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49 results on '"Olechnovič, Kliment"'

1. Modeling SARS‐CoV‐2 proteins in the CASP‐commons experiment

Author

Kryshtafovych, Andriy, Moult, John, Billings, Wendy M, Della Corte, Dennis, Fidelis, Krzysztof, Kwon, Sohee, Olechnovič, Kliment, Seok, Chaok, Venclovas, Česlovas, Won, Jonghun, and participants, CASP‐COVID

Subjects
Biochemistry and Cell Biology, Bioinformatics and Computational Biology, Biological Sciences, Prevention, COVID-19, Genome, Viral, Humans, Models, Molecular, Protein Conformation, Protein Domains, SARS-CoV-2, Viral Proteins, Viroporin Proteins, CASP, COVID, EMA, model accuracy, protein structure prediction, CASP-COVID participants, Mathematical Sciences, Information and Computing Sciences, Bioinformatics, Biological sciences, Mathematical sciences
Abstract

Critical Assessment of Structure Prediction (CASP) is an organization aimed at advancing the state of the art in computing protein structure from sequence. In the spring of 2020, CASP launched a community project to compute the structures of the most structurally challenging proteins coded for in the SARS-CoV-2 genome. Forty-seven research groups submitted over 3000 three-dimensional models and 700 sets of accuracy estimates on 10 proteins. The resulting models were released to the public. CASP community members also worked together to provide estimates of local and global accuracy and identify structure-based domain boundaries for some proteins. Subsequently, two of these structures (ORF3a and ORF8) have been solved experimentally, allowing assessment of both model quality and the accuracy estimates. Models from the AlphaFold2 group were found to have good agreement with the experimental structures, with main chain GDT_TS accuracy scores ranging from 63 (a correct topology) to 87 (competitive with experiment).

Published
2021

2. Comparative analysis of methods for evaluation of protein models against native structures

Author

Olechnovič, Kliment, Monastyrskyy, Bohdan, Kryshtafovych, Andriy, and Venclovas, Česlovas

Subjects
Biochemistry and Cell Biology, Bioinformatics and Computational Biology, Biological Sciences, Computational Biology, Models, Molecular, Protein Conformation, Proteins, Mathematical Sciences, Information and Computing Sciences, Bioinformatics, Biological sciences, Information and computing sciences, Mathematical sciences
Abstract

MotivationMeasuring discrepancies between protein models and native structures is at the heart of development of protein structure prediction methods and comparison of their performance. A number of different evaluation methods have been developed; however, their comprehensive and unbiased comparison has not been performed.ResultsWe carried out a comparative analysis of several popular model assessment methods (RMSD, TM-score, GDT, QCS, CAD-score, LDDT, SphereGrinder and RPF) to reveal their relative strengths and weaknesses. The analysis, performed on a large and diverse model set derived in the course of three latest community-wide CASP experiments (CASP10-12), had two major directions. First, we looked at general differences between the scores by analyzing distribution, correspondence and correlation of their values as well as differences in selecting best models. Second, we examined the score differences taking into account various structural properties of models (stereochemistry, hydrogen bonds, packing of domains and chain fragments, missing residues, protein length and secondary structure). Our results provide a solid basis for an informed selection of the most appropriate score or combination of scores depending on the task at hand.Supplementary informationSupplementary data are available at Bioinformatics online.

Published
2019

6. TemStaPro: protein thermostability prediction using sequence representations from protein language models.

Author

Pudžiuvelytė, Ieva, Olechnovič, Kliment, Godliauskaite, Egle, Sermokas, Kristupas, Urbaitis, Tomas, Gasiunas, Giedrius, and Kazlauskas, Darius

Subjects
*LANGUAGE models, *PROTEIN models, *PROTEIN stability, *DEEP learning, *AMINO acid sequence, *MACHINE learning
Abstract

