Search

Your search keyword '"Elofsson, Arne"' showing total 1,151 results
Start Over Author "Elofsson, Arne"
1,151 results on '"Elofsson, Arne"'

51. A “grappling hook” interaction connects self-assembly and chaperone activity of Nucleophosmin 1

Author

Saluri, Mihkel, primary, Leppert, Axel, additional, Gese, Genis Valentin, additional, Sahin, Cagla, additional, Lama, Dilraj, additional, Kaldmäe, Margit, additional, Chen, Gefei, additional, Elofsson, Arne, additional, Allison, Timothy M, additional, Arsenian-Henriksson, Marie, additional, Johansson, Jan, additional, Lane, David P, additional, Hällberg, B Martin, additional, and Landreh, Michael, additional

Published
2023
Full Text
View/download PDF

56. Multiplexed selectivity screening of anti-GPCR antibodies

Author

Dahl, Leo, primary, Kotliar, Ilana B., additional, Bendes, Annika, additional, Dodig-Crnković, Tea, additional, Fromm, Samuel, additional, Elofsson, Arne, additional, Uhlén, Mathias, additional, Sakmar, Thomas P., additional, and Schwenk, Jochen M., additional

Published
2022
Full Text
View/download PDF

59. A “grappling hook” interaction balances self-assembly and chaperone activity of Nucleophosmin 1

Author

Saluri, Mihkel, primary, Leppert, Axel, additional, Gese, Genis Valentin, additional, Sahin, Cagla, additional, Lama, Dilraj, additional, Kaldmäe, Margit, additional, Chen, Gefei, additional, Elofsson, Arne, additional, Allison, Timothy M., additional, Arsenian-Henriksson, Marie, additional, Johansson, Jan, additional, Lane, David P., additional, Hällberg, B. Martin, additional, and Landreh, Michael, additional

Published
2022
Full Text
View/download PDF

68. Discovery of widespread transcription initiation at microsatellites predictable by sequence-based deep neural network

Author

Grapotte, Mathys, Elofsson, Arne, Sachenkova, Oxana, Bréhélin, Laurent, Lecellier, Charles-Henri, Grapotte, Mathys, Elofsson, Arne, Sachenkova, Oxana, Bréhélin, Laurent, and Lecellier, Charles-Henri

Abstract

Using the Cap Analysis of Gene Expression (CAGE) technology, the FANTOM5 consortium provided one of the most comprehensive maps of transcription start sites (TSSs) in several species. Strikingly, ~72% of them could not be assigned to a specific gene and initiate at unconventional regions, outside promoters or enhancers. Here, we probe these unassigned TSSs and show that, in all species studied, a significant fraction of CAGE peaks initiate at microsatellites, also called short tandem repeats (STRs). To confirm this transcription, we develop Cap Trap RNA-seq, a technology which combines cap trapping and long read MinION sequencing. We train sequence-based deep learning models able to predict CAGE signal at STRs with high accuracy. These models unveil the importance of STR surrounding sequences not only to distinguish STR classes, but also to predict the level of transcription initiation. Importantly, genetic variants linked to human diseases are preferentially found at STRs with high transcription initiation level, supporting the biological and clinical relevance of transcription initiation at STRs. Together, our results extend the repertoire of non-coding transcription associated with DNA tandem repeats and complexify STR polymorphism., For correction, see: Grapotte, M., Saraswat, M., Bessière, C. et al. Author Correction: Discovery of widespread transcription initiation at microsatellites predictable by sequence-based deep neural network. Nat Commun 13, 1200 (2022). DOI: 10.1038/s41467-022-28758-y

Published
2022
Full Text
View/download PDF

69. Scoring of protein-protein docking models utilizing predicted interface residues

Author

Pozzati, Gabriele, Kundrotas, Petras, Elofsson, Arne, Pozzati, Gabriele, Kundrotas, Petras, and Elofsson, Arne

Abstract

Scoring docking solutions is a difficult task, and many methods have been developed for this purpose. In docking, only a handful of the hundreds of thousands of models generated by docking algorithms are acceptable, causing difficulties when developing scoring functions. Today's best scoring functions can significantly increase the number of top-ranked models but still fail for most targets. Here, we examine the possibility of utilizing predicted interface residues to score docking models generated during the scan stage of a docking algorithm. Many methods have been developed to infer the regions of a protein surface that interact with another protein, but most have not been benchmarked using docking algorithms. This study systematically tests different interface prediction methods for scoring >300.000 low-resolution rigid-body template free docking decoys. Overall we find that contact-based interface prediction by BIPSPI is the best method to score docking solutions, with >12% of first ranked docking models being acceptable. Additional experiments indicated precision as a high-importance metric when estimating interface prediction quality, focusing on docking constraints production. Finally, we discussed several limitations for adopting interface predictions as constraints in a docking protocol.

