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8 results on '"Chiara Pallara"'

1. pyDock scoring for the new modeling challenges in docking: Protein-peptide, homo-multimers, and domain-domain interactions

Author

Chiara Pallara, Brian Jiménez-García, Miguel Romero, Juan Fernández-Recio, and Iain H. Moal

Subjects
0301 basic medicine, 03 medical and health sciences, 030104 developmental biology, 030102 biochemistry & molecular biology, Structural Biology, Computer science, Server, Macromolecular docking, Data mining, computer.software_genre, Molecular Biology, Biochemistry, computer
Abstract

The 6th CAPRI edition included new modelling challenges, such as the prediction of protein-peptide complexes, and the modelling of homo-oligomers and domain-domain interactions as part of the first joint CASP-CAPRI experiment. Other non-standard targets included the prediction of interfacial water positions and the modelling of the interactions between proteins and nucleic acids. We have participated in all proposed targets of this CAPRI edition both as predictors and as scorers, with new protocols to efficiently use our docking and scoring scheme pyDock in a large variety of scenarios. In addition, we have participated for the first time in the server section, with our recently developed webserver, pyDockWeb. Excluding the CASP-CAPRI cases, we submitted acceptable models (or better) for 7 out of the 18 evaluated targets as predictors, 4 out of the 11 targets as scorers, and 6 out of the 18 targets as servers. The overall success rates were below those in past CAPRI editions. This shows the challenging nature of this last edition, with many difficult targets for which no participant submitted a single acceptable model. Interestingly, we submitted acceptable models for 83% of the evaluated protein-peptide targets. As for the 25 cases of the CASP-CAPRI experiment, in which we used a larger variety of modelling techniques (template-based, symmetry restraints, literature information, etc.), we submitted acceptable models for 56% of the targets. In summary, this CAPRI edition showed that pyDock scheme can be efficiently adapted to the increasing variety of problems that the protein interactions field is currently facing. This article is protected by copyright. All rights reserved.

Published
2016
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2. Blind prediction of interfacial water positions in CAPRI

Author

Pravin Muthu, Joy Sarmiento, John Wieting, Thom Vreven, Hasup Lee, Dima Kozakov, Haruki Nakamura, Julie C. Mitchell, Juan Fernández-Recio, Haim J. Wolfson, Sergei Grudinin, Yuko Tsuchiya, Iain H. Moal, Efrat Farkash, Chiara Pallara, Petras J. Kundrotas, Howook Hwang, Chaok Seok, Panagiotis L. Kastritis, Hahnbeom Park, Xiaoqin Zou, Junsu Ko, Justyna Aleksandra Wojdyla, Brian G. Pierce, Christophe Schmitz, Colin Kleanthous, Sanbo Qin, Shoshana J. Wodak, Paul A. Bates, Matsuyuki Shirota, Solène Grosdidier, Idit Buch, Ilya A. Vakser, Krishna Praneeth Kilambi, Jianqing Xu, Matthieu Chavent, Sandor Vajda, Adrien S. J. Melquiond, Marc F. Lensink, Shen You Huang, Martin Zacharias, David W. Ritchie, Brian Jiménez-García, Marc van Dijk, Ezgi Karaca, Yoichi Murakami, Daron M. Standley, Albert Solernou, Laura Pérez-Cano, Yang Shen, Miriam Eisenstein, Jeffrey J. Gray, Alexandre M. J. J. Bonvin, Zhiping Weng, Georgy Derevyanko, Kengo Kinoshita, Huan-Xiang Zhou, and Eiji Kanamori

Subjects
0303 health sciences, 010304 chemical physics, Chemistry, 01 natural sciences, Biochemistry, Molecular Docking Simulation, Force field (chemistry), Protein–protein interaction, 03 medical and health sciences, Crystallography, Molecular recognition, Protein structure, Structural Biology, Docking (molecular), 0103 physical sciences, Critical assessment, Macromolecular docking, Biological system, Molecular Biology, 030304 developmental biology
Abstract

