Carlos A., Ichiishi, Eichiro, Marze, Nicholas, Kuroda, Daisuke, Burman, Shourya S., Gray, Jeffrey J., Chermak, Edrisse, Cavallo, Luigi, Oliva, Romina, Tovchigrechko, Andrey, Wodak, Shoshana J., Barcelona Supercomputing Center, Lesink, Marc F., Velankar, Sameer, Kryshtafovych, Andriy, Huang, Shen-You, Schneidman-Duhovny, Dina, Sali, Andrej, Segura, Joan, Fernandez-Fuentes, Narcis, Shruthi, Viswanath, Elber, Ron, Grudinin, Sergei, Popov, Petr, Neveu, Emilie, Lee, Hasup, Baek, Minkyung, Park, Sangwoo, Heo, Lim, Lee, Gyu R., Seok, Chaok, Qin, Sanbo, Zhou, Huan-Xiang, Ritchie, David W., Maigret, Bernard, Devignes, Marie-Dominique, Ghoorah, Anisah, Torchala, Mieczyslaw, Chaleil, Raphaël A.G., Bates, Paul A., Ben-Zeev, Efrat, Eisenstein, Miriam, Negi, Surendra S., Weng, Zhiping, Vreven, Thom, Pierce, Brian G., Borrman, Tyler M., Yu, Jinchao, Ochsenbein, Françoise, Guerois, Raphaël, Vangone, Anna, Rodrigues, Joao P.G.L.M., Zundert, Gydo van, Nellen, Mehdi, Xue, Li, Karaca, Ezgi, Melquiond, Adrien S.J., Visscher, Koen, Kastritis, Panagiotis L., Bonvin, Alexandre M.J.J., Xianjin, Xu, Qiu, Liming, Yan, Chengfei, Li, Jilong, Ma, Zhiwei, Cheng, Jianlin, Zou, Xiaoqin, Shen, Yang, Peterson, Lenna X., Kim, Hyung-Rae, Roy, Amit, Han, Xusi, Esquivel-Rodriguez, Juan, Kihara, Daisuke, Yu, Xiaofeng, Bruce, Neil J., Fuller, Jonathan C., Wade, Rebecca C., Anishchenko, Ivan, Kundrotas, Petras J., Vakser, Ilya A., Imai, Kenichiro, Yamada, Kazunori, Oda, Toshiyuki, Nakamura, Tsukasa, Tomii, Kentaro, Pallara, Chiara, Romero-Durana, Miguel, Jiménez-García, Brian, Moal, Iain H., Fernández-Recio, Juan, Joung, Jong Y., Kim, Jong Y., Joo, Keehyoung, Lee, Jooyoung, Kozakov, Dima, Vajda, Sandor, Mottarella, Scott, Hall, David R., Beglov, Dmitri, Mamonov, Artem, Xia, Bing, Bohnuud, Tanggis, del Carpio, Carlos A., Ichiishi, Eichiro, Marze, Nicholas, Kuroda, Daisuke, Burman, Shourya S., Gray, Jeffrey J., Chermak, Edrisse, Cavallo, Luigi, Oliva, Romina, Tovchigrechko, Andrey, and Wodak, Shoshana J.

Abstract

We present the results for CAPRI Round 30, the first joint CASP-CAPRI experiment, which brought together experts from the protein structure prediction and protein–protein docking communities. The Round comprised 25 targets from amongst those submitted for the CASP11 prediction experiment of 2014. The targets included mostly homodimers, a few homotetramers, and two heterodimers, and comprised protein chains that could readily be modeled using templates from the Protein Data Bank. On average 24 CAPRI groups and 7 CASP groups submitted docking predictions for each target, and 12 CAPRI groups per target participated in the CAPRI scoring experiment. In total more than 9500 models were assessed against the 3D structures of the corresponding target complexes. Results show that the prediction of homodimer assemblies by homology modeling techniques and docking calculations is quite successful for targets featuring large enough subunit interfaces to represent stable associations. Targets with ambiguous or inaccurate oligomeric state assignments, often featuring crystal contact-sized interfaces, represented a confounding factor. For those, a much poorer prediction performance was achieved, while nonetheless often providing helpful clues on the correct oligomeric state of the protein. The prediction performance was very poor for genuine tetrameric targets, where the inaccuracy of the homology-built subunit models and the smaller pair-wise interfaces severely limited the ability to derive the correct assembly mode. Our analysis also shows that docking procedures tend to perform better than standard homology modeling techniques and that highly accurate models of the protein components are not always required to identify their association modes with acceptable accuracy., We are most grateful to the PDBe at the European Bioinformatics Institute in Hinxton, UK, for hosting the CAPRI website. Our deepest thanks go to all the structural biologists and to the following structural genomics initiatives: Northeast Structural Genomics Consortium, Joint Center for Structural Genomics, NatPro PSI:Biology, New York Structural Genomics Research Center, Midwest Center for Structural Genomics, Structural Genomics Consortium, for contributing the targets for this joint CASP-CAPRI experiment. MFL acknowledges support from the FRABio FR3688 Research Federation “Structural & Functional Biochemistry of Biomolecular Assemblies.”, Peer Reviewed, Postprint (published version)

