J. Richardson, The Anatomy and Taxonomy of Protein Structure, Adv Protein Chem, vol.34, pp.167-339, 1981.
DOI : 10.1016/S0065-3233(08)60520-3

J. Thornton, Disulphide bridges in globular proteins, Journal of Molecular Biology, vol.151, issue.2, pp.261-287, 1981.
DOI : 10.1016/0022-2836(81)90515-5

A. Ceroni, P. Frasconi, A. Passerini, and A. Vullo, Predicting the Disulfide Bonding State of Cysteines with Combinations of Kernel Machines. The Journal of VLSI Signal Processing-Systems for Signal, Image, and Video Technology, pp.287-295, 2003.

Y. Chen, Y. Lin, C. Lin, and J. Hwang, Prediction of the bonding states of cysteines Using the support vector machines based on multiple feature vectors and cysteine state sequences, Proteins: Structure, Function, and Bioinformatics, vol.99, issue.4, pp.1036-1042, 2004.
DOI : 10.1073/pnas.252633099

P. Fariselli, P. Riccobelli, and R. Casadio, Role of evolutionary information in predicting the disulfide-bonding state of cysteine in proteins, Proteins: Structure, Function, and Genetics, vol.55, issue.3, pp.340-346, 1999.
DOI : 10.1103/PhysRevE.55.811

A. Fiser, M. Cserzo, E. Tudos, and I. Simon, Different sequence environments of cysteines and half cystines in proteins Application to predict disulfide forming residues, FEBS Letters, vol.335, issue.2, pp.117-120, 1992.
DOI : 10.1038/335045a0

URL : http://onlinelibrary.wiley.com/doi/10.1016/0014-5793(92)80419-H/pdf

A. Fiser and I. Simon, Predicting the oxidation state of cysteines by multiple sequence alignment, Bioinformatics, vol.16, issue.3, pp.251-256, 2000.
DOI : 10.1093/bioinformatics/16.3.251

URL : https://academic.oup.com/bioinformatics/article-pdf/16/3/251/681766/160251.pdf

A. Fiser and I. Simon, Predicting Redox State of Cysteines in Proteins, Methods Enzymol, vol.353, pp.10-21, 2002.
DOI : 10.1016/S0076-6879(02)53032-9

P. Frasconi, A. Passerini, and A. Vullo, A Two-Stage SVM Architecture for Predicting the Disulfide Bonding State of Cysteines. IEEE NNSP International Workshop: special session on signal processing and neural networks for bionformatics, 2002.
DOI : 10.1109/nnsp.2002.1030014

URL : http://www-dsi.dsi.unifi.it/~paolo/ps/NNSP-02-cysteines.pdf

P. Martelli, P. Fariselli, and R. Casadio, Prediction of disulfide-bonded cysteines in proteomes with a hidden neural network, PROTEOMICS, vol.4, issue.6, pp.1665-1671, 2004.
DOI : 10.1002/pmic.200300745

P. Martelli, P. Fariselli, L. Malaguti, and R. Casadio, Prediction of the disulfide bonding state of cysteines in proteins with hidden neural networks, Protein Engineering, Design and Selection, vol.15, issue.12, pp.951-953, 2002.
DOI : 10.1021/bi992922o

P. Martelli, P. Fariselli, L. Malaguti, and R. Casadio, Prediction of the disulfide-bonding state of cysteines in proteins at 88% accuracy, Protein Science, vol.98, issue.(Suppl.1), pp.2735-2739, 2002.
DOI : 10.1073/pnas.041615798

M. Mucchielli-giorgi, S. Hazout, and P. Tuffery, Predicting the disulfide bonding state of cysteines using protein descriptors, Proteins: Structure, Function, and Bioinformatics, vol.264, issue.3, pp.243-249, 2002.
DOI : 10.1006/jmbi.1996.0664

S. Muskal, S. Holbrook, and S. Kim, Prediction of the disulfide-bonding state of cysteine in proteins, "Protein Engineering, Design and Selection", vol.3, issue.8, pp.667-672, 1990.
DOI : 10.1093/protein/3.8.667

A. Passerini and P. Frasconi, Learning to discriminate between ligand-bound and disulfide-bound cysteines, Protein Engineering Design and Selection, vol.17, issue.4, pp.367-373, 2004.
DOI : 10.1093/protein/gzh042

