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Publications of the year

Doctoral Dissertations and Habilitation Theses

Articles in International Peer-Reviewed Journals

  • 2J. E. Fajardo, R. Shrestha, N. Gil, A. Belsom, S. Crivelli, C. Czaplewski, K. Fidelis, S. Grudinin, M. Karasikov, A. Karczyńska, A. Kryshtafovych, A. Leitner, A. Liwo, E. Lubecka, B. Monastyrskyy, G. Pagès, J. Rappsilber, A. Sieradzan, C. Sikorska, E. Trabjerg, A. Fiser.

    Assessment of chemical‐crosslink‐assisted protein structure modeling in CASP13, in: Proteins - Structure, Function and Bioinformatics, 2019, forthcoming. [ DOI : 10.1002/prot.25816 ]

    https://hal.inria.fr/hal-02315542
  • 3G. Fonti, M. Marcaida, L. Bryan, S. Träger, A. Kalantzi, P.-Y. J. Helleboid, D. Demurtas, M. Tully, S. Grudinin, D. Trono, B. Fierz, M. Dal Peraro.

    KAP1 is an antiparallel dimer with a functional asymmetry, in: Life Science Alliance, August 2019, vol. 2, no 4, e201900349. [ DOI : 10.26508/lsa.201900349 ]

    https://hal.archives-ouvertes.fr/hal-02291553
  • 4G. Hura, C. Hodge, D. Rosenberg, D. Guzenko, J. Duarte, B. Monastyrskyy, S. Grudinin, A. Kryshtafovych, J. Tainer, K. Fidelis, S. Tsutakawa.

    Small angle X‐ray scattering‐assisted protein structure prediction in CASP13 and emergence of solution structure differences, in: Proteins - Structure, Function and Bioinformatics, 2019, pp. 1-17, forthcoming. [ DOI : 10.1002/prot.25827 ]

    https://hal.inria.fr/hal-02315292
  • 5M. Kadukova, V. Chupin, S. Grudinin.

    Docking rigid macrocycles using Convex-PL, AutoDock Vina, and RDKit in the D3R Grand Challenge 4, in: Journal of Computer-Aided Molecular Design, November 2019, pp. 1-10. [ DOI : 10.1007/s10822-019-00263-3 ]

    https://hal.archives-ouvertes.fr/hal-02434514
  • 6M. Karasikov, G. Pagès, S. Grudinin.

    Smooth orientation-dependent scoring function for coarse-grained protein quality assessment, in: Bioinformatics, August 2019, vol. 35, no 16, pp. 2801–2808. [ DOI : 10.1093/bioinformatics/bty1037 ]

    https://hal.inria.fr/hal-01971128
  • 7M. Lensink, G. Brysbaert, N. Nadzirin, S. Velankar, R. A. Chaleil, T. Gerguri, P. Bates, E. Laine, A. Carbone, S. Grudinin, R. Kong, R. Liu, X. Xu, H. Shi, S. Chang, M. Eisenstein, A. Karczyńska, C. Czaplewski, E. Lubecka, A. Lipska, P. Krupa, M. Mozolewska, Ł. Golon, S. Samsonov, A. Liwo, S. Crivelli, G. Pagès, M. Karasikov, M. Kadukova, Y. Yan, S. Huang, M. Rosell, L. A. Rodríguez‐Lumbreras, M. Romero‐Durana, L. Díaz‐Bueno, J. Fernandez‐Recio, C. Christoffer, G. Terashi, W. Shin, T. Aderinwale, S. Raghavendra Maddhuri Venkata Subram, D. Kihara, D. Kozakov, S. Vajda, K. Porter, D. Padhorny, I. Desta, D. Beglov, M. Ignatov, S. Kotelnikov, I. Moal, D. Ritchie, I. Chauvot de Beauchêne, B. Maigret, M. E. R. Echartea, D. Barradas‐Bautista, Z. Cao, L. Cavallo, R. Oliva, Y. Cao, Y. Shen, M. Baek, T. Park, H. Woo, C. Seok, M. Braitbard, L. Bitton, D. Scheidman‐Duhovny, J. DapkŪnas, K. Olechnovič, Č. Venclovas, P. J. Kundrotas, S. Belkin, D. Chakravarty, V. Badal, I. A. Vakser, T. Vreven, S. Vangaveti, T. M. Borrman, Z. Weng, J. D. Guest, R. Gowthaman, B. G. Pierce, X. Xu, R. Duan, L. Qiu, J. Hou, B. Ryan Merideth, Z. Ma, J. Cheng, X. Zou, P. Koukos, J. Roel‐Touris, F. Ambrosetti, C. Geng, J. Schaarschmidt, M. Trellet, A. S. Melquiond, L. Xue, B. Jiménez‐García, C. Noort, R. Honorato, A. M. Bonvin, S. J. Wodak.