Published
2016

29. Prediction of homoprotein and heteroprotein complexes by protein docking and template‐based modeling: A CASP‐CAPRI experiment

Author

Lensink, Marc F., primary, Velankar, Sameer, additional, Kryshtafovych, Andriy, additional, Huang, Shen‐You, additional, Schneidman‐Duhovny, Dina, additional, Sali, Andrej, additional, Segura, Joan, additional, Fernandez‐Fuentes, Narcis, additional, Viswanath, Shruthi, additional, Elber, Ron, additional, Grudinin, Sergei, additional, Popov, Petr, additional, Neveu, Emilie, additional, Lee, Hasup, additional, Baek, Minkyung, additional, Park, Sangwoo, additional, Heo, Lim, additional, Rie Lee, Gyu, additional, Seok, Chaok, additional, Qin, Sanbo, additional, Zhou, Huan‐Xiang, additional, Ritchie, David W., additional, Maigret, Bernard, additional, Devignes, Marie‐Dominique, additional, Ghoorah, Anisah, additional, Torchala, Mieczyslaw, additional, Chaleil, Raphaël A.G., additional, Bates, Paul A., additional, Ben‐Zeev, Efrat, additional, Eisenstein, Miriam, additional, Negi, Surendra S., additional, Weng, Zhiping, additional, Vreven, Thom, additional, Pierce, Brian G., additional, Borrman, Tyler M., additional, Yu, Jinchao, additional, Ochsenbein, Françoise, additional, Guerois, Raphaël, additional, Vangone, Anna, additional, Rodrigues, João P.G.L.M., additional, van Zundert, Gydo, additional, Nellen, Mehdi, additional, Xue, Li, additional, Karaca, Ezgi, additional, Melquiond, Adrien S.J., additional, Visscher, Koen, additional, Kastritis, Panagiotis L., additional, Bonvin, Alexandre M.J.J., additional, Xu, Xianjin, additional, Qiu, Liming, additional, Yan, Chengfei, additional, Li, Jilong, additional, Ma, Zhiwei, additional, Cheng, Jianlin, additional, Zou, Xiaoqin, additional, Shen, Yang, additional, Peterson, Lenna X., additional, Kim, Hyung‐Rae, additional, Roy, Amit, additional, Han, Xusi, additional, Esquivel‐Rodriguez, Juan, additional, Kihara, Daisuke, additional, Yu, Xiaofeng, additional, Bruce, Neil J., additional, Fuller, Jonathan C., additional, Wade, Rebecca C., additional, Anishchenko, Ivan, additional, Kundrotas, Petras J., additional, Vakser, Ilya A., additional, Imai, Kenichiro, additional, Yamada, Kazunori, additional, Oda, Toshiyuki, additional, Nakamura, Tsukasa, additional, Tomii, Kentaro, additional, Pallara, Chiara, additional, Romero‐Durana, Miguel, additional, Jiménez‐García, Brian, additional, Moal, Iain H., additional, Férnandez‐Recio, Juan, additional, Joung, Jong Young, additional, Kim, Jong Yun, additional, Joo, Keehyoung, additional, Lee, Jooyoung, additional, Kozakov, Dima, additional, Vajda, Sandor, additional, Mottarella, Scott, additional, Hall, David R., additional, Beglov, Dmitri, additional, Mamonov, Artem, additional, Xia, Bing, additional, Bohnuud, Tanggis, additional, Del Carpio, Carlos A., additional, Ichiishi, Eichiro, additional, Marze, Nicholas, additional, Kuroda, Daisuke, additional, Roy Burman, Shourya S., additional, Gray, Jeffrey J., additional, Chermak, Edrisse, additional, Cavallo, Luigi, additional, Oliva, Romina, additional, Tovchigrechko, Andrey, additional, and Wodak, Shoshana J., additional

Published
2016
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30. Cross-React: a new structural bioinformatics method for predicting allergen cross-reactivity.

