A review of algorithmic techniques for disulfide-bond determination.

Disulfide bonds play an important role in understanding protein folding, evolution, and in studies related to determining structural and functional properties of specific proteins. At the state-of-the-art, a large number of computational techniques have been proposed for determining disulfide bonds. Operating across the gamut of input data, from pure sequence-based information to spectra from mass spectrometry, these techniques provide researchers with a variety of methodological choices and trade-offs. Techniques for disulfide-bond determination are also underpinned by a rich variety of algorithmic formulations. Analysis of these algorithms can provide valuable cues towards choosing a particular technique and understanding its results. Further, their study is critical in developing the next generation of techniques. This paper discusses the importance and applicability of disulfide-bond determination in understanding protein structure and function and provides a detailed review of computational approaches to this problem.

[1]  Anders Krogh,et al.  Hidden Neural Networks , 1999, Neural Computation.

[2]  A. M. B. DOUGLAS,et al.  X-Ray Crystallography , 1947, Nature.

[3]  H. Gilbert,et al.  Thiol/disulfide exchange equilibria and disulfide bond stability. , 1995, Methods in enzymology.

[4]  S. Patterson,et al.  Mass spectrometry and proteomics. , 2000, Physiological genomics.

[5]  M J Sternberg,et al.  Side‐chain conformational entropy in protein folding , 1995, Protein science : a publication of the Protein Society.

[6]  David Fenyö,et al.  Peer Reviewed: Internet-Based Analytical Chemistry Resources: A Model Project , 1996 .

[7]  Piero Fariselli,et al.  Prediction of the disulfide bonding state of cysteines in proteins with hidden neural networks. , 2002, Protein engineering.

[8]  D Fenyö.  A software tool for the analysis of mass spectrometric disulfide mapping experiments , 1997, Comput. Appl. Biosci..

[9]  D. McRee.  Practical Protein Crystallography , 1993 .

[10]  R. Raines,et al.  Contribution of disulfide bonds to the conformational stability and catalytic activity of ribonuclease A. , 2000, European journal of biochemistry.

[11]  Koreaki Ito,et al.  Respiratory chain strongly oxidizes the CXXC motif of DsbB in the Escherichia coli disulfide bond formation pathway , 1999, The EMBO journal.

[12]  Wen-Bo Xu,et al.  Prediction of the disulfide-bonding state of cysteines in proteins based on dipeptide composition. , 2004, Biochemical and biophysical research communications.

[13]  H. Morris,et al.  A new method for rapid assignment of S-S bridges in proteins. , 1985, Biochemical and biophysical research communications.

[14]  B. Matthews,et al.  Substantial increase of protein stability by multiple disulphide bonds , 1989, Nature.

[15]  H. Scheraga,et al.  Statistical mechanics of noncovalent bonds in polyamino acids. VIII. Covalent loops in proteins , 1965 .

[16]  P. Hogg,et al.  Disulfide bonds as switches for protein function. , 2003, Trends in biochemical sciences.

[17]  L A Mirny,et al.  How to derive a protein folding potential? A new approach to an old problem. , 1996, Journal of molecular biology.

[18]  H. Gilbert.  Molecular and cellular aspects of thiol-disulfide exchange. , 2006, Advances in enzymology and related areas of molecular biology.

[19]  Ten-Yang Yen,et al.  An algorithmic approach to automated high-throughput identification of disulfide connectivity in proteins using tandem mass spectrometry. , 2007, Computational systems bioinformatics. Computational Systems Bioinformatics Conference.

[20]  John L. Klepeis,et al.  Prediction of β‐sheet topology and disulfide bridges in polypeptides , 2003, J. Comput. Chem..

[21]  P. Pucci,et al.  Assignment of protein disulphides by a computer method using mass spectrometric data , 1996, FEBS letters.

[22]  Jenn-Kang Hwang,et al.  Prediction of disulfide connectivity from protein sequences , 2005, Proteins.