    Blind prediction of homo‐ and hetero‐ protein complexes: The CASP13‐CAPRI experiment, in: Proteins - Structure, Function and Bioinformatics, October 2019, vol. 87, no 12, pp. 1200-1221. [ DOI : 10.1002/prot.25838 ]

    https://hal.inria.fr/hal-02320974
  • 8G. Pagès, B. Charmettant, S. Grudinin.

    Protein model quality assessment using 3D oriented convolutional neural networks, in: Bioinformatics, September 2019, vol. 35, no 18, pp. 3313–3319. [ DOI : 10.1093/bioinformatics/btz122 ]

    https://hal.inria.fr/hal-01899468
  • 9G. Pagès, S. Grudinin.

    DeepSymmetry : Using 3D convolutional networks for identification of tandem repeats and internal symmetries in protein structures, in: Bioinformatics, June 2019, pp. 1-24, https://arxiv.org/abs/1810.12026, forthcoming. [ DOI : 10.1093/bioinformatics/btz454 ]

    https://hal.inria.fr/hal-01903624
  • 10P. Popov, S. Grudinin, A. Kurdiuk, P. Buslaev, S. Redon.

    Controlled‐advancement rigid‐body optimization of nanosystems, in: Journal of Computational Chemistry, October 2019, vol. 40, no 27, pp. 2391-2399. [ DOI : 10.1002/jcc.26016 ]

    https://hal.inria.fr/hal-02315276
  • 11F. Rousse, S. Redon.

    Incremental solver for Orbital-Free Density Functional Theory, in: Journal of Computational Chemistry, September 2019, vol. 40, no 23, pp. 2013-2027. [ DOI : 10.1002/jcc.25854 ]

    https://hal.inria.fr/hal-02135603

Other Publications

  • 12S. Grudinin, E. Laine, A. Hoffmann.

    Predicting protein functional motions: an old recipe with a new twist, September 2019, working paper or preprint. [ DOI : 10.1101/703652 ]

    https://hal.archives-ouvertes.fr/hal-02291552
  • 13A. H. Larsen, Y. Wang, A. Bottaro, S. Grudinin, L. Arleth, K. Lindorff-Larsen.

    Combining molecular dynamics simulations with small-angle X-ray and neutron scattering data to study multi-domain proteins in solution, January 2020, working paper or preprint. [ DOI : 10.1101/2019.12.26.888834 ]

    https://hal.archives-ouvertes.fr/hal-02434585
References in notes
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    Effect of nano layered silicates on automotive polyurethane refinish clear coat, in: Progress in Organic Coatings, 2007, vol. 60, no 2, pp. 99–104.

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    The Cambridge Structural Database: a quarter of a million crystal structures and rising, in: Acta Crystallographica Section B, Jun 2002, vol. 58, no 3 Part 1, pp. 380–388.

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    A Morse manipulator molecule for the modulation of metallic shockley surface states, in: Chemical Physics Letters, 2007, vol. 434, pp. 280-285. [ DOI : 10.1016/j.cplett.2006.12.021 ]

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    A semi-empirical study of polyacene molecules adsorbed on a Cu(1 1 0) surface, in: Surface Science, 2006, vol. 600, no 16, pp. 3243–3251.

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  • 18A. Arkhipov, P. Freddolino, K. Imada, K. Namba, K. Schulten.

    Coarse-grained molecular dynamics simulations of a rotating bacterial flagellum, in: Biophysical Journal, 2006, vol. 91, pp. 4589-4597.
  • 19S. Artemova, S. Grudinin, S. Redon.

    A comparison of neighbor search algorithms for large rigid molecules, in: Journal of Computational Chemistry, 2011, vol. 32, no 13, pp. 2865–2877.

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    Adaptively Restrained Particle Simulations, in: Phys. Rev. Lett., Nov 2012, vol. 109.