We report the first assessment of blind predictions of water positions at protein-protein interfaces, performed as part of the critical assessment of predicted interactions (CAPRI) community-wide experiment. Groups submitting docking predictions for the complex of the DNase domain of colicin E2 and Im2 immunity protein (CAPRI Target 47), were invited to predict the positions of interfacial water molecules using the method of their choice. The predictions-20 groups submitted a total of 195 models-were assessed by measuring the recall fraction of water-mediated protein contacts. Of the 176 high- or medium-quality docking models-a very good docking performance per se-only 44% had a recall fraction above 0.3, and a mere 6% above 0.5. The actual water positions were in general predicted to an accuracy level no better than 1.5 A, and even in good models about half of the contacts represented false positives. This notwithstanding, three hotspot interface water positions were quite well predicted, and so was one of the water positions that is believed to stabilize the loop that confers specificity in these complexes. Overall the best interface water predictions was achieved by groups that also produced high-quality docking models, indicating that accurate modelling of the protein portion is a determinant factor. The use of established molecular mechanics force fields, coupled to sampling and optimization procedures also seemed to confer an advantage. Insights gained from this analysis should help improve the prediction of protein-water interactions and their role in stabilizing protein complexes.

Published
2013
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3. Community-wide evaluation of methods for predicting the effect of mutations on protein-protein interactions

Author

Howook Hwang, Shiyong Liu, Xiaoqin Zou, Huan-Xiang Zhou, Hideaki Umeyama, Paul A. Bates, Hahnbeom Park, Yangyu Huang, Xiaolei Zhu, Marianne Rooman, Rudi Agius, David Baker, Sarel J. Fleishman, Dimitri Gillis, Eiji Kanamori, Yuko Tsuchiya, Sandor Vajda, Panagiotis L. Kastritis, Brian Jimenez, Thom Vreven, Xiufeng Yang, Hiromitsu Shimoyama, Nan Zhao, Zhiping Weng, Sheng-You Huang, Mikael Trellet, Chaok Seok, Samuel C. Flores, Miguel Romero-Durana, Sanbo Qin, Michael S. Pacella, Julie C. Mitchell, Mayuko Takeda-Shitaka, Dmitri Beglov, Jeffrey J. Gray, Shoshana J. Wodak, Rocco Moretti, Martin Zacharias, Dmitry Korkin, Dima Kozakov, João P. G. L. M. Rodrigues, Haruki Nakamura, Juan Esquivel-Rodríguez, Mieczyslaw Torchala, Yves Dehouck, Alexandre M. J. J. Bonvin, David R. Hall, Mitsuo Iwadate, Krishna Praneeth Kilambi, Jamica Sarmiento, Daron M. Standley, Joël Janin, Omar N. A. Demerdash, Brian G. Pierce, Chiara Pallara, Meng Cui, Shusuke Teraguchi, Petr Popov, Hasup Lee, Haotian Li, Juan Fernández-Recio, Laura Pérez-Cano, Sergei Grudinin, Sameer Velankar, Daisuke Kihara, Xiaofeng Ji, Genki Terashi, Yi Xiao, Shide Liang, and Iain H. Moal

Subjects
Genetics, 0303 health sciences, Mutation, 010304 chemical physics, Fitness landscape, Stability (learning theory), Computational biology, Yeast display, Biology, medicine.disease_cause, 01 natural sciences, Biochemistry, Deep sequencing, Protein–protein interaction, 03 medical and health sciences, Structural Biology, 0103 physical sciences, medicine, CASP, Saturated mutagenesis, Molecular Biology, 030304 developmental biology
Abstract

Community-wide blind prediction experiments such as CAPRI and CASP provide an objective measure of the current state of predictive methodology. Here we describe a community-wide assessment of methods to predict the effects of mutations on protein-protein interactions. Twenty-two groups predicted the effects of comprehensive saturation mutagenesis for two designed influenza hemagglutinin binders and the results were compared with experimental yeast display enrichment data obtained using deep sequencing. The most successful methods explicitly considered the effects of mutation on monomer stability in addition to binding affinity, carried out explicit side-chain sampling and backbone relaxation, evaluated packing, electrostatic, and solvation effects, and correctly identified around a third of the beneficial mutations. Much room for improvement remains for even the best techniques, and large-scale fitness landscapes should continue to provide an excellent test bed for continued evaluation of both existing and new prediction methodologies.