Published
2022
Full Text
View/download PDF

70. Complementing machine learning-based structure predictions with native mass spectrometry

Author

Allison, Timothy M., Degiacomi, Matteo T., Marklund, Erik G., Jovine, Luca, Elofsson, Arne, Benesch, Justin L. P., Landreh, Michael, Allison, Timothy M., Degiacomi, Matteo T., Marklund, Erik G., Jovine, Luca, Elofsson, Arne, Benesch, Justin L. P., and Landreh, Michael

Abstract

The advent of machine learning-based structure prediction algorithms such as AlphaFold2 (AF2) and RoseTTa Fold have moved the generation of accurate structural models for the entire cellular protein machinery into the reach of the scientific community. However, structure predictions of protein complexes are based on user-provided input and may require experimental validation. Mass spectrometry (MS) is a versatile, time-effective tool that provides information on post-translational modifications, ligand interactions, conformational changes, and higher-order oligomerization. Using three protein systems, we show that native MS experiments can uncover structural features of ligand interactions, homology models, and point mutations that are undetectable by AF2 alone. We conclude that machine learning can be complemented with MS to yield more accurate structural models on a small and large scale.

Published
2022
Full Text
View/download PDF

71. The relationship between ageing and changes in the human blood and brain methylomes

Author

Bryant, Patrick, Elofsson, Arne, Bryant, Patrick, and Elofsson, Arne

Abstract

Changes in DNA methylation have been found to be strongly correlated with age, enabling the creation of ‘epigenetic clocks’. Previously, studies on the relationship between ageing and DNA methylation have assumed a linear relationship. Here, we show that several markers show a non-linear behaviour. In particular, we observe a tendency for saturation with age, especially in the cerebellum. Further, we show that the relationships between significant methylation changes and ageing are different in different tissues. We suggest a straightforward method of assessing all methylation-age relationships and cluster them according to their relative fold change. Our fold change selection outperforms the most common epigenetic clocks in predicting age for the cerebellum, but not for Blood or the Frontal Cortex. Further, we find that the saturation of methylation observed at older ages for the cerebellum explains why epigenetic clocks consistently underestimate the age there. The findings imply that assuming linear correlations might cause biologically important markers to be missed.