Motivation Reliable prediction of protein thermostability from its sequence is valuable for both academic and industrial research. This prediction problem can be tackled using machine learning and by taking advantage of the recent blossoming of deep learning methods for sequence analysis. These methods can facilitate training on more data and, possibly, enable the development of more versatile thermostability predictors for multiple ranges of temperatures. Results We applied the principle of transfer learning to predict protein thermostability using embeddings generated by protein language models (pLMs) from an input protein sequence. We used large pLMs that were pre-trained on hundreds of millions of known sequences. The embeddings from such models allowed us to efficiently train and validate a high-performing prediction method using over one million sequences that we collected from organisms with annotated growth temperatures. Our method, TemStaPro (Temperatures of Stability for Proteins), was used to predict thermostability of CRISPR-Cas Class II effector proteins (C2EPs). Predictions indicated sharp differences among groups of C2EPs in terms of thermostability and were largely in tune with previously published and our newly obtained experimental data. Availability and implementation TemStaPro software and the related data are freely available from https://github.com/ievapudz/TemStaPro and https://doi.org/10.5281/zenodo.7743637. [ABSTRACT FROM AUTHOR]

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

Author

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

Published
2023
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11. Discriminating physiological from non‐physiological interfaces in structures of protein complexes: A community‐wide study

Author

Schweke, Hugo, primary, Xu, Qifang, additional, Tauriello, Gerardo, additional, Pantolini, Lorenzo, additional, Schwede, Torsten, additional, Cazals, Frédéric, additional, Lhéritier, Alix, additional, Fernandez‐Recio, Juan, additional, Rodríguez‐Lumbreras, Luis Angel, additional, Schueler‐Furman, Ora, additional, Varga, Julia K., additional, Jiménez‐García, Brian, additional, Réau, Manon F., additional, Bonvin, Alexandre M. J. J., additional, Savojardo, Castrense, additional, Martelli, Pier‐Luigi, additional, Casadio, Rita, additional, Tubiana, Jérôme, additional, Wolfson, Haim J., additional, Oliva, Romina, additional, Barradas‐Bautista, Didier, additional, Ricciardelli, Tiziana, additional, Cavallo, Luigi, additional, Venclovas, Česlovas, additional, Olechnovič, Kliment, additional, Guerois, Raphael, additional, Andreani, Jessica, additional, Martin, Juliette, additional, Wang, Xiao, additional, Terashi, Genki, additional, Sarkar, Daipayan, additional, Christoffer, Charles, additional, Aderinwale, Tunde, additional, Verburgt, Jacob, additional, Kihara, Daisuke, additional, Marchand, Anthony, additional, Correia, Bruno E., additional, Duan, Rui, additional, Qiu, Liming, additional, Xu, Xianjin, additional, Zhang, Shuang, additional, Zou, Xiaoqin, additional, Dey, Sucharita, additional, Dunbrack, Roland L., additional, Levy, Emmanuel D., additional, and Wodak, Shoshana J., additional

Published
2023
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13. 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

14. Discriminating physiological from non-physiological interfaces in structures of protein complexes: A community-wide study

Author

Schweke, Hugo, Xu, Qifang, Tauriello, Gerardo, Pantolini, Lorenzo, Schwede, Torsten, Cazals, Frédéric, Lhéritier, Alix, Fernandez-Recio, Juan, Rodríguez-Lumbreras, Luis Angel, Schueler-Furman, Ora, Varga, Julia K., Jiménez-García, Brian, Réau, Manon F., Bonvin, Alexandre M.J.J., Savojardo, Castrense, Martelli, Pier Luigi, Casadio, Rita, Tubiana, Jérôme, Wolfson, Haim J., Oliva, Romina, Barradas-Bautista, Didier, Ricciardelli, Tiziana, Cavallo, Luigi, Venclovas, Česlovas, Olechnovič, Kliment, Guerois, Raphael, Andreani, Jessica, Martin, Juliette, Wang, Xiao, Terashi, Genki, Sarkar, Daipayan, Christoffer, Charles, Aderinwale, Tunde, Verburgt, Jacob, Kihara, Daisuke, Marchand, Anthony, Correia, Bruno E., Duan, Rui, Qiu, Liming, Xu, Xianjin, Zhang, Shuang, Zou, Xiaoqin, Dey, Sucharita, Dunbrack, Roland L., Levy, Emmanuel D., Wodak, Shoshana J., Schweke, Hugo, Xu, Qifang, Tauriello, Gerardo, Pantolini, Lorenzo, Schwede, Torsten, Cazals, Frédéric, Lhéritier, Alix, Fernandez-Recio, Juan, Rodríguez-Lumbreras, Luis Angel, Schueler-Furman, Ora, Varga, Julia K., Jiménez-García, Brian, Réau, Manon F., Bonvin, Alexandre M.J.J., Savojardo, Castrense, Martelli, Pier Luigi, Casadio, Rita, Tubiana, Jérôme, Wolfson, Haim J., Oliva, Romina, Barradas-Bautista, Didier, Ricciardelli, Tiziana, Cavallo, Luigi, Venclovas, Česlovas, Olechnovič, Kliment, Guerois, Raphael, Andreani, Jessica, Martin, Juliette, Wang, Xiao, Terashi, Genki, Sarkar, Daipayan, Christoffer, Charles, Aderinwale, Tunde, Verburgt, Jacob, Kihara, Daisuke, Marchand, Anthony, Correia, Bruno E., Duan, Rui, Qiu, Liming, Xu, Xianjin, Zhang, Shuang, Zou, Xiaoqin, Dey, Sucharita, Dunbrack, Roland L., Levy, Emmanuel D., and Wodak, Shoshana J.