Published
2013
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4. Prediction of homo- and hetero-protein complexes by protein docking and template-based modeling: a CASP-CAPRI experiment

Author

Eichiro Ichiishi, Dmitri Beglov, Bernard Maigret, Gyu Rie Lee, Artem B. Mamonov, Shoshana J. Wodak, Jonathan C. Fuller, Dima Kozakov, Jong Young Joung, Petr Popov, Xiaofeng Yu, Keehyoung Joo, João P. G. L. M. Rodrigues, Anna Vangone, Koen M. Visscher, Xiaoqin Zou, Paul A. Bates, Andriy Kryshtafovych, Shourya S. Roy Burman, Daisuke Kihara, Romina Oliva, Efrat Ben-Zeev, Jeffrey J. Gray, Yang Shen, Li C. Xue, Sameer Velankar, Emilie Neveu, Shruthi Viswanath, Dina Schneidman-Duhovny, Juan Esquivel-Rodríguez, Mieczyslaw Torchala, Amit Roy, Alexandre M. J. J. Bonvin, David R. Hall, Tanggis Bohnuud, Xusi Han, David W. Ritchie, Ron Elber, Daisuke Kuroda, Zhiwei Ma, Joan Segura, Carlos A. Del Carpio, Nicholas A. Marze, Jong Yun Kim, Andrej Sali, Petras J. Kundrotas, Ezgi Karaca, Neil J. Bruce, Chaok Seok, Panagiotis L. Kastritis, Shen You Huang, Ilya A. Vakser, Lim Heo, Sanbo Qin, Raphael A. G. Chaleil, Adrien S. J. Melquiond, Miguel Romero-Durana, Anisah W. Ghoorah, Surendra S. Negi, Andrey Tovchigrechko, Françoise Ochsenbein, Narcis Fernandez-Fuentes, Liming Qiu, Miriam Eisenstein, Mehdi Nellen, Marie-Dominique Devignes, Lenna X. Peterson, Jinchao Yu, Minkyung Baek, Brian G. Pierce, Hasup Lee, Toshiyuki Oda, Rebecca C. Wade, Raphael Guerois, Juan Fernández-Recio, Iain H. Moal, Edrisse Chermak, Sergei Grudinin, Sangwoo Park, Ivan Anishchenko, Chengfei Yan, Thom Vreven, Kentaro Tomii, Bing Xia, Hyung Rae Kim, Chiara Pallara, Jooyoung Lee, Kazunori D. Yamada, Xianjin Xu, Kenichiro Imai, Zhiping Weng, Luigi Cavallo, Tyler M. Borrman, Jianlin Cheng, Marc F. Lensink, Huan-Xiang Zhou, Jilong Li, Gydo C. P. van Zundert, Brian Jiménez-García, Tsukasa Nakamura, Scott E. Mottarella, Sandor Vajda, Institut de Recherche Interdisciplinaire [Villeneuve d'Ascq] ( IRI ), Université de Lille, Sciences et Technologies-Université de Lille, Droit et Santé-Centre National de la Recherche Scientifique ( CNRS ), European Molecular Biology Laboratory, European Bioinformatics Institute, Genome Center [UC Davis], University of California at Davis, Research Support Computing [Columbia], University of Missouri-Columbia, Bioinformatics Consortium and Department of Computer Science [Columbia], Department of Bioengineering and Therapeutic Sciences, University of California [San Francisco] ( UCSF ), Department of Pharmaceutical Chemistry, Department of Bioengineering and Therapeutic Sciences, Department of Pharmaceutical Chemistry, University of California [San Francisco] ( UCSF ) -California Institute for Quantitative Biosciences, GN7 of the National Institute for Bioinformatics (INB) and Biocomputing Unit, Centro Nacional de Biotecnología (CSIC), Institute of Biological, Environmental and Rural Sciences ( IBERS ), Institute for Computational Engineering and Sciences [Austin] ( ICES ), University of Texas at Austin [Austin], Department of Computer Science, Department of Chemistry, 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 ), Université Pierre Mendès France - Grenoble 2 ( UPMF ) -Université Joseph Fourier - Grenoble 1 ( UJF ) -Institut Polytechnique de Grenoble - Grenoble Institute of Technology-Centre National de la Recherche Scientifique ( CNRS ) -Université Grenoble Alpes ( UGA ) -Université Pierre Mendès France - Grenoble 2 ( UPMF ) -Université Joseph Fourier - Grenoble 1 ( UJF ) -Institut Polytechnique de Grenoble - Grenoble Institute of Technology-Centre National de la Recherche Scientifique ( CNRS ) -Université Grenoble Alpes ( UGA ) -Institut National Polytechnique de Grenoble ( INPG ), Moscow Institute of Physics and Technology [Moscow] ( MIPT ), Seoul National University [Seoul], Florida State University [Tallahassee] ( FSU ), Computational Algorithms for Protein Structures and Interactions ( CAPSID ), Inria Nancy - Grand Est, Institut National de Recherche en Informatique et en Automatique ( Inria ) -Institut National de Recherche en Informatique et en Automatique ( Inria ) -Department of Complex Systems, Artificial Intelligence & Robotics ( LORIA - AIS ), Laboratoire Lorrain de Recherche en Informatique et ses Applications ( LORIA ), Institut National de Recherche en Informatique et en Automatique ( Inria ) -Université de Lorraine ( UL ) -Centre National de la Recherche Scientifique ( CNRS ) -Institut National de Recherche en Informatique et en Automatique ( Inria ) -Université de Lorraine ( UL ) -Centre National de la Recherche Scientifique ( CNRS ) -Laboratoire Lorrain de Recherche en Informatique et ses Applications ( LORIA ), Institut National de Recherche en Informatique et en Automatique ( Inria ) -Université de Lorraine ( UL ) -Centre National de la Recherche Scientifique ( CNRS ) -Université de Lorraine ( UL ) -Centre National de la Recherche Scientifique ( CNRS ), University of Mauritius, Biomolecular Modelling Laboratory, The Francis Crick Institute, Lincoln's Inn Fields Laboratory, G-INCPM, Weizmann Institute of Science, Chemical Research Support [Rehovot], Sealy Center for Structural Biology and Molecular Biophysics, The University of Texas Medical Branch ( UTMB ), Program in Bioinformatics and Integrative Biology [Worcester], University of Massachusetts Medical School [Worcester] ( UMASS ), Institut de Biologie Intégrative de la Cellule ( I2BC ), Université Paris-Saclay-Centre National de la Recherche Scientifique ( CNRS ) -Commissariat à l'énergie atomique et aux énergies alternatives ( CEA ) -Université Paris-Sud - Paris 11 ( UP11 ), Bijvoet Center for Biomolecular Research [Utrecht], Utrecht University [Utrecht], Dalton Cardiovascular Research Center [Columbia], Department of Computer Science [Columbia], Informatics Intitute, Department of Biochemistry, University of Missouri, UNIVERSITY OF MISSOURI, Toyota Technological Institute at Chicago [Chicago] ( TTIC ), Department of Biological Sciences, Purdue University, Purdue University [West Lafayette], Department of Computer Science [Purdue], Bioinformatics and Computational Biosciences Branch, Rocky Mountain Laboratories, Molecular and Cellular Modeling Group, Heidelberg Institute of Theoretical Studies, Center for Molecular Biology ( ZMBH ), Universität Heidelberg [Heidelberg], Interdisciplinary Center for Scientific Computing ( IWR ), Department of Molecular Biosciences [Lawrence], University of Kansas [Lawrence] ( KU ), Computational Biology Research Center ( CBRC ), National Institute of Advanced Industrial Science and Technology ( AIST ), Graduate School of Frontier Sciences, The University of Tokyo, Joint BSC-CRG-IRB Research Program in Computational Biology, Barcelona Supercomputing Center - Centro Nacional de Supercomputacion ( BSC - CNS ), Center for In-Silico Protein Science, Korea Institute for Advanced Study ( KIAS ), Center for Advanced Computation, Department of Biomedical Engineering [Boston], Boston University [Boston] ( BU ), Institute of Biological Diversity, International Pacific Institute of Indiana, Drosophila Genetic Resource Center, Kyoto Institute of Technology, International University of Health and Welfare Hospital ( IUHW Hospital ), International University of Health and Welfare Hospital, Department of Chemical and Biomolecular Engineering [Baltimore], Johns Hopkins University ( JHU ), Program in Molecular Biophysics [Baltimore], King Abdullah University of Science and Technology ( KAUST ), University of Naples, J Craig Venter Institute, Structural Biology Research Center, VIB, 1050 Brussels, Belgium, Institut de Recherche Interdisciplinaire [Villeneuve d'Ascq] (IRI), Université de Lille, Sciences et Technologies-Université de Lille, Droit et Santé-Centre National de la Recherche Scientifique (CNRS), European Bioinformatics Institute [Hinxton] (EMBL-EBI), EMBL Heidelberg, University of California [Davis] (UC Davis), University of California (UC)-University of California (UC), University of Missouri [Columbia] (Mizzou), University of Missouri System, University of California [San Francisco] (UC San Francisco), Centro Nacional de Biotecnología [Madrid] (CNB-CSIC), Consejo Superior de Investigaciones Científicas [Madrid] (CSIC)-Consejo Superior de Investigaciones Científicas [Madrid] (CSIC), Institute of Biological, Environmental and Rural Sciences (IBERS), Institute for Computational Engineering and Sciences [Austin] (ICES), Algorithms for Modeling and Simulation of Nanosystems (NANO-D), Institut National de Recherche en Informatique et en Automatique (Inria)-Institut National de Recherche en Informatique et en Automatique (Inria)-Institut polytechnique de Grenoble - Grenoble Institute of Technology (Grenoble INP )-Laboratoire Jean Kuntzmann (LJK ), Institut polytechnique de Grenoble - Grenoble Institute of Technology (Grenoble INP )-Institut National de Recherche en Informatique et en Automatique (Inria)-Centre National de la Recherche Scientifique (CNRS)-Université Grenoble Alpes [2016-2019] (UGA [2016-2019])-Centre National de la Recherche Scientifique (CNRS)-Université Grenoble Alpes [2016-2019] (UGA [2016-2019]), Moscow Institute of Physics and Technology [Moscow] (MIPT), Seoul National University [Seoul] (SNU), Florida State University [Tallahassee] (FSU), Computational Algorithms for Protein Structures and Interactions (CAPSID), Institut National de Recherche en Informatique et en Automatique (Inria)-Institut National de Recherche en Informatique et en Automatique (Inria)-Department of Complex Systems, Artificial Intelligence & Robotics (LORIA - AIS), Laboratoire Lorrain de Recherche en Informatique et ses Applications (LORIA), Institut National de Recherche en Informatique et en Automatique (Inria)-Université de Lorraine (UL)-Centre National de la Recherche Scientifique (CNRS)-Institut National de Recherche en Informatique et en Automatique (Inria)-Université de Lorraine (UL)-Centre National de la Recherche Scientifique (CNRS)-Laboratoire Lorrain de Recherche en Informatique et ses Applications (LORIA), Institut National de Recherche en Informatique et en Automatique (Inria)-Université de Lorraine (UL)-Centre National de la Recherche Scientifique (CNRS)-Université de Lorraine (UL)-Centre National de la Recherche Scientifique (CNRS), Biomolecular Modelling Laboratory [London], The Francis Crick Institute [London], Weizmann Institute of Science [Rehovot, Israël], The University of Texas Medical Branch (UTMB), University of Massachusetts Medical School [Worcester] (UMASS), University of Massachusetts System (UMASS)-University of Massachusetts System (UMASS), Institut de Biologie Intégrative de la Cellule (I2BC), Commissariat à l'énergie atomique et aux énergies alternatives (CEA)-Université Paris-Saclay-Centre National de la Recherche Scientifique (CNRS), Assemblage moléculaire et intégrité du génome (AMIG), Département Biochimie, Biophysique et Biologie Structurale (B3S), Commissariat à l'énergie atomique et aux énergies alternatives (CEA)-Université Paris-Saclay-Centre National de la Recherche Scientifique (CNRS)-Commissariat à l'énergie atomique et aux énergies alternatives (CEA)-Université Paris-Saclay-Centre National de la Recherche Scientifique (CNRS)-Institut de Biologie Intégrative de la Cellule (I2BC), Commissariat à l'énergie atomique et aux énergies alternatives (CEA)-Université Paris-Saclay-Centre National de la Recherche Scientifique (CNRS)-Commissariat à l'énergie atomique et aux énergies alternatives (CEA)-Université Paris-Saclay-Centre National de la Recherche Scientifique (CNRS), University of Missouri System-University of Missouri System, Toyota Technological Institute at Chicago [Chicago] (TTIC), Department of Biological Sciences [Lafayette IN], Heidelberg Institute for Theoretical Studies (HITS ), Center for Molecular Biology (ZMBH), Universität Heidelberg [Heidelberg] = Heidelberg University, Interdisciplinary Center for Scientific Computing (IWR), University of Kansas [Lawrence] (KU), Computational Biology Research Center (CBRC), National Institute of Advanced Industrial Science and Technology (AIST), The University of Tokyo (UTokyo), Barcelona Supercomputing Center - Centro Nacional de Supercomputacion (BSC - CNS), Korea Institute for Advanced Study (KIAS), Boston University [Boston] (BU), International University of Health and Welfare Hospital (IUHW Hospital), Johns Hopkins University (JHU), King Abdullah University of Science and Technology (KAUST), University of Naples Federico II = Università degli studi di Napoli Federico II, J. Craig Venter Institute, VIB-VUB Center for Structural Biology [Bruxelles], VIB [Belgium], Centre National de la Recherche Scientifique (CNRS)-Université de Lille, Droit et Santé-Université de Lille, Sciences et Technologies, University of California-University of California, University of California [San Francisco] (UCSF), Centre National de la Recherche Scientifique (CNRS)-Université de Lorraine (UL)-Institut National de Recherche en Informatique et en Automatique (Inria)-Centre National de la Recherche Scientifique (CNRS)-Université de Lorraine (UL)-Institut National de Recherche en Informatique et en Automatique (Inria)-Laboratoire Lorrain de Recherche en Informatique et ses Applications (LORIA), Centre National de la Recherche Scientifique (CNRS)-Université de Lorraine (UL)-Institut National de Recherche en Informatique et en Automatique (Inria)-Centre National de la Recherche Scientifique (CNRS)-Université de Lorraine (UL), University of Naples Federico II, Barcelona Supercomputing Center, NMR Spectroscopy, and Sub NMR Spectroscopy