Published
2022
Full Text
View/download PDF

72. DisProt in 2022 : improved quality and accessibility of protein intrinsic disorder annotation

Author

Quaglia, Federica, Mészáros, Bálint, Salladini, Edoardo, Hatos, András, Pancsa, Rita, Chemes, Lucía B., Pajkos, Mátyás, Lazar, Tamas, Peña-Díaz, Samuel, Santos, Jaime, Ács, Veronika, Farahi, Nazanin, Fichó, Erzsébet, Aspromonte, Maria Cristina, Bassot, Claudio, Chasapi, Anastasia, Davey, Norman E., Davidović, Radoslav, Dobson, Laszlo, Elofsson, Arne, Erdős, Gábor, Gaudet, Pascale, Giglio, Michelle, Glavina, Juliana, Iserte, Javier, Iglesias, Valentín, Kálmán, Zsófia, Lambrughi, Matteo, Leonardi, Emanuela, Longhi, Sonia, Macedo-Ribeiro, Sandra, Maiani, Emiliano, Marchetti, Julia, Marino-Buslje, Cristina, Mészáros, Attila, Monzon, Alexander Miguel, Minervini, Giovanni, Nadendla, Suvarna, Nilsson, Juliet F., Novotný, Marian, Ouzounis, Christos A., Palopoli, Nicolás, Papaleo, Elena, Barbosa Pereira, Pedro José, Pozzati, Gabriele, Promponas, Vasilis J., Pujols, Jordi, Sanchez Rocha, Alma Carolina, Salas, Martin, Rodriguez Sawicki, Luciana, Schad, Eva, Shenoy, Aditi, Szaniszló, Tamás, Tsirigos, Konstantinos D., Veljkovic, Nevena, Parisi, Gustavo, Ventura, Salvador, Dosztányi, Zsuzsanna, Tompa, Peter, Tosatto, Silvio C. E., Piovesan, Damiano, Quaglia, Federica, Mészáros, Bálint, Salladini, Edoardo, Hatos, András, Pancsa, Rita, Chemes, Lucía B., Pajkos, Mátyás, Lazar, Tamas, Peña-Díaz, Samuel, Santos, Jaime, Ács, Veronika, Farahi, Nazanin, Fichó, Erzsébet, Aspromonte, Maria Cristina, Bassot, Claudio, Chasapi, Anastasia, Davey, Norman E., Davidović, Radoslav, Dobson, Laszlo, Elofsson, Arne, Erdős, Gábor, Gaudet, Pascale, Giglio, Michelle, Glavina, Juliana, Iserte, Javier, Iglesias, Valentín, Kálmán, Zsófia, Lambrughi, Matteo, Leonardi, Emanuela, Longhi, Sonia, Macedo-Ribeiro, Sandra, Maiani, Emiliano, Marchetti, Julia, Marino-Buslje, Cristina, Mészáros, Attila, Monzon, Alexander Miguel, Minervini, Giovanni, Nadendla, Suvarna, Nilsson, Juliet F., Novotný, Marian, Ouzounis, Christos A., Palopoli, Nicolás, Papaleo, Elena, Barbosa Pereira, Pedro José, Pozzati, Gabriele, Promponas, Vasilis J., Pujols, Jordi, Sanchez Rocha, Alma Carolina, Salas, Martin, Rodriguez Sawicki, Luciana, Schad, Eva, Shenoy, Aditi, Szaniszló, Tamás, Tsirigos, Konstantinos D., Veljkovic, Nevena, Parisi, Gustavo, Ventura, Salvador, Dosztányi, Zsuzsanna, Tompa, Peter, Tosatto, Silvio C. E., and Piovesan, Damiano

Abstract

The Database of Intrinsically Disordered Proteins (DisProt, URL: https://disprot.org) is the major repository of manually curated annotations of intrinsically disordered proteins and regions from the literature. We report here recent updates of DisProt version 9, including a restyled web interface, refactored Intrinsically Disordered Proteins Ontology (IDPO), improvements in the curation process and significant content growth of around 30%. Higher quality and consistency of annotations is provided by a newly implemented reviewing process and training of curators. The increased curation capacity is fostered by the integration of DisProt with APICURON, a dedicated resource for the proper attribution and recognition of biocuration efforts. Better interoperability is provided through the adoption of the Minimum Information About Disorder (MIADE) standard, an active collaboration with the Gene Ontology (GO) and Evidence and Conclusion Ontology (ECO) consortia and the support of the ELIXIR infrastructure.

Published
2022
Full Text
View/download PDF

73. Limits and potential of combined folding and docking

Author

Pozzati, Gabriele, Zhu, Wensi, Bassot, Claudio, Lamb, John, Kundrotas, Petras, Elofsson, Arne, Pozzati, Gabriele, Zhu, Wensi, Bassot, Claudio, Lamb, John, Kundrotas, Petras, and Elofsson, Arne

Abstract

Motivation: In the last decade, de novo protein structure prediction accuracy for individual proteins has improved significantly by utilising deep learning (DL) methods for harvesting the co-evolution information from large multiple sequence alignments (MSAs). The same approach can, in principle, also be used to extract information about evolutionary-based contacts across protein-protein interfaces. However, most earlier studies have not used the latest DL methods for inter-chain contact distance prediction. This article introduces a fold-and-dock method based on predicted residue-residue distances with trRosetta. Results: The method can simultaneously predict the tertiary and quaternary structure of a protein pair, even when the structures of the monomers are not known. The straightforward application of this method to a standard dataset for protein-protein docking yielded limited success. However, using alternative methods for generating MSAs allowed us to dock accurately significantly more proteins. We also introduced a novel scoring function, PconsDock, that accurately separates 98% of correctly and incorrectly folded and docked proteins. The average performance of the method is comparable to the use of traditional, template-based or ab initio shape-complementarity-only docking methods. Moreover, the results of conventional and fold-and-dock approaches are complementary, and thus a combined docking pipeline could increase overall docking success significantly. This methodology contributed to the best model for one of the CASP14 oligomeric targets, H1065.