Abstract

Reliably scoring and ranking candidate models of protein complexes and assigning their oligomeric state from the structure of the crystal lattice represent outstanding challenges. A community-wide effort was launched to tackle these challenges. The latest resources on protein complexes and interfaces were exploited to derive a benchmark dataset consisting of 1677 homodimer protein crystal structures, including a balanced mix of physiological and non-physiological complexes. The non-physiological complexes in the benchmark were selected to bury a similar or larger interface area than their physiological counterparts, making it more difficult for scoring functions to differentiate between them. Next, 252 functions for scoring protein-protein interfaces previously developed by 13 groups were collected and evaluated for their ability to discriminate between physiological and non-physiological complexes. A simple consensus score generated using the best performing score of each of the 13 groups, and a cross-validated Random Forest (RF) classifier were created. Both approaches showed excellent performance, with an area under the Receiver Operating Characteristic (ROC) curve of 0.93 and 0.94, respectively, outperforming individual scores developed by different groups. Additionally, AlphaFold2 engines recalled the physiological dimers with significantly higher accuracy than the non-physiological set, lending support to the reliability of our benchmark dataset annotations. Optimizing the combined power of interface scoring functions and evaluating it on challenging benchmark datasets appears to be a promising strategy.

Published
2023

15. Discriminating physiological from non-physiological interfaces in structures of protein complexes: A community-wide study

Author

Sub NMR Spectroscopy, NMR Spectroscopy, Schweke, Hugo, Xu, Qifang, Tauriello, Gerardo, Pantolini, Lorenzo, Schwede, Torsten, Cazals, Frédéric, Lhéritier, Alix, Fernandez-Recio, Juan, Rodríguez-Lumbreras, Luis Angel, Schueler-Furman, Ora, Varga, Julia K., Jiménez-García, Brian, Réau, Manon F., Bonvin, Alexandre M.J.J., Savojardo, Castrense, Martelli, Pier Luigi, Casadio, Rita, Tubiana, Jérôme, Wolfson, Haim J., Oliva, Romina, Barradas-Bautista, Didier, Ricciardelli, Tiziana, Cavallo, Luigi, Venclovas, Česlovas, Olechnovič, Kliment, Guerois, Raphael, Andreani, Jessica, Martin, Juliette, Wang, Xiao, Terashi, Genki, Sarkar, Daipayan, Christoffer, Charles, Aderinwale, Tunde, Verburgt, Jacob, Kihara, Daisuke, Marchand, Anthony, Correia, Bruno E., Duan, Rui, Qiu, Liming, Xu, Xianjin, Zhang, Shuang, Zou, Xiaoqin, Dey, Sucharita, Dunbrack, Roland L., Levy, Emmanuel D., Wodak, Shoshana J., Sub NMR Spectroscopy, NMR Spectroscopy, Schweke, Hugo, Xu, Qifang, Tauriello, Gerardo, Pantolini, Lorenzo, Schwede, Torsten, Cazals, Frédéric, Lhéritier, Alix, Fernandez-Recio, Juan, Rodríguez-Lumbreras, Luis Angel, Schueler-Furman, Ora, Varga, Julia K., Jiménez-García, Brian, Réau, Manon F., Bonvin, Alexandre M.J.J., Savojardo, Castrense, Martelli, Pier Luigi, Casadio, Rita, Tubiana, Jérôme, Wolfson, Haim J., Oliva, Romina, Barradas-Bautista, Didier, Ricciardelli, Tiziana, Cavallo, Luigi, Venclovas, Česlovas, Olechnovič, Kliment, Guerois, Raphael, Andreani, Jessica, Martin, Juliette, Wang, Xiao, Terashi, Genki, Sarkar, Daipayan, Christoffer, Charles, Aderinwale, Tunde, Verburgt, Jacob, Kihara, Daisuke, Marchand, Anthony, Correia, Bruno E., Duan, Rui, Qiu, Liming, Xu, Xianjin, Zhang, Shuang, Zou, Xiaoqin, Dey, Sucharita, Dunbrack, Roland L., Levy, Emmanuel D., and Wodak, Shoshana J.