Subjects
0301 basic medicine, Protein Conformation, alpha-Helical, Protein Folding, Computer science, International Cooperation, Amino Acid Motifs, Oligomer state, Homoprotein, DATA-BANK, computer.software_genre, Molecular Docking Simulation, Biochemistry, CAPRI Round 30, DESIGN, Structural Biology, ALIGN, Blind prediction, AFFINITY, Protein interaction, Enginyeria biomèdica [Àrees temàtiques de la UPC], ZDOCK, Oligomer State, computer.file_format, Articles, Protein structure prediction, Proteïnes--Investigació, 3. Good health, WEB SERVER, CASP, Thermodynamics, Data mining, CAPRI, Protein docking, Molecular Biology, Algorithms, INTERFACES, Protein Binding, [ INFO.INFO-MO ] Computer Science [cs]/Modeling and Simulation, Bioinformatics, STRUCTURAL BIOLOGY, Computational biology, Molecular Dynamics Simulation, Article, 03 medical and health sciences, [ INFO.INFO-BI ] Computer Science [cs]/Bioinformatics [q-bio.QM], Heteroprotein, Humans, Protein binding, Macromolecular docking, Protein Interaction Domains and Motifs, Homology modeling, ALGORITHM, Protein-protein docking, Internet, Binding Sites, Models, Statistical, 030102 biochemistry & molecular biology, Bacteria, Sequence Homology, Amino Acid, Computational Biology, Proteins, Protein Data Bank, [INFO.INFO-MO]Computer Science [cs]/Modeling and Simulation, Protein Structure, Tertiary, 030104 developmental biology, Structural biology, Docking (molecular), Protein structure, Protein Conformation, beta-Strand, Protein Multimerization, oligomer state, blind prediction, protein interaction, protein docking, [INFO.INFO-BI]Computer Science [cs]/Bioinformatics [q-bio.QM], computer, Software
Abstract

