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Bibliography

Major publications by the team in recent years
  • 1F. Cazals, P. Kornprobst (editors)

    Modeling in Computational Biology and Medicine: A Multidisciplinary Endeavor, Springer, 2013. [ DOI : 10.1007/978-3-642-31208-3 ]

    http://hal.inria.fr/hal-00845616
  • 2D. Agarwal, J. Araujo, C. Caillouet, F. Cazals, D. Coudert, S. Pérennes.

    Connectivity Inference in Mass Spectrometry based Structure Determination, in: European Symposium on Algorithms (Springer LNCS 8125), Sophia Antipolis, France, H. Bodlaender, G. Italiano (editors), Springer, 2013, pp. 289–300.

    http://hal.inria.fr/hal-00849873
  • 3D. Agarwal, C. Caillouet, D. Coudert, F. Cazals.

    Unveiling Contacts within Macro-molecular assemblies by solving Minimum Weight Connectivity Inference Problems, in: Molecular and Cellular Proteomics, 2015, vol. 14, pp. 2274–2282. [ DOI : 10.1074/mcp.M114.047779 ]

    https://hal.archives-ouvertes.fr/hal-01078378
  • 4J. Carr, D. Mazauric, F. Cazals, D. J. Wales.

    Energy landscapes and persistent minima, in: The Journal of Chemical Physics, 2016, vol. 144, no 5, 4 p. [ DOI : 10.1063/1.4941052 ]

    https://www.repository.cam.ac.uk/handle/1810/253412
  • 5F. Cazals, F. Chazal, T. Lewiner.

    Molecular shape analysis based upon the Morse-Smale complex and the Connolly function, in: ACM SoCG, San Diego, USA, 2003, pp. 351-360.
  • 6F. Cazals, T. Dreyfus, D. Mazauric, A. Roth, C. Robert.

    Conformational Ensembles and Sampled Energy Landscapes: Analysis and Comparison, in: J. of Computational Chemistry, 2015, vol. 36, no 16, pp. 1213–1231. [ DOI : 10.1002/jcc.23913 ]

    https://hal.archives-ouvertes.fr/hal-01076317
  • 7F. Cazals, T. Dreyfus, S. Sachdeva, N. Shah.

    Greedy Geometric Algorithms for Collections of Balls, with Applications to Geometric Approximation and Molecular Coarse-Graining, in: Computer Graphics Forum, 2014, vol. 33, no 6, pp. 1–17. [ DOI : 10.1111/cgf.12270 ]

    http://hal.inria.fr/hal-00777892
  • 8F. Cazals, C. Karande.

    An algorithm for reporting maximal c-cliques, in: Theoretical Computer Science, 2005, vol. 349, no 3, pp. 484–490.
  • 9T. Dreyfus, V. Doye, F. Cazals.

    Assessing the Reconstruction of Macro-molecular Assemblies with Toleranced Models, in: Proteins: structure, function, and bioinformatics, 2012, vol. 80, no 9, pp. 2125–2136.
  • 10T. Dreyfus, V. Doye, F. Cazals.

    Probing a Continuum of Macro-molecular Assembly Models with Graph Templates of Sub-complexes, in: Proteins: structure, function, and bioinformatics, 2013, vol. 81, no 11, pp. 2034–2044. [ DOI : 10.1002/prot.24313 ]

    http://hal.inria.fr/hal-00849795
  • 11N. Malod-Dognin, A. Bansal, F. Cazals.

    Characterizing the Morphology of Protein Binding Patches, in: Proteins: structure, function, and bioinformatics, 2012, vol. 80, no 12, pp. 2652–2665.
  • 12S. Marillet, P. Boudinot, F. Cazals.

    High Resolution Crystal Structures Leverage Protein Binding Affinity Predictions, in: Proteins: structure, function, and bioinformatics, 2015, vol. 1, no 84, pp. 9–20. [ DOI : 10.1002/prot.24946 ]

    https://hal.inria.fr/hal-01159641
  • 13A. Roth, T. Dreyfus, C. Robert, F. Cazals.

    Hybridizing rapidly growing random trees and basin hopping yields an improved exploration of energy landscapes, in: J. Comp. Chem., 2016, vol. 37, no 8, pp. 739–752. [ DOI : 10.1002/jcc.24256 ]

    https://hal.inria.fr/hal-01191028
Publications of the year

Articles in International Peer-Reviewed Journals

  • 14P. Bonami, D. Mazauric, Y. Vaxès.

