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1. New prediction categories in CASP15

Author

Kryshtafovych, Andriy, Antczak, Maciej, Szachniuk, Marta, Zok, Tomasz, Kretsch, Rachael C, Rangan, Ramya, Pham, Phillip, Das, Rhiju, Robin, Xavier, Studer, Gabriel, Durairaj, Janani, Eberhardt, Jerome, Sweeney, Aaron, Topf, Maya, Schwede, Torsten, Fidelis, Krzysztof, and Moult, John

Subjects
Biological Sciences, Bioinformatics and Computational Biology, Protein Conformation, Proteins, Models, Molecular, Computational Biology, Ligands, 3D structure prediction, CASP15, protein structure, protein-ligand complexes, RNA structure, Mathematical Sciences, Information and Computing Sciences, Bioinformatics, Biological sciences, Mathematical sciences
Abstract

Prediction categories in the Critical Assessment of Structure Prediction (CASP) experiments change with the need to address specific problems in structure modeling. In CASP15, four new prediction categories were introduced: RNA structure, ligand-protein complexes, accuracy of oligomeric structures and their interfaces, and ensembles of alternative conformations. This paper lists technical specifications for these categories and describes their integration in the CASP data management system.

Published
2023

9. New prediction categories in CASP15

Author

Kryshtafovych, Andriy, primary, Antczak, Maciej, additional, Szachniuk, Marta, additional, Kretsch, Rachael C., additional, Rangan, Ramya, additional, Pham, Phillip, additional, Das, Rhiju, additional, Robin, Xavier, additional, Studer, Gabriel, additional, Durairaj, Janani, additional, Eberhardt, Jerome, additional, Sweeney, Aaron, additional, Topf, Maya, additional, Schwede, Torsten, additional, Fidelis, Krzysztof, additional, and Moult, John, additional

Published
2023
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10. What is hidden in the darkness? Deep-learning assisted large-scale protein family curation uncovers novel protein families and folds

Author

Durairaj, Janani, primary, Waterhouse, Andrew, additional, Mets, Toomas, additional, Brodiazhenko, Tetiana, additional, Abdullah, Minhal, additional, Studer, Gabriel, additional, Akdel, Mehmet, additional, Andreeva, Antonina, additional, Bateman, Alex, additional, Hauryliuk, Vasili, additional, Tenson, Tanel, additional, Schwede, Torsten, additional, and Pereira, Joana, additional

Published
2023
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11. Assessment of the assessment—All about complexes.

Author

Studer, Gabriel, Tauriello, Gerardo, and Schwede, Torsten

Abstract

Predicting model quality is a fundamental component of any modeling procedure, and blind assessment of these methods constitutes a crucial aspect of the Critical Assessment of Protein Structure Prediction (CASP) experiment. Historically, the main focus was on assessing methods that predict global and per-residue accuracies in tertiary structure models. This focus shifted with the community's increased efforts in modeling complexes and assemblies. We asked the community to process the models from the CASP15 assembly category and provide estimates of the accuracy of the predicted quaternary structure, both globally and at the local interface level. Besides identifying remarkable accuracy of modeling groups in assessing their own predictions, we set up a benchmarking pipeline to highlight different aspects of quaternary structure models and introduced a simple consensus EMA method as baseline. While participating methods showed commendable performance, the baseline was difficult to surpass. It is important to point out that prediction performance varies for the individual CASP targets, highlighting potential areas of improvement and challenges ahead. [ABSTRACT FROM AUTHOR]

Published
2023
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13. 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
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15. A Comprehensive Mapping of the Druggable Cavities within the SARS-CoV-2 Therapeutically Relevant Proteins by Combining Pocket and Docking Searches as Implemented in Pockets 2.0

Author

Gervasoni, Silvia, Vistoli, Giulio, Talarico, Carmine, Manelfi, Candida, Beccari, Andrea R., Studer, Gabriel, Tauriello, Gerardo, Waterhouse, Andrew Mark, Schwede, Torsten, and Pedretti, Alessandro