Published
2022
Full Text
View/download PDF

84. Towards a structurally resolved human protein interaction network

Author

Elofsson, Arne, primary, Burke, David, additional, Bryant, Patrick, additional, Barrio-Hernandez, Inigo, additional, Memon, Danish, additional, Pozzati, Gabriele, additional, Shenoy, Aditi, additional, Zhu, Wensi, additional, Dunham, Alistair, additional, Albanese, Pascal, additional, Keller, Andrew, additional, Scheltema, Richard, additional, Bruce, James, additional, Leitner, Alexander, additional, Kundrotas, Petras, additional, and Beltrao, Pedro, additional

Published
2021
Full Text
View/download PDF

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

Author

Kryshtafovych, Andriy, Moult, John, Billings, Wendy, Della Corte, Dennis, Fidelis, Krzysztof, Kwon, Sohee, Olechnovič, Kliment, Seok, Chaok, Venclovas, Česlovas, Won, Jonghun, Adhikari, Badri, Adiyaman, Recep, Aguirre-Plans, Joaquim, Anishchenko, Ivan, Baek, Minkyung, Baker, David, Baldassarre, Frederico, Barger, Jacob, Bhattacharya, Sutanu, Bhattacharya, Debswapna, Bitton, Mor, Cao, Renzhi, Cheng, Jianlin, Christoffer, Charles, Czaplewski, Cezary, Elofsson, Arne, Faraggi, Eshel, Feig, Michael, Fernandez-Fuentes, Narcis, Grishin, Nick, Grudinin, Sergei, Guo, Zhiye, Hanazono, Yuya, Hassabis, Demis, Hedelius, Bryce, Heo, Lim, Hiranuma, Naozumi, Hunt, Cassandra, Igashov, Ilia, Ishida, Takashi, Jernigan, Robert, Jones, David, Jumper, John, Kadukova, Maria, Kandathil, Shaun, Keasar, Chen, Kihara, Daisuke, Kinch, Lisa, Kiyota, Yasuomi, Kloczkowski, Andrzje, Kohli, Pushmeet, Kogut, Mateusz, Laine, Elodie, Lilley, Cade, Liu, Jian, Liwo, Adam, Lubecka, Emilia, Mondal, Arup, Morris, Connor, Mcguffin, Liam, Molina, Alexis, Nakamura, Tsukasa, Oliva, Baldo, Perez, Alberto, Pozzati, Gabriele, Sarkar, Daipayan, Sato, Rin, Schwede, Torsten, Shrestha, Bikash, Sidi, Tomer, Studer, Gabriel, Shuvo, Md Hossain, Takeda-Shitaka, Mayuko, Takei, Yuma, Terashi, Genki, Tomii, Kentaro, Tsuchiya, Yuko, Tunyasuvunakool, Kathryn, Waliner, Björn, Wu, Tianqi, Xu, Jinbo, Yamamori, Yu, Zhang, Chengxin, Zhang, Yang, Zheng, Wei, University of California [Davis] (UC Davis), University of California (UC), Institute for Bioscience and Biotechnology Research [Rockville, MD, États-Unis] (IBBR), University of Maryland [College Park], University of Maryland System-University of Maryland System, Brigham Young University (BYU), Seoul National University [Seoul] (SNU), Vilnius University [Vilnius], University of Missouri [St. Louis], University of Missouri System, University of Reading (UOR), Universitat Pompeu Fabra [Barcelona] (UPF), Biorobotics Lab (University of Washington), University of Washington [Seattle], Royal Institute of Technology [Stockholm] (KTH ), Auburn University (AU), Ben-Gurion University of the Negev (BGU), Pacific Lutheran