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7 results on '"Nicholas A. Marze"'

1. Efficient flexible backbone protein–protein docking for challenging targets

Author

Shourya S. Roy Burman, William Sheffler, Jeffrey J. Gray, and Nicholas A. Marze

Subjects
0301 basic medicine, Statistics and Probability, Protein Conformation, Computer science, Ligands, 01 natural sciences, Biochemistry, 03 medical and health sciences, Protein structure, 0103 physical sciences, Macromolecular docking, Molecular Biology, Conformational ensembles, Conformational isomerism, 010304 chemical physics, Ligand, Protein protein, Proteins, Original Papers, Computer Science Applications, Computational Mathematics, 030104 developmental biology, Computational Theory and Mathematics, Docking (molecular), Algorithm, Algorithms, Software, Protein Binding
Abstract

Motivation Binding-induced conformational changes challenge current computational docking algorithms by exponentially increasing the conformational space to be explored. To restrict this search to relevant space, some computational docking algorithms exploit the inherent flexibility of the protein monomers to simulate conformational selection from pre-generated ensembles. As the ensemble size expands with increased flexibility, these methods struggle with efficiency and high false positive rates. Results Here, we develop and benchmark RosettaDock 4.0, which efficiently samples large conformational ensembles of flexible proteins and docks them using a novel, six-dimensional, coarse-grained score function. A strong discriminative ability allows an eight-fold higher enrichment of near-native candidate structures in the coarse-grained phase compared to RosettaDock 3.2. It adaptively samples 100 conformations each of the ligand and the receptor backbone while increasing computational time by only 20–80%. In local docking of a benchmark set of 88 proteins of varying degrees of flexibility, the expected success rate (defined as cases with ≥50% chance of achieving 3 near-native structures in the 5 top-ranked ones) for blind predictions after resampling is 77% for rigid complexes, 49% for moderately flexible complexes and 31% for highly flexible complexes. These success rates on flexible complexes are a substantial step forward from all existing methods. Additionally, for highly flexible proteins, we demonstrate that when a suitable conformer generation method exists, the method successfully docks the complex. Availability and implementation As a part of the Rosetta software suite, RosettaDock 4.0 is available at https://www.rosettacommons.org to all non-commercial users for free and to commercial users for a fee. Supplementary information Supplementary data are available at Bioinformatics online.

Published
2018
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2. Macromolecular modeling and design in Rosetta: recent methods and frameworks

Author

Jack Maguire, Ragul Gowthaman, Marion F. Sauer, Georg Kuenze, Tanja Kortemme, Benjamin Basanta, Indigo Chris King, Jens Meiler, Rhiju Das, Ora Schueler-Furman, Nicholas A. Marze, Brandon Frenz, Christoffer Norn, Julia Koehler Leman, Jason W. Labonte, Kala Bharath Pilla, Lei Shi, Sergey Lyskov, Brian D. Weitzner, Nir London, Karen R. Khar, Jaume Bonet, Nawsad Alam, Andreas Scheck, Alexander M. Sevy, Lars Malmström, Thomas Huber, Christopher Bystroff, Lior Zimmerman, Lorna Dsilva, Bruno E. Correia, Roland L. Dunbrack, Sergey Ovchinnikov, Rocco Moretti, Scott Horowitz, Phil Bradley, Frank DiMaio, Noah Ollikainen, Brian Kuhlman, Jeffrey J. Gray, Melanie L. Aprahamian, Andrew Leaver-Fay, Santrupti Nerli, Brian Koepnick, Xingjie Pan, Manasi A. Pethe, Andrew M. Watkins, Summer B. Thyme, Enrique Marcos, Vikram Khipple Mulligan, Hahnbeom Park, Po-Ssu Huang, David K. Johnson, Daniel-Adriano Silva, Patrick Barth, Shannon Smith, Caleb Geniesse, Jason K. Lai, Patrick Conway, Amelie Stein, Jeliazko R. Jeliazkov, David Baker, Dominik Gront, Kalli Kappel, Firas Khatib, Robert Kleffner, Brian J. Bender, Richard Bonneau, Kyle A. Barlow, Joseph H. Lubin, Shourya S. Roy Burman, Nikolaos G. Sgourakis, Yuval Sedan, Ryan E. Pavlovicz, Kristin Blacklock, Seth Cooper, Barak Raveh, Alisa Khramushin, John Karanicolas, Justin B. Siegel, Sharon L. Guffy, Brian G. Pierce, Alex Ford, Darwin Y. Fu, Orly Marcu, Gideon Lapidoth, Brian Coventry, René M. de Jong, Shane O’Conchúir, Thomas W. Linsky, William R. Schief, Rebecca F. Alford, Scott E. Boyken, Sagar D. Khare, Maria Szegedy, Ray Yu-Ruei Wang, Steven M. Lewis, Hamed Khakzad, Timothy M. Jacobs, Frank D. Teets, Lukasz Goldschmidt, Daisuke Kuroda, Steffen Lindert, P. Douglas Renfrew, Yifan Song, Jared Adolf-Bryfogle, Michael S. Pacella, and Aliza B. Rubenstein

