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NANO-D - 2017
NANO-D - 2017
Project-Team Nano-d
Personnel
Overall Objectives
Research Program
New Software and Platforms
New Results
Partnerships and Cooperations
Dissemination
Bibliography
Project-Team Nano-d
Personnel
Overall Objectives
Research Program
New Software and Platforms
New Results
Partnerships and Cooperations
Dissemination
Bibliography


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Bibliography

Publications of the year

Articles in International Peer-Reviewed Journals

  • 1M. El Houasli, B. Maigret, M.-D. Devignes, A. W. Ghoorah, S. Grudinin, D. Ritchie.

    Modeling and minimizing CAPRI round 30 symmetrical protein complexes from CASP-11 structural models, in: Proteins: Structure, Function, and Genetics, March 2017, vol. 85, no 3, pp. 463–469. [ DOI : 10.1002/prot.25182 ]

    https://hal.inria.fr/hal-01388654
  • 2S. Grudinin, M. Garkavenko, A. Kazennov.

    Pepsi-SAXS : an adaptive method for rapid and accurate computation of small-angle X-ray scattering profiles, in: Acta Crystallographica Section D: Biological Crystallography, May 2017, vol. D73, pp. 449 - 464. [ DOI : 10.1107/S2059798317005745 ]

    https://hal.inria.fr/hal-01516719
  • 3I. Y. Gushchin, I. I. Melkinov, V. Polovinkin, A. Ishchenko, A. Yuzhakova, P. Buslaev, G. Bourenkov, S. Grudinin, E. Round, T. Balandin, V. Borshchevskiy, D. Willbold, G. Leonard, G. Büldt, A. Popov, V. I. Gordeliy.

    Mechanism of transmembrane signaling by sensor histidine kinases, in: Science, June 2017, vol. 356, no 6342, eaah6345 p. [ DOI : 10.1126/science.aah6345 ]

    https://hal.archives-ouvertes.fr/hal-01526454
  • 4A. Hoffmann, S. Grudinin.

    NOLB: Nonlinear Rigid Block Normal Mode Analysis Method, in: Journal of Chemical Theory and Computation, April 2017, vol. 13, no 5, pp. 2123-2134. [ DOI : 10.1021/acs.jctc.7b00197 ]

    https://hal.inria.fr/hal-01505843
  • 5A. Hoffmann, V. Perrier, S. Grudinin.

    A novel fast Fourier transform accelerated off-grid exhaustive search method for cryo-electron microscopy fitting, in: Journal of Applied Crystallography, August 2017, vol. 50, no 4, pp. 1036-1047. [ DOI : 10.1107/S1600576717008172 ]

    https://hal.inria.fr/hal-01553293
  • 6A. Ishchenko, E. Round, V. Borshchevskiy, S. Grudinin, I. Y. Gushchin, J. Klare, A. Remeeva, V. Polovinkin, P. Utrobin, T. Balandin, M. Engelhard, G. Büldt, V. I. Gordeliy.

    New Insights on Signal Propagation by Sensory Rhodopsin II/Transducer Complex, in: Scientific Reports, February 2017, vol. 7, 41811 p. [ DOI : 10.1038/srep41811 ]

    https://hal.inria.fr/hal-01458744
  • 7L. Jaillet, S. Artemova, S. Redon.

    IM-UFF: Extending the universal force field for interactive molecular modeling, in: Journal of Molecular Graphics and Modelling, October 2017, vol. 77, pp. 350 - 362. [ DOI : 10.1016/j.jmgm.2017.08.023 ]

    https://hal.inria.fr/hal-01676519
  • 8M. Kadukova, S. Grudinin.

    Convex-PL: a novel knowledge-based potential for protein-ligand interactions deduced from structural databases using convex optimization, in: Journal of Computer-Aided Molecular Design, October 2017, vol. 31, no 10, pp. 943–958. [ DOI : 10.1007/s10822-017-0068-8 ]

    https://hal.inria.fr/hal-01591154
  • 9M. Kadukova, S. Grudinin.