Subjects
docking simulations, pocket druggability, Binding Sites, Protein Conformation, SARS-CoV-2, Pneumonia, Viral, Drug Repositioning, COVID-19, pocket search, virtual screening, Antiviral Agents, Article, blind docking, Molecular Docking Simulation, lcsh:Chemistry, Betacoronavirus, Viral Proteins, lcsh:Biology (General), lcsh:QD1-999, Humans, Coronavirus Infections, Pandemics, lcsh:QH301-705.5, Protein Binding
Abstract

(1) Background: Virtual screening studies on the therapeutically relevant proteins of the severe acute respiratory syndrome Coronavirus 2 (SARS-CoV-2) require a detailed characterization of their druggable binding sites, and, more generally, a convenient pocket mapping represents a key step for structure-based in silico studies, (2) Methods: Along with a careful literature search on SARS-CoV-2 protein targets, the study presents a novel strategy for pocket mapping based on the combination of pocket (as performed by the well-known FPocket tool) and docking searches (as performed by PLANTS or AutoDock/Vina engines), such an approach is implemented by the Pockets 2.0 plug-in for the VEGA ZZ suite of programs, (3) Results: The literature analysis allowed the identification of 16 promising binding cavities within the SARS-CoV-2 proteins and the here proposed approach was able to recognize them showing performances clearly better than those reached by the sole pocket detection, and (4) Conclusions: Even though the presented strategy should require more extended validations, this proved successful in precisely characterizing a set of SARS-CoV-2 druggable binding pockets including both orthosteric and allosteric sites, which are clearly amenable for virtual screening campaigns and drug repurposing studies. All results generated by the study and the Pockets 2.0 plug-in are available for download.

Published
2020

17. 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

21. SWISS-MODEL: homology modelling of protein structures and complexes

Author

Waterhouse, Andrew, primary, Bertoni, Martino, additional, Bienert, Stefan, additional, Studer, Gabriel, additional, Tauriello, Gerardo, additional, Gumienny, Rafal, additional, Heer, Florian T, additional, de Beer, Tjaart A P, additional, Rempfer, Christine, additional, Bordoli, Lorenza, additional, Lepore, Rosalba, additional, and Schwede, Torsten, additional

Published
2018
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22. Efficient algorithms in protein modelling

Author

Studer, Gabriel, Schwede, Torsten, and Maier, Timm

Abstract

Proteins are key players in the complex world of living cells. No matter whether they are involved in enzymatic reactions, inter-cell communication or numerous other processes, knowledge of their structure is vital for a detailed understanding of their function. However, structure determination by experiment is often a laborious process that cannot keep up with the ever increasing pace of sequencing methodologies. As a consequence, the gap between proteins where we only know the sequence and the proteins where we additionally have detailed structural information is growing rapidly. Computational modelling methods that extrapolate structural information from homologous structures have established themselves as a valuable complement to experiment and help bridging this gap. This thesis addresses two key aspects in protein modelling. (1) It investigates and improves methodologies that assign reliability estimates to protein models, so called quality estimation (QE) methods. Even a human expert cannot immediately detect errors introduced in the modelling process, thus the importance of automated methods performing this task. (2) It assesses the available methods that perform the modelling itself, discusses solutions for current shortcomings and provides efficient implementations thereof. When detecting errors in protein models, many knowledge based methods are biased towards the physio-chemical properties observed in soluble protein structures. This limits their applicability for the important class of membrane protein models. In an effort to improve the situation, QMEANBrane has been developed. QMEANBrane is specifically designed to detect local errors in membrane protein models by membrane specific statistical potentials of mean force that nowadays approach statistical saturation given the increase of available experimental data. Considering the improvement of quality estimation for soluble proteins, instead of solely applying the widely used statistical potentials of mean force, QMEANDisCo incorporates the observed structural variety of experimentally determined protein structures homologous to the model being assessed. Valuable ensemble information can be gathered without the need of actually depending on a large ensemble of protein models, thus circumventing a main limitation of consensus QE methods. Apart from improving QE methods, in an effort of implementing and extending state-of-the-art modelling algorithms, the lack of a free and efficient modelling engine became obvious. No available modelling engine provided an open-source codebase as a basis for novel, innovative algorithms and, at the same time, had no restrictions for usage. This contradicts our efforts to make protein modelling available to all biochemists and molecular biologists worldwide. As a consequence we implemented a new free and open modelling engine from scratch - ProMod3. ProMod3 allows to combine extremely efficient, state-of-the-art modelling algorithms in a flexible manner to solve various modelling problems. To weaken the dogma of one template one model, basic algorithms have been explored to incorporate structural information from multiple templates into one protein model. The algorithms are built using ProMod3 and have extensively been tested in the context of the CAMEO continuous evaluation platform. The result is a highly competitive modelling pipeline that excels with extremely low runtimes and excellent performance.

