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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.

    The Structural Bioinformatics Library: modeling in biomolecular science and beyond, in: Bioinformatics, 2017, vol. 7, no 33, pp. 1–8. [ DOI : 10.1093/bioinformatics/btw752 ]

    http://sbl.inria.fr
  • 7F. 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
  • 8F. 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
  • 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

  • 14J.-C. Bermond, A. Chaintreau, G. Ducoffe, D. Mazauric.

    How long does it take for all users in a social network to choose their communities?, in: Discrete Applied Mathematics, 2019, vol. 270, pp. 37-57. [ DOI : 10.1016/j.dam.2019.07.023 ]

    https://hal.inria.fr/hal-02264327
  • 15F. Cazals, D. Mazauric, R. Tetley, R. Watrigant.

    Comparing Two Clusterings Using Matchings between Clusters of Clusters, in: ACM Journal of Experimental Algorithmics, December 2019, vol. 24, no 1, pp. 1-41. [ DOI : 10.1145/3345951 ]

    https://hal.inria.fr/hal-02425599
  • 16F. Cazals, R. Tetley.

    Characterizing molecular flexibility by combining least root mean square deviation measures, in: Proteins - Structure, Function and Bioinformatics, March 2019, vol. 87, no 5, pp. 380-389. [ DOI : 10.1002/prot.25658 ]

    https://hal.inria.fr/hal-02425596
  • 17C. O. Sorzano, A. Jiménez, J. Mota, J. L. Vilas, D. Maluenda, M. Martínez, E. Ramírez-Aportela, T. Majtner, J. Segura, R. Sánchez-García, Y. Rancel, L. del Caño, P. Conesa, R. Melero, S. Jonic, J. Vargas, F. Cazals, Z. Freyberg, J. Krieger, I. Bahar, R. Marabini, J. M. Carazo.

    Survey of the analysis of continuous conformational variability of biological macromolecules by electron microscopy, in: Acta crystallographica. Section F, Structural biology communications, January 2019, vol. 75, no 1, pp. 19-32. [ DOI : 10.1107/S2053230X1801510 ]

    https://hal.inria.fr/hal-01968170

International Conferences with Proceedings

  • 18F. Cazals, A. Lhéritier.

    Low-Complexity Nonparametric Bayesian Online Prediction with Universal Guarantees, in: NeurIPS 2019 - Thirty-third Conference on Neural Information Processing Systems, Vancouver, Canada, December 2019.

    https://hal.inria.fr/hal-02425602

Internal Reports

  • 19A. Chevallier, F. Cazals.

    Wang-Landau Algorithm: an adapted random walk to boost convergence, Inria Sophia Antipolis, France, November 2019, no RR-9223, pp. 1-34.

    https://hal.archives-ouvertes.fr/hal-01919860
  • 20F. Havet, D. Mazauric, V.-H. Nguyen, R. Watrigant.

    Overlaying a hypergraph with a graph with bounded maximum degree, Inria Sophia Antipolis, February 2019, no RR-9258.

    https://hal.inria.fr/hal-02025469

Other Publications

  • 21J.-C. Bermond, D. Mazauric, V. Misra, P. Nain.

    Distributed Link Scheduling in Wireless Networks, January 2019, working paper or preprint.

    https://hal.inria.fr/hal-01977266
References in notes
  • 22F. 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.
  • 23F. 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.
  • 24F. 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.
  • 25O. Becker, A. D. Mackerell, B. Roux, M. Watanabe.

    Computational Biochemistry and Biophysics, M. Dekker, 2001.
  • 26A.-C. Camproux, R. Gautier, P. Tuffery.

    A Hidden Markov Model derived structural alphabet for proteins, in: J. Mol. Biol., 2004, pp. 591-605.
  • 27M. L. Connolly.

    Analytical molecular surface calculation, in: J. Appl. Crystallogr., 1983, vol. 16, no 5, pp. 548–558.
  • 28R. Dunbrack.

    Rotamer libraries in the 21st century, in: Curr Opin Struct Biol, 2002, vol. 12, no 4, pp. 431-440.
  • 29A. 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.
  • 30A. Fersht.

    Structure and Mechanism in Protein Science: A Guide to Enzyme Catalysis and Protein Folding, Freeman, 1999.
  • 31M. 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.
  • 32H. Gohlke, G. Klebe.

    Statistical potentials and scoring functions applied to protein-ligand binding, in: Curr. Op. Struct. Biol., 2001, vol. 11, pp. 231-235.
  • 33J. 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.
  • 34V. K. Krivov, M. Karplus.

    Hidden complexity of free energy surfaces for peptide (protein) folding, in: PNAS, 2004, vol. 101, no 41, pp. 14766-14770.
  • 35E. 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.
  • 36I. 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.
  • 37J. 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.
  • 38M. Pettini.

    Geometry and Topology in Hamiltonian Dynamics and Statistical Mechanics, Springer, 2007.
  • 39E. 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.
  • 40D. Rajamani, S. Thiel, S. Vajda, C. Camacho.

    Anchor residues in protein-protein interactions, in: PNAS, 2004, vol. 101, no 31, pp. 11287-11292.
  • 41D. 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.
  • 42F. Richards.

    Areas, volumes, packing and protein structure, in: Ann. Rev. Biophys. Bioeng., 1977, vol. 6, pp. 151-176.
  • 43G. 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.
  • 44G. Schreiber, L. Serrano.

    Folding and binding: an extended family business, in: Current Opinion in Structural Biology, 2005, vol. 15, no 1, pp. 1–3.
  • 45M. 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.
  • 46C. 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.
  • 47S. Wodak, J. Janin.

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


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