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7. High-Strength Welding of Silica Glass Using Double-Pulse Femtosecond Laser under Non-Optical Contact Conditions.

Author

Gao, Zheng, He, Jiahua, Jia, Xianshi, Yi, Zhaoxi, Li, Cheng, Zhang, Shifu, Wang, Cong, and Duan, Ji'an

Subjects
LASER welding, TIME delay systems, MICROELECTRONIC packaging, FEMTOSECOND lasers, WELDING
Abstract

Ultrafast laser welding technology for transparent materials has developed rapidly in recent years; however, high-strength non-optical contact transparent material welding has been a challenge. This work presents a welding method for silica glass using a double-pulse femtosecond (fs) laser and optimizes the laser processing parameters to enhance the welding performance. The welding characteristics of silica glass are analyzed under different time delays by controlling the pulse delay of double pulses. In addition to comprehensively study the influence of various experimental conditions on double-pulse fs laser welding, multi-level tests are designed for five factors, including average laser power, pulse delay, scanning interval, scanning speed, and repetition rate. Finally, by optimizing the parameters, a welding strength of 57.15 MPa is achieved at an average power of 3500 mW, repetition rate of 615 kHz, pulse delay of 66.7 ps, scanning interval of 10 µm, and scanning speed of 1000 µm/s. This work introduces a new approach to glass welding and presents optimal parameters for achieving higher welding strength, which can be widely used in aerospace, microelectronic packaging, microfluidics, and other fields. [ABSTRACT FROM AUTHOR]

Published
2024
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12. Combination of DNA ploidy, stroma, and nucleotyping predicting prognosis and tailoring adjuvant chemotherapy duration in stage III colon cancer.

Author

Peng, Jianhong, Zhang, Weili, He, Jiahua, Wang, Weifeng, Li, Weihao, Mao, Lijun, Dong, Yuejin, Lu, Zhenhai, Pan, Zhizhong, Zhou, Chi, and Wu, Xiaojun

Abstract

Introduction: DNA ploidy (P), stroma fraction (S), and nucleotyping (N) collectively known as PSN, have proven prognostic accuracy in stage II colorectal cancer (CRC). However, few studies have reported on the prognostic value of the PSN panel in stage III colon cancer patients receiving capecitabine and oxaliplatin adjuvant chemotherapy. Objectives: This study aimed to validate PSN's prognostic impact on stage III colon cancer, identifying candidates for optimized adjuvant chemotherapy duration. Design: A retrospective analysis was conducted on a cohort of stage III colon cancer patients from April 2008 to June 2020. Methods: Postoperative pathological samples from stage III colon cancer patients who underwent radical surgery and postoperative adjuvant chemotherapy at Sun Yat-sen University Cancer Center were retrospectively collected. Automated digital imaging assessed PSN, categorizing risk groups. Kaplan–Meier, Cox regression, and time-dependent receiver operating characteristic analysis compared model validity. Results: Significant differences in 5-year disease-free survival (DFS) and overall survival (OS) were noted among PSN-based low-, moderate-, and high-risk groups (DFS: 92.10% versus 83.62% versus 79.80%, p = 0.029; OS: 96.69% versus 93.99% versus 90.12%, p = 0.016). PSN emerged as an independent prognostic factor for DFS [hazard ratio (HR) = 1.409, 95% confidence interval (CI): 1.002–1.981, p = 0.049] and OS (HR = 1.720, 95% CI: 1.127–2.624, p = 0.012). The PSN model, incorporating perineural invasion and tumor location, displayed superior area under the curve for 5-year (0.692 versus 0.553, p = 0.020) and 10-year (0.694 versus 0.532, p = 0.006) DFS than TNM stage. In the PSN high-risk group, completing eight cycles of adjuvant chemotherapy significantly improved 5-year DFS and OS compared to four to seven cycles (DFS: 89.43% versus 71.52%, p = 0.026; OS: 96.77% versus 85.46%, p = 0.007). Conclusion: The PSN panel effectively stratifies stage III colon cancer, aiding in optimized adjuvant chemotherapy duration determination. [ABSTRACT FROM AUTHOR]

Published
2024
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13. Tumor-derived lactate promotes resistance to bevacizumab treatment by facilitating autophagy enhancer protein RUBCNL expression through histone H3 lysine 18 lactylation (H3K18la) in colorectal cancer

Author

Li, Weihao, primary, Zhou, Chi, additional, Yu, Long, additional, Hou, Zhenlin, additional, Liu, Huashan, additional, Kong, Lingheng, additional, Xu, Yanbo, additional, He, Jiahua, additional, Lan, Jin, additional, Ou, Qingjian, additional, Fang, Yujing, additional, Lu, Zhenhai, additional, Wu, Xiaojun, additional, Pan, Zhizhong, additional, Peng, Jianhong, additional, and Lin, Junzhong, additional

Published
2023
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14. Tumor-derived lactate promotes resistance to bevacizumab treatment by facilitating autophagy enhancer protein RUBCNL expression through histone H3 lysine 18 lactylation (H3K18la) in colorectal cancer.