Published
2017

24. Assessing the local structural quality of transmembrane protein models using statistical potentials (QMEANBrane)

Author

Studer, Gabriel, Biasini, Marco, Schwede, Torsten, Studer, Gabriel, Biasini, Marco, and Schwede, Torsten

Abstract

Motivation: Membrane proteins are an important class of biological macromolecules involved in many cellular key processes including signalling and transport. They account for one third of genes in the human genome and >50% of current drug targets. Despite their importance, experimental structural data are sparse, resulting in high expectations for computational modelling tools to help fill this gap. However, as many empirical methods have been trained on experimental structural data, which is biased towards soluble globular proteins, their accuracy for transmembrane proteins is often limited. Results: We developed a local model quality estimation method for membrane proteins (‘QMEANBrane') by combining statistical potentials trained on membrane protein structures with a per-residue weighting scheme. The increasing number of available experimental membrane protein structures allowed us to train membrane-specific statistical potentials that approach statistical saturation. We show that reliable local quality estimation of membrane protein models is possible, thereby extending local quality estimation to these biologically relevant molecules. Availability and implementation: Source code and datasets are available on request. Contact: torsten.schwede@unibas.ch Supplementary Information: Supplementary data are available at Bioinformatics online

Published
2017

25. SWISS-MODEL: modelling protein tertiary and quaternary structure using evolutionary information

Author

Biasini, Marco, Bienert, Stefan, Waterhouse, Andrew, Arnold, Konstantin, Studer, Gabriel, Schmidt, Tobias, Kiefer, Florian, Cassarino, Tiziano Gallo, Bertoni, Martino, Bordoli, Lorenza, Schwede, Torsten, Biasini, Marco, Bienert, Stefan, Waterhouse, Andrew, Arnold, Konstantin, Studer, Gabriel, Schmidt, Tobias, Kiefer, Florian, Cassarino, Tiziano Gallo, Bertoni, Martino, Bordoli, Lorenza, and Schwede, Torsten

Abstract

Protein structure homology modelling has become a routine technique to generate 3D models for proteins when experimental structures are not available. Fully automated servers such as SWISS-MODEL with user-friendly web interfaces generate reliable models without the need for complex software packages or downloading large databases. Here, we describe the latest version of the SWISS-MODEL expert system for protein structure modelling. The SWISS-MODEL template library provides annotation of quaternary structure and essential ligands and co-factors to allow for building of complete structural models, including their oligomeric structure. The improved SWISS-MODEL pipeline makes extensive use of model quality estimation for selection of the most suitable templates and provides estimates of the expected accuracy of the resulting models. The accuracy of the models generated by SWISS-MODEL is continuously evaluated by the CAMEO system. The new web site allows users to interactively search for templates, cluster them by sequence similarity, structurally compare alternative templates and select the ones to be used for model building. In cases where multiple alternative template structures are available for a protein of interest, a user-guided template selection step allows building models in different functional states. SWISS-MODEL is available at http://swissmodel.expasy.org

Published
2017

27. Continuous Automated Model EvaluatiOn (CAMEO) complementing the critical assessment of structure prediction in CASP12.