University [Tacoma] (PLU), University of Missouri [Columbia] (Mizzou), Purdue University [West Lafayette], University of Gdańsk (UG), Stockholm University, Indiana University - Purdue University Indianapolis (IUPUI), Indiana University System, Michigan State University [East Lansing], Michigan State University System, Aberystwyth University, University of Texas Southwestern Medical Center [Dallas], Données, Apprentissage et Optimisation (DAO), Laboratoire Jean Kuntzmann (LJK), Institut National de Recherche en Informatique et en Automatique (Inria)-Centre National de la Recherche Scientifique (CNRS)-Université Grenoble Alpes (UGA)-Institut polytechnique de Grenoble - Grenoble Institute of Technology (Grenoble INP ), Université Grenoble Alpes (UGA)-Institut National de Recherche en Informatique et en Automatique (Inria)-Centre National de la Recherche Scientifique (CNRS)-Université Grenoble Alpes (UGA)-Institut polytechnique de Grenoble - Grenoble Institute of Technology (Grenoble INP ), Université Grenoble Alpes (UGA), National Institutes for Quantum and Radiological Science and Technology (QST), DeepMind [London], DeepMind Technologies, Moscow Institute of Physics and Technology [Moscow] (MIPT), Tokyo Institute of Technology [Tokyo] (TITECH), Iowa State University (ISU), University College of London [London] (UCL), Kitasato University, Ohio State University [Columbus] (OSU), Biologie Computationnelle et Quantitative = Laboratory of Computational and Quantitative Biology (LCQB), Sorbonne Université (SU)-Centre National de la Recherche Scientifique (CNRS)-Institut de Biologie Paris Seine (IBPS), Institut National de la Santé et de la Recherche Médicale (INSERM)-Sorbonne Université (SU)-Centre National de la Recherche Scientifique (CNRS)-Institut National de la Santé et de la Recherche Médicale (INSERM)-Centre National de la Recherche Scientifique (CNRS), University of Florida [Gainesville] (UF), Barcelona Supercomputing Center - Centro Nacional de Supercomputacion (BSC - CNS), Tohoku University [Sendai], University of Basel (Unibas), National Institute of Advanced Industrial Science and Technology (AIST), Linköping University (LIU), Toyota Technological Institute at Chicago [Chicago] (TTIC), University of Michigan [Ann Arbor], University of Michigan System, University of California, University of Maryland [Baltimore], Algorithms for Modeling and Simulation of Nanosystems (NANO-D), Inria Grenoble - Rhône-Alpes, Institut National de Recherche en Informatique et en Automatique (Inria)-Institut National de Recherche en Informatique et en Automatique (Inria)-Laboratoire Jean Kuntzmann (LJK), and Université Grenoble Alpes (UGA)-Centre National de la Recherche Scientifique (CNRS)-Université Grenoble Alpes (UGA)-Institut polytechnique de Grenoble - Grenoble Institute of Technology (Grenoble INP )

