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NANO-D - 2016
NANO-D - 2016
Project-Team Nano-d
Members
Overall Objectives
Research Program
Application Domains
New Software and Platforms
New Results
Partnerships and Cooperations
Dissemination
Bibliography
Project-Team Nano-d
Members
Overall Objectives
Research Program
Application Domains
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

  • 1S. Artemova, L. Jaillet, S. Redon.

    Automatic molecular structure perception for the universal force field, in: Journal of Computational Chemistry, March 2016. [ DOI : 10.1002/jcc.24309 ]

    https://hal.inria.fr/hal-01282433
  • 2P. Buslaev, V. I. Gordeliy, S. Grudinin, I. Y. Gushchin.

    Principal component analysis of lipid molecule conformational changes in molecular dynamics simulations, in: Journal of Chemical Theory and Computation, March 2016, vol. 12, no 3, pp. 1019–1028. [ DOI : 10.1021/acs.jctc.5b01106 ]

    https://hal.inria.fr/hal-01258167
  • 3L. Debreu, E. Neveu, E. Simon, F.-X. Le Dimet, A. Vidard.

    Multigrid solvers and multigrid preconditioners for the solution of variational data assimilation problems, in: Quarterly Journal of the Royal Meteorological Society, January 2016, vol. 142, no 694, pp. 515–528. [ DOI : 10.1002/qj.2676 ]

    https://hal.inria.fr/hal-01246349
  • 4M. 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, October 2016. [ DOI : 10.1002/prot.25182 ]

    https://hal.inria.fr/hal-01388654
  • 5S. Grudinin, M. Kadukova, A. Eisenbarth, S. Marillet, F. Cazals.

    Predicting binding poses and affinities for protein-ligand complexes in the 2015 D3R Grand Challenge using a physical model with a statistical parameter estimation, in: Journal of Computer-Aided Molecular Design, September 2016, vol. 30, no 9, pp. 791–804. [ DOI : 10.1007/s10822-016-9976-2 ]

    https://hal.inria.fr/hal-01377738
  • 6S. Grudinin, P. Popov, E. Neveu, G. Cheremovskiy.

    Predicting Binding Poses and Affinities in the CSAR 2013―2014 Docking Exercises Using the Knowledge-Based Convex-PL Potential, in: Journal of Chemical Information and Modeling, June 2016, vol. 56, no 6, pp. 1053–1062. [ DOI : 10.1021/acs.jcim.5b00339 ]

    https://hal.inria.fr/hal-01258022
  • 7M. Kadukova, S. Grudinin.

    Knodle: A Support Vector Machines-Based Automatic Perception of Organic Molecules from 3D Coordinates, in: Journal of Chemical Information and Modeling, July 2016, vol. 56, no 8, pp. 1410–1419. [ DOI : 10.1021/acs.jcim.5b00512 ]

    https://hal.inria.fr/hal-01381010
  • 8M. F. Lensink, S. Velankar, A. Kryshtafovych, S.-Y. Huang, D. Schneidman-Duhovy, A. Sali, J. Segura, N. Fernandez-Fuentes, S. Viswanath, R. Elber, S. Grudinin, P. Popov, E. Neveu, H. Lee, M. Baek, S. Park, L. Heo, G. R. Lee, C. Seok, S. Qin, H.-X. Zhou, D. W. Ritchie, B. Maigret, M.-D. Devignes, A. Ghoorah, M. Torchala, R. A.G. Chaleil, P. A. Bates, E. Ben-Zeev, M. Eisenstein, S. Negi S., T. Vreven, B. G. Pierce, T. M. Borrman, J. Yu, F. Ochsenbein, Z. Weng, R. Guerois, A. Vangone, J. P. Rodrigues, G. van Zundert, M. Nellen, L. Xue, E. Karaca, A. S. J. Melquiond, K. Visscher, P. L. Kastritis, A. M. J. J. Bonvin, X. Xu, L. Qiu, C. Yan, J. Li, Z. Ma, J. Cheng, X. Zou, Y. Sheng, L. X. Peterson, H.-R. Kim, A. Roy, X. Han, J. Esquivel-Rodríguez, D. Kihara, X. Yu, N. J. Bruce, J. C. Fuller, R. C. Wade, I. Anishchenko, P. J. Kundrotas, I. A. Vakser, K. Imai, K. Yamada, T. Oda, T. Nakamura, K. Tomii, C. Pallara, M. Romero-Durana, B. Jiménez-García, I. H. Moal, J. Fernández-Recio, J. Y. Joung, J. Y. Kim, K. Joo, J. Lee, D. Kozakov, S. Vajda, S. Mottarella, D. R. Hall, D. Beglov, A. Mamonov, B. Xia, T. Bohnuud, C. A. Del Carpio, E. Ichiishi, N. Marze, D. Kuroda, S. S. R. Burman, J. J. Gray, E. Chermak, L. Cavallo, R. Oliva, A. Tovchigrechko, S. J. Wodak.