Author

Haas, Jürgen, Barbato, Alessandro, Behringer, Dario, Studer, Gabriel, Roth, Steven, Bertoni, Martino, Mostaguir, Khaled, Gumienny, Rafal, and Schwede, Torsten

Abstract

Abstract: Every second year, the community experiment “Critical Assessment of Techniques for Structure Prediction” (CASP) is conducting an independent blind assessment of structure prediction methods, providing a framework for comparing the performance of different approaches and discussing the latest developments in the field. Yet, developers of automated computational modeling methods clearly benefit from more frequent evaluations based on larger sets of data. The “Continuous Automated Model EvaluatiOn (CAMEO)” platform complements the CASP experiment by conducting fully automated blind prediction assessments based on the weekly pre‐release of sequences of those structures, which are going to be published in the next release of the PDB Protein Data Bank. CAMEO publishes weekly benchmarking results based on models collected during a 4‐day prediction window, on average assessing ca. 100 targets during a time frame of 5 weeks. CAMEO benchmarking data is generated consistently for all participating methods at the same point in time, enabling developers to benchmark and cross‐validate their method's performance, and directly refer to the benchmarking results in publications. In order to facilitate server development and promote shorter release cycles, CAMEO sends weekly email with submission statistics and low performance warnings. Many participants of CASP have successfully employed CAMEO when preparing their methods for upcoming community experiments. CAMEO offers a variety of scores to allow benchmarking diverse aspects of structure prediction methods. By introducing new scoring schemes, CAMEO facilitates new development in areas of active research, for example, modeling quaternary structure, complexes, or ligand binding sites. [ABSTRACT FROM AUTHOR]

Published
2018
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28. Simultaneous analysis of large-scale RNAi screens for pathogen entry

Author

Rämö, Pauli, Drewek, Anna, Arrieumerlou, Cécile, Beerenwinkel, Niko, Ben-Tekaya, Houchaima, Cardel, Bettina, Casanova, Alain, Conde-Alvarez, Raquel, Cossart, Pascale, Csúcs, Gábor, Eicher, Simone, Emmenlauer, Mario, Greber, Urs, Hardt, Wolf-Dietrich, Helenius, Ari, Kasper, Christoph, Kaufmann, Andreas, Kreibich, Saskia, Kühbacher, Andreas, Kunszt, Peter, Low, Shyan Huey, Mercer, Jason, Mudrak, Daria, Muntwiler, Simone, Pelkmans, Lucas, Pizarro-Cerdá, Javier, Podvinec, Michael, Pujadas, Eva, Rinn, Bernd, Rouilly, Vincent, Schmich, Fabian, Siebourg-Polster, Juliane, Snijder, Berend, Stebler, Michael, Studer, Gabriel, Szczurek, Ewa, Truttmann, Matthias, von Mering, Christian; https://orcid.org/0000-0001-7734-9102, Vonderheit, Andreas, Yakimovich, Artur, Bühlmann, Peter, Dehio, Christoph, Rämö, Pauli, Drewek, Anna, Arrieumerlou, Cécile, Beerenwinkel, Niko, Ben-Tekaya, Houchaima, Cardel, Bettina, Casanova, Alain, Conde-Alvarez, Raquel, Cossart, Pascale, Csúcs, Gábor, Eicher, Simone, Emmenlauer, Mario, Greber, Urs, Hardt, Wolf-Dietrich, Helenius, Ari, Kasper, Christoph, Kaufmann, Andreas, Kreibich, Saskia, Kühbacher, Andreas, Kunszt, Peter, Low, Shyan Huey, Mercer, Jason, Mudrak, Daria, Muntwiler, Simone, Pelkmans, Lucas, Pizarro-Cerdá, Javier, Podvinec, Michael, Pujadas, Eva, Rinn, Bernd, Rouilly, Vincent, Schmich, Fabian, Siebourg-Polster, Juliane, Snijder, Berend, Stebler, Michael, Studer, Gabriel, Szczurek, Ewa, Truttmann, Matthias, von Mering, Christian; https://orcid.org/0000-0001-7734-9102, Vonderheit, Andreas, Yakimovich, Artur, Bühlmann, Peter, and Dehio, Christoph