Published
2023

16. Discriminating physiological from non-physiological interfaces in structures of protein complexes: A community-wide study

Author

National Institutes of Health (US), National Science Foundation (US), Netherlands eScience Center, Netherlands Organization for Scientific Research, European Commission, Agencia Estatal de Investigación (España), Ministerio de Ciencia, Innovación y Universidades (España), Generalitat de Catalunya, European Research Council, Israel Science Foundation, Israel Academy of Sciences and Humanities, Schweke, Hugo, Xu, Qifang, Tauriello, Gerardo, Pantolini, Lorenzo, Schwede, Torsten, Cazals, Frédéric, Lhéritier, Alix, Fernández-Recio, Juan, Rodríguez-Lumbreras, Luis A., Schueler-Furman, Ora, Varga, Julia K., Jiménez-García, Brian, Réau, Manon F., Bonvin, Alexandre M. J. J., Savojardo, Castrense, Martelli, Pier-Luigi, Casadio, Rita, Tubiana, Jérôme, Wolfson, Haim J., Oliva, Romina, Barradas-Bautista, Didier, Ricciardelli, Tiziana, Cavallo, Luigi, Venclovas, Česlovas, Olechnovič, Kliment, Guerois, Raphael, Andreani, Jessica, Martin, Juliette, Wang, Xiao, Terashi, Genki, Sarkar, Daipayan, Christoffer, Charles, Aderinwale, Tunde, Verburgt, Jacob, Kihara, Daisuke, Marchand, Anthony, Correia, Bruno E., Duan, Rui, Qiu, Liming, Xu, Xianjin, Zhang, Shuang, Zou, Xiaoqin, Dey, Sucharita, Dunbrack, Roland L., Levy, Emmanuel D., Wodak, Shoshana J., National Institutes of Health (US), National Science Foundation (US), Netherlands eScience Center, Netherlands Organization for Scientific Research, European Commission, Agencia Estatal de Investigación (España), Ministerio de Ciencia, Innovación y Universidades (España), Generalitat de Catalunya, European Research Council, Israel Science Foundation, Israel Academy of Sciences and Humanities, Schweke, Hugo, Xu, Qifang, Tauriello, Gerardo, Pantolini, Lorenzo, Schwede, Torsten, Cazals, Frédéric, Lhéritier, Alix, Fernández-Recio, Juan, Rodríguez-Lumbreras, Luis A., Schueler-Furman, Ora, Varga, Julia K., Jiménez-García, Brian, Réau, Manon F., Bonvin, Alexandre M. J. J., Savojardo, Castrense, Martelli, Pier-Luigi, Casadio, Rita, Tubiana, Jérôme, Wolfson, Haim J., Oliva, Romina, Barradas-Bautista, Didier, Ricciardelli, Tiziana, Cavallo, Luigi, Venclovas, Česlovas, Olechnovič, Kliment, Guerois, Raphael, Andreani, Jessica, Martin, Juliette, Wang, Xiao, Terashi, Genki, Sarkar, Daipayan, Christoffer, Charles, Aderinwale, Tunde, Verburgt, Jacob, Kihara, Daisuke, Marchand, Anthony, Correia, Bruno E., Duan, Rui, Qiu, Liming, Xu, Xianjin, Zhang, Shuang, Zou, Xiaoqin, Dey, Sucharita, Dunbrack, Roland L., Levy, Emmanuel D., and Wodak, Shoshana J.