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

Published
2016
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5. Tetramerization-defects of p53 result in aberrant ubiquitylation and transcriptional activity

Author

Rosa Farràs, Chiara Pallara, Christine Blattner, Rosa Barrio, Ronald T. Hay, Valérie Lang, Lorea Zabaleta, Amaia Zabala, Roland Hjerpe, Mónica Torres-Ramos, Manuel S. Rodriguez, Juan Fernández-Recio, Arkaitz Carracedo, Fabienne Aillet, Fernando Lopitz-Otsoa, and Sofia Lobato-Gil

Subjects
Transcriptional Activation, Cancer Research, Proteasome Endopeptidase Complex, Mutant, BH3 interacting-domain death agonist, Bcl-2-associated X protein, Transcription (biology), Cell Line, Tumor, Gene expression, Genetics, E2F1, Humans, Point Mutation, Research Articles, biology, Wild type, Ubiquitination, Proto-Oncogene Proteins c-mdm2, General Medicine, Molecular biology, Molecular Docking Simulation, Oncology, Proteasome, Proteolysis, biology.protein, Mutagenesis, Site-Directed, Molecular Medicine, Protein Multimerization, Tumor Suppressor Protein p53
Abstract

The tumor suppressor p53 regulates the expression of genes involved in cell cycle progression, senescence and apoptosis. Here, we investigated the effect of single point mutations in the oligomerization domain (OD) on tetramerization, transcription, ubiquitylation and stability of p53. As predicted by docking and molecular dynamics simulations, p53 OD mutants show functional defects on transcription, Mdm2-dependent ubiquitylation and 26S proteasome-mediated degradation. However, mutants unable to form tetramers are well degraded by the 20S proteasome. Unexpectedly, despite the lower structural stability compared to WT p53, p53 OD mutants form heterotetramers with WT p53 when expressed transiently or stably in cells wild type or null for p53. In consequence, p53 OD mutants interfere with the capacity of WT p53 tetramers to be properly ubiquitylated and result in changes of p53-dependent protein expression patterns, including the pro-apoptotic proteins Bax and PUMA under basal and adriamycin-induced conditions. Importantly, the patient derived p53 OD mutant L330R (OD1) showed the more severe changes in p53-dependent gene expression. Thus, in addition to the well-known effects on p53 stability, ubiquitylation defects promote changes in p53-dependent gene expression with implications on some of its functions.