Subjects
atomic-accuracy, Models, Molecular, Computer science, Macromolecular Substances, Protein Conformation, Interoperability, computational design, Score, antibody structures, Biochemistry, Article, homing endonuclease specificity, 03 medical and health sciences, Software, Molecular Biology, 030304 developmental biology, 0303 health sciences, business.industry, Proteins, Usability, fold determination, Cell Biology, Molecular Docking Simulation, variable region, Docking (molecular), protein-structure prediction, small-molecule docking, Modeling and design, Peptidomimetics, User interface, Software engineering, business, de-novo design, sparse nmr data, Biotechnology
Abstract

The Rosetta software for macromolecular modeling, docking and design is extensively used in laboratories worldwide. During two decades of development by a community of laboratories at more than 60 institutions, Rosetta has been continuously refactored and extended. Its advantages are its performance and interoperability between broad modeling capabilities. Here we review tools developed in the last 5 years, including over 80 methods. We discuss improvements to the score function, user interfaces and usability. Rosetta is available at ., This Perspective reviews tools developed over the past five years in the macromolecular modeling, docking and design software Rosetta.

Published
2019

3. Modeling oblong proteins and water-mediated interfaces with RosettaDock in CAPRI rounds 28-35

Author

Frank DiMaio, Jeliazko R. Jeliazkov, Nicholas A. Marze, Scott E. Boyken, Jeffrey J. Gray, and Shourya S. Roy Burman

Subjects
0301 basic medicine, Computer science, Computational biology, Biochemistry, Molecular Docking Simulation, Protein–protein interaction, 03 medical and health sciences, 030104 developmental biology, Protein structure, Structural Biology, Docking (molecular), Searching the conformational space for docking, DOCK, Fluorescent protein, Critical assessment, Molecular Biology, Simulation
Abstract

The 28th-35th rounds of the Critical Assessment of PRotein Interactions (CAPRI) served as a practical benchmark for our RosettaDock protein-protein docking protocols, highlighting strengths and weaknesses of the approach. We achieved acceptable or better quality models in three out of 11 targets. For the two α-repeat protein-green fluorescent protein (αrep-GFP) complexes, we used a novel ellipsoidal partial-global docking method (Ellipsoidal Dock) to generate models with 2.2 A/1.5 A interface RMSD, capturing 49%/42% of the native contacts, for the 7-/5-repeat αrep complexes. For the DNase-immunity protein complex, we used a new predictor of hydrogen-bonding networks, HBNet with Bridging Waters, to place individual water models at the complex interface; models were generated with 1.8 A interface RMSD and 12% native water contacts recovered. The targets for which RosettaDock failed to create an acceptable model were typically difficult in general, as six had no acceptable models submitted by any CAPRI predictor. The UCH-L5-RPN13 and UCH-L5-INO80G de-ubiquitinating enzyme-inhibitor complexes comprised inhibitors undergoing significant structural changes upon binding, with the partners being highly interwoven in the docked complexes. Our failure to predict the nucleosome-enzyme complex in Target 95 was largely due to tight constraints we placed on our model based on sparse biochemical data suggesting two specific cross-interface interactions, preventing the correct structure from being sampled. While RosettaDock's three successes show that it is a state-of-the-art docking method, the difficulties with highly flexible and multi-domain complexes highlight the need for better flexible docking and domain-assembly methods. Proteins 2017; 85:479-486. © 2016 Wiley Periodicals, Inc.

Published
2016
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4. Blind prediction performance of RosettaAntibody 3.0: Grafting, relaxation, kinematic loop modeling, and full CDR optimization

Author

Daisuke Kuroda, Nicholas A. Marze, Jeffrey J. Gray, Brian D. Weitzner, and Jianqing Xu

Subjects
Computer science, Complementarity determining region, Kinematics, Antigen binding site, Biochemistry, Template, Structural Biology, Angstrom, Homology modeling, Loop modeling, Unavailability, Molecular Biology, Algorithm, Simulation
Abstract

Antibody Modeling Assessment II (AMA-II) provided an opportunity to benchmark RosettaAntibody on a set of 11 unpublished antibody structures. RosettaAntibody produced accurate, physically realistic models, with all framework regions and 42 of the 55 non-H3 CDR loops predicted to under an Angstrom. The performance is notable when modeling H3 on a homology framework, where RosettaAntibody produced the best model among all participants for four of the 11 targets, two of which were predicted with sub-Angstrom accuracy. To improve RosettaAntibody, we pursued the causes of model errors. The most common limitation was template unavailability, underscoring the need for more antibody structures and/or better de novo loop methods. In some cases, better templates could have been found by considering residues outside of the CDRs. De novo CDR H3 modeling remains challenging at long loop lengths, but constraining the C-terminal end of H3 to a kinked conformation allows near-native conformations to be sampled more frequently. We also found that incorrect VL -VH orientations caused models with low H3 RMSDs to score poorly, suggesting that correct VL -VH orientations will improve discrimination between near-native and incorrect conformations. These observations will guide the future development of RosettaAntibody.