Author

Negi, Surendra S. and Braun, Werner

Subjects
*CROSS reactions (Immunology), *STRUCTURAL bioinformatics, *ALLERGENS, *DATABASES, *EPITOPES
Abstract

The phenomenon of cross-reactivity between allergenic proteins plays an important role to understand how the immune system recognizes different antigen proteins. Allergen proteins are known to cross-react if their sequence comparison shows a high sequence identity which also implies that the proteins have a similar 3D fold. In such cases, linear sequence alignment methods are frequently used to predict cross-reactivity between allergenic proteins. However, the prediction of cross-reactivity between distantly related allergens continues to be a challenging task. To overcome this problem, we developed a new structure-based computational method, Cross-React, to predict cross-reactivity between allergenic proteins available in the Structural Database of Allergens (SDAP). Our method is based on the hypothesis that we can find surface patches on 3D structures of potential allergens with amino acid compositions similar to an epitope in a known allergen. We applied the Cross-React method to a diverse set of seven allergens, and successfully identified several cross-reactive allergens with high to moderate sequence identity which have also been experimentally shown to cross-react. Based on these findings, we suggest that Cross- React can be used as a predictive tool to assess protein allergenicity and cross-reactivity. [ABSTRACT FROM AUTHOR]

Published
2017
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37. Validation of a Phage Display and Computational Algorithm by Mapping a Conformational Epitope of Bla g 2.

Author

Tiwari, Ruby, Negi, Surendra S., Braun, Benjamin, Braun, Werner, Pomés, Anna, Chapman, Martin D., Goldblum, Randall M., and Midoro-Horiuti, Terumi

Subjects
*ALLERGIC rhinitis, *ASTHMA, *MONOCLONAL antibodies, *AMINO acid analysis, *EPITOPES
Abstract

Background: Bla g 2, one of the major cockroach allergens, induces a strong IgE response against conformational epitopes, and on reexposure, sensitized individuals often display symptoms of allergic rhinitis and asthma. The aim of the current study was to perform a test of the efficacy of a modified phage display screening, characterization of selected phages and an automated algorithm, EpiSearch, in locating an important conformational epitope. Methods: The monoclonal antibody 7C11, which partially inhibits the binding of patient IgE antibodies to Bla g 2, was used to screen a random peptide phage library. After 3 rounds of panning, 32 phage clones were isolated and the amino acid sequences of their peptides were determined. The relative affinity and specificity of the binding of these peptides to 7C11 were tested in ELISAs. The amino acid composition of these peptides was then matched with clusters of residues on the surface of the 3-dimensional (3D) structure of Bla g 2, using our EpiSearch algorithm. Results: The amino acid sequences of the peptides on selected phages differed at only one position, occupied by 1 of 2 negatively charged residues. The two 12-mer sequences bound to 7C11 with similar avidity and specificity. There was good concordance between the residues in the 3D clusters identified from our phage display/computational method with the co-crystal structural analysis. Conclusion: Conformational epitopes may be mapped through screening of clones from random peptide phage display libraries and EpiSearch. Copyright © 2011 S. Karger AG, Basel [ABSTRACT FROM AUTHOR]

Published
2012
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38. AllergenAI: a deep learning model predicting allergenicity based on protein sequence.

Author

Yang C, Negi SS, Schein CH, Braun W, and Kim P

Abstract

Innovations in protein engineering can help redesign allergenic proteins to reduce adverse reactions in sensitive individuals. To accomplish this aim, a better knowledge of the molecular properties of allergenic proteins and the molecular features that make a protein allergenic is needed. We present a novel AI-based tool, AllergenAI, to quantify the allergenic potential of a given protein. Our approach is solely based on protein sequences, differentiating it from previous tools that use some knowledge of the allergens' physicochemical and other properties in addition to sequence homology. We used the collected data on protein sequences of allergenic proteins as archived in the three well-established databases, SDAP 2.0, COMPARE, and AlgPred 2, to train a convolutional neural network and assessed its prediction performance by cross-validation. We then used Allergen AI to find novel potential proteins of the cupin family in date palm, spinach, maize, and red clover plants with a high allergenicity score that might have an adverse allergenic effect on sensitive individuals. By analyzing the feature importance scores (FIS) of vicilins, we identified a proline-alanine-rich (P-A) motif in the top 50% of FIS regions that overlapped with known IgE epitope regions of vicilin allergens. Furthermore, using∼ 1600 allergen structures in our SDAP database, we showed the potential to incorporate 3D information in a CNN model. Future, incorporating 3D information in training data should enhance the accuracy. AllergenAI is a novel foundation for identifying the critical features that distinguish allergenic proteins.

Published
2024
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39. Conformational IgE epitopes of peanut allergens Ara h 2 and Ara h 6.