Subjects
Models, Molecular, 2019-20 coronavirus outbreak, Research groups, Protein Conformation, Computer science, Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), Model accuracy, Genome, Viral, computer.software_genre, Biochemistry, SARS‐CoV‐2, Viroporin Proteins, Domain (software engineering), Viral Proteins, 03 medical and health sciences, Protein Domains, Structural Biology, EMA, Humans, CASP, Molecular Biology, Research Articles, ComputingMilieux_MISCELLANEOUS, 030304 developmental biology, COVID, 0303 health sciences, SARS-CoV-2, 030302 biochemistry & molecular biology, COVID-19, Protein structure prediction, Model quality, Critical assessment, Data mining, [INFO.INFO-BI]Computer Science [cs]/Bioinformatics [q-bio.QM], computer, Research Article
Abstract

International audience; 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
Full Text
View/download PDF

88. DisProt in 2022: improved quality and accessibility of protein intrinsic disorder annotation

Author

Quaglia, Federica, primary, Mészáros, Bálint, additional, Salladini, Edoardo, additional, Hatos, András, additional, Pancsa, Rita, additional, Chemes, Lucía B, additional, Pajkos, Mátyás, additional, Lazar, Tamas, additional, Peña-Díaz, Samuel, additional, Santos, Jaime, additional, Ács, Veronika, additional, Farahi, Nazanin, additional, Fichó, Erzsébet, additional, Aspromonte, Maria Cristina, additional, Bassot, Claudio, additional, Chasapi, Anastasia, additional, Davey, Norman E, additional, Davidović, Radoslav, additional, Dobson, Laszlo, additional, Elofsson, Arne, additional, Erdős, Gábor, additional, Gaudet, Pascale, additional, Giglio, Michelle, additional, Glavina, Juliana, additional, Iserte, Javier, additional, Iglesias, Valentín, additional, Kálmán, Zsófia, additional, Lambrughi, Matteo, additional, Leonardi, Emanuela, additional, Longhi, Sonia, additional, Macedo-Ribeiro, Sandra, additional, Maiani, Emiliano, additional, Marchetti, Julia, additional, Marino-Buslje, Cristina, additional, Mészáros, Attila, additional, Monzon, Alexander Miguel, additional, Minervini, Giovanni, additional, Nadendla, Suvarna, additional, Nilsson, Juliet F, additional, Novotný, Marian, additional, Ouzounis, Christos A, additional, Palopoli, Nicolás, additional, Papaleo, Elena, additional, Pereira, Pedro José Barbosa, additional, Pozzati, Gabriele, additional, Promponas, Vasilis J, additional, Pujols, Jordi, additional, Rocha, Alma Carolina Sanchez, additional, Salas, Martin, additional, Sawicki, Luciana Rodriguez, additional, Schad, Eva, additional, Shenoy, Aditi, additional, Szaniszló, Tamás, additional, Tsirigos, Konstantinos D, additional, Veljkovic, Nevena, additional, Parisi, Gustavo, additional, Ventura, Salvador, additional, Dosztányi, Zsuzsanna, additional, Tompa, Peter, additional, Tosatto, Silvio C E, additional, and Piovesan, Damiano, additional

Published
2021
Full Text
View/download PDF

90. Towards a structurally resolved human protein interaction network

Author

Burke, David F., primary, Bryant, Patrick, additional, Barrio-Hernandez, Inigo, additional, Memon, Danish, additional, Pozzati, Gabriele, additional, Shenoy, Aditi, additional, Zhu, Wensi, additional, Dunham, Alistair S, additional, Albanese, Pascal, additional, Keller, Andrew, additional, Scheltema, Richard A., additional, Bruce, James E., additional, Leitner, Alexander, additional, Kundrotas, Petras, additional, Beltrao, Pedro, additional, and Elofsson, Arne, additional

Published
2021
Full Text
View/download PDF

92. A structural biology community assessment of AlphaFold 2 applications

Author

Akdel, Mehmet, primary, Pires, Douglas E V, additional, Porta Pardo, Eduard, additional, Jänes, Jürgen, additional, Zalevsky, Arthur O, additional, Mészáros, Bálint, additional, Bryant, Patrick, additional, Good, Lydia L., additional, Laskowski, Roman A, additional, Pozzati, Gabriele, additional, Shenoy, Aditi, additional, Zhu, Wensi, additional, Kundrotas, Petras, additional, Ruiz Serra, Victoria, additional, Rodrigues, Carlos H M, additional, Dunham, Alistair S, additional, Burke, David, additional, Borkakoti, Neera, additional, Velankar, Sameer, additional, Frost, Adam, additional, Lindorff-Larsen, Kresten, additional, Valencia, Alfonso, additional, Ovchinnikov, Sergey, additional, Durairaj, Janani, additional, Ascher, David B, additional, Thornton, Janet M, additional, Davey, Norman E, additional, Stein, Amelie, additional, Elofsson, Arne, additional, Croll, Tristan I, additional, and Beltrao, Pedro, additional

