Cysteine frameworks

Conotoxins, the disulfide rich conopeptides, are classified according to three schemes: the similarities between the ER signal sequence of the conotoxin precursors (gene superfamilies), the cysteine patterns of conotoxin mature peptide regions (cysteine frameworks), and the specificities to pharmacological targets (pharmacological families). This page provides a brief introduction to the cysteine frameworks used in ConoServer. The two other classification schemes are detailed in separate pages accessible from the menu on the left. A more comprehensive discussion of the conopeptide classification schemes can be found in Kaas et al. Toxicon 2010 [1].

During the maturation process of proteins in eukaryote, cysteine residues are oxidised and form disulfide bridges, which are important to maintain protein shapes. Through this shape, disulfide bonds play en essential role for the stability and the activity of proteins. Cysteine frameworks are defined in ConoServer by the arrangement of cysteines along the primary sequence of the conopeptide mature region in the precursor protein. As described in Table 1, the cysteine frameworks are defined by the number of cysteines and the number of residues (none or at least one) between consecutive cysteines.

At an early stage of conopeptide discovery, the disulfide connectivities were also included in the definition. Because the cysteine frameworks are now only defined according to the primary sequence, they can be attributed without determining the disulfide connectivities [1]. Table 1 also gives the disulfide connectivies that have been presently identified for each cysteine frameworks. Additional connectivities may be discovered in the future for each cysteine framework.

Table 1: Cysteine frameworks used in ConoServer. This table is automatically generated and therefore kept up-to-date with the content of ConoServer. The second column provides the cysteine pattern that define the cysteine frameworks. The third column contains the number of cysteines involved in each pattern. The fourth column provides the cystine connectivities that have been identified for this frameworks. The disulfide connectivities are not part of the definition of the cysteine frameworks in ConoServer.

Framework	Cystine pattern	# cysteines	Connectivity	Reference
I	CC-C-C	4	I-III, II-IV	Gray,W.R. et al. (1981) J. Biol. Chem. 256:4734-4740
II	CCC-C-C-C	6		Ramilo,C. et al. (1992) Biochemistry 31:9919-9926
III	CC-C-C-CC	6		Sato,S. et al. (1983) FEBS Lett. 155:277-280
IV	CC-C-C-C-C	6	I-V, II-III, IV-VI	Fainzilber,M. et al. (1995) Biochemistry 34:8649-8656
V	CC-CC	4	I-III, II-IV	Walker,C.S. et al. (1999) J. Biol. Chem. 274:30664-30671
VI/VII	C-C-CC-C-C	6	I-IV, II-V, III-VI	Olivera,B.M. et al. (1984) Biochemistry 23:5087-5090
VIII	C-C-C-C-C-C-C-C-C-C	10		England,L.J. et al. (1998) Science 281:575-578
IX	C-C-C-C-C-C	6	I-IV, II-V, III-VI	Lirazan,M.B. et al. (2000) Biochemistry 39:1583-1588
X	CC-C.[PO]C	4	I-IV, II-III	Balaji,R.A. et al. (2000) J. Biol. Chem. 275:39516-39522
XI	C-C-CC-CC-C-C	8	I-IV, II-VI, III-VII, V-VIII	Jimenez,E.C. et al. (2003) J. Neurochem. 85:610-621
XII	C-C-C-C-CC-C-C	8		Brown,M.A. et al. (2005) Biochemistry 44:9150-9159
XIII	C-C-C-CC-C-C-C	8		Aguilar,M.B. et al. (2005) Biochemistry 44:11130-11136
XIV	C-C-C-C	4	I-III, II-IV	Moller,C. et al. (2005) Biochemistry 44:15986-15996
XV	C-C-CC-C-C-C-C	8		Peng,C. et al. (2008) Peptides 29:985-991
XVI	C-C-CC	4		Pi,C. et al. (2006) Genomics 88:809-819
XVII	C-C-CC-C-CC-C	8		Yuan,D.D. et al. (2008) Peptides 29:1521-1525
XVIII	C-C-CC-CC	6		Chen,J.S. et al. (1999) J Nat Toxins 8:341-349
XIX	C-C-C-CCC-C-C-C-C	10		Chen,P. et al. (2008) Toxicon 52:139-145
XX	C-CC-C-CC-C-C-C-C	10		Loughnan,M.L. et al. (2009) Biochemistry 48:3717-3729
XXI	CC-C-C-C-CC-C-C-C	10		Möller,C. and Marí,F. (2011) Biopolymers 96:158-165
XXII	C-C-C-C-C-C-C-C	8		Elliger,C.A. et al. (2011) Toxicon 57:311-322
XXIII	C-C-C-CC-C	6		Ye,M. et al. (2012) J Biol Chem 287:14973-14983
XXIV	C-CC-C	4		Luo,S. et al. (2013) PLoS ONE 8
XXV	C-C-C-C-CC	6		Aguilar,M.B. et al. (2013) Peptides 41:38-44
XXVI	C-C-C-C-CC-CC	8		Bernáldez,J. et al. (2013) Mar Drugs 11:1188-1202
XXVII	C-C-C-CCC-C-C	8		Jin,A.H. et al. (2015) Proc. Biol. Sci. 282
XXVIII	C-C-C-CC-C-C-C-C-C	10		Lu et al. (2017) Peptides, 94, pp.64-70 94:64-70
XXIX	CCC-C-CC-C-C	8		Bernáldez-Sarabia,J. et al. (2019) Toxins 11:128
XXX	C-C-CCC-C-C-C-CC	10		Bernáldez-Sarabia,J. et al. (2019) Toxins 11:128
XXXII	C-CC-C-C-C	6		Kancherla,A.K. et al. (2015) ACS Chem. Biol. 10:1847-1860
XXXIII	C-C-C-C-C-C-C-C-C-C-C-C	12		Pardos-Blas,J.R. et al. (2019) Marine drugs 17:453

[1]	Kaas,Q. et al. (2010) Toxicon 55:1491-1509

Please cite:
Kaas Q, Yu R, Jin AH, Dutertre S and Craik DJ. ConoServer: updated content, knowledge, and discovery tools in the conopeptide database. Nucleic Acids Research (2012) 40(Database issue):D325-30
Kaas Q, Westermann JC, Halai R, Wang CK and Craik DJ. ConoServer, a database for conopeptide sequences and structures. Bioinformatics (2008) 24(3):445-6

ConoServer is managed at the Institute of Molecular Bioscience IMB, Brisbane, Australia.

The database and computational tools found on this website may be used for academic research only, provided that it is referred to ConoServer, the database of conotoxins (http://www.conoserver.org) and the above reference is cited. For any other use please contact David Craik (d.craik@imb.uq.edu.au).

Contacts:
David Craik
Quentin Kaas

Last updated: Friday 12 August 2022