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    The Protein Data Bank, in: Nucleic Acids Research, 2000, vol. 28, no 1, pp. 235-242. [ DOI : 10.1093/nar/28.1.235 ]

    http://nar.oxfordjournals.org/content/28/1/235.abstract
  • 22X. Blanc, C. Le Bris, F. Legoll.

    Analysis of a prototypical multiscale method coupling atomistic and continuum mechanics, in: ESAIM: Mathematical Modelling and Numerical Analysis, 2005, vol. 39, no 04, pp. 797-826.

    http://dx.doi.org/10.1051/m2an:2005035
  • 23M. Bosson, S. Grudinin, X. Bouju, S. Redon.

    Interactive physically-based structural modeling of hydrocarbon systems, in: Journal of Computational Physics, 2012, vol. 231, no 6, pp. 2581–2598.

    http://www.sciencedirect.com/science/article/pii/S0021999111007042
  • 24D. W. Brenner.

    Empirical potential for hydrocarbons for use in simulating the chemical vapor deposition of diamond films, in: Phys. Rev. B, Nov 1990, vol. 42, pp. 9458–9471.

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  • 25T. Cagin, G. Wang, R. Martin, G. Zamanakos, N. Vaidehi, D. T. Mainz, W. A. Goddard III.

    Multiscale modeling and simulation methods with applications to dendritic polymers, in: Computational and Theoretical Polymer Science, 2001, vol. 11, no 5, pp. 345–356.

    http://www.sciencedirect.com/science/article/pii/S1089315601000265
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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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  • 27Q. 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
  • 28X. 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
  • 29S. 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.
  • 30M. 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.
  • 31E. Darve.

    The Fast Multipole Method: Numerical Implementation, in: Journal of Computational Physics, 2000.
  • 32H. 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
  • 33S. 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
  • 34G. 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.

    http://www.sciencedirect.com/science/article/pii/S0257897210003178
  • 35Z. Gaieb, S. Liu, S. Gathiaka, M. Chiu, H. Yang, C. Shao, V. A. Feher, W. P. Walters, B. Kuhn, M. G. Rudolph, S. K. Burley, M. K. Gilson, R. E. Amaro.

    D3R Grand Challenge 2: blind prediction of protein–ligand poses, affinity rankings, and relative binding free energies, in: Journal of Computer-Aided Molecular Design, 2018, vol. 32, no 1, pp. 1–20.

    https://doi.org/10.1007/s10822-017-0088-4
  • 36Z. Gaieb, C. D. Parks, M. Chiu, H. Yang, C. Shao, W. P. Walters, M. H. Lambert, N. Nevins, S. D. Bembenek, M. K. Ameriks, T. Mirzadegan, S. K. Burley, R. E. Amaro, M. K. Gilson.

    D3R Grand Challenge 3: blind prediction of protein–ligand poses and affinity rankings, in: Journal of Computer-Aided Molecular Design, 2019, vol. 33, no 1, pp. 1–20.

    https://doi.org/10.1007/s10822-018-0180-4
  • 37M. Goldberg, R. Langer, X. Jia.

    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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  • 38J.-H. He.

    An elementary introduction to recently developed asymptotic methods and nanomechanics in textile engineering, in: International Journal of Modern Physics B, 2008, vol. 22, no 21, pp. 3487-3578.
  • 39S. Helveg.

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

    http://dx.doi.org/10.1017/S1431927610055005
  • 40A. Heyden, D. G. Truhlar.

    Conservative Algorithm for an Adaptive Change of Resolution in Mixed Atomistic/Coarse-Grained Multiscale Simulations, in: Journal of Chemical Theory and Computation, 2008, vol. 4, no 2, pp. 217-221.

    http://pubs.acs.org/doi/abs/10.1021/ct700269m
  • 41V. Hornak, R. Abel, A. Okur, B. Strockbine, A. Roitberg, C. Simmerling.

    Comparison of multiple Amber force fields and development of improved protein backbone parameters, in: Proteins: Structure, Function, and Bioinformatics, 2006, vol. 65, no 3, pp. 712–725.

    http://dx.doi.org/10.1002/prot.21123
  • 42A. Jahn, G. Hinselmann, N. Fechner, A. Zell.

