P. Rose, A. Prli?, C. Bi, W. Bluhm, C. Christie et al., The RCSB Protein Data Bank: views of structural biology for basic and applied research and education, Nucleic Acids Research, vol.69, issue.D1, pp.345-356, 2015.
DOI : 10.1002/prot.21507

C. Uniprot, UniProt: a hub for protein information, Nucleic Acids Res, vol.43, pp.204-212, 2015.

W. Kabsch and C. Sander, On the use of sequence homologies to predict protein structure: identical pentapeptides can have completely different conformations., Proceedings of the National Academy of Sciences, vol.81, issue.4, pp.1075-1078, 1984.
DOI : 10.1073/pnas.81.4.1075
URL : http://www.pnas.org/content/81/4/1075.full.pdf

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 : 10.1080/07391102.1988.10506425

R. Karchin, M. Cline, and K. Karplus, Evaluation of local structure alphabets based on residue burial, Proteins: Structure, Function, and Bioinformatics, vol.7, issue.Suppl3, pp.508-518, 2004.
DOI : 10.1016/S0022-2836(63)80023-6

B. Offmann, M. Tyagi, and A. De-brevern, Local Protein Structures. Current Bioinformatics, vol.33, pp.165-202, 2007.

M. Tyagi, A. Bornot, B. Offmann, and A. De-brevern, Protein short loop prediction in terms of a structural alphabet, Computational Biology and Chemistry, vol.33, issue.4, pp.329-333, 2009.
DOI : 10.1016/j.compbiolchem.2009.06.002
URL : https://hal.archives-ouvertes.fr/inserm-00396485

H. Cheng, T. Sen, R. Jernigan, and A. Kloczkowski, Consensus Data Mining (CDM) Protein Secondary Structure Prediction Server: Combining GOR V and Fragment Database Mining (FDM), Bioinformatics, vol.189, issue.1, pp.2628-2630, 2007.
DOI : 10.1128/JB.01238-06

C. Bystroff, K. Simons, K. Han, and D. Baker, Local sequence-structure correlations in proteins, Current Opinion in Biotechnology, vol.7, issue.4, pp.417-421, 1996.
DOI : 10.1016/S0958-1669(96)80117-0

K. Simons, C. Kooperberg, E. Huang, and D. Baker, Assembly of protein tertiary structures from fragments with similar local sequences using simulated annealing and bayesian scoring functions, Journal of Molecular Biology, vol.268, issue.1, pp.209-225, 1997.
DOI : 10.1006/jmbi.1997.0959

R. Kolodny, P. Koehl, L. Guibas, and M. Levitt, Small Libraries of Protein Fragments Model Native Protein Structures Accurately, Journal of Molecular Biology, vol.323, issue.2, pp.297-307, 2002.
DOI : 10.1016/S0022-2836(02)00942-7

A. Joseph, G. Agarwal, S. Mahajan, J. Gelly, L. Swapna et al., A short survey on protein blocks, Biophysical Reviews, vol.30, issue.3, pp.137-147, 2010.
DOI : 10.1021/jp012090f
URL : https://hal.archives-ouvertes.fr/inserm-00512823

A. De-brevern, C. Etchebest, and S. Hazout, Bayesian Probabilistic Approach for Predicting Backbone, Proteins, vol.287, pp.271-287, 2000.
URL : https://hal.archives-ouvertes.fr/inserm-00132821

M. Dudev and C. Lim, Discovering structural motifs using a structural alphabet: Application to magnesium-binding sites, BMC Bioinformatics, vol.8, issue.1, pp.106-17389049, 2007.
DOI : 10.1186/1471-2105-8-106

C. Wu, Y. Chen, and C. Lim, A structural-alphabet-based strategy for finding structural motifs across protein families, Nucleic Acids Research, vol.67, issue.14, pp.150-20525797, 2010.
DOI : 10.1002/prot.21366

B. Schneider, . Cerny´jcerny´cerny´j, D. Svozil, P. Cech, J. Gelly et al., Bioinformatic analysis of the protein/DNA interface, Nucleic Acids Research, vol.67, issue.1, pp.3381-3394, 2014.
DOI : 10.1007/s00018-009-0131-2
URL : https://hal.archives-ouvertes.fr/inserm-00926088

M. Tyagi, A. De-brevern, N. Srinivasan, and B. Offmann, Protein structure mining using a structural alphabet, Proteins: Structure, Function, and Bioinformatics, vol.5, issue.2, pp.920-937, 2008.
DOI : 10.2174/157489307781662105
URL : https://hal.archives-ouvertes.fr/inserm-00176443

A. Joseph, N. Srinivasan, and A. De-brevern, Improvement of protein structure comparison using a structural alphabet, Biochimie, vol.93, issue.9, pp.1434-1445, 2011.
DOI : 10.1016/j.biochi.2011.04.010
URL : https://hal.archives-ouvertes.fr/inserm-00646245

S. Mahajan, A. De-brevern, Y. Sanejouand, N. Srinivasan, and B. Offmann, Use of a structural alphabet to find compatible folds for amino acid sequences, Protein Science, vol.4, issue.1, pp.145-153, 2015.
DOI : 10.1371/journal.pone.0004981
URL : https://hal.archives-ouvertes.fr/hal-01677393