URL : https://academic.oup.com/peds/article-pdf/17/4/367/4694446/gzh042.pdf

J. Song, M. Wang, W. Li, and W. Xu, Prediction of the disulfide-bonding state of cysteines in proteins based on dipeptide composition, Biochemical and Biophysical Research Communications, vol.318, issue.1, pp.142-147, 2004.
DOI : 10.1016/j.bbrc.2004.03.189

D. Sharma and K. Rajarathnam, 13C NMR chemical shifts can predict disulfide bond formation, Journal of Biomolecular NMR, vol.18, issue.2, pp.165-171, 2000.
DOI : 10.1023/A:1008398416292

P. Fariselli and R. Casadio, Prediction of disulfide connectivity in proteins, Bioinformatics, vol.17, issue.10, pp.957-964, 2001.
DOI : 10.1093/bioinformatics/17.10.957

URL : https://academic.oup.com/bioinformatics/article-pdf/17/10/957/698296/170957.pdf

F. Ferre and P. Clote, Disulfide connectivity prediction using secondary structure information and diresidue frequencies, Bioinformatics, vol.9, issue.5, pp.2336-2346, 2005.
DOI : 10.1093/bioinformatics/9.5.499

URL : https://academic.oup.com/bioinformatics/article-pdf/21/10/2336/539489/bti328.pdf

F. Ferre and P. Clote, DiANNA: a web server for disulfide connectivity prediction, Nucleic Acids Research, vol.33, issue.Web Server, pp.230-232, 2005.
DOI : 10.1093/nar/gki412

URL : https://academic.oup.com/nar/article-pdf/33/suppl_2/W230/7622954/gki412.pdf

C. Tsai, B. Chen, C. Chan, H. Liu, and C. Kao, Improving disulfide connectivity prediction with sequential distance between oxidized cysteines, Bioinformatics, vol.21, issue.8, pp.4416-4419, 2005.
DOI : 10.1093/bioinformatics/bti179

URL : https://academic.oup.com/bioinformatics/article-pdf/21/24/4416/432986/bti715.pdf

A. Vullo and P. Frasconi, A recursive connectionist approach for predicting disulfide connectivity in proteins, Proceedings of the 2003 ACM symposium on Applied computing , SAC '03, pp.66-71, 2003.
DOI : 10.1145/952532.952550

A. Vullo and P. Frasconi, Disulfide connectivity prediction using recursive neural networks and evolutionary information, Bioinformatics, vol.20, issue.5, pp.653-659, 2004.
DOI : 10.1093/bioinformatics/btg463

URL : https://academic.oup.com/bioinformatics/article-pdf/20/5/653/565587/btg463.pdf

E. Zhao, H. Liu, C. Tsai, H. Tsai, C. Chan et al., Cysteine separations profiles on protein sequences infer disulfide connectivity, Bioinformatics, vol.40, issue.31, pp.1415-1420, 2005.
DOI : 10.1021/bi010409g

URL : https://academic.oup.com/bioinformatics/article-pdf/21/8/1415/692448/bti179.pdf

B. Hazes and B. Dijkstra, Model building of disulfide bonds in proteins with known three-dimensional structure, "Protein Engineering, Design and Selection", vol.2, issue.2, pp.119-125, 1988.
DOI : 10.1093/protein/2.2.119

URL : https://pure.rug.nl/ws/files/3376659/1988ProteinEngHazes.pdf

R. Sowdhamini, N. Srinivasan, B. Shoichet, D. Santi, C. Ramakrishnan et al., Stereochemical modeling of disulfide bridges. Criteria for introduction into proteins by site-directed mutagenesis, "Protein Engineering, Design and Selection", vol.3, issue.2, pp.95-103, 1989.
DOI : 10.1093/protein/3.2.95

J. Overington, Z. Zhu, A. Sali, M. Johnson, R. Sowdhamini et al., Molecular recognition in protein families: A database of aligned three-dimensional structures of related proteins, Biochemical Society Transactions, vol.21, issue.3, pp.597-604, 1993.
DOI : 10.1042/bst0210597