Abstract

Reliably scoring and ranking candidate models of protein complexes and assigning their oligomeric state from the structure of the crystal lattice represent outstanding challenges. A community-wide effort was launched to tackle these challenges. The latest resources on protein complexes and interfaces were exploited to derive a benchmark dataset consisting of 1677 homodimer protein crystal structures, including a balanced mix of physiological and non-physiological complexes. The non-physiological complexes in the benchmark were selected to bury a similar or larger interface area than their physiological counterparts, making it more difficult for scoring functions to differentiate between them. Next, 252 functions for scoring protein-protein interfaces previously developed by 13 groups were collected and evaluated for their ability to discriminate between physiological and non-physiological complexes. A simple consensus score generated using the best performing score of each of the 13 groups, and a cross-validated Random Forest (RF) classifier were created. Both approaches showed excellent performance, with an area under the Receiver Operating Characteristic (ROC) curve of 0.93 and 0.94, respectively, outperforming individual scores developed by different groups. Additionally, AlphaFold2 engines recalled the physiological dimers with significantly higher accuracy than the non-physiological set, lending support to the reliability of our benchmark dataset annotations. Optimizing the combined power of interface scoring functions and evaluating it on challenging benchmark datasets appears to be a promising strategy.

Published
2023

17. 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

18. Prediction of protein assemblies, the next frontier: The CASP14‐CAPRI experiment

Author

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

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

Author

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

Abstract

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

Published
2021

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

Author

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

Published
2021

27. Estimation of model accuracy in CASP13

Author

Cheng, Jianlin, Choe, Myong‐Ho, Elofsson, Arne, Han, Kun‐Sop, Hou, Jie, Maghrabi, Ali H. A., McGuffin, Liam J., Menéndez‐Hurtado, David, Olechnovič, Kliment, Schwede, Torsten, Studer, Gabriel, Uziela, Karolis, Venclovas, Česlovas, and Wallner, Björn

Abstract

Methods to reliably estimate the accuracy of 3D models of proteins are both a fundamental part of most protein folding pipelines and important for reliable identification of the best models when multiple pipelines are used. Here, we describe the progress made from CASP12 to CASP13 in the field of estimation of model accuracy (EMA) as seen from the progress of the most successful methods in CASP13. We show small but clear progress, that is, several methods perform better than the best methods from CASP12 when tested on CASP13 EMA targets. Some progress is driven by applying deep learning and residue‐residue contacts to model accuracy prediction. We show that the best EMA methods select better models than the best servers in CASP13, but that there exists a great potential to improve this further. Also, according to the evaluation criteria based on local similarities, such as lDDT and CAD, it is now clear that single model accuracy methods perform relatively better than consensus‐based methods.

Published
2019

29. Blind prediction of homo‐ and hetero‐protein complexes: The CASP13‐CAPRI experiment

Author

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

Published
2019
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32. Methods for the analysis and assessment of the three-dimensional structures of proteins and nucleic acids: development and applications

Author

Olechnovič, Kliment and Venclovas, Česlovas

Subjects
Quantitative Biology::Biomolecules, bioinformatics, geometry, biochemistry, Computer Science::Computational Geometry
Abstract

This dissertation describes three novel effective methods for the analysis and evaluation of biomolecular structures. The presented methods construct and utilize the Voronoi tessellation of atomic balls and the tessellation-derived interatomic contact areas. The first method, Voronota, is a method for computing the vertices of the Voronoi diagram of balls. It is capable of processing macromolecular structures efficiently by exploiting common patterns of atomic spatial arrangements. The second method, CAD-score (Contact Area Difference Score), is a highly effective method for the comparison of different conformations of macromolecules, for example, native and modeled structures. It is universally applicable for the comparison of structures of all the major types of macromolecules (proteins, nucleic acids and their complexes). The third method, VoroMQA (Voronoi diagram­-based Model Quality Assessment), is a method for the evaluation of predicted protein structures when the native structure is unknown. It efficiently combines the idea of knowledge-based statistical potential with the concept of interatomic contact areas derived from the Voronoi tessellation of atomic balls, it consistently outperforms other statistical potential-based methods. The main conclusion of the presented studies is that Voronoi tessellation-derived contact areas effectively capture important structural features of biological macromolecules.