Y. Ghouzam, G. Postic, A. De-brevern, and J. Gelly, Improving protein fold recognition with hybrid profiles combining sequence and structure evolution, Bioinformatics, vol.82, pp.3782-3791, 2015.
DOI : 10.1021/ci800178a

O. Zimmermann and H. Ulrich, LOCUSTRA: Accurate Prediction of Local Protein Structure Using a Two-Layer Support Vector Machine Approach, Journal of Chemical Information and Modeling, vol.48, issue.9, pp.1903-1908, 2008.
DOI : 10.1021/ci800178a

V. Suresh, S. Parthasarathy, and . Svm-pb-pred, SVM-PB-Pred: SVM Based Protein Block Prediction Method Using Sequence Profiles and Secondary Structures, Protein & Peptide Letters, vol.21, issue.8, pp.736-742, 2014.
DOI : 10.2174/09298665113209990064

H. Rangwala, C. Kauffman, and G. Karypis, svmPRAT: SVM-based Protein Residue Annotation Toolkit, BMC Bioinformatics, vol.10, issue.1, p.439, 2009.
DOI : 10.1186/1471-2105-10-439
URL : https://bmcbioinformatics.biomedcentral.com/track/pdf/10.1186/1471-2105-10-439?site=bmcbioinformatics.biomedcentral.com

Q. Dong, X. Wang, L. Lin, and Y. Wang, Analysis and prediction of protein local structure based on structure alphabets, Proteins: Structure, Function, and Bioinformatics, vol.6, issue.Database issue, pp.163-172, 2008.
DOI : 10.1186/1471-2105-6-291

G. Karypis and . Yasspp, YASSPP: Better kernels and coding schemes lead to improvements in protein secondary structure prediction, Proteins: Structure, Function, and Bioinformatics, vol.21, issue.3, pp.575-586, 2006.
DOI : 10.1002/prot.10408
URL : http://www.cs.umn.edu/tech_reports_upload/tr2005/05-028.pdf

J. Garnier, J. Gibrat, and B. Robson, [32] GOR method for predicting protein secondary structure from amino acid sequence, Methods Enzym, vol.266, pp.540-553, 1996.
DOI : 10.1016/S0076-6879(96)66034-0

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/pro.5560070103
URL : https://hal.archives-ouvertes.fr/inserm-00143564

D. Jones, Protein secondary structure prediction based on position-specific scoring matrices 1 1Edited by G. Von Heijne, Journal of Molecular Biology, vol.292, issue.2, pp.195-202, 1999.
DOI : 10.1006/jmbi.1999.3091

A. De-brevern, C. Benros, R. Gautier, H. Valadié, 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

S. Altschul, W. Gish, W. Miller, E. Myers, and D. 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

M. Tyagi, V. Gowri, N. Srinivasan, A. De-brevern, . De et al., A substitution matrix for structural alphabet based on structural alignment of homologous proteins and its applications, Proteins: Structure, Function, and Bioinformatics, vol.272, issue.1, pp.32-39, 2006.
DOI : 10.1016/j.bbagen.2005.05.016
URL : https://hal.archives-ouvertes.fr/inserm-00133760

P. Argos, Analysis of sequence-similar pentapeptides in unrelated protein tertiary structures, Journal of Molecular Biology, vol.197, issue.2, pp.331-379, 1987.
DOI : 10.1016/0022-2836(87)90127-6

V. Suresh, L. Liu, D. Adjeroh, and X. Zhou, RPI-Pred: predicting ncRNA-protein interaction using sequence and structural information, Nucleic Acids Research, vol.30, issue.3, pp.1370-1379, 2015.
DOI : 10.1093/nar/gkf512
URL : https://academic.oup.com/nar/article-pdf/43/3/1370/17436652/gkv020.pdf

J. Luo, L. Liu, S. Venkateswaran, Q. Song, and X. Zhou, RPI-Bind: a structure-based method for accurate identification of RNA-protein binding sites, Scientific Reports, vol.4, issue.Suppl 1, pp.614-28377624, 2017.
DOI : 10.1186/1752-0509-4-S1-S3

L. Nguyen, X. Dang, T. Le, T. Saethang, V. Tran et al., Predicting ??eta-Turns and ??eta-Turn Types Using a Novel Over-Sampling Approach, Journal of Biomedical Science and Engineering, vol.07, issue.11, pp.927-940, 2014.
DOI : 10.4236/jbise.2014.711090
URL : http://www.scirp.org/journal/PaperDownload.aspx?paperID=49809

B. Webb and A. Sali, Comparative protein structure modeling using MODELLER, Curr Protoc Bioinforma, vol.5, issue.6, pp.1-32, 2014.
DOI : 10.1002/cpbi.3
URL : http://europepmc.org/articles/pmc5031415?pdf=render

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

W. Kabsch and C. Sander, Dictionary of protein secondary structure: Pattern recognition of hydrogen-bonded and geometrical features, Biopolymers, vol.33, issue.12, pp.2577-2637, 1983.
DOI : 10.1016/0005-2795(73)90350-4