Author

Chen X, Negi SS, Liao S, Gao V, Braun W, and Dreskin SC

Subjects
2S Albumins, Plant immunology, 2S Albumins, Plant metabolism, Allergens immunology, Allergens metabolism, Amino Acid Sequence, Antigens, Plant immunology, Antigens, Plant metabolism, Arachis immunology, Binding Sites, Cell Surface Display Techniques, Consensus Sequence, Epitope Mapping methods, Epitopes immunology, Epitopes metabolism, Glycoproteins immunology, Glycoproteins metabolism, Humans, Immunoglobulin E chemistry, Immunoglobulin E metabolism, Peanut Hypersensitivity immunology, Peptide Library, Protein Binding, 2S Albumins, Plant chemistry, Allergens chemistry, Antigens, Plant chemistry, Epitopes chemistry, Glycoproteins chemistry, Immunoglobulin E immunology, Models, Molecular, Protein Conformation
Abstract

Background: Cross-linking of IgE antibody by specific epitopes on the surface of mast cells is a prerequisite for triggering symptoms of peanut allergy. IgE epitopes are frequently categorized as linear or conformational epitopes. Although linear IgE-binding epitopes of peanut allergens have been defined, little is known about conformational IgE-binding epitopes., Objective: To identify clinically relevant conformational IgE epitopes of the two most important peanut allergens, Ara h 2 and Ara h 6, using phage peptide library., Methods: A phage 12mer peptide library was screened with allergen-specific IgE from 4 peanut-allergic patients. Binding of the mimotopes to IgE from a total of 29 peanut-allergic subjects was measured by ELISA. The mimotope sequences were mapped on the surface areas of Ara h 2 and Ara h 6 using EpiSearch., Results: Forty-one individual mimotopes were identified that specifically bind anti- Ara h 2/Ara h 6 IgE as well as rabbit anti-Ara h 2 and anti-Ara h 6 IgG. Sequence alignment showed that none of the mimotope sequences match a linear segment of the Ara h 2 or Ara h 6 sequences. EpiSearch analysis showed that all the mimotopes mapped to surface patches of Ara h 2 and Ara h 6. Eight of the mimotopes were recognized by more than 90% of the patients, suggesting immunodominance. Each patient had distinct IgE recognition patterns but the recognition frequency was not correlated to the concentration of peanut specific IgE or to clinical history., Conclusions: The mimotopes identified in this study represent conformational epitopes. Identification of similar surface patches on Ara h 2 and Ara h 6 further underscores the similarities between these two potent allergens., (© 2016 John Wiley & Sons Ltd.)

Published
2016
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40. Rational engineering of human cytochrome P450 2B6 for enhanced expression and stability: importance of a Leu264->Phe substitution.

Author

Kumar S, Zhao Y, Sun L, Negi SS, Halpert JR, and Muralidhara BK

Subjects
Amino Acid Substitution, Chaperonin 10 biosynthesis, Chaperonin 60 biosynthesis, Crystallography, X-Ray, Cytochrome P-450 CYP2B6, Cytochrome P-450 Enzyme System genetics, Enzyme Stability genetics, Escherichia coli genetics, Hot Temperature, Humans, Leucine chemistry, Leucine genetics, Molecular Chaperones biosynthesis, Mutation, Phenylalanine chemistry, Phenylalanine genetics, Protein Conformation, Substrate Specificity, Thermodynamics, Cytochrome P-450 Enzyme System biosynthesis, Cytochrome P-450 Enzyme System chemistry, Protein Engineering
Abstract

Despite the emerging importance of human P450 2B6 in xenobiotic metabolism, thorough biochemical and biophysical characterization has been impeded as a result of low expression in Escherichia coli. Comparison with similar N-terminal truncated and C-terminal His-tagged constructs (rat P450 2B1dH, rabbit 2B4dH, and dog 2B11dH) revealed that P450 2B6dH showed the lowest thermal stability, catalytic tolerance to temperature, and chemical stability against guanidinium chloride-induced denaturation. Eleven P450 2B6dH mutants were rationally engineered based on sequence comparison with the three other P450 2B enzymes and the solvent accessibility of residues in the ligand-free crystal structure of P450 2B4dH. L198M, L264F, and L390P showed approximately 3-fold higher expression than P450 2B6dH. L264F alone showed enhanced stability against thermal and chemical denaturation compared with P450 2B6dH and was characterized further functionally. L264F showed similar preferential inhibition by pyridine over imidazole derivatives as P450 2B6dH. The Leu(264)-->Phe substitution did not alter the K(s) for inhibitors or the substrate benzphetamine, the K(m) for 7-ethoxy-4-(trifluoromethyl)coumarin, or the benzphetamine metabolite profiles. The enhanced stability and monodisperse nature of L264F made it suitable for isothermal titration calorimetry studies. Interaction of 1-benzylimidazole with L264F yielded a clear binding isotherm with a distinctly different thermodynamic signature from P450 2B4dH. The inhibitor docked differently in the binding pocket of a P450 2B6 homology model than in 2B4, highlighting the different chemistry of the active site of these two enzymes. Thus, L264F is a good candidate to further explore the unique structure-function relationships of P450 2B6 using X-ray crystallography and solution thermodynamics.

Published
2007
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