Published
2017

33. Baltymų ir nukleorūgščių erdvinių struktūrų analizės ir vertinimo metodai: kūrimas ir taikymas

Author

Olechnovič, Kliment and Venclovas, Česlovas

Subjects
Quantitative Biology::Biomolecules, bioinformatics, geometry, biochemistry, Computer Science::Computational Geometry
Abstract

This dissertation describes three novel effective methods for the analysis and evaluation of biomolecular structures. The presented methods construct and utilize the Voronoi tessellation of atomic balls and the tessellation-derived interatomic contact areas. The first method, Voronota, is a method for computing the vertices of the Voronoi diagram of balls. It is capable of processing macromolecular structures efficiently by exploiting common patterns of atomic spatial arrangements. The second method, CAD-score (Contact Area Difference Score), is a highly effective method for the comparison of different conformations of macromolecules, for example, native and modeled structures. It is universally applicable for the comparison of structures of all the major types of macromolecules (proteins, nucleic acids and their complexes). The third method, VoroMQA (Voronoi diagram­-based Model Quality Assessment), is a method for the evaluation of predicted protein structures when the native structure is unknown. It efficiently combines the idea of knowledge-based statistical potential with the concept of interatomic contact areas derived from the Voronoi tessellation of atomic balls, it consistently outperforms other statistical potential-based methods. The main conclusion of the presented studies is that Voronoi tessellation-derived contact areas effectively capture important structural features of biological macromolecules.

Published
2017

34. Template‐based modeling of diverse protein interactions in CAPRI rounds 38‐45.

Author

Dapkūnas, Justas, Kairys, Visvaldas, Olechnovič, Kliment, and Venclovas, Česlovas

Abstract

Structures of proteins complexed with other proteins, peptides, or ligands are essential for investigation of molecular mechanisms. However, the experimental structures of protein complexes of interest are often not available. Therefore, computational methods are widely used to predict these structures, and, of those methods, template‐based modeling is the most successful. In the rounds 38‐45 of the Critical Assessment of PRediction of Interactions (CAPRI), we applied template‐based modeling for 9 of 11 protein‐protein and protein‐peptide interaction targets, resulting in medium and high‐quality models for six targets. For the protein‐oligosaccharide docking targets, we used constraints derived from template structures, and generated models of at least acceptable quality for most of the targets. Apparently, high flexibility of oligosaccharide molecules was the main cause preventing us from obtaining models of higher quality. We also participated in the CAPRI scoring challenge, the goal of which was to identify the highest quality models from a large pool of decoys. In this experiment, we tested VoroMQA, a scoring method based on interatomic contact areas. The results showed VoroMQA to be quite effective in scoring strongly binding and obligatory protein complexes, but less successful in the case of transient interactions. We extensively used manual intervention in both CAPRI modeling and scoring experiments. This oftentimes allowed us to select the correct templates from available alternatives and to limit the search space during the model scoring. [ABSTRACT FROM AUTHOR]

Published
2020
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40. Modeling of protein complexes in CAPRI Round 37 using template‐based approach combined with model selection.

Author

Dapkūnas, Justas, Olechnovič, Kliment, and Venclovas, Česlovas

Abstract

Abstract: We participated in Round 37 of the Critical Assessment of PRediction of Interactions (CAPRI), held jointly with the 12th edition of the Critical Assessment of protein Structure Prediction (CASP12), having two major objectives. First, we intended to test the utility of our PPI3D web server in finding and selecting templates for comparative modeling of structures of protein complexes. Our second aim was to evaluate the ability of our model accuracy estimation method VoroMQA to score and rank structural models for protein‐protein interactions. Using sequence search in PPI3D and HHpred servers we identified multimeric templates for 7 of 11 CAPRI targets, and models of at least acceptable quality were constructed for 6 of them. The clustering and visual analysis features implemented in the PPI3D software were instrumental in detecting alternative protein‐protein interaction interfaces among the identified templates. When a single binding mode was observed for homologous proteins, the structural modeling of the protein complex was fairly straightforward, whereas choosing the correct interaction template from several alternatives turned out to be a difficult task requiring manual intervention. The combination of full structure and interaction interface VoroMQA scores effectively ranked structural models of protein complexes and selected models of better quality from the CAPRI Scoring sets. The overall results show possible uses of PPI3D and VoroMQA in structural modeling of protein‐protein interactions and suggest ways for further improvements of both methods. [ABSTRACT FROM AUTHOR]