    Prediction of homo- and hetero-protein complexes by protein docking and template-based modeling: a CASP-CAPRI experiment, in: Proteins - Structure, Function and Bioinformatics, September 2016, vol. 84, no S1, pp. 323–348. [ DOI : 10.1002/prot.25007 ]

    https://hal.inria.fr/hal-01309105
  • 9P.-L. Manteaux, C. Wojtan, R. Narain, S. Redon, F. Faure, M.-P. Cani.

    Adaptive Physically Based Models in Computer Graphics, in: Computer Graphics Forum, 2016. [ DOI : 10.1111/cgf.12941 ]

    https://hal.inria.fr/hal-01367170
  • 10E. Neveu, D. Ritchie, P. Popov, S. Grudinin.

    PEPSI-Dock: a detailed data-driven protein–protein interaction potential accelerated by polar Fourier correlation, in: Bioinformatics, August 2016, vol. 32, no 7, pp. i693-i701. [ DOI : 10.1093/bioinformatics/btw443 ]

    https://hal.archives-ouvertes.fr/hal-01358645
  • 11S. Redon, G. Stoltz, Z. Trstanova.

    Error Analysis of Modified Langevin Dynamics, in: Journal of Statistical Physics, August 2016, vol. 164, no 4, pp. 735–771. [ DOI : 10.1007/s10955-016-1544-6 ]

    https://hal.archives-ouvertes.fr/hal-01263700
  • 12D. W. Ritchie, S. Grudinin.

    Spherical polar Fourier assembly of protein complexes with arbitrary point group symmetry, in: Journal of Applied Crystallography, February 2016, vol. 49, no 1, pp. 158-167. [ DOI : 10.1107/S1600576715022931 ]

    https://hal.inria.fr/hal-01261402

International Conferences with Proceedings

  • 13R. Pogodin, A. Katrutsa, S. Grudinin.

    Quadratic Programming Approach to Fit Protein Complexes into Electron Density Maps, in: Information Technology and Systems 2016, Repino, St. Petersburg, Russia, September 2016.

    https://hal.inria.fr/hal-01419380
  • 14A. Riazanov, M. Karasikov, S. Grudinin.

    Inverse Protein Folding Problem via Quadratic Programming, in: Information Technology and Systems 2016, Repino, St. Petersburg, Russia, September 2016, pp. 561-568.

    https://hal.inria.fr/hal-01419374

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

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    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
  • 30X. 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
  • 31G. M. Clore.

    Visualizing lowly-populated regions of the free energy landscape of macromolecular complexes by paramagnetic relaxation enhancement, in: Molecular Biosystems, 2008, vol. 4, no 11, pp. 1058–1069.
  • 32S. 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.
  • 33M. 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.
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    The Fast Multipole Method: Numerical Implementation, in: Journal of Computational Physics, 2000.
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    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
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    MUSCLE: multiple sequence alignment with high accuracy and high throughput, in: Nucleic acids research, 2004, vol. 32, no 5, pp. 1792–1797.
  • 37S. 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
  • 38G. 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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    Nanostructured materials for applications in drug delivery and tissue engineering, in: Journal of Biomaterials Science, Polymer Edition, 2007, vol. 18, no 3, pp. 241-268. [ DOI : doi:10.1163/156856207779996931 ]

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    PIPER: an FFT-based protein docking program with pairwise potentials, in: Proteins: Structure, Function, and Bioinformatics, 2006, vol. 65, no 2, pp. 392–406.
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    Comparative Assessment of Scoring Functions on an Updated Benchmark: 2. Evaluation Methods an General Results, in: J. Chem. Inf. Model., Jun 2014, vol. 54, no 6, pp. 1717-36.

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