    Maximum flow under proportional delay constraint, in: Theoretical Computer Science, 2017, vol. 689, pp. 58-66. [ DOI : 10.1016/j.tcs.2017.05.034 ]

    https://hal.inria.fr/hal-01571232
  • 15F. Cazals, T. Dreyfus.

    The Structural Bioinformatics Library: modeling in biomolecular science and beyond, in: Bioinformatics, April 2017, vol. 33, no 8. [ DOI : 10.1093/bioinformatics/btw752 ]

    https://hal.inria.fr/hal-01570848
  • 16S. Fleischer, S. Ries, P. Shen, A. Lhéritier, F. Cazals, G. R. Burmester, T. Dörner, S. Fillatreau.

    Anti-interleukin-6 signalling therapy rebalances the disrupted cytokine production of B cells from patients with active rheumatoid arthritis, in: European Journal of Immunology, September 2017. [ DOI : 10.1002/eji.201747191 ]

    https://hal.inria.fr/hal-01671956
  • 17S. Marillet, M.-P. Lefranc, P. Boudinot, F. Cazals.

    Novel Structural Parameters of Ig–Ag Complexes Yield a Quantitative Description of Interaction Specificity and Binding Affinity, in: Frontiers in Immunology, February 2017, vol. 8. [ DOI : 10.3389/fimmu.2017.00034 ]

    https://hal.inria.fr/hal-01570846

International Conferences with Proceedings

  • 18N. Cohen, F. Havet, D. Mazauric, I. Sau, R. Watrigant.

    Complexity Dichotomies for the Minimum F -Overlay Problem, in: IWOCA 2017 - 28th International Workshop on Combinatorial Algorithms, Newcastle, Australia, July 2017, 12 p.

    https://hal.inria.fr/hal-01571229
  • 19N. Lascano, G. Gallardo, R. Deriche, D. Mazauric, D. Wassermann.

    Extracting the Groupwise Core Structural Connectivity Network: Bridging Statistical and Graph-Theoretical Approaches, in: Information Processing in Medical Imaging, Boone, United States, 2017, https://arxiv.org/abs/1701.01311.

    https://hal.inria.fr/hal-01426870

Internal Reports

  • 20F. Cazals, D. Mazauric, R. Tetley, R. Watrigant.

    Comparing two clusterings using matchings between clusters of clusters, Inria Sophia Antipolis - Méditerranée ; Universite Cote d'Azur, April 2017, no RR-9063.

    https://hal.inria.fr/hal-01514872

Scientific Popularization

  • 21D. Mazauric.

    Tour de cartes - La magie des graphes et du binaire, 2017, 2 p, Posters expliquant un tour de cartes qui utilise les graphes et le codage binaire.

    https://hal.inria.fr/hal-01671009
  • 22D. Mazauric.

    Transmission de pensée - La magie du binaire, 2017, 13 p, Posters expliquant le binaire avec un tour de magie.

    https://hal.inria.fr/hal-01670180
References in notes
  • 23F. Alber, S. Dokudovskaya, L. Veenhoff, W. Zhang, J. Kipper, D. Devos, A. Suprapto, O. Karni-Schmidt, R. Williams, B. Chait, M. Rout, A. Sali.

    Determining the architectures of macromolecular assemblies, in: Nature, Nov 2007, vol. 450, pp. 683-694.
  • 24F. Alber, S. Dokudovskaya, L. Veenhoff, W. Zhang, J. Kipper, D. Devos, A. Suprapto, O. Karni-Schmidt, R. Williams, B. Chait, A. Sali, M. Rout.

    The molecular architecture of the nuclear pore complex, in: Nature, 2007, vol. 450, no 7170, pp. 695–701.
  • 25F. Alber, F. Förster, D. Korkin, M. Topf, A. Sali.

    Integrating Diverse Data for Structure Determination of Macromolecular Assemblies, in: Ann. Rev. Biochem., 2008, vol. 77, pp. 11.1–11.35.
  • 26O. Becker, A. D. Mackerell, B. Roux, M. Watanabe.

    Computational Biochemistry and Biophysics, M. Dekker, 2001.
  • 27A.-C. Camproux, R. Gautier, P. Tuffery.

    A Hidden Markov Model derived structural alphabet for proteins, in: J. Mol. Biol., 2004, pp. 591-605.
  • 28M. L. Connolly.

    Analytical molecular surface calculation, in: J. Appl. Crystallogr., 1983, vol. 16, no 5, pp. 548–558.
  • 29R. Dunbrack.

