N. Alexandrov and I. Shindyalov, PDP: protein domain parser, Bioinformatics, vol.19, issue.3, pp.429-430, 2003.
DOI : 10.1093/bioinformatics/btg006

URL : http://bioinformatics.oxfordjournals.org/cgi/content/short/19/3/429

S. F. Altschul, W. Gish, W. Miller, E. W. Myers, and D. J. Lipman, Basic local alignment search tool, Journal of Molecular Biology, vol.215, issue.3, pp.403-410, 1990.
DOI : 10.1016/S0022-2836(05)80360-2

A. Bairoch, B. Boeckmann, S. Ferro, and E. Gasteiger, Swiss-Prot: Juggling between evolution and stability, Swiss- Prot: juggling between evolution and stability, pp.39-55, 2004.
DOI : 10.1093/bib/5.1.39

URL : http://bib.oxfordjournals.org/cgi/content/short/5/1/39

C. Benros, A. G. De-brevern, C. Etchebest, and S. Hazout, Assessing a novel approach for predicting local 3D protein structures from sequence, Proteins: Structure, Function, and Bioinformatics, vol.30, issue.23, pp.865-880, 2006.
DOI : 10.1002/prot.20815

URL : https://hal.archives-ouvertes.fr/inserm-00133180

C. Benros, A. De-brevern, and S. Hazout, Hybrid protein model (HPM): a method for building a library of overlapping local structural prototypes. Sensitivity study and improvements of the training, 2003 IEEE XIII Workshop on Neural Networks for Signal Processing (IEEE Cat. No.03TH8718), pp.53-72, 2003.
DOI : 10.1109/NNSP.2003.1318004

URL : https://hal.archives-ouvertes.fr/inserm-00133639

C. Benros, A. De-brevern, and S. Hazout, Predicting Local Structural Candidates from Sequence by the Hybrid Protein Model " Approach; in 12th Intelligent Systems for Glasgow Bystroff C and Baker D 1998 Prediction of local structure in proteins using a library of sequence-structure motifs, ISMB) / 3rd the European Conference on Computational Biology (ECCB), pp.565-577, 2004.

A. C. Camproux, A. G. Brevern, S. Hazout, and P. Tufféry, Exploring the use of a structural alphabet for structural prediction of protein loops, Theoretical Chemistry Accounts: Theory, Computation, and Modeling (Theoretica Chimica Acta), vol.106, issue.1-2, pp.28-35, 2001.
DOI : 10.1007/s002140100261

URL : https://hal.archives-ouvertes.fr/inserm-00134555

A. C. Camproux, R. Gautier, and P. Tuffery, A Hidden Markov Model Derived Structural Alphabet for Proteins, Journal of Molecular Biology, vol.339, issue.3, pp.591-605, 2004.
DOI : 10.1016/j.jmb.2004.04.005

A. C. Camproux, P. Tuffery, L. Buffat, C. Andre, and J. Boisvieux, Analyzing patterns between regular secondary structures using short structural building blocks defined by a hidden Markov model, Theoretical Chemistry Accounts: Theory, Computation, and Modeling (Theoretica Chimica Acta), vol.101, issue.1-3, pp.33-40, 1999.
DOI : 10.1007/s002140050402

A. C. Camproux, P. Tuffery, J. P. Chevrolat, and J. Boisvieux, Hidden Markov model approach for identifying the modular framework of the protein backbone, Protein Engineering Design and Selection, vol.12, issue.12, pp.1063-1073, 1999.
DOI : 10.1093/protein/12.12.1063

D. Chivian, D. E. Kim, L. Malmstrom, J. Schonbrun, C. A. Rohl et al., Prediction of CASP-6 structures using automated Robetta protocols, Proteins, pp.61-157, 2005.