Author

Li, Weihao, Zhou, Chi, Yu, Long, Hou, Zhenlin, Liu, Huashan, Kong, Lingheng, Xu, Yanbo, He, Jiahua, Lan, Jin, Ou, Qingjian, Fang, Yujing, Lu, Zhenhai, Wu, Xiaojun, Pan, Zhizhong, Peng, Jianhong, and Lin, Junzhong

Subjects
BEVACIZUMAB, COLORECTAL cancer, TUBULINS, PROTEIN expression, AUTOPHAGY, CANCER cell proliferation
Abstract

Bevacizumab plays an important role in the first and second line treatment for metastatic colorectal cancer (CRC). And induction of hypoxia and the tumors response to it plays an important role in determining the efficacy of antiangiogenic therapy while the connection between them remains unclear. Here, we found that lactate accumulated in the tumor environment of CRC and acted as substrates for histone lactylation, and this process was further induced by cellular enhanced glycolysis in hypoxia. We determined that CRC patients resistant to bevacizumab treatment presented with elevated levels of histone lactylation and inhibition of histone lactylation efficiently suppressed CRC tumorigenesis, progression and survival in hypoxia. Histone lactylation promoted the transcription of RUBCNL/Pacer, facilitating autophagosome maturation through interacting with BECN1 (beclin 1) and mediating the recruitment and function of the class III phosphatidylinositol 3-kinase complex, which had a crucial role in hypoxic cancer cells proliferation and survival. Moreover, combining inhibition of histone lactylation and macroautophagy/autophagy with bevacizumab treatment demonstrated remarkable treatment efficacy in bevacizumab-resistance patients-derived pre-clinical models. These findings delivered a new exploration and important supplement of metabolic reprogramming-epigenetic regulation, and provided a new strategy for improving clinical efficacy of bevacizumab in CRC by inhibition of histone lactylation. Abbreviations: 2-DG: 2-deoxy-D-glucose; BECN1: beclin 1; CQ: chloroquine; CRC: colorectal cancer; DMOG: dimethyloxalylglycine; H3K18la: histone H3 lysine 18 lactylation; MAP1LC3B/LC3B: microtubule associated protein 1 light chain 3 beta; Nala: sodium lactate; PDO: patient-derived orgnoid; PDX: patient-derived xenograft; RUBCNL/Pacer: rubicon like autophagy enhancer; SQSTM1/p62: sequestosome 1. [ABSTRACT FROM AUTHOR]

Published
2024
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15. Impact of AlphaFold on Structure Prediction of Protein Complexes: The CASP15-CAPRI Experiment