    Docking of small molecules to farnesoid X receptors using AutoDock Vina with the Convex-PL potential: lessons learned from D3R Grand Challenge 2, in: Journal of Computer-Aided Molecular Design, October 2017. [ DOI : 10.1007/s10822-017-0062-1 ]

    https://hal.inria.fr/hal-01591157
  • 10P.-L. Manteaux, C. Wojtan, R. Narain, S. Redon, F. Faure, M.-P. Cani.

    Adaptive Physically Based Models in Computer Graphics, in: Computer Graphics Forum, September 2017, vol. 36, no 6, pp. 312–337. [ DOI : 10.1111/cgf.12941 ]

    https://hal.inria.fr/hal-01367170
  • 11M. K. Nguyen, L. Jaillet, S. Redon.

    As-Rigid-As-Possible molecular interpolation paths, in: Journal of Computer-Aided Molecular Design, April 2017, vol. 31, no 4, pp. 403 - 417. [ DOI : 10.1007/s10822-017-0012-y ]

    https://hal.inria.fr/hal-01676132
  • 12K. K. Singh, S. Redon.

    Adaptively Restrained Molecular Dynamics in LAMMPS, in: Modelling and Simulation in Materials Science and Engineering, June 2017, vol. 25, no 5, 055013 p. [ DOI : 10.1088/1361-651X/aa7345 ]

    https://hal.archives-ouvertes.fr/hal-01525253
  • 13K. K. Singh, S. Redon.

    Single-pass Incremental Force Updates for Adaptively Restrained Molecular Dynamics, in: Journal of Computational Chemistry, December 2017. [ DOI : 10.1002/jcc.25126 ]

    https://hal.inria.fr/hal-01635863

International Conferences with Proceedings

  • 14K. K. Singh, D. F. Marin, S. Redon.

    Parallel Adaptively Restrained Molecular Dynamics, in: 2017 International Conference on High Performance Computing & Simulation (HPCS), Genova, Italy, IEEE, July 2017, pp. 308 - 314. [ DOI : 10.1109/HPCS.2017.55 ]

    https://hal.inria.fr/hal-01591466

Other Publications

References in notes
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    Interactive physically-based structural modeling of hydrocarbon systems, in: Journal of Computational Physics, 2012, vol. 231, no 6, pp. 2581 - 2598. [ DOI : 10.1016/j.jcp.2011.12.006 ]

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    Multiscale modeling and simulation methods with applications to dendritic polymers, in: Computational and Theoretical Polymer Science, 2001, vol. 11, no 5, pp. 345 - 356. [ DOI : 10.1016/S1089-3156(01)00026-5 ]

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  • 30D. Camacho, Y. Niquet.

    Application of Keating's valence force field model to non-ideal wurtzite materials, in: Physica E: Low-dimensional Systems and Nanostructures, 2010, vol. 42, no 5, pp. 1361–1364.
  • 31E. Cances, F. Castella, P. Chartier, E. Faou, C. L. Bris, F. Legoll, G. Turinici.

    Long-time averaging for integrable Hamiltonian dynamics, in: Numerische Mathematik, 2005, vol. 100, pp. 211-232, 10.1007/s00211-005-0599-0.

    http://dx.doi.org/10.1007/s00211-005-0599-0
  • 32Q. Chaudhry, M. Scotter, J. Blackburn, B. Ross, A. Boxall, L. Castle, R. Aitken, R. Watkins.

    Applications and implications of nanotechnologies for the food sector, in: Food Additives & Contaminants: Part A, 2008, vol. 25, no 3, pp. 241-258.

    http://www.tandfonline.com/doi/abs/10.1080/02652030701744538
  • 33X. Chen, S. S. Mao.

    Titanium Dioxide Nanomaterials: Synthesis, Properties, Modifications, and Applications, in: ChemInform, 2007, vol. 38, no 41.

    http://dx.doi.org/10.1002/chin.200741216
  • 34S. Cooper, F. Khatib, A. Treuille, J. Barbero, J. Lee, M. Beenen, A. Leaver-Fay, D. Baker, Z. Popovic, F. Players.

    Predicting protein structures with a multiplayer online game, in: Nature, 2010, vol. 466, pp. 756-760.
  • 35M. Curreli, A. H. Nadershahi, G. Shahi.