N. Colloc-'h, C. Etchebest, E. Thoreau, B. Henrissat, and J. Mornon, Comparison of three algorithms for the assignment of secondary structure in proteins: the advantages of a consensus assignment, "Protein Engineering, Design and Selection", vol.6, issue.4, pp.377-382, 1993.
DOI : 10.1093/protein/6.4.377

URL : https://hal.archives-ouvertes.fr/hal-00310605

J. A. Cuff and G. Barton, Evaluation and improvement of multiple sequence methods for protein secondary structure prediction, Proteins: Structure, Function, and Genetics, vol.266, issue.4, pp.508-519, 1999.
DOI : 10.1002/(SICI)1097-0134(19990301)34:4<508::AID-PROT10>3.0.CO;2-4

A. G. De-brevern, C. Benros, R. Gautier, H. Valadie, S. Hazout et al., Local backbone structure prediction of proteins, In Silico Biol, vol.4, pp.381-386, 2004.
URL : https://hal.archives-ouvertes.fr/inserm-00132872

A. G. De-brevern, A. Camproux, S. Hazout, C. Etchebest, and P. Tuffery, Protein structural alphabets: beyond the secondary structure description; in Recent research developments in protein engineering, pp.319-331, 2001.

A. G. De-brevern, C. Etchebest, and S. Hazout, Bayesian probabilistic approach for predicting backbone structures in terms of protein blocks, Proteins: Structure, Function, and Genetics, vol.7, issue.3, pp.271-287, 2000.
DOI : 10.1002/1097-0134(20001115)41:3<271::AID-PROT10>3.0.CO;2-Z

URL : https://hal.archives-ouvertes.fr/inserm-00132821

A. De-brevern and S. Hazout, Hybrid Protein Model (HPM): a method to compact protein 3D-structure information and physicochemical properties, Proceedings Seventh International Symposium on String Processing and Information Retrieval. SPIRE 2000, pp.1-49, 2000.
DOI : 10.1109/SPIRE.2000.878179

A. De-brevern and S. Hazout, Compacting local protein folds with a "hybrid protein model", Theoretical Chemistry Accounts: Theory, Computation, and Modeling (Theoretica Chimica Acta), vol.106, issue.1-2, pp.36-47, 2001.
DOI : 10.1007/s002140000227

A. De-brevern and S. Hazout, 'Hybrid Protein Model' for optimally defining 3D protein structure fragments, Bioinformatics, vol.19, issue.3, pp.345-353, 2003.
DOI : 10.1093/bioinformatics/btf859

URL : https://hal.archives-ouvertes.fr/inserm-00133632

A. G. De-brevern, H. Valadie, S. Hazout, and C. Etchebest, Extension of a local backbone description using a structural alphabet: A new approach to the sequence-structure relationship, Protein Science, vol.40, issue.(1/2), pp.2871-2886, 2002.
DOI : 10.1110/ps.0220502

URL : https://hal.archives-ouvertes.fr/inserm-00143374

A. G. De-brevern, H. Wong, C. Tournamille, C. Y. , L. Van-kim et al., A structural model of a seven-transmembrane helix receptor: The Duffy antigen/receptor for chemokine (DARC), Biochimica et Biophysica Acta (BBA) - General Subjects, vol.1724, issue.3, pp.288-306, 2005.
DOI : 10.1016/j.bbagen.2005.05.016

URL : https://hal.archives-ouvertes.fr/inserm-00143373

D. Eisenberg, The discovery of the alpha-helix and beta-sheet, the principal structural features of proteins, Proc. Natl. Acad. Sci. USA, pp.11207-11210, 2003.

M. Errami, C. Geourjon, and G. Deleage, Detection of unrelated proteins in sequences multiple alignments by using predicted secondary structures, Bioinformatics, vol.19, issue.4, pp.506-512, 2003.
DOI : 10.1093/bioinformatics/btg016

URL : https://hal.archives-ouvertes.fr/hal-00313744

J. Espadaler, N. Fernandez-fuentes, A. Hermoso, E. Querol, F. X. Aviles et al., ArchDB: automated protein loop classifi cation as a tool for structural genomics; Nucleic Acids Res, pp.32-185, 2004.