Author

Lensink, Marc, Brysbaert, Guillaume, Raouraoua, Nessim, Bates, Paul, Giulini, Marco, Honorato, Rodrigo Vargas, Noort, Charlotte van, Teixeira, João, Bonvin, Alexandre M.J.J., Kong, Ren, Shi, Hang, Lu, Xufeng, Chang, Shan, Liu, Jian, Guo, Zhiye, Chen, Xiao, Morehead, Alex, Roy, Raj, Wu, Tianqi, Giri, Nabin, Quadir, Farhan, Chen, Chen, Cheng, Jianlin, Carpio, Carlos Del, Ichiishi, Eichiro, Luis, Rodriguez-Lumbreras A, Fernández-Recio, Juan, Harmalkar, Ameya, Chu, Lee-Shin, Canner, Sam, Smanta, Rituparna, Gray, Jeffrey, Li, Hao, Lin, Peicong, He, Jiahua, Tao, Huanyu, Huang, Shengyou, Roel, Jorge, Jimenez-Garcia, Brian, Christoffer, Charles, Anika, Jain J, Kagaya, Yuki, Kannan, Harini, Nakamura, Tsukasa, Terashi, Genki, Verburgt, Jacob, Zhang, Yuanyuan, Zhang, Zicong, Fujuta, Hayato, Sekijima, Masakazu, Kihara, Daisuke, Khan, Omeir, Kotelnikov, Sergei, Ghani, Usman, Padhorny, Dzmitry, Beglov, Dmitri, Vajda, Sandor, Kozakov, Dima, Surendra, Negi S, Ricciardelli, Tiziana, Barradas-Bautista, Didier, Cao, Zhen, Chawla, Mohit, Cavallo, Luigi, Oliva, Romina, Yin, Rujie, Cheung, Melyssa, Guest, Johnathan, Lee, Jessica, Pierce, Brian, Shor, Ben, Cohen, Tomer, Halfon, Matan, Schneidman-Duhovny, Dina, Zhu, Shaowen, Sun, Yuanfei, Shen, Yang, Maszota-Zieleniak, Martyna, Krzysztof, Bojarski K, Lubecka, Emilia, Marcisz, Mateusz, Danielsson, Annemarie, Dziadek, Lukasz, Gaardlos, Margrethe, Giełdoń, Artur, Liwo, Jozef, Samsonov, Sergey, Slusarz, Rafal, Zieba, Karolina, Sieradzan, Adam, Czaplewski, Cezary, Kobayashi, Shinpei, Miyakawa, Yuta, Kiyota, Yasuomi, Takeda-Shitaka, Mayuko, Olechnovič, Kliment, Valančauskas, Lukas, Dapkūnas, Justas, Venclovas, Ceslovas, Wallner, Björn, Yang, Lin, Hou, Chengyu, He, Xiaodong, Guo, Shuai, Jiang, Shenda, Ma, Xiaoliang, Duan, Rui, Qiu, Liming, Xu, Xianjin, Zou, Xiaoqin, Velankar, Sameer, Shoshana, Wodak J, Lensink, Marc, Brysbaert, Guillaume, Raouraoua, Nessim, Bates, Paul, Giulini, Marco, Honorato, Rodrigo Vargas, Noort, Charlotte van, Teixeira, João, Bonvin, Alexandre M.J.J., Kong, Ren, Shi, Hang, Lu, Xufeng, Chang, Shan, Liu, Jian, Guo, Zhiye, Chen, Xiao, Morehead, Alex, Roy, Raj, Wu, Tianqi, Giri, Nabin, Quadir, Farhan, Chen, Chen, Cheng, Jianlin, Carpio, Carlos Del, Ichiishi, Eichiro, Luis, Rodriguez-Lumbreras A, Fernández-Recio, Juan, Harmalkar, Ameya, Chu, Lee-Shin, Canner, Sam, Smanta, Rituparna, Gray, Jeffrey, Li, Hao, Lin, Peicong, He, Jiahua, Tao, Huanyu, Huang, Shengyou, Roel, Jorge, Jimenez-Garcia, Brian, Christoffer, Charles, Anika, Jain J, Kagaya, Yuki, Kannan, Harini, Nakamura, Tsukasa, Terashi, Genki, Verburgt, Jacob, Zhang, Yuanyuan, Zhang, Zicong, Fujuta, Hayato, Sekijima, Masakazu, Kihara, Daisuke, Khan, Omeir, Kotelnikov, Sergei, Ghani, Usman, Padhorny, Dzmitry, Beglov, Dmitri, Vajda, Sandor, Kozakov, Dima, Surendra, Negi S, Ricciardelli, Tiziana, Barradas-Bautista, Didier, Cao, Zhen, Chawla, Mohit, Cavallo, Luigi, Oliva, Romina, Yin, Rujie, Cheung, Melyssa, Guest, Johnathan, Lee, Jessica, Pierce, Brian, Shor, Ben, Cohen, Tomer, Halfon, Matan, Schneidman-Duhovny, Dina, Zhu, Shaowen, Sun, Yuanfei, Shen, Yang, Maszota-Zieleniak, Martyna, Krzysztof, Bojarski K, Lubecka, Emilia, Marcisz, Mateusz, Danielsson, Annemarie, Dziadek, Lukasz, Gaardlos, Margrethe, Giełdoń, Artur, Liwo, Jozef, Samsonov, Sergey, Slusarz, Rafal, Zieba, Karolina, Sieradzan, Adam, Czaplewski, Cezary, Kobayashi, Shinpei, Miyakawa, Yuta, Kiyota, Yasuomi, Takeda-Shitaka, Mayuko, Olechnovič, Kliment, Valančauskas, Lukas, Dapkūnas, Justas, Venclovas, Ceslovas, Wallner, Björn, Yang, Lin, Hou, Chengyu, He, Xiaodong, Guo, Shuai, Jiang, Shenda, Ma, Xiaoliang, Duan, Rui, Qiu, Liming, Xu, Xianjin, Zou, Xiaoqin, Velankar, Sameer, and Shoshana, Wodak J