Published
2018
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45. The PPI3D web server for searching, analyzing and modeling protein--protein interactions in the context of 3D structures.

Author

Dapkũnas, Justas, Timinskas, Albertas, Olechnovič, Kliment, Margelevičius, Mindaugas, Dičiũnas, Rytis, and Venclovas, Česlovas

Subjects
INTERNET servers, DATABASE searching, PROTEIN-protein interactions, CLUSTERING of particles, CENTROIDAL Voronoi tessellations
Abstract

The PPI3D web server is focused on searching and analyzing the structural data on protein--protein interactions. Reducing the data redundancy by clustering and analyzing the properties of interaction interfaces using Voronoi tessellation makes this software a highly effective tool for addressing different questions related to protein interactions. [ABSTRACT FROM AUTHOR]

Published
2017
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46. CAD-score: A new contact area difference-based function for evaluation of protein structural models.

Author

Olechnovič, Kliment, Kulberkytė, Eleonora, and Venclovas, Česlovas

Abstract

Evaluation of protein models against the native structure is essential for the development and benchmarking of protein structure prediction methods. Although a number of evaluation scores have been proposed to date, many aspects of model assessment still lack desired robustness. In this study we present CAD-score, a new evaluation function quantifying differences between physical contacts in a model and the reference structure. The new score uses the concept of residue-residue contact area difference (CAD) introduced by Abagyan and Totrov (J Mol Biol 1997; 268:678-685). Contact areas, the underlying basis of the score, are derived using the Voronoi tessellation of protein structure. The newly introduced CAD-score is a continuous function, confined within fixed limits, free of any arbitrary thresholds or parameters. The built-in logic for treatment of missing residues allows consistent ranking of models of any degree of completeness. We tested CAD-score on a large set of diverse models and compared it to GDT-TS, a widely accepted measure of model accuracy. Similarly to GDT-TS, CAD-score showed a robust performance on single-domain proteins, but displayed a stronger preference for physically more realistic models. Unlike GDT-TS, the new score revealed a balanced assessment of domain rearrangement, removing the necessity for different treatment of single-domain, multi-domain, and multi-subunit structures. Moreover, CAD-score makes it possible to assess the accuracy of inter-domain or inter-subunit interfaces directly. In addition, the approach offers an alternative to the superposition-based model clustering. The CAD-score implementation is available both as a web server and a standalone software package at http://www.ibt.lt/bioinformatics/cad-score/. Proteins 2013. © 2012 Wiley Periodicals, Inc. [ABSTRACT FROM AUTHOR]

Published
2013
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47. Discriminating physiological from non‐physiological interfaces in structures of protein complexes: A community‐wide study

Author

Schweke, Hugo, Xu, Qifang, Tauriello, Gerardo, Pantolini, Lorenzo, Schwede, Torsten, Cazals, Frédéric, Lhéritier, Alix, Fernandez‐Recio, Juan, Rodríguez‐Lumbreras, Luis Angel, Schueler‐Furman, Ora, Varga, Julia K., Jiménez‐García, Brian, Réau, Manon F., Bonvin, Alexandre M. J. J., Savojardo, Castrense, Martelli, Pier‐Luigi, Casadio, Rita, Tubiana, Jérôme, Wolfson, Haim J., Oliva, Romina, Barradas‐Bautista, Didier, Ricciardelli, Tiziana, Cavallo, Luigi, Venclovas, Česlovas, Olechnovič, Kliment, Guerois, Raphael, Andreani, Jessica, Martin, Juliette, Wang, Xiao, Terashi, Genki, Sarkar, Daipayan, Christoffer, Charles, Aderinwale, Tunde, Verburgt, Jacob, Kihara, Daisuke, Marchand, Anthony, Correia, Bruno E., Duan, Rui, Qiu, Liming, Xu, Xianjin, Zhang, Shuang, Zou, Xiaoqin, Dey, Sucharita, Dunbrack, Roland L., Levy, Emmanuel D., and Wodak, Shoshana J.