Published
2021
Full Text
View/download PDF

95. Critical assessment of protein intrinsic disorder prediction

Author

Necci, Marco, Piovesan, Damiano, Hoque Md, Tamjidul, Walsh, Ian, Iqbal, Sumaiya, Vendruscolo, Michele, Sormanni, Pietro, Wang, Chen, Raimondi, Daniele, Sharma, Ronesh, Zhou, Yaoqi, Litfin, Thomas, Galzitskaya Oxana, Valerianovna, Lobanov Michail, Yu, Vranken, Wim, Wallner, Björn, Mirabello, Claudio, Malhis, Nawar, Dosztányi, Zsuzsanna, Erdős, Gábor, Mészáros, Bálint, Gao, Jianzhao, Wang, Kui, Hu, Gang, Wu, Zhonghua, Sharma, Alok, Hanson, Jack, Paliwal, Kuldip, Callebaut, Isabelle, Bitard-Feildel, Tristan, Orlando, Gabriele, Peng, Zhenling, Xu, Jinbo, Wang, Sheng, Jones David, T., Cozzetto, Domenico, Meng, Fanchi, Yan, Jing, Gsponer, Jörg, Cheng, Jianlin, Wu, Tianqi, Kurgan, Lukasz, Promponas Vasilis, J., Tamana, Stella, Marino-Buslje, Cristina, Martínez-Pérez, Elizabeth, Chasapi, Anastasia, Ouzounis, Christos, Dunker A., Keith, Kajava Andrey, V., Leclercq Jeremy, Y., Aykac-Fas, Burcu, Lambrughi, Matteo, Maiani, Emiliano, Papaleo, Elena, Chemes Lucia, Beatriz, Álvarez, Lucía, González-Foutel Nicolás, S., Iglesias, Valentin, Pujols, Jordi, Ventura, Salvador, Palopoli, Nicolás, Benítez Guillermo, Ignacio, Parisi, Gustavo, Bassot, Claudio, Elofsson, Arne, Govindarajan, Sudha, Lamb, John, Salvatore, Marco, Hatos, András, Monzon Alexander, Miguel, Bevilacqua, Martina, Mičetić, Ivan, Minervini, Giovanni, Paladin, Lisanna, Quaglia, Federica, Leonardi, Emanuela, Davey, Norman, Horvath, Tamas, Kovacs Orsolya, Panna, Murvai, Nikoletta, Pancsa, Rita, Schad, Eva, Szabo, Beata, Tantos, Agnes, Macedo-Ribeiro, Sandra, Manso Jose, Antonio, Pereira Pedro José, Barbosa, Davidović, Radoslav, Veljkovic, Nevena, Hajdu-Soltész, Borbála, Pajkos, Mátyás, Szaniszló, Tamás, Guharoy, Mainak, Lazar, Tamas, Macossay-Castillo, Mauricio, Tompa, Peter, Tosatto Silvio C., E., Caid, Predictors, DisProt, Curators, Università degli Studi di Padova = University of Padua (Unipd), Institut de minéralogie, de physique des matériaux et de cosmochimie (IMPMC), Muséum national d'Histoire naturelle (MNHN)-Institut de recherche pour le développement [IRD] : UR206-Sorbonne Université (SU)-Centre National de la Recherche Scientifique (CNRS), Necci, Marco [0000-0001-9377-482X], Piovesan, Damiano [0000-0001-8210-2390], Tosatto, Silvio C. E. [0000-0003-4525-7793], Apollo - University of Cambridge Repository, Informatics and Applied Informatics, Chemistry, Basic (bio-) Medical Sciences, Department of Bio-engineering Sciences, Faculty of Sciences and Bioengineering Sciences, Structural Biology Brussels, Tosatto, Silvio CE [0000-0003-4525-7793], ANR-17-CE12-0016,FUNBRCA2,Caractérisation d'un nouveau site de liaison à l'ADN dans la protéine BRCA2(2017), Universita degli Studi di Padova, CAID Predictors, and DisProt Curators

Subjects
Protein Folding, Protein Conformation, Computer science, 631/45/612, analysis, [SDV]Life Sciences [q-bio], purl.org/becyt/ford/1.7 [https], MESH: Amino Acid Sequence, Biochemistry, purl.org/becyt/ford/1 [https], Protein structure, MESH: Protein Conformation, 631/114/2398, Databases, Protein, Biological sciences, ComputingMilieux_MISCELLANEOUS, MESH: Intrinsically Disordered Proteins, 0303 health sciences, 030302 biochemistry & molecular biology, disorder, Critical assessment, Protein folding, Protein Binding, Biotechnology, MESH: Computational Biology, MESH: Databases, Protein, disorder prediction, MESH: Protein Folding, Computational biology, Intrinsically disordered proteins, Orders of magnitude (entropy), 03 medical and health sciences, MESH: Software, Computational platforms and environments, 631/114/2411, Machine learning, Molecule, MESH: Protein Binding, [INFO]Computer Science [cs], Amino Acid Sequence, Molecular Biology, 030304 developmental biology, business.industry, Deep learning, Computational Biology, Proteins, Cell Biology, 631/114/1305, Intrinsically Disordered Proteins, CAID, 631/114/794, Protein structure predictions, Artificial intelligence, business, Software
Abstract