C. Etchebest, C. Benros, S. Hazout, and A. De-brevern, A structural alphabet for local protein structures: Improved prediction methods, Proteins: Structure, Function, and Bioinformatics, vol.20, issue.4, pp.810-827, 2005.
DOI : 10.1002/prot.20458

URL : https://hal.archives-ouvertes.fr/inserm-00143564

J. S. Fetrow, M. J. Palumbo, and G. Berg, Patterns, structures, and amino acid frequencies in structural building blocks, a protein secondary structure classification scheme, Proteins: Structure, Function, and Genetics, vol.19, issue.2, pp.249-271, 1997.
DOI : 10.1002/(SICI)1097-0134(199702)27:2<249::AID-PROT11>3.0.CO;2-M

L. Fourrier, C. Benros, and A. De-brevern, Use of a structural alphabet for analysis of short loops connecting repetitive structures, BMC Bioinformatics, vol.5, issue.1, p.58, 2004.
DOI : 10.1186/1471-2105-5-58

URL : https://hal.archives-ouvertes.fr/inserm-00112104

J. C. Gelly, A. De-brevern, and S. Hazout, 'Protein Peeling': an approach for splitting a 3D protein structure into compact fragments, Bioinformatics, vol.22, issue.2, pp.129-133, 2006.
DOI : 10.1093/bioinformatics/bti773

URL : https://hal.archives-ouvertes.fr/inserm-00133725

A. Girod, M. Ried, C. Wobus, H. Lahm, K. Leike et al., Genetic capsid modifi cations allow effi cient re-targeting of adeno-associated virus type 2, Nature Medicine, vol.5, issue.12, p.1438, 1999.
DOI : 10.1038/71021

J. A. Hartigan and M. Wong, Algorithm AS 136: A K-Means Clustering Algorithm, Applied Statistics, vol.28, issue.1, pp.100-115, 1979.
DOI : 10.2307/2346830

S. Henikoff and J. Henikoff, Amino acid substitution matrices from protein blocks., Proc. Natl. Acad. Sci. USA, pp.10915-10919, 1992.
DOI : 10.1073/pnas.89.22.10915

URL : https://www.ncbi.nlm.nih.gov/pmc/articles/PMC50453/pdf

W. Humphrey, A. Dalke, and K. Schulten, VMD: Visual molecular dynamics, Journal of Molecular Graphics, vol.14, issue.1, pp.33-38, 1996.
DOI : 10.1016/0263-7855(96)00018-5

C. Hunter and S. , Protein fragment clustering and canonical local shapes, Proteins: Structure, Function, and Bioinformatics, vol.281, issue.4, pp.580-588, 2003.
DOI : 10.1002/prot.10309

C. Hunter and S. , Protein local structure prediction from sequence, Proteins: Structure, Function, and Bioinformatics, vol.7, issue.Suppl 1, pp.572-579, 2003.
DOI : 10.1002/prot.10310

D. Jones, Protein secondary structure prediction based on position-specific scoring matrices, Journal of Molecular Biology, vol.292, issue.2, pp.195-202, 1999.
DOI : 10.1006/jmbi.1999.3091

W. Jurkowski, M. Brylinski, L. Konieczny, Z. Wiiniowski, and R. , Conformational subspace in simulation of early-stage protein folding, Proteins: Structure, Function, and Bioinformatics, vol.13, issue.Suppl 3, pp.115-127, 2004.
DOI : 10.1002/prot.20002

S. Karchin, . Cruz, R. Usa-karchin, M. Cline, Y. Mandel-gutfreund et al., Hidden Markov models that use predicted local structure for fold recognition: Alphabets of backbone geometry, Proteins: Structure, Function, and Bioinformatics, vol.323, issue.1/2, pp.504-514, 2003.
DOI : 10.1002/prot.10369

T. Kohonen, Self-organized formation of topologically correct feature maps, Biological Cybernetics, vol.13, issue.1, pp.59-69, 1982.
DOI : 10.1007/BF00337288

R. Koradi, M. Billeter, and K. Wuthrich, MOLMOL: A program for display and analysis of macromolecular structures, Journal of Molecular Graphics, vol.14, issue.1, pp.29-32, 1996.
DOI : 10.1016/0263-7855(96)00009-4

R. Kuang, C. Leslie, and A. Yang, Protein backbone angle prediction with machine learning approaches, Bioinformatics, vol.20, issue.10, pp.1612-1621, 2004.
DOI : 10.1093/bioinformatics/bth136