    Rotamer libraries in the 21st century, in: Curr Opin Struct Biol, 2002, vol. 12, no 4, pp. 431-440.
  • 30A. Fernandez, R. Berry.

    Extent of Hydrogen-Bond Protection in Folded Proteins: A Constraint on Packing Architectures, in: Biophysical Journal, 2002, vol. 83, pp. 2475-2481.
  • 31A. Fersht.

    Structure and Mechanism in Protein Science: A Guide to Enzyme Catalysis and Protein Folding, Freeman, 1999.
  • 32M. Gerstein, F. Richards.

    Protein geometry: volumes, areas, and distances, in: The international tables for crystallography (Vol F, Chap. 22), M. G. Rossmann, E. Arnold (editors), Springer, 2001, pp. 531–539.
  • 33H. Gohlke, G. Klebe.

    Statistical potentials and scoring functions applied to protein-ligand binding, in: Curr. Op. Struct. Biol., 2001, vol. 11, pp. 231-235.
  • 34J. Janin, S. Wodak, M. Levitt, B. Maigret.

    Conformations of amino acid side chains in proteins, in: J. Mol. Biol., 1978, vol. 125, pp. 357–386.
  • 35V. K. Krivov, M. Karplus.

    Hidden complexity of free energy surfaces for peptide (protein) folding, in: PNAS, 2004, vol. 101, no 41, pp. 14766-14770.
  • 36E. Meerbach, C. Schutte, I. Horenko, B. Schmidt.

    Metastable Conformational Structure and Dynamics: Peptides between Gas Phase and Aqueous Solution, in: Analysis and Control of Ultrafast Photoinduced Reactions. Series in Chemical Physics 87, O. Kuhn, L. Wudste (editors), Springer, 2007.
  • 37I. Mihalek, O. Lichtarge.

    On Itinerant Water Molecules and Detectability of Protein-Protein Interfaces through Comparative Analysis of Homologues, in: JMB, 2007, vol. 369, no 2, pp. 584–595.
  • 38J. Mintseris, B. Pierce, K. Wiehe, R. Anderson, R. Chen, Z. Weng.

    Integrating statistical pair potentials into protein complex prediction, in: Proteins, 2007, vol. 69, pp. 511–520.
  • 39M. Pettini.

    Geometry and Topology in Hamiltonian Dynamics and Statistical Mechanics, Springer, 2007.
  • 40E. Plaku, H. Stamati, C. Clementi, L. Kavraki.

    Fast and Reliable Analysis of Molecular Motion Using Proximity Relations and Dimensionality Reduction, in: Proteins: Structure, Function, and Bioinformatics, 2007, vol. 67, no 4, pp. 897–907.
  • 41D. Rajamani, S. Thiel, S. Vajda, C. Camacho.

    Anchor residues in protein-protein interactions, in: PNAS, 2004, vol. 101, no 31, pp. 11287-11292.
  • 42D. Reichmann, O. Rahat, S. Albeck, R. Meged, O. Dym, G. Schreiber.

    From The Cover: The modular architecture of protein-protein binding interfaces, in: PNAS, 2005, vol. 102, no 1, pp. 57-62.
  • 43F. Richards.

    Areas, volumes, packing and protein structure, in: Ann. Rev. Biophys. Bioeng., 1977, vol. 6, pp. 151-176.
  • 44G. Rylance, R. Johnston, Y. Matsunaga, C.-B. Li, A. Baba, T. Komatsuzaki.

    Topographical complexity of multidimensional energy landscapes, in: PNAS, 2006, vol. 103, no 49, pp. 18551-18555.
  • 45G. Schreiber, L. Serrano.

    Folding and binding: an extended family business, in: Current Opinion in Structural Biology, 2005, vol. 15, no 1, pp. 1–3.
  • 46M. Sippl.

    Calculation of Conformational Ensembles from Potential of Mean Force: An Approach to the Knowledge-based prediction of Local Structures in Globular Proteins, in: J. Mol. Biol., 1990, vol. 213, pp. 859-883.
  • 47C. Summa, M. Levitt, W. DeGrado.

    An atomic environment potential for use in protein structure prediction, in: JMB, 2005, vol. 352, no 4, pp. 986–1001.
  • 48S. Wodak, J. Janin.

    Structural basis of macromolecular recognition, in: Adv. in protein chemistry, 2002, vol. 61, pp. 9–73.


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