Intrinsically disordered proteins, defying the traditional protein structure–function paradigm, are a challenge to study experimentally. Because a large part of our knowledge rests on computational predictions, it is crucial that their accuracy is high. The Critical Assessment of protein Intrinsic Disorder prediction (CAID) experiment was established as a community-based blind test to determine the state of the art in prediction of intrinsically disordered regions and the subset of residues involved in binding. A total of 43 methods were evaluated on a dataset of 646 proteins from DisProt. The best methods use deep learning techniques and notably outperform physicochemical methods. The top disorder predictor has Fmax = 0.483 on the full dataset and Fmax = 0.792 following filtering out of bona fide structured regions. Disordered binding regions remain hard to predict, with Fmax = 0.231. Interestingly, computing times among methods can vary by up to four orders of magnitude., Results are presented from the first Critical Assessment of protein Intrinsic Disorder prediction (CAID) experiment, a community-based blind test to determine the state of the art in predicting intrinsically disordered regions in proteins.

Published
2021
Full Text
View/download PDF

96. Accurate contact-based modelling of repeat proteins predicts the structure of new repeats protein families

Author

Bassot, Claudio and Elofsson, Arne

Subjects
Protein Structure Comparison, Models, Molecular, Protein Structure, Computer and Information Sciences, Bioinformatics, Protein Conformation, QH301-705.5, Protein Structure Prediction, Research and Analysis Methods, Biochemistry, Machine Learning, Database and Informatics Methods, Protein Domains, Artificial Intelligence, Macromolecular Structure Analysis, Biology (General), Molecular Biology, Organisms, Biology and Life Sciences, Proteins, Eukaryota, Computational Biology, Structural Proteins, Sequence Analysis, Sequence Alignment, Research Article
Abstract

Repeat proteins are abundant in eukaryotic proteomes. They are involved in many eukaryotic specific functions, including signalling. For many of these proteins, the structure is not known, as they are difficult to crystallise. Today, using direct coupling analysis and deep learning it is often possible to predict a protein’s structure. However, the unique sequence features present in repeat proteins have been a challenge to use direct coupling analysis for predicting contacts. Here, we show that deep learning-based methods (trRosetta, DeepMetaPsicov (DMP) and PconsC4) overcomes this problem and can predict intra- and inter-unit contacts in repeat proteins. In a benchmark dataset of 815 repeat proteins, about 90% can be correctly modelled. Further, among 48 PFAM families lacking a protein structure, we produce models of forty-one families with estimated high accuracy., Author summary Repeat proteins are widespread among organisms and particularly abundant in eukaryotic proteomes. Their primary sequence presents repetition in the amino acid sequences that origin structures with repeated folds/domains. Although the repeated units often can be recognised from the sequence alone, often structural information is missing. Here, we used contact prediction for predicting the structure of repeats protein directly from their primary sequences. We benchmark the methods on a dataset comprehensive of all the known repeated structures. We evaluate the contact predictions and the obtained models for different classes of repeat proteins. Further, we develop and benchmark a quality assessment (QA) method specific for repeat proteins. Finally, we used the prediction pipeline for all PFAM repeat families without resolved structures and found that forty-one of them could be modelled with high accuracy.

Published
2021

100. A structural biology community assessment of AlphaFold 2 applications

Author

Akdel, Mehmet, Pires, Douglas EV., Porta Prado, Eduard, Jänes, Jürgen, Zalevsky, Arthur O., Mészáros, Bálint, Bryant, Patrick, Good, Lydia L., Laskowski, Roman A., Pozzati, Gabriele, Shenoy, Aditi, Zhu, Wensi, Kundrotas, Petras, Ruiz Serra, Victoria, Rodrigues, Carlos H. M., Dunham, Alistair S., Burke, David, Borkakoti, Neera, Velankar, Sameer, Frost, Adam, Lindorff-Larsen, Kresten, Valencia, Alfonso, Ovchinnikov, Sergey, Durairaj, Janani, Ascher, David B., Thornton, Janet M., Davey, Norman E., Stein, Amelie, Elofsson, Arne, Croll, Tristan I., and Beltrao, Pedro

Abstract

bioRxiv

Published
2021
Full Text
View/download PDF

Catalog

Books, media, physical & digital resources
See catalog results