Abstract

We present the results for CAPRI Round 54, the 5th joint CASP-CAPRI protein assembly prediction challenge. The Round offered 37 targets, including 14 homo-dimers, 3 homo-trimers, 13 hetero-dimers including 3 antibody-antigen complexes, and 7 large assemblies. On average ~70 CASP and CAPRI predictor groups, including more than 20 automatics servers, submitted models for each target. A total of 21941 models submitted by these groups and by 15 CAPRI scorer groups were evaluated using the CAPRI model quality measures and the DockQ score consolidating these measures. The prediction performance was quantified by a weighted score based on the number of models of acceptable quality or higher submitted by each group among their 5 best models. Results show substantial progress achieved across a significant fraction of the 60+ participating groups. High-quality models were produced for about 40% for the targets compared to 8% two years earlier, a remarkable improvement resulting from the wide use of the AlphaFold2 and AlphaFold-Multimer software. Creative use was made of the deep learning inference engines affording the sampling of a much larger number of models and enriching the multiple sequence alignments with sequences from various sources. Wide use was also made of the AlphaFold confidence metrics to rank models, permitting top performing groups to exceed the results of the public AlphaFold-Multimer version used as a yard stick. This notwithstanding, performance remained poor for complexes with antibodies and nanobodies, where evolutionary relationships between the binding partners are lacking, and for complexes featuring conformational flexibility, clearly indicating that the prediction of protein complexes remains a challenging problem.

Published
2023

16. 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

17. 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

18. 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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19. 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

