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Original Articles

Comparison study of two kernel-based learning algorithms for predicting the distance range between antibody interface residues and antigen surface

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Pages 697-707 | Received 29 Sep 2006, Accepted 07 Feb 2007, Published online: 02 Jul 2007

References

  • Webster , D. M. , Henry , A. H. and Rees , A. R. 1994 . Antibody–antigen interactions . Current Opinion in Structural Biology , 4 : 123 – 129 .
  • Bath , T. N. , Bentley , G. A. , Fischmann , T. O. , Boulot , G. , Poljak and Small , R. J. 1990 . Rearrangements in structures of Fv and Fab fragments of antibody D1.3 on antigen binding . Nature , 347 : 483 – 485 .
  • Stanfield , R. L. , Fieser , T. M. , Lerner , R. A. and Wilson , I. A. 1990 . Crystal structures of an antibody to a peptide and its complex with peptide antigen at 2.8 Å . Science , 248 : 712 – 719 .
  • Chothia , C. , Lesk , A. M. Gherardi , E. 1992 . Structural repertoire of the human Vh segments . Journal of Molecular Biology , 227 : 799 – 817 .
  • Collman , P. M. , Laver , W. G. , Varghese , J. N. , Baker , A. T. , Tulloch , P. A. , Air , G. M. and Webster , R. G. 1987 . Three-dimensional structure of a complex of antibody with influenza virus neuraminidase . Nature , 326 : 358 – 363 .
  • Xiang , J. , Sha , Y. , Prasad , L. and Delbaere , L. T.J. 1996 . Complementarity determining region residues aspartic acid at H55 serine at tyrosines at H97 andL96 play important roles in the B72.3 antibody-TAG72 antigen interaction . Protein Engineering , 9 : 539 – 543 .
  • Iba , Y. , Hayshi , N. , Sawada , I. , Titani , K. and Kurosawa , Y. 1998 . Changes in the specificity of antibodies against steroid antigens by introduction of mutations into complementarity-determining regions of Vh domain . Protein Engineering , 11 : 361 – 370 .
  • Dougan , D. A. , Malby , R. L. and Grunen , I. C. 1998 . Effects of substitutions in the binding surface of an antibody on antigen affinity . Protein Engineering , 11 : 65 – 74 .
  • Berman , H. M. , Westbrook , J. , Feng , Z. , Gilliland , G. , Bhat , T. N. , Weissig , H. , Shindyalov , I. N. and Bourne , P. E. 2000 . The Protein Data Bank . Nucleic Acids Research , 28 : 235 – 242 .
  • Rost , B. and Sander , C. 1994 . Conservation and prediction of solvent accessibility in protein families . Proteins , 20 : 216 – 226 .
  • Holbrook , S. R. , Muskal , S. M. and Kim , S. H. 1990 . Predicting surface exposure of amino acids from protein sequence . Protein Engineering , 3 : 659 – 665 .
  • Naderi , H. , Sadeghi , M. , Arab , S. and Moosavi , M. A. 2001 . Prediction of protein surface accessibility with information theory . Proteins , 42 : 452 – 459 .
  • Li , X. and Pan , X. 2001 . New method for accurate prediction of solvent accessibility from protein sequence . Proteins , 42 : 1 – 5 .
  • Pascarella , S. , Persio , R. , Bossa , F. and Argos , P. 1998 . Easy method to predict solvent accessibility from multiple protein sequence alignments . Proteins , 32 : 190 – 199 .
  • Kabsch , W. and Sander , C. 1983 . Dictionary of protein secondary structure: pattern recognition of hydrogen-bonded and geometrical features . Biopolymers , 22 : 2577 – 2637 .
  • Jones , S. and Thornton , J. M. 1996 . Principles of protein–protein interactions . Proceedings of the National Academy of Sciences of the USA , 93 : 13 – 20 .
  • Fariselli , P. , Pazos , F. , Valencia , A. and Casadio , R. 2002 . Prediction of protein–protein interaction sites in heterocomplexes with neural networks . European Journal of Biochemistry , 269 : 1356 – 1361 .