    Emergence of nanomedical devices for the diagnosis and treatment of cancer: the journey from basic science to commercialization, in: International Journal of Technology Transfer and Commercialisation, 2008, vol. 7, no 4, pp. 290-307.
  • 36E. Darve.

    The Fast Multipole Method: Numerical Implementation, in: Journal of Computational Physics, 2000.
  • 37H. Dietz, S. M. Douglas, W. M. Shih.

    Folding DNA into Twisted and Curved Nanoscale Shapes, in: Science, 2009, vol. 325, no 5941, pp. 725-730. [ DOI : 10.1126/science.1174251 ]

    http://www.sciencemag.org/content/325/5941/725.abstract
  • 38P. Durand, G. Trinquier, Y.-H. Sanejouand.

    a New Approach for Determining Low-Frequency Normal Modes in Macromolecules, in: Biopolymers, jun 1994, vol. 34, no 6, pp. 759–771.

    http://dx.doi.org/10.1002/bip.360340608
  • 39S. J. Fleishman, T. A. Whitehead, D. C. Ekiert, C. Dreyfus, J. E. Corn, E.-M. Strauch, I. A. Wilson, D. Baker.

    Computational Design of Proteins Targeting the Conserved Stem Region of Influenza Hemagglutinin, in: Science, 2011, vol. 332, no 6031, pp. 816-821. [ DOI : 10.1126/science.1202617 ]

    http://www.sciencemag.org/content/332/6031/816.abstract
  • 40G. Fox-Rabinovich, B. Beake, K. Yamamoto, M. Aguirre, S. Veldhuis, G. Dosbaeva, A. Elfizy, A. Biksa, L. Shuster.

    Structure, properties and wear performance of nano-multilayered TiAlCrSiYN/TiAlCrN coatings during machining of Ni-based aerospace superalloys, in: Surface and Coatings Technology, 2010, vol. 204, pp. 3698 - 3706. [ DOI : 10.1016/j.surfcoat.2010.04.050 ]

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    Structure and Dynamics of Nanocatalysts, in: Microscopy and Microanalysis, 2010, vol. 16, no Supplement S2, pp. 1712-1713.

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    Knowledge-based prediction of protein-ligand interactions, 2017.
  • 53M. Kadukova, S. Grudinin.

    Using machine learning to predict protein-ligand interactions, 2017.
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    Indicating the development stage of nanotechnology in the textile and clothing industry, in: International Journal of Nanotechnology, 2007, vol. 4, no 6, pp. 667-679.
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    Nanoelectronics from the bottom up, in: Nature materials, 2007, vol. 6, no 11, pp. 841-850.
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    Communications: A systematic method for locating transition structures of A + B→X type reactions, in: The Journal of Chemical Physics, jun 2010, vol. 132, no 24, 241102 p.

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    Adaptive Multiscale Molecular Dynamics of Macromolecular Fluids, in: Phys. Rev. Lett., Dec 2010, vol. 105, 237802.

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    Hydrogen Storage in Carbon Nanotubes through the Formation of Stable C-H Bonds, in: Nano Letters, 2008, vol. 8, no 1, pp. 162-167, PMID: 18088150.

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    A macromolecule in a solvent: Adaptive resolution molecular dynamics simulation, in: The Journal of Chemical Physics, 2007, vol. 126, no 13, 134902.

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    Multiscale Simulation of Soft Matter: From Scale Bridging to Adaptive Resolution, in: Annual Review of Physical Chemistry, 2008, vol. 59, no 1, pp. 545-571.

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    Domain Motions in Phosphoglycerate Kinase using Hierarchical NEIMO Molecular Dynamics Simulations, in: The Journal of Physical Chemistry A, 2000, vol. 104, no 11, pp. 2375-2383.

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    Statistics of polymer rings in the melt: a numerical simulation study, in: Physical Biology, 2009, vol. 6, no 2, 025013 p.

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    ReaxFF: A Reactive Force Field for Hydrocarbons, in: The Journal of Physical Chemistry A, 2001, vol. 105, no 41, pp. 9396-9409.

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