20. Impact of AlphaFold on Structure Prediction of Protein Complexes: The CASP15-CAPRI Experiment

Author

NMR Spectroscopy, Sub NMR Spectroscopy, Lensink, Marc, Brysbaert, Guillaume, Raouraoua, Nessim, Bates, Paul, Giulini, Marco, Honorato, Rodrigo Vargas, Noort, Charlotte van, Teixeira, João, Bonvin, Alexandre M.J.J., Kong, Ren, Shi, Hang, Lu, Xufeng, Chang, Shan, Liu, Jian, Guo, Zhiye, Chen, Xiao, Morehead, Alex, Roy, Raj, Wu, Tianqi, Giri, Nabin, Quadir, Farhan, Chen, Chen, Cheng, Jianlin, Carpio, Carlos Del, Ichiishi, Eichiro, Luis, Rodriguez-Lumbreras A, Fernández-Recio, Juan, Harmalkar, Ameya, Chu, Lee-Shin, Canner, Sam, Smanta, Rituparna, Gray, Jeffrey, Li, Hao, Lin, Peicong, He, Jiahua, Tao, Huanyu, Huang, Shengyou, Roel, Jorge, Jimenez-Garcia, Brian, Christoffer, Charles, Anika, Jain J, Kagaya, Yuki, Kannan, Harini, Nakamura, Tsukasa, Terashi, Genki, Verburgt, Jacob, Zhang, Yuanyuan, Zhang, Zicong, Fujuta, Hayato, Sekijima, Masakazu, Kihara, Daisuke, Khan, Omeir, Kotelnikov, Sergei, Ghani, Usman, Padhorny, Dzmitry, Beglov, Dmitri, Vajda, Sandor, Kozakov, Dima, Surendra, Negi S, Ricciardelli, Tiziana, Barradas-Bautista, Didier, Cao, Zhen, Chawla, Mohit, Cavallo, Luigi, Oliva, Romina, Yin, Rujie, Cheung, Melyssa, Guest, Johnathan, Lee, Jessica, Pierce, Brian, Shor, Ben, Cohen, Tomer, Halfon, Matan, Schneidman-Duhovny, Dina, Zhu, Shaowen, Sun, Yuanfei, Shen, Yang, Maszota-Zieleniak, Martyna, Krzysztof, Bojarski K, Lubecka, Emilia, Marcisz, Mateusz, Danielsson, Annemarie, Dziadek, Lukasz, Gaardlos, Margrethe, Giełdoń, Artur, Liwo, Jozef, Samsonov, Sergey, Slusarz, Rafal, Zieba, Karolina, Sieradzan, Adam, Czaplewski, Cezary, Kobayashi, Shinpei, Miyakawa, Yuta, Kiyota, Yasuomi, Takeda-Shitaka, Mayuko, Olechnovič, Kliment, Valančauskas, Lukas, Dapkūnas, Justas, Venclovas, Ceslovas, Wallner, Björn, Yang, Lin, Hou, Chengyu, He, Xiaodong, Guo, Shuai, Jiang, Shenda, Ma, Xiaoliang, Duan, Rui, Qiu, Liming, Xu, Xianjin, Zou, Xiaoqin, Velankar, Sameer, Shoshana, Wodak J, NMR Spectroscopy, Sub NMR Spectroscopy, Lensink, Marc, Brysbaert, Guillaume, Raouraoua, Nessim, Bates, Paul, Giulini, Marco, Honorato, Rodrigo Vargas, Noort, Charlotte van, Teixeira, João, Bonvin, Alexandre M.J.J., Kong, Ren, Shi, Hang, Lu, Xufeng, Chang, Shan, Liu, Jian, Guo, Zhiye, Chen, Xiao, Morehead, Alex, Roy, Raj, Wu, Tianqi, Giri, Nabin, Quadir, Farhan, Chen, Chen, Cheng, Jianlin, Carpio, Carlos Del, Ichiishi, Eichiro, Luis, Rodriguez-Lumbreras A, Fernández-Recio, Juan, Harmalkar, Ameya, Chu, Lee-Shin, Canner, Sam, Smanta, Rituparna, Gray, Jeffrey, Li, Hao, Lin, Peicong, He, Jiahua, Tao, Huanyu, Huang, Shengyou, Roel, Jorge, Jimenez-Garcia, Brian, Christoffer, Charles, Anika, Jain J, Kagaya, Yuki, Kannan, Harini, Nakamura, Tsukasa, Terashi, Genki, Verburgt, Jacob, Zhang, Yuanyuan, Zhang, Zicong, Fujuta, Hayato, Sekijima, Masakazu, Kihara, Daisuke, Khan, Omeir, Kotelnikov, Sergei, Ghani, Usman, Padhorny, Dzmitry, Beglov, Dmitri, Vajda, Sandor, Kozakov, Dima, Surendra, Negi S, Ricciardelli, Tiziana, Barradas-Bautista, Didier, Cao, Zhen, Chawla, Mohit, Cavallo, Luigi, Oliva, Romina, Yin, Rujie, Cheung, Melyssa, Guest, Johnathan, Lee, Jessica, Pierce, Brian, Shor, Ben, Cohen, Tomer, Halfon, Matan, Schneidman-Duhovny, Dina, Zhu, Shaowen, Sun, Yuanfei, Shen, Yang, Maszota-Zieleniak, Martyna, Krzysztof, Bojarski K, Lubecka, Emilia, Marcisz, Mateusz, Danielsson, Annemarie, Dziadek, Lukasz, Gaardlos, Margrethe, Giełdoń, Artur, Liwo, Jozef, Samsonov, Sergey, Slusarz, Rafal, Zieba, Karolina, Sieradzan, Adam, Czaplewski, Cezary, Kobayashi, Shinpei, Miyakawa, Yuta, Kiyota, Yasuomi, Takeda-Shitaka, Mayuko, Olechnovič, Kliment, Valančauskas, Lukas, Dapkūnas, Justas, Venclovas, Ceslovas, Wallner, Björn, Yang, Lin, Hou, Chengyu, He, Xiaodong, Guo, Shuai, Jiang, Shenda, Ma, Xiaoliang, Duan, Rui, Qiu, Liming, Xu, Xianjin, Zou, Xiaoqin, Velankar, Sameer, and Shoshana, Wodak J

Published
2023

23. Effect of cold rolling on microstructure and mechanical property of a novel (Fe50Mn30Co10Cr10)97C2Mo1high entropy alloy

Author

Wang, Yuzhe, Chen, Jian, Ding, Rengen, Wang, Weili, He, Jiahua, and Zhou, Xueyang