Published
2013

6. Correction: Structural Basis for Rab1 De-AMPylation by the Legionella pneumophila Effector SidD

Author

Adriana L. Rojas, Aitor Hierro, Lisa N. Kinch, M. Ramona Neunuebel, Juan Fernández-Recio, Chiara Pallara, Jacqueline Brady, Igor Tascón, Matthias P. Machner, and Yang Chen

Subjects
biology, Immunology, Library science, biology.organism_classification, Microbiology, Legionella pneumophila, Light source, Sequence homology, SIDD, Virology, Genetics, Rapid access, Parasitology, Molecular Biology
Abstract

Two errors were made in this article. First, a reference was omitted from the second sentence of the fourth paragraph in the Introduction section. The sentence should read: SidD lacks any obvious sequence homology with the AT-N or other known proteins, although fold recognition analysis of the N-terminal portion of SidD predicted limited resemblance with members of the metal-dependent protein phosphatase (PPM) family (Rigden 2011). The reference is: Rigden DJ. (2011) Identification and modelling of a PPM protein phosphatase fold in the Legionella pneumophila deAMPylase SidD. FEBS Lett. 585:2749-54. doi: 10.1016/j.febslet.2011.08.006. Second, there were some omissions from the acknowledgements section, which should instead read as follows: We thank K. B. Decker, Y. Lin, and A.H. Gaspar for comments on the manuscript, and A. Vidaurrazaga for assistance with biochemical experiments. This study made use of the European Synchrotron Radiation Facility (ESRF, Grenoble, France) under the Block Allocation Group (BAG) MX1420, the Diamond Light Source (Oxfordshire, UK) under the rapid access mode MX7512 and BAG MX8302, the beamline PROXIMA1 at the SOLEIL synchrotron (Saint-Aubin, France), the X-ray crystallography platform and proteomics platform (member of CIBERehd and ProteoRed-ISCIII) at CIC bioGUNE (Derio, Spain), and the SGIker analytical facility at UPV/EHU (Leioa, Spain). We thank all the staff from these facilities for technical and human support. Access to synchrotron facilities was part funded by the the BioStruct-X project (proposal number 2460).

Published
2013
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7. Expanding the frontiers of protein-protein modeling: from docking and scoring to binding affinity predictions and other challenges

Author

Miguel Romero-Durana, Iain H. Moal, Brian Jiménez-García, Solène Grosdidier, Chiara Pallara, Juan Fernández-Recio, Laura Pérez-Cano, Albert Solernou, and Carles Pons

Subjects
Computer science, Protein Conformation, Carbohydrates, Computational biology, 01 natural sciences, Biochemistry, 03 medical and health sciences, X-Ray Diffraction, Structural Biology, 0103 physical sciences, Scattering, Small Angle, Molecular Biology, Simulation, 030304 developmental biology, Binding affinities, High rate, 0303 health sciences, 010304 chemical physics, Protein protein, Computational Biology, Proteins, Water, Molecular Docking Simulation, Docking (molecular), Mutation, Software, Protein Binding
Abstract

In addition to protein-protein docking, this CAPRI edition included new challenges, like protein-water and protein-sugar interactions, or the prediction of binding affinities and ΔΔG changes upon mutation. Regarding the standard protein-protein docking cases, our approach, mostly based on the pyDock scheme, submitted correct models as predictors and as scorers for 67% and 57% of the evaluated targets, respectively. In this edition, available information on known interface residues hardly made any difference for our predictions. In one of the targets, the inclusion of available experimental small-angle X-ray scattering (SAXS) data using our pyDockSAXS approach slightly improved the predictions. In addition to the standard protein-protein docking assessment, new challenges were proposed. One of the new problems was predicting the position of the interface water molecules, for which we submitted models with 20% and 43% of the water-mediated native contacts predicted as predictors and scorers, respectively. Another new problem was the prediction of protein-carbohydrate binding, where our submitted model was very close to being acceptable. A set of targets were related to the prediction of binding affinities, in which our pyDock scheme was able to discriminate between natural and designed complexes with area under the curve = 83%. It was also proposed to estimate the effect of point mutations on binding affinity. Our approach, based on machine learning methods, showed high rates of correctly classified mutations for all cases. The overall results were highly rewarding, and show that the field is ready to move forward and face new interesting challenges in interactomics. Proteins 2013; 81:2192-2200. © 2013 Wiley Periodicals, Inc.