Abstract

BACKGROUND: Large-scale RNAi screening has become an important technology for identifying genes involved in biological processes of interest. However, the quality of large-scale RNAi screening is often deteriorated by off-targets effects. In order to find statistically significant effector genes for pathogen entry, we systematically analyzed entry pathways in human host cells for eight pathogens using image-based kinome-wide siRNA screens with siRNAs from three vendors. We propose a Parallel Mixed Model (PMM) approach that simultaneously analyzes several non-identical screens performed with the same RNAi libraries. RESULTS: We show that PMM gains statistical power for hit detection due to parallel screening. PMM allows incorporating siRNA weights that can be assigned according to available information on RNAi quality. Moreover, PMM is able to estimate a sharedness score that can be used to focus follow-up efforts on generic or specific gene regulators. By fitting a PMM model to our data, we found several novel hit genes for most of the pathogens studied. CONCLUSIONS: Our results show parallel RNAi screening can improve the results of individual screens. This is currently particularly interesting when large-scale parallel datasets are becoming more and more publicly available. Our comprehensive siRNA dataset provides a public, freely available resource for further statistical and biological analyses in the high-content, high-throughput siRNA screening field.

Published
2014

29. Simultaneous analysis of large-scale RNAi screens for pathogen entry

Author

Rämö, Pauli, primary, Drewek, Anna, additional, Arrieumerlou, Cécile, additional, Beerenwinkel, Niko, additional, Ben-Tekaya, Houchaima, additional, Cardel, Bettina, additional, Casanova, Alain, additional, Conde-Alvarez, Raquel, additional, Cossart, Pascale, additional, Csúcs, Gábor, additional, Eicher, Simone, additional, Emmenlauer, Mario, additional, Greber, Urs, additional, Hardt, Wolf-Dietrich, additional, Helenius, Ari, additional, Kasper, Christoph, additional, Kaufmann, Andreas, additional, Kreibich, Saskia, additional, Kühbacher, Andreas, additional, Kunszt, Peter, additional, Low, Shyan Huey, additional, Mercer, Jason, additional, Mudrak, Daria, additional, Muntwiler, Simone, additional, Pelkmans, Lucas, additional, Pizarro-Cerdá, Javier, additional, Podvinec, Michael, additional, Pujadas, Eva, additional, Rinn, Bernd, additional, Rouilly, Vincent, additional, Schmich, Fabian, additional, Siebourg-Polster, Juliane, additional, Snijder, Berend, additional, Stebler, Michael, additional, Studer, Gabriel, additional, Szczurek, Ewa, additional, Truttmann, Matthias, additional, von Mering, Christian, additional, Vonderheit, Andreas, additional, Yakimovich, Artur, additional, Bühlmann, Peter, additional, and Dehio, Christoph, additional

Published
2014
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33. SWISS-MODEL: modelling protein tertiary and quaternary structure using evolutionary information

Author

Biasini, Marco, Bienert, Stefan, Waterhouse, Andrew, Arnold, Konstantin, Studer, Gabriel, Schmidt, Tobias, Kiefer, Florian, Cassarino, Tiziano Gallo, Bertoni, Martino, Bordoli, Lorenza, Schwede, Torsten, Biasini, Marco, Bienert, Stefan, Waterhouse, Andrew, Arnold, Konstantin, Studer, Gabriel, Schmidt, Tobias, Kiefer, Florian, Cassarino, Tiziano Gallo, Bertoni, Martino, Bordoli, Lorenza, and Schwede, Torsten

Abstract

Protein structure homology modelling has become a routine technique to generate 3D models for proteins when experimental structures are not available. Fully automated servers such as SWISS-MODEL with user-friendly web interfaces generate reliable models without the need for complex software packages or downloading large databases. Here, we describe the latest version of the SWISS-MODEL expert system for protein structure modelling. The SWISS-MODEL template library provides annotation of quaternary structure and essential ligands and co-factors to allow for building of complete structural models, including their oligomeric structure. The improved SWISS-MODEL pipeline makes extensive use of model quality estimation for selection of the most suitable templates and provides estimates of the expected accuracy of the resulting models. The accuracy of the models generated by SWISS-MODEL is continuously evaluated by the CAMEO system. The new web site allows users to interactively search for templates, cluster them by sequence similarity, structurally compare alternative templates and select the ones to be used for model building. In cases where multiple alternative template structures are available for a protein of interest, a user-guided template selection step allows building models in different functional states. SWISS-MODEL is available at http://swissmodel.expasy.org