URL : http://bioinformatics.oxfordjournals.org/cgi/content/short/20/10/1612

S. Kullback and R. A. Leibler, On Information and Sufficiency, The Annals of Mathematical Statistics, vol.22, issue.1, pp.79-86, 1951.
DOI : 10.1214/aoms/1177729694

J. Martin, G. Letellier, A. Marin, J. Taly, A. De-brevern et al., Protein secondary structure assignment revisited: a detailed analysis of different assignment methods, BMC Structural Biology, vol.5, issue.1, p.17, 2005.
DOI : 10.1186/1472-6807-5-17

URL : https://hal.archives-ouvertes.fr/inserm-00090199

E. Milner-white, Situations of gamma-turns in proteins. Their relation to alpha-helices, beta-sheets and ligand binding sites, J. Mol. Biol, vol.216, pp.386-397, 1990.

A. G. Murzin, S. E. Brenner, T. Hubbard, and C. Chothia, SCOP: a structural classifi cation of proteins database for the investigation of sequences and structures, J. Mol. Biol, vol.247, pp.536-540, 1995.

G. Némethy and M. Printz, The ?? Turn, a Possible Folded Conformation of the Polypeptide Chain. Comparison with the ?? Turn, Macromolecules, vol.5, issue.6, pp.755-758, 1972.
DOI : 10.1021/ma60030a017

B. Oliva, P. A. Bates, E. Querol, F. Aviles, and M. J. Sternberg, An automated classification of the structure of protein loops, Journal of Molecular Biology, vol.266, issue.4, pp.814-830, 1997.
DOI : 10.1006/jmbi.1996.0819

C. A. Orengo, A. D. Michie, S. Jones, D. T. Jones, M. Swindells et al., CATH ??? a hierarchic classification of protein domain structures, Structure, vol.5, issue.8, pp.1093-1108, 1997.
DOI : 10.1016/S0969-2126(97)00260-8

L. Pauling and R. Corey, Atomic coordinates and structure factors for two helical confi gurations of polypeptide chains, Proc. Natl. Acad. Sci. USA, pp.235-240, 1951.

L. Pauling and R. Corey, The pleated sheet, a new layer confi guration of polypeptide chains, Proc. Natl. Acad. Sci. USA, pp.251-256, 1951.

J. Pei and N. Grishin, Combining evolutionary and structural information for local protein structure prediction, Proteins: Structure, Function, and Bioinformatics, vol.42, issue.Suppl 5, pp.782-794, 2004.
DOI : 10.1002/prot.20158

T. N. Petersen, C. Lundegaard, M. Nielsen, H. Bohr, J. Bohr et al., Prediction of protein secondary structure at 80% accuracy, Proteins: Structure, Function, and Genetics, vol.9, issue.1, pp.17-20, 2000.
DOI : 10.1002/1097-0134(20001001)41:1<17::AID-PROT40>3.0.CO;2-F

G. Pollastri and A. Mclysaght, Porter: a new, accurate server for protein secondary structure prediction, Bioinformatics, vol.21, issue.8, pp.1719-1720, 2005.
DOI : 10.1093/bioinformatics/bti203

G. Pollastri, D. Przybylski, B. Rost, and P. Baldi, Improving the prediction of protein secondary structure in three and eight classes using recurrent neural networks and profiles, Proteins: Structure, Function, and Genetics, vol.47, issue.2, pp.228-235, 2002.
DOI : 10.1002/prot.10082

S. J. Prestrelski, A. Williams, . Jr, and M. Liebman, Generation of a substructure library for the description and classification of protein secondary structure. I. Overview of the methods and results, Proteins: Structure, Function, and Genetics, vol.14, issue.4, pp.430-439, 1992.
DOI : 10.1002/prot.340140404

L. Rabiner, A tutorial on hidden Markov models and selected application in speech recognition, Proc. IEEE, pp.257-286, 1989.