[23]  Robert J Chalkley,et al.  Bioinformatic methods to exploit mass spectrometric data for proteomic applications. , 2005, Methods in enzymology.

[24]  D. Smith,et al.  Strategies for locating disulfide bonds in proteins. , 1990, Methods in enzymology.

[25]  CysMap and CysJoin: Database and tools for protein disulphides localisation , 2005, FEBS letters.

[26]  T. Takao,et al.  Fast atom bombardment (FAB) mass spectra of protein digests: Hen and duck egg‐white lysozymes , 1984 .

[27]  Cheng-Yan Kao,et al.  Disulfide connectivity prediction with 70% accuracy using two‐level models , 2006, Proteins.

[28]  B. Chait,et al.  Mass spectrometry of microscale chemical reaction products produced at the surface of organic solids , 1985 .

[29]  D. Craik,et al.  The cystine knot motif in toxins and implications for drug design. , 2001, Toxicon : official journal of the International Society on Toxinology.

[30]  A. Fiser,et al.  Different sequence environments of cysteines and half cystines in proteins Application to predict disulfide forming residues , 1992, FEBS letters.

[31]  Alessio Ceroni,et al.  DISULFIND: a disulfide bonding state and cysteine connectivity prediction server , 2006, Nucleic Acids Res..

[32]  Paolo Frasconi,et al.  Disulfide connectivity prediction using recursive neural networks and evolutionary information , 2004, Bioinform..

[33]  Paolo Frasconi,et al.  A two-stage SVM architecture for predicting the disulfide bonding state of cysteines , 2002, Proceedings of the 12th IEEE Workshop on Neural Networks for Signal Processing.

[34]  Paolo Frasconi,et al.  Learning to discriminate between ligand-bound and disulfide-bound cysteines. , 2004, Protein engineering, design & selection : PEDS.

[35]  H. Scheraga,et al.  Disulfide bonds and protein folding. , 2000, Biochemistry.

[36]  András Fiser,et al.  Predicting the oxidation state of cysteines by multiple sequence alignment , 2000, Bioinform..

[37]  Rahul Singh,et al.  MS2DB: A Mass-Based Hashing Algorithm for the Identification of Disulfide Linkage Patterns in Protein Utilizing Mass Spectrometric Data , 2007, Twentieth IEEE International Symposium on Computer-Based Medical Systems (CBMS'07).

[38]  Peter Clote,et al.  Disulfide connectivity prediction using secondary structure information and diresidue frequencies , 2005, Bioinform..

[39]  Guoli Wang,et al.  PISCES: a protein sequence culling server , 2003, Bioinform..

[40]  H. Versteeg,et al.  Evidence for activation of tissue factor by an allosteric disulfide bond. , 2006, Biochemistry.

[41]  Piero Fariselli,et al.  Prediction of disulfide connectivity in proteins , 2001, Bioinform..

[42]  Vineet Bafna,et al.  Mass spectrometry and computational proteomics , 2004 .

[43]  Vladimir Brusic,et al.  CysView: protein classification based on cysteine pairing patterns , 2004, Nucleic Acids Res..

[44]  C. Sander,et al.  Database of homology‐derived protein structures and the structural meaning of sequence alignment , 1991, Proteins.

[45]  P. Andrews,et al.  A new approach for detection and assignment of disulfide bonds in peptides. , 1986, Analytical biochemistry.

[46]  Rahul Singh,et al.  MS2DB: An Algorithmic Approach to Determine Disulfide Linkage Patterns , 2006, 19th IEEE Symposium on Computer-Based Medical Systems (CBMS'06).

[47]  P. Lyu,et al.  Relationship between protein structures and disulfide‐bonding patterns , 2003, Proteins.

[48]  Ting Chen,et al.  Algorithms for identifying protein cross-links via tandem mass spectrometry , 2001, J. Comput. Biol..