  • Brown , M. P.S. , Grundy , W. N. , Cristianini , D. , Lin , N. and Sugnet , C. 2000 . Knowledge-based analysis of microarray gene expression data using support vector machines . Proceedings of the National Academy of Sciences of the USA , 97 : 262 – 267 .
  • Minakuchi , Y. and Konagaya , A. 2004 . Prediction of protein–protein interaction sites using support vector machines . Protein Engineering Design and Selection , 17 : 165 – 173 .
  • Yan , C. H. , Dobbs , D. and Honavar , V. 2004 . A two-stage classifier for identification of protein–protein interface residues . Bioinformatics , 20 : i371 – i378 .
  • Haussler , D. 1999 . “ Convolution kernels on discrete structures ” . Santa Cruz, CA : University of California . Technical Report UCSC-CRL-99-10
  • Vapnik , V. N. 2000 . The Nature of Statistical Learning Theory , (2nd edn) , New York : Springer .
  • Cristianini , N. and Shawe-Taylor , J. 2000 . An Introduction to Support Vector Machines , Cambridge : Cambridge University Press .
  • Jones , S. and Thornton , J. M. 1997 . Analysis of protein–protein interaction sites using surface patches . Journal of Molecular Biology , 272 : 121 – 132 .
  • Jones , S. and Thornton , J. M. 1997 . Prediction of protein–protein interaction sites using patch analysis . Journal of Molecular Biology , 272 : 133 – 143 .
  • Kou , G. , Peng , Y. , Shi , Y. and Chen , Z. 2006 . “ A new multi-criteria convex quadratic programming model for credit analysis ” . In International Conference on Computational Science 2006, Lecture Notes in Computer Scienc , Edited by: Alexandrov , V. N. , van Albada , G. D. , Sloot , P. M.A. and Dongarra , J. Vol. 3994 , 476 – 484 . Berlin : Springer-Verlag .
  • Kou , G. , Peng , Y. , Shi , Y. , Wise , M. and Xu , W. 2005 . Discovering credit cardholders’ behavior by multiple criteria linear programming . Annals of Operations Research , 135 : 261 – 274 .
  • Vapnik , V. N. and Chervonenkis , A. 1964 . A note on one class of perceptrons . Automatics and Remote Control , 25 : 821 – 837 .
  • Cortes , C. and Vapnik , V. 1995 . Support-vector network . Machine Learning , 20 : 273 – 297 .
  • LIBSVM (Version 2.71) Available online at: http://www.csie.ntu.edu.tw/∼cjlin/libsvm (accessed 20 March 2007)
  • Petrovsky , N. and Brusic , V. 2002 . Computational immunology: the coming of age . Immunology and Cell Biology , 80 : 248 – 254 .
  • Easwarakumar , K. S. and Muragan , A. 2005 . Possibilities of constructing two pictures in DNA computing: Part I . International Journal of Computer Mathematics , 82 : 1307 – 1321 .
  • Easwarakumar , K. S. and Muragan , A. 2006 . Possibilities of constructing two pictures in DNA computing: Part II . International Journal of Computer Mathematics , 83 : 1 – 20 .
  • Bradley , P. S. , Fayyad , U. M. and Magasarian , O. L. 1999 . Mathematical programming for data mining: formulations and challenges . INFORMS Journal on Computing , 11 : 217 – 238 .
  • Li , J. , Liu , J. , Xu , W. and Shi , Y. 2004 . “ Support vector machines approach to credit assessment ” . In International Conference on Computational Science 2004, Lecture Notes in Computer Scienc , Edited by: Sloot , P. M.A. , Abramson , D. , Bogdanov , A. V. , Dongarra , J. J. , Zomaya , A. Y. and Gorbachev , Y. E. Vol. 2658 , 892 – 899 . Berlin : Springer-Verlag .
  • Olson , D. and Shi , Y. 2006 . Introduction to Business Data Mining , New York : McGraw-Hill .

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