34. Assessing the local structural quality of transmembrane protein models using statistical potentials (QMEANBrane)

Author

Studer, Gabriel, Biasini, Marco, Schwede, Torsten, Studer, Gabriel, Biasini, Marco, and Schwede, Torsten

Abstract

Motivation: Membrane proteins are an important class of biological macromolecules involved in many cellular key processes including signalling and transport. They account for one third of genes in the human genome and >50% of current drug targets. Despite their importance, experimental structural data are sparse, resulting in high expectations for computational modelling tools to help fill this gap. However, as many empirical methods have been trained on experimental structural data, which is biased towards soluble globular proteins, their accuracy for transmembrane proteins is often limited. Results: We developed a local model quality estimation method for membrane proteins (‘QMEANBrane') by combining statistical potentials trained on membrane protein structures with a per-residue weighting scheme. The increasing number of available experimental membrane protein structures allowed us to train membrane-specific statistical potentials that approach statistical saturation. We show that reliable local quality estimation of membrane protein models is possible, thereby extending local quality estimation to these biologically relevant molecules. Availability and implementation: Source code and datasets are available on request. Contact: torsten.schwede@unibas.ch Supplementary Information: Supplementary data are available at Bioinformatics online

35. Global profiling of protein complex dynamics with an experimental library of protein interaction markers.

Author

Dörig C, Marulli C, Peskett T, Volkmar N, Pantolini L, Studer G, Paleari C, Frommelt F, Schwede T, de Souza N, Barral Y, and Picotti P

Abstract

Methods to systematically monitor protein complex dynamics are needed. We introduce serial ultrafiltration combined with limited proteolysis-coupled mass spectrometry (FLiP-MS), a structural proteomics workflow that generates a library of peptide markers specific to changes in PPIs by probing differences in protease susceptibility between complex-bound and monomeric forms of proteins. The library includes markers mapping to protein-binding interfaces and markers reporting on structural changes that accompany PPI changes. Integrating the marker library with LiP-MS data allows for global profiling of protein-protein interactions (PPIs) from unfractionated lysates. We apply FLiP-MS to Saccharomyces cerevisiae and probe changes in protein complex dynamics after DNA replication stress, identifying links between Spt-Ada-Gcn5 acetyltransferase activity and the assembly state of several complexes. FLiP-MS enables protein complex dynamics to be probed on any perturbation, proteome-wide, at high throughput, with peptide-level structural resolution and informing on occupancy of binding interfaces, thus providing both global and molecular views of a system under study., (© 2024. The Author(s).)

Published
2024
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36. 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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37. Modeling of Protein Tertiary and Quaternary Structures Based on Evolutionary Information.

Author

Studer G, Tauriello G, Bienert S, Waterhouse AM, Bertoni M, Bordoli L, Schwede T, and Lepore R

Subjects
Computational Biology, Models, Molecular, Proteins classification, Sequence Homology, Amino Acid, Structural Homology, Protein, Proteins chemistry
Abstract

Proteins are subject to evolutionary forces that shape their three-dimensional structure to meet specific functional demands. The knowledge of the structure of a protein is therefore instrumental to gain information about the molecular basis of its function. However, experimental structure determination is inherently time consuming and expensive, making it impossible to follow the explosion of sequence data deriving from genome-scale projects. As a consequence, computational structural modeling techniques have received much attention and established themselves as a valuable complement to experimental structural biology efforts. Among these, comparative modeling remains the method of choice to model the three-dimensional structure of a protein when homology to a protein of known structure can be detected.The general strategy consists of using experimentally determined structures of proteins as templates for the generation of three-dimensional models of related family members (targets) of which the structure is unknown. This chapter provides a description of the individual steps needed to obtain a comparative model using SWISS-MODEL, one of the most widely used automated servers for protein structure homology modeling.

Published
2019
Full Text
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