C. Chuang, C. Chen, J. Yang, P. Lyu, and J. Hwang, Relationship between protein structures and disulfide-bonding patterns, Proteins: Structure, Function, and Bioinformatics, vol.231, issue.1, pp.1-5, 2003.
DOI : 10.1006/jmbi.1993.1334

J. Mas, P. Aloy, M. Marti-renom, B. Oliva, C. Blanco-aparicio et al., Protein similarities beyond disulphide bridge topology, Journal of Molecular Biology, vol.284, issue.3, pp.541-548, 1998.
DOI : 10.1006/jmbi.1998.2194

V. Abkevich and E. Shakhnovich, What can Disulfide Bonds Tell Us about Protein Energetics, Function and Folding: Simulations and Bioninformatics Analysis, Journal of Molecular Biology, vol.300, issue.4, pp.975-985, 2000.
DOI : 10.1006/jmbi.2000.3893

M. Berkmen, D. Boyd, and J. Beckwith, The Nonconsecutive Disulfide Bond of Escherichia coli Phytase (AppA) Renders It Dependent on the Protein-disulfide Isomerase, DsbC, Journal of Biological Chemistry, vol.280, issue.12, pp.11387-11394, 2005.
DOI : 10.1074/jbc.M411774200

S. Betz, Disulfide bonds and the stability of globular proteins, Protein Science, vol.32, issue.10, pp.1551-1558, 1993.
DOI : 10.1111/j.1399-3011.1990.tb00958.x

URL : http://onlinelibrary.wiley.com/doi/10.1002/pro.5560021002/pdf

J. Clarke, A. Hounslow, C. Bond, A. Fersht, and V. Daggett, The effects of disulfide bonds on the denatured state of barnase, Protein Science, vol.296, issue.12, pp.2394-2404, 2000.
DOI : 10.1021/jp964020s

M. Matsumura and B. Matthews, Control of enzyme activity by an engineered disulfide bond, Science, vol.243, issue.4892, pp.792-794, 1989.
DOI : 10.1126/science.2916125

M. Matsumura and B. Matthews, [16] Stabilization of functional proteins by introduction of multiple disulfide bonds, Methods Enzymol, vol.202, pp.336-356, 1991.
DOI : 10.1016/0076-6879(91)02018-5

O. Mayans, J. Wuerges, S. Canela, M. Gautel, and M. Wilmanns, Structural Evidence for a Possible Role of Reversible Disulphide Bridge Formation in the Elasticity of the Muscle Protein Titin, Structure, vol.9, issue.4, pp.331-340, 2001.
DOI : 10.1016/S0969-2126(01)00591-3

W. Wedemeyer, E. Welker, M. Narayan, and H. Scheraga, Biochemistry, vol.39, issue.15, pp.4207-4216, 2000.
DOI : 10.1021/bi992922o

E. Welker, W. Wedemeyer, M. Narayan, and H. Scheraga, Biochemistry, vol.40, issue.31, pp.409059-9064, 2001.
DOI : 10.1021/bi010409g

E. Drakopoulou, J. Vizzavona, J. Neyton, V. Aniort, F. Bouet et al.,

J. Torrance, G. Bartlett, C. Porter, and J. Thornton, Using a Library of Structural Templates to Recognise Catalytic Sites and Explore their Evolution in Homologous Families, Journal of Molecular Biology, vol.347, issue.3, pp.565-581, 2005.
DOI : 10.1016/j.jmb.2005.01.044

Y. Dai and J. Tang, Intra-A chain disulfide bond (A6-11) of insulin is essential for displaying its activity, Biochem Mol Biol Int, vol.33, issue.6, pp.1049-1053, 1994.