Abstract

Effect of cold rolling on microstructural evolution and mechanical properties of a novel (Fe50Mn30Co10Cr10)97C2Mo1high entropy alloy has been investigated via electron microscopy and tensile testing. The results show that the alloy is mainly deformed via dislocation and twinning due to its low stacking fault energy. With increasing thickness reduction, the dislocation density first increases, up to a maximum of ∼29.3 × 1014 m−2 at CR 60 %, then reduces, to 21.1 × 1014 m−2 at CR 90 %. The width and spacing of the deformation twins gradually decrease with an increase of thickness reduction. TEM examination also reveals that shear bands produce in CR 40 % sample, and that its volume fraction gradually increases with increasing thickness reduction. Such microstructural evolution with cold rolling, gives rise to a quick enhancement of yield strength of the alloy (1926 MPa for CR 90 % vs. 239 MPa for the as-homogenized sample) whereas a clear loss in ductility. However, the cold-rolled samples at low to medium strains show a good combination of strength and ductility, e.g., CR 20 % sample has a yield strength of 800 MPa (, which is 3.3 times higher than that of as-homogenized HEA) and an elongation of 30 %, while 1300 MPa yield strength and 15 % elongation for CR 40 % sample.

Published
2023
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25. Improvement of cryo-EM maps by simultaneous local and non-local deep learning.

Author

He, Jiahua, Li, Tao, and Huang, Sheng-You

Subjects
DEEP learning, MULTISCALE modeling
Abstract

Cryo-EM has emerged as the most important technique for structure determination of macromolecular complexes. However, raw cryo-EM maps often exhibit loss of contrast at high resolution and heterogeneity over the entire map. As such, various post-processing methods have been proposed to improve cryo-EM maps. Nevertheless, it is still challenging to improve both the quality and interpretability of EM maps. Addressing the challenge, we present a three-dimensional Swin-Conv-UNet-based deep learning framework to improve cryo-EM maps, named EMReady, by not only implementing both local and non-local modeling modules in a multiscale UNet architecture but also simultaneously minimizing the local smooth L1 distance and maximizing the non-local structural similarity between processed experimental and simulated target maps in the loss function. EMReady was extensively evaluated on diverse test sets of 110 primary cryo-EM maps and 25 pairs of half-maps at 3.0–6.0 Å resolutions, and compared with five state-of-the-art map post-processing methods. It is shown that EMReady can not only robustly enhance the quality of cryo-EM maps in terms of map-model correlations, but also improve the interpretability of the maps in automatic de novo model building. Map post-processing is crucial for cryo-EM modeling building. Here, the authors present a deep learning approach to improve both the quality and interpretability of cryo-EM maps by simultaneously considering local and non-local effects. [ABSTRACT FROM AUTHOR]

Published
2023
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29. Advances in Bi 2 WO 6 -Based Photocatalysts for Degradation of Organic Pollutants.

Author

Jiang, Haiyan, He, Jiahua, Deng, Changyi, Hong, Xiaodong, and Liang, Bing

Subjects
*PHOTOCATALYSTS, *POLLUTANTS, *METAL sulfides, *WATER pollution, *TUNGSTEN trioxide, *PHOTOCATALYSIS, *TUNGSTATES
Abstract

With the rapid development of modern industries, water pollution has become an urgent problem that endangers the health of human and wild animals. The photocatalysis technique is considered an environmentally friendly strategy for removing organic pollutants in wastewater. As an important member of Bi-series semiconductors, Bi2WO6 is widely used for fabricating high-performance photocatalysts. In this review, the recent advances of Bi2WO6-based photocatalysts are summarized. First, the controllable synthesis, surface modification and heteroatom doping of Bi2WO6 are introduced. In the respect of Bi2WO6-based composites, existing Bi2WO6-containing binary composites are classified into six types, including Bi2WO6/carbon or MOF composite, Bi2WO6/g-C3N4 composite, Bi2WO6/metal oxides composite, Bi2WO6/metal sulfides composite, Bi2WO6/Bi-series composite, and Bi2WO6/metal tungstates composite. Bi2WO6-based ternary composites are classified into four types, including Bi2WO6/g-C3N4/X, Bi2WO6/carbon/X, Bi2WO6/Au or Ag-based materials/X, and Bi2WO6/Bi-series semiconductors/X. The design, microstructure, and photocatalytic performance of Bi2WO6-based binary and ternary composites are highlighted. Finally, aimed at the existing problems in Bi2WO6-based photocatalysts, some solutions and promising research trends are proposed that would provide theoretical and practical guidelines for developing high-performance Bi2WO6-based photocatalysts. [ABSTRACT FROM AUTHOR]

Published
2022
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36. PMap : unlocking the performance genes of HPC applications