Subjects
complexes, physiological, interface, AlphaFold, prediction, protein
Abstract

Reliably scoring and ranking candidate models of protein complexes and assigning their oligomeric state from the structure of the crystal lattice represent outstanding challenges. A community-wide effort was launched to tackle these challenges. The latest resources on protein complexes and interfaces were exploited to derive a benchmark dataset consisting of 1677 homodimer protein crystal structures, including a balanced mix of physiological and non-physiological complexes. The non-physiological complexes in the benchmark were selected to bury a similar or larger interface area than their physiological counterparts, making it more difficult for scoring functions to differentiate between them. Next, 252 functions for scoring protein-protein interfaces previously developed by 13 groups were collected and evaluated for their ability to discriminate between physiological and non-physiological complexes. A simple consensus score generated using the best performing score of each of the 13 groups, and a cross-validated Random Forest (RF) classifier were created. Both approaches showed excellent performance, with an area under the Receiver Operating Characteristic (ROC) curve of 0.93 and 0.94, respectively, outperforming individual scores developed by different groups. Additionally, AlphaFold2 engines recalled the physiological dimers with significantly higher accuracy than the non-physiological set, lending support to the reliability of our benchmark dataset annotations. Optimizing the combined power of interface scoring functions and evaluating it on challenging benchmark datasets appears to be a promising strategy.

48. Contact Area-Based Structural Analysis of Proteins and Their Complexes Using CAD-Score.

Author

Olechnovič K and Venclovas Č

Subjects
Algorithms, Binding Sites, Protein Binding, Software, Computational Biology methods, Protein Conformation, Proteins chemistry
Abstract

Quantifying discrepancies between computationally derived and native (reference) structures is an essential step in the development and comparison of protein modeling and protein-protein docking methods. Measuring conformational differences of proteins or protein complexes is also important in other areas of structural biology such as molecular dynamics and crystallography. There are multiple scores to do that. However, nearly all of them, whether superposition-based (e.g., RMSD) or superposition-free, use distances to measure similarity. CAD-score is conceptually different as it uses physical contacts represented as contact areas. Such representation makes it possible to quantify differences of both structures and surfaces (e.g., protein-protein interfaces and binding sites) using the same framework. A number of studies have found CAD-score to be among the most robust scores. The method is implemented both as a web server and as standalone software available at http://bioinformatics.lt/software/cad-score . Here, we describe how to use the standalone CAD-score software for comparison and analysis of protein structures, interfaces, and binding sites.

Published
2020
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49. Estimation of model accuracy in CASP13.

Author

Cheng J, Choe MH, Elofsson A, Han KS, Hou J, Maghrabi AHA, McGuffin LJ, Menéndez-Hurtado D, Olechnovič K, Schwede T, Studer G, Uziela K, Venclovas Č, and Wallner B

Subjects
Algorithms, Databases, Protein, Models, Molecular, Protein Folding, Proteins chemistry, Proteins genetics, Sequence Alignment, Sequence Analysis, Protein, Computational Biology, Protein Conformation, Proteins ultrastructure, Software
Abstract

Methods to reliably estimate the accuracy of 3D models of proteins are both a fundamental part of most protein folding pipelines and important for reliable identification of the best models when multiple pipelines are used. Here, we describe the progress made from CASP12 to CASP13 in the field of estimation of model accuracy (EMA) as seen from the progress of the most successful methods in CASP13. We show small but clear progress, that is, several methods perform better than the best methods from CASP12 when tested on CASP13 EMA targets. Some progress is driven by applying deep learning and residue-residue contacts to model accuracy prediction. We show that the best EMA methods select better models than the best servers in CASP13, but that there exists a great potential to improve this further. Also, according to the evaluation criteria based on local similarities, such as lDDT and CAD, it is now clear that single model accuracy methods perform relatively better than consensus-based methods., (© 2019 Wiley Periodicals, Inc.)

Published
2019
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