C. Eigenbrot, R. M. Kossiakoff, and A. , Structural effects induced by removal of a disulfide-bridge: the X-ray structure of the C30A/C51A mutant of basic pancreatic trypsin inhibitor at 1.6 ??, "Protein Engineering, Design and Selection", vol.3, issue.7, pp.591-598, 1990.
DOI : 10.1093/protein/3.7.591

T. Li, H. Yamane, T. Arakawa, L. Narhi, and J. Philo, Effect of the intermolecular disulfide bond on the conformation and stability of glial cell line-derived neurotrophic factor, Protein Engineering, Design and Selection, vol.15, issue.1, pp.59-64, 2002.
DOI : 10.1021/bi00391a031

R. Doolittle, The genealogy of some recently evolved vertebrate proteins, Trends in Biochemical Sciences, vol.10, issue.6, pp.233-237, 1985.
DOI : 10.1016/0968-0004(85)90140-9

P. Mallick, D. Boutz, D. Eisenberg, and T. Yeates, Genomic evidence that the intracellular proteins of archaeal microbes contain disulfide bonds, Proceedings of the National Academy of Sciences, vol.5, issue.2, pp.999679-9684, 2002.
DOI : 10.2741/scan

R. Ladenstein and B. Ren, Reconsideration of an early dogma, saying ???there is no evidence for disulfide bonds in proteins from archaea???, Extremophiles, vol.32, issue.1, pp.29-38, 2008.
DOI : 10.1042/bj3151001

H. Nakashima and K. Nishikawa, Discrimination of Intracellular and Extracellular Proteins Using Amino Acid Composition and Residue-pair Frequencies, Journal of Molecular Biology, vol.238, issue.1, pp.54-61, 1994.
DOI : 10.1006/jmbi.1994.1267

K. Nishikawa, Y. Kubota, and T. Ooi, Classification of Proteins into Groups Based on Amino Acid Composition and Other Characters. II. Grouping into Four Types, The Journal of Biochemistry, vol.94, issue.3, pp.997-1007, 1983.
DOI : 10.1093/oxfordjournals.jbchem.a134443

R. Thangudu, P. Sharma, N. Srinivasan, and B. Offmann, Analycys: A database for conservation and conformation of disulphide bonds in homologous protein domains, Proteins: Structure, Function, and Bioinformatics, vol.22, issue.90001, pp.255-261, 2007.
DOI : 10.1002/prot.21318

URL : https://hal.archives-ouvertes.fr/hal-01198478

H. Berman, J. Westbrook, Z. Feng, G. Gilliland, T. Bhat et al., The Protein Data Bank, Nucleic Acids Research, vol.28, issue.1, pp.235-242, 2000.
DOI : 10.1093/nar/28.1.235

O. Emanuelsson, H. Nielsen, S. Brunak, and G. Von-heijne, Predicting Subcellular Localization of Proteins Based on their N-terminal Amino Acid Sequence, Journal of Molecular Biology, vol.300, issue.4, pp.1005-1016, 2000.
DOI : 10.1006/jmbi.2000.3903

K. Nakai and P. Horton, PSORT: a program for detecting sorting signals in proteins and predicting their subcellular localization, Trends in Biochemical Sciences, vol.24, issue.1, pp.34-36, 1999.
DOI : 10.1016/S0968-0004(98)01336-X

S. Hua and Z. Sun, Support vector machine approach for protein subcellular localization prediction, Bioinformatics, vol.17, issue.8, pp.721-728, 2001.
DOI : 10.1093/bioinformatics/17.8.721

R. Russell and G. Barton, Multiple protein sequence alignment from tertiary structure comparison: Assignment of global and residue confidence levels, Proteins: Structure, Function, and Genetics, vol.47, issue.2, pp.309-323, 1992.
DOI : 10.1107/S0108768191010315

P. Argos and M. Rossmann, A method to determine heavy-atom positions for virus structures, Acta Crystallographica Section B Structural Crystallography and Crystal Chemistry, vol.32, issue.11, pp.2975-2979, 1976.
DOI : 10.1107/S0567740876009394

URL : http://journals.iucr.org/b/issues/1976/11/00/a13751/a13751.pdf

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

B. Lee and F. Richards, The interpretation of protein structures: Estimation of static accessibility, Journal of Molecular Biology, vol.55, issue.3, pp.379-400, 1971.
DOI : 10.1016/0022-2836(71)90324-X

S. Hubbard and J. Thornton, Naccess V2.1.1, Atomic solvent accessible area calculations, 1993.

A. Pintar, O. Carugo, and S. Pongor, DPX: for the analysis of the protein core, Bioinformatics, vol.19, issue.2, pp.313-314, 2003.
DOI : 10.1093/bioinformatics/19.2.313

URL : https://academic.oup.com/bioinformatics/article-pdf/19/2/313/1060165/190313.pdf