Author

He, Jiahua

Subjects
UCSD Dissertations, Academic Computer science. (Discipline)
Abstract

Performance modeling, the science of understanding and predicting application performance, is important but challenging. High Performance Computing (HPC) with large- scale applications and aggressive technologies, such as dynamic computational grids, hybrid computing platforms, and innovative storage systems, further complicates the task. This dissertation proposed and proved the hypothesis that a small number of performance primitives can be extracted from HPC applications and leveraged for fast application performance modeling and prediction even on large-scale dynamic systems. PMap : a set of methods and tools to extract, measure, and analyze performance primitives in HPC applications are proposed, implemented, and verified under these challenging environments. Two production computational grids, Teragrid and Geon, were monitored with periodically running benchmarks for about half a year. Their performance fluctuated in the 50% range. However, simple benchmarks that serve as performance primitives can be used to predict application performance with a relative error as low as 9%. To map program constructs to the best matched hardware components in hybrid computing platforms, an automatic idioms (performance primitives) recognition method was proposed and implemented based on the open source compiler Open64. With the NAS Parallel Benchmark (NPB) as a case study, the prototype system is about 90% accurate compared with idiom classfication by a human expert. The performance of the idiom benchmarks with their corresponding in- stances in the NPB codes on two different platforms were compared with different methods. The approximation accuracy is up to 97%. With the HPC data challenge and emerging storage technologies, a flash-based supercomputer DASH was designed, built, and tuned. A large parameter space was swept by fast and reliable measurements developed to investigate varying design options, and the results showed that performance can be improved by as much as 9x with appropriate existing technologies developed here. Finally, the PMaC framework was extended to model and predict application performance on flash storage systems. Results showed that the total I/O time can be predicted with reasonable error of 15%. The end result of this body of work is that the performance of applications on supercomputers can be understood by mapping their performance genetics

Published
2011

37. Effect of Ho Addition on the Glass-Forming Ability and Crystallization Behaviors of Zr 54 Cu 29 Al 10 Ni 7 Bulk Metallic Glass.

Author

Lu, Shuaidan, Li, Xiaoxiao, Liang, Xiaoyu, He, Jiahua, Shao, Wenting, Li, Kuanhe, and Chen, Jian

Subjects
METALLIC glasses, CRYSTALLIZATION, DIFFERENTIAL scanning calorimetry, HOLMIUM, TRANSMISSION electron microscopy, ACTIVATION energy, DEBYE temperatures
Abstract

The effect of holmium (Ho) addition on the glass-forming ability (GFA) and crystallization behaviors of Zr54Cu29Al10Ni7 bulk metallic glass (BMGs) were studied by employing differential scanning calorimetry (DSC), X-ray diffraction (XRD), and transmission electron microscopy (TEM). The characteristic temperatures and activation energies of crystallization were obtained from DSC data. Classical kinetic modes were used to evaluate the crystallization processes of Zr54Cu29Al10Ni7 and Zr48Cu29Ni7Al10Ho6 BMGs. The results showed that Ho addition reduces the activation energy in the original crystallization period of Zr-based BMG and improves the nucleation, which is due to the formation of simpler compounds, such as CuZr2, Cu2Ho, and Al3Zr5. [ABSTRACT FROM AUTHOR]

Published
2022
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46. Topology-independent and global protein structure alignment through an FFT-based algorithm.

Author

Wen, Zeyu, He, Jiahua, and Huang, Sheng-You

Subjects
*PROTEIN structure, *MORPHOLOGY, *SEARCH algorithms, *COMPUTATIONAL biology, *ALGORITHMS, *FAST Fourier transforms
Abstract

Motivation Protein structure alignment is one of the fundamental problems in computational structure biology. A variety of algorithms have been developed to address this important issue in the past decade. However, due to their heuristic nature, current structure alignment methods may suffer from suboptimal alignment and/or over-fragmentation and thus lead to a biologically wrong alignment in some cases. To overcome these limitations, we have developed an accurate topology-independent and global structure alignment method through an FFT-based exhaustive search algorithm, which is referred to as FTAlign. Results Our FTAlign algorithm was extensively tested on six commonly used datasets and compared with seven state-of-the-art structure alignment approaches, TMalign, DeepAlign, Kpax, 3DCOMB, MICAN, SPalignNS and CLICK. It was shown that FTAlign outperformed the other methods in reproducing manually curated alignments and obtained a high success rate of 96.7 and 90.0% on two gold-standard benchmarks, MALIDUP and MALISAM, respectively. Moreover, FTAlign also achieved the overall best performance in terms of biologically meaningful structure overlap (SO) and TMscore on both the sequential alignment test sets including MALIDUP, MALISAM and 64 difficult cases from HOMSTRAD, and the non-sequential sets including MALIDUP-NS, MALISAM-NS, 199 topology-different cases, where FTAlign especially showed more advantage for non-sequential alignment. Despite its global search feature, FTAlign is also computationally efficient and can normally complete a pairwise alignment within one second. Availability and implementation http://huanglab.phys.hust.edu.cn/ftalign/. [ABSTRACT FROM AUTHOR]

Published
2020
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49. PepBDB: a comprehensive structural database of biological peptide–protein interactions.