J. S. Richardson, E. D. Getzoff, and D. Richardson, The beta bulge: a common small unit of nonrepetitive protein structure., Proc. Natl. Acad. Sci. USA, pp.2574-2578, 1978.
DOI : 10.1073/pnas.75.6.2574

C. S. Ring, D. G. Kneller, R. Langridge, and F. Cohen, Taxonomy and conformational analysis of loops in proteins, Journal of Molecular Biology, vol.224, issue.3, pp.685-699, 1992.
DOI : 10.1016/0022-2836(92)90553-V

C. A. Rohl and A. Doig, -helix/coil transitions in isolated peptides, Protein Science, vol.31, issue.8, pp.1687-1696, 1996.
DOI : 10.1002/pro.5560050822

URL : https://hal.archives-ouvertes.fr/hal-01407662

O. Sander, I. Sommer, and T. Lengauer, Local protein structure prediction using discriminative models, BMC Bioinformatics, 2006.

R. A. Sayle and E. Milner-white, RASMOL: biomolecular graphics for all, Trends in Biochemical Sciences, vol.20, issue.9, p.374, 1995.
DOI : 10.1016/S0968-0004(00)89080-5

J. Schuchhardt, G. Schneider, J. Reichelt, D. Schomburg, and P. Wrede, Local structural motifs of protein backbones are classified by self-organizing neural networks, "Protein Engineering, Design and Selection", vol.9, issue.10, pp.833-842, 1996.
DOI : 10.1093/protein/9.10.833

B. L. Sibanda and J. Thornton, [5] Conformation of ?? hairpins in protein structures: Classification and diversity in homologous structures, Methods Enzymol, vol.202, pp.59-82, 1991.
DOI : 10.1016/0076-6879(91)02007-V

R. Sowdhamini and T. Blundell, An automatic method involving cluster analysis of secondary structures for the identification of domains in proteins, Protein Science, vol.25, issue.3, pp.506-520, 1995.
DOI : 10.1002/pro.5560040317

A. V. Tendulkar, A. A. Joshi, M. A. Sohoni, and P. Wangikar, Clustering of Protein Structural Fragments Reveals Modular Building Block Approach of Nature, Journal of Molecular Biology, vol.338, issue.3, pp.611-629, 2004.
DOI : 10.1016/j.jmb.2004.02.047

J. D. Thompson, D. G. Higgins, and T. Gibson, CLUSTAL W: improving the sensitivity of progressive multiple sequence alignment through sequence weighting, position-specific gap penalties and weight matrix choice, Nucleic Acids Research, vol.22, issue.22, pp.4673-4680, 1994.
DOI : 10.1093/nar/22.22.4673

H. H. Tsai, C. J. Tsai, M. B. , and N. , In silico protein design by combinatorial assembly of protein building blocks, Protein Science, vol.267, issue.10, pp.2753-2765, 2004.
DOI : 10.1110/ps.04774004

M. Tyagi, P. Sharma, C. Swamy, F. Cadet, N. Srinivasan et al., PBE): A web-based protein structure analysis server using a structural alphabet, Protein Block Expert Nucleic Acids Res, 2006.
URL : https://hal.archives-ouvertes.fr/inserm-00133751

R. Unger, D. Harel, S. Wherland, and J. Sussman, A 3D building blocks approach to analyzing and predicting structure of proteins, Proteins: Structure, Function, and Genetics, vol.5, issue.4, pp.355-373, 1989.
DOI : 10.1002/prot.340050410

R. Unger and J. Sussman, The importance of short structural motifs in protein structure analysis, Journal of Computer-Aided Molecular Design, vol.6, issue.4, pp.457-472, 1993.
DOI : 10.1007/BF02337561

R. T. Wintjens, M. J. Rooman, and S. J. Wodak, Automatic Classification and Analysis of ????-Turn Motifs in Proteins, Journal of Molecular Biology, vol.255, issue.1, pp.235-253, 1996.
DOI : 10.1006/jmbi.1996.0020

J. Wojcik, J. Mornon, and J. Chomilier, New efficient statistical sequence-dependent structure prediction of short to medium-sized protein loops based on an exhaustive loop classification, Journal of Molecular Biology, vol.289, issue.5, pp.1469-1490, 1999.
DOI : 10.1006/jmbi.1999.2826