    Optimal assignment methods for ligand-based virtual screening, in: Journal of Cheminformatics, 2009, vol. 1, no 1, pp. 1–23.

    https://doi.org/10.1186/1758-2946-1-14
  • 43C. Joachim, H. Tang, F. Moresco, G. Rapenne, G. Meyer.

    The design of a nanoscale molecular barrow, in: Nanotechnology, 2002, vol. 13, no 3, 330 p.

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  • 44M. 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, 2017, vol. 31, no 10.

    https://doi.org/10.1007/s10822-017-0068-8
  • 45L. Kalé, R. Skeel, M. Bhandarkar, R. Brunner, A. Gursoy, N. Krawetz, J. Phillips, A. Shinozaki, K. Varadarajan, K. Schulten.

    NAMD2: Greater Scalability for Parallel Molecular Dynamics, in: Journal of Computational Physics, 1999, vol. 151, no 1, pp. 283–312.

    http://www.sciencedirect.com/science/article/pii/S0021999199962010
  • 46Z. Li, H. A. Scheraga.

    Monte Carlo-minimization approach to the multiple-minima problem in protein folding, in: Proceedings of the National Academy of Sciences, 1987, vol. 84, no 19, pp. 6611-6615.

    http://www.pnas.org/content/84/19/6611.abstract
  • 47Y. Li, M. Su, Z. Liu, J. Li, J. Liu, L. Han, R. Wang.

    Assessing protein–ligand interaction scoring functions with the CASF-2013 benchmark, in: Nature protocols, 2018, vol. 13, no 4, 666 p.
  • 48L. Lo, Y. Li, K. Yeung, C. Yuen.

    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.
  • 49J. R. Lopez-Blanco, P. Chacon.

    KORP: Knowledge-based 6D potential for fast protein and loop modeling, in: Bioinformatics, 2019, vol. 35, no 17, pp. 3013–3019.

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  • 50W. Lu, C. M. Lieber.

    Nanoelectronics from the bottom up, in: Nature materials, 2007, vol. 6, no 11, pp. 841-850.
  • 51S. O. Nielsen, P. B. Moore, B. Ensing.

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

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  • 52A. Nikitin, X. Li, Z. Zhang, H. Ogasawara, H. Dai, A. Nilsson.

    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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  • 53M. Praprotnik, L. Delle Site, K. Kremer.

    Adaptive resolution scheme for efficient hybrid atomistic-mesoscale molecular dynamics simulations of dense liquids, in: Phys. Rev. E, Jun 2006, vol. 73, 066701.

    http://link.aps.org/doi/10.1103/PhysRevE.73.066701
  • 54M. Praprotnik, S. Matysiak, L. D. Site, K. Kremer, C. Clementi.

    Adaptive resolution simulation of liquid water, in: Journal of Physics: Condensed Matter, 2007, vol. 19, no 29, 292201.

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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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  • 57P. Procacci, T. Darden, M. Marchi.

    A Very Fast Molecular Dynamics Method To Simulate Biomolecular Systems with Realistic Electrostatic Interactions, in: The Journal of Physical Chemistry, 1996, vol. 100, no 24, pp. 10464-10468.

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  • 58X. Qian, T. Schlick.

    Efficient multiple-time-step integrators with distance-based force splitting for particle-mesh-Ewald molecular dynamics simulations, in: Journal of Chemical Physics, 2002, vol. 116, pp. 5971-5983.
  • 59D. W. Ritchie, G. J. Kemp.

    Protein docking using spherical polar Fourier correlations, in: Proteins: Structure, Function, and Bioinformatics, 2000, vol. 39, no 2, pp. 178–194.
  • 60M. C. Roco.

    The long view of nanotechnology development: the National Nanotechnology Initiative at 10 years, in: Journal of Nanoparticle Research, 2010.
  • 61B. Rooks.

    A shorter product development time with digital mock-up, in: Assembly Automation, 1998, vol. 18, no 1, pp. 34-38. [ DOI : doi:10.1108/01445159810201405 ]

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  • 62R. Rossi, M. Isorce, S. Morin, J. Flocard, K. Arumugam, S. Crouzy, M. Vivaudou, S. Redon.

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    Assembly of lipoprotein particles revealed by coarse-grained molecular dynamics simulations, in: Journal of Structural Biology, 2007, vol. 157, pp. 579-592.
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    http://pubs.acs.org/doi/abs/10.1021/jp004368u


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