Author

Wen, Zeyu, He, Jiahua, Tao, Huanyu, and Huang, Sheng-You

Subjects
*PROTEIN-protein interactions, *BIOLOGICAL databases, *PEPTIDES, *MOLECULAR docking, *DRUG design
Abstract

Summary A structural database of peptide–protein interactions is important for drug discovery targeting peptide-mediated interactions. Although some peptide databases, especially for special types of peptides, have been developed, a comprehensive database of cleaned peptide–protein complex structures is still not available. Such cleaned structures are valuable for docking and scoring studies in structure-based drug design. Here, we have developed PepBDB—a curated Pep tide B inding D ata B ase of biological complex structures from the Protein Data Bank (PDB). PepBDB presents not only cleaned structures but also extensive information about biological peptide–protein interactions, and allows users to search the database with a variety of options and interactively visualize the search results. Availability and implementation PepBDB is available at http://huanglab.phys.hust.edu.cn/pepbdb/. [ABSTRACT FROM AUTHOR]

Published
2019
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50. Chromosomal instability is associated with prognosis and efficacy of bevacizumab after resection of colorectal cancer liver metastasis.

Author

Li W, Lan J, Zhou C, Yang R, Wang J, He J, Xiao B, Ou Q, Fang Y, Fan W, Lin J, Pan Z, Peng J, and Wu X

Subjects
Humans, Male, Female, Middle Aged, Prognosis, Aged, Antineoplastic Agents, Immunological therapeutic use, Adult, Interleukin-6 metabolism, Vascular Endothelial Growth Factor A metabolism, Retrospective Studies, Bevacizumab therapeutic use, Bevacizumab administration & dosage, Chromosomal Instability, Colorectal Neoplasms pathology, Colorectal Neoplasms genetics, Colorectal Neoplasms drug therapy, Liver Neoplasms secondary, Liver Neoplasms genetics, Liver Neoplasms drug therapy, Liver Neoplasms surgery, Hepatectomy
Abstract

Introduction: Individualized treatment of colorectal cancer liver metastases (CRLM) remains challenging due to differences in the severity of metastatic disease and tumour biology. Exploring specific prognostic risk subgroups is urgently needed. The current study aimed to investigate the prognostic value of chromosomal instability (CIN) in patients with initially resectable CRLM and the predictive value of CIN for the efficacy of bevacizumab., Methods: Ninety-one consecutive patients with initially resectable CRLM who underwent curative liver resection from 2006 to 2018 at Sun Yat-sen University Cancer Center were selected for analysis. CIN was evaluated by automated digital imaging systems. Immunohistochemistry (IHC) was performed to detect interleukin-6 (IL-6), vascular endothelial growth factor A (VEGFA) and CD31 expression in paraffin-embedded specimens. Recurrence-free survival (RFS) and overall survival (OS) were analysed using the Kaplan-Meier method and Cox regression models., Results: Patients with high chromosomal instability (CIN-H) had a worse 3-year RFS rate (HR, 1.953; 95% CI, 1.001-3.810; p  = 0.049) and a worse 3-year OS rate (HR, 2.449; 95% CI, 1.150-5.213; p  = 0.016) than those with low chromosomal instability (CIN-L). CIN-H was identified as an independent prognostic factor for RFS (HR, 2.569; 95% CI, 1.078-6.121; p  = 0.033) and OS (HR, 3.852; 95% CI, 1.173-12.645; p  = 0.026) in the multivariate analysis. The protein levels of IL-6, VEGFA and CD31 were upregulated in patients in the CIN-H group compared to those in the CIN-L group in both primary tumour and liver metastases tissues. Among them, 22 patients with recurrent tumours were treated with first-line bevacizumab treatment and based on the clinical response assessment, disease control rates were adversely associated with chromosomal instability ( p  = 0.043)., Conclusions: Our study showed that high chromosomal instability is a negative prognostic factor for patients with initially resectable CRLM after liver resection. CIN may have positive correlations with angiogenesis through expression of IL-6-VEGFA axis and be used as a potential predictor of efficacy of bevacizumab.

Published
2024
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