[49]  R C Beavis,et al.  Implementation of an algorithm for modeling disulfide bond patterns using mass spectrometry. , 2003, Journal of proteome research.

[50]  K. Guruprasad,et al.  Analysis of disulphide bond connectivity patterns in protein tertiary structure. , 2006, International journal of biological macromolecules.

[51]  S. Betz.  Disulfide bonds and the stability of globular proteins , 1993, Protein science : a publication of the Protein Society.

[52]  J. Gorman,et al.  Protein Disulfide Bond Determination by Mass Spectrometry , 2003 .

[53]  Song Jiang-ning,et al.  Cooperativity of the oxidization of cysteines in globular proteins. , 2004, Journal of theoretical biology.

[54]  Birgit Schilling,et al.  MS2Assign, automated assignment and nomenclature of tandem mass spectra of chemically crosslinked peptides , 2003, Journal of the American Society for Mass Spectrometry.

[55]  Pierre Baldi,et al.  Large‐scale prediction of disulphide bridges using kernel methods, two‐dimensional recursive neural networks, and weighted graph matching , 2005, Proteins.

[56]  B. Schmidt,et al.  Allosteric disulfide bonds. , 2006, Biochemistry.

[57]  Cheng-Yan Kao,et al.  Improving disulfide connectivity prediction with sequential distance between oxidized cysteines , 2005, Bioinform..

[58]  Cheng-Yan Kao,et al.  Cysteine separations profiles on protein sequences infer disulfide connectivity , 2005, Bioinform..

[59]  Jenn-Kang Hwang,et al.  Predicting disulfide connectivity patterns , 2007, Proteins.

[60]  T. Alber,et al.  Entropic effects of disulphide bonds on protein stability , 1994, Nature Structural Biology.

[61]  Pierre Baldi,et al.  The Principled Design of Large-Scale Recursive Neural Network Architectures--DAG-RNNs and the Protein Structure Prediction Problem , 2003, J. Mach. Learn. Res..

[62]  P Tufféry,et al.  Predicting the disulfide bonding state of cysteines using protein descriptors , 2002, Proteins.

[63]  B. Chait.  Mass Spectrometry: Bottom-Up or Top-Down? , 2006, Science.

[64]  Hajime Nakamura,et al.  Thioredoxin and its related molecules: update 2005. , 2005, Antioxidants & redox signaling.

[65]  Peter Clote,et al.  DiANNA: a web server for disulfide connectivity prediction , 2005, Nucleic Acids Res..

[66]  Steven M. Muskal,et al.  Prediction of the disulfide-bonding state of cysteine in proteins. , 1990, Protein engineering.

[67]  Harold Neil Gabow,et al.  Implementation of algorithms for maximum matching on nonbipartite graphs , 1973 .

[68]  Piero Fariselli,et al.  Prediction of disulfide‐bonded cysteines in proteomes with a hidden neural network , 2004, Proteomics.

[69]  Jon Beckwith,et al.  Protein disulfide bond formation in prokaryotes. , 2003, Annual review of biochemistry.

[70]  J. W. Wong,et al.  Allosteric Disulfide Bonds , 2006 .

[71]  Jenn-Kang Hwang,et al.  Prediction of the bonding states of cysteines Using the support vector machines based on multiple feature vectors and cysteine state sequences , 2004, Proteins.

[72]  W. Kabsch,et al.  Dictionary of protein secondary structure: Pattern recognition of hydrogen‐bonded and geometrical features , 1983, Biopolymers.

[73]  Chris Sander.  Databases of homology-derived protein structures , 1990 .

[74]  P Fariselli,et al.  Role of evolutionary information in predicting the disulfide‐bonding state of cysteine in proteins , 1999, Proteins.

[75]  Malin M. Young,et al.  High throughput protein fold identification by using experimental constraints derived from intramolecular cross-links and mass spectrometry , 2000, Proc. Natl. Acad. Sci. USA.