Published
2014
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5. 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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6. Improved prediction of antibody VL-VH orientation

Author

Nicholas A. Marze, Jeffrey J. Gray, and Sergey Lyskov

Subjects
0301 basic medicine, Models, Molecular, 030102 biochemistry & molecular biology, Computer science, Grafting (decision trees), Frame (networking), Computational Biology, Bioengineering, Computational biology, Orientation (graph theory), Single-Domain Antibodies, Biochemistry, Protein Structure, Secondary, 03 medical and health sciences, 030104 developmental biology, Template, Antigen, Paratope, Original Article, Homology modeling, Databases, Protein, Molecular Biology, Protocol (object-oriented programming), Biotechnology
Abstract

Antibodies are important immune molecules with high commercial value and therapeutic interest because of their ability to bind diverse antigens. Computational prediction of antibody structure can quickly reveal valuable information about the nature of these antigen-binding interactions, but only if the models are of sufficient quality. To achieve high model quality during complementarity-determining region (CDR) structural prediction, one must account for the VL-VH orientation. We developed a novel four-metric VL-VH orientation coordinate frame. Additionally, we extended the CDR grafting protocol in RosettaAntibody with a new method that diversifies VL-VH orientation by using 10 VL-VH orientation templates rather than a single one. We tested the multiple-template grafting protocol on two datasets of known antibody crystal structures. During the template-grafting phase, the new protocol improved the fraction of accurate VL-VH orientation predictions from only 26% (12/46) to 72% (33/46) of targets. After the full RosettaAntibody protocol, including CDR H3 remodeling and VL-VH re-orientation, the new protocol produced more candidate structures with accurate VL-VH orientation than the standard protocol in 43/46 targets (93%). The improved ability to predict VL-VH orientation will bolster predictions of other parts of the paratope, including the conformation of CDR H3, a grand challenge of antibody homology modeling.

Published
2015

7. Catabolism of Branched Chain Amino Acids Contributes Significantly to Synthesis of Odd-Chain and Even-Chain Fatty Acids in 3T3-L1 Adipocytes

Author

Nicholas A. Marze, Maciek R. Antoniewicz, and Scott B. Crown

Subjects
Glutamine, lcsh:Medicine, Biology, Gas Chromatography-Mass Spectrometry, chemistry.chemical_compound, Mice, Valine, 3T3-L1 Cells, Adipocytes, Animals, Lactic Acid, lcsh:Science, Fatty acid synthesis, Cell Proliferation, chemistry.chemical_classification, Carbon Isotopes, Multidisciplinary, Catabolism, lcsh:R, Fatty Acids, Fatty acid, Cell Differentiation, Fatty acid synthase, Kinetics, Glucose, chemistry, Biochemistry, Odd-chain fatty acid, biology.protein, lcsh:Q, Leucine, Isoleucine, Amino Acids, Branched-Chain, Metabolic Networks and Pathways, Research Article
Abstract

The branched chain amino acids (BCAA) valine, leucine and isoleucine have been implicated in a number of diseases including obesity, insulin resistance, and type 2 diabetes mellitus, although the mechanisms are still poorly understood. Adipose tissue plays an important role in BCAA homeostasis by actively metabolizing circulating BCAA. In this work, we have investigated the link between BCAA catabolism and fatty acid synthesis in 3T3-L1 adipocytes using parallel 13C-labeling experiments, mass spectrometry and model-based isotopomer data analysis. Specifically, we performed parallel labeling experiments with four fully 13C-labeled tracers, [U-13C]valine, [U-13C]leucine, [U-13C]isoleucine and [U-13C]glutamine. We measured mass isotopomer distributions of fatty acids and intracellular metabolites by GC-MS and analyzed the data using the isotopomer spectral analysis (ISA) framework. We demonstrate that 3T3-L1 adipocytes accumulate significant amounts of even chain length (C14:0, C16:0 and C18:0) and odd chain length (C15:0 and C17:0) fatty acids under standard cell culture conditions. Using a novel GC-MS method, we demonstrate that propionyl-CoA acts as the primer on fatty acid synthase for the production of odd chain fatty acids. BCAA contributed significantly to the production of all fatty acids. Leucine and isoleucine contributed at least 25% to lipogenic acetyl-CoA pool, and valine and isoleucine contributed 100% to lipogenic propionyl-CoA pool. Our results further suggest that low activity of methylmalonyl-CoA mutase and mass action kinetics of propionyl-CoA on fatty acid synthase result in high rates of odd chain fatty acid synthesis in 3T3-L1 cells. Overall, this work provides important new insights into the connection between BCAA catabolism and fatty acid synthesis in adipocytes and underscores the high capacity of adipocytes for metabolizing BCAA.

Published
2015

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