Published
2013

8. Correction: Structural Basis for Rab1 De-AMPylation by the Legionella pneumophila Effector SidD

Author

Adriana L. Rojas, Lisa N. Kinch, Aitor Hierro, Igor Tascón, Juan Fernández-Recio, M. Ramona Neunuebel, Jacqueline Brady, Chiara Pallara, Yang Chen, and Matthias P. Machner

Subjects
Bacterial Diseases, Crystallography, X-Ray, Biochemistry, Legionella pneumophila, Molecular Cell Biology, Macromolecular Structure Analysis, Phosphoprotein Phosphatases, Signaling in Cellular Processes, Small GTPase, Magnesium ion, lcsh:QH301-705.5, Macromolecular Complex Analysis, biology, Effector, Cellular Structures, Enzymes, Cell biology, Molecular Docking Simulation, Protein Phosphatase 2C, Infectious Diseases, SIDD, Medicine, Membranes and Sorting, Research Article, Signal Transduction, lcsh:Immunologic diseases. Allergy, Immunology, Legionella, Ras Signaling, Microbiology, Structure-Activity Relationship, Bacterial Proteins, Virology, Genetics, Humans, Protein Structure, Quaternary, Biology, Microbial Pathogens, Molecular Biology, Adenylylation, Computational Biology, RAB1, Correction, biology.organism_classification, Adenosine Monophosphate, rab1 GTP-Binding Proteins, Subcellular Organelles, lcsh:Biology (General), Docking (molecular), Enzyme Structure, Parasitology, lcsh:RC581-607
Abstract

The covalent attachment of adenosine monophosphate (AMP) to proteins, a process called AMPylation (adenylylation), has recently emerged as a novel theme in microbial pathogenesis. Although several AMPylating enzymes have been characterized, the only known virulence protein with de-AMPylation activity is SidD from the human pathogen Legionella pneumophila. SidD de-AMPylates mammalian Rab1, a small GTPase involved in secretory vesicle transport, thereby targeting the host protein for inactivation. The molecular mechanisms underlying Rab1 recognition and de-AMPylation by SidD are unclear. Here, we report the crystal structure of the catalytic region of SidD at 1.6 Å resolution. The structure reveals a phosphatase-like fold with additional structural elements not present in generic PP2C-type phosphatases. The catalytic pocket contains a binuclear metal-binding site characteristic of hydrolytic metalloenzymes, with strong dependency on magnesium ions. Subsequent docking and molecular dynamics simulations between SidD and Rab1 revealed the interface contacts and the energetic contribution of key residues to the interaction. In conjunction with an extensive structure-based mutational analysis, we provide in vivo and in vitro evidence for a remarkable adaptation of SidD to its host cell target Rab1 which explains how this effector confers specificity to the reaction it catalyses., Author Summary The covalent attachment of adenosine monophosphate (AMP) to proteins, a process called AMPylation (adenylylation), has recently emerged as a novel theme in microbial pathogenesis. While AMPylases from various pathogenic microorganisms have recently been characterized, the only virulence protein with de-AMPylation activity known to date is the Legionella pneumophila effector SidD which catalyzes AMP removal from the host GTPase Rab1. Thus, both AMPylation and de-AMPylation constitute a novel catalytic mechanism to precisely control the function and membrane dynamics of a host Rab GTPase. In spite of this pivotal role, the molecular mechanism of AMP removal and the structural determinants for Rab1 recognition by SidD have remained largely unexplored. Here, we present the crystal structure of the de-adenylylation domain of SidD and reveal the catalytic mechanism of Rab1 de-adenylylation. Surprisingly, the structure of SidD is not related to the other known enzyme with de-AMPylation activity, the Escherichia coli GS-ATase. Instead, the catalitic domain of SidD is remarkably similar to that of the metal-dependent protein phosphatases (PPMs), however with distinctive structural features to distinguish AMPylated Rab1 from similarly modified substrates. Importantly, we provide a model for the SidD-Rab1 complex which sheds light into the specific details of substrate recognition and catalysis by this virulence factor.

Published
2013

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