A computational approach for the identification of small GTpases based on preprocessed amino acid sequences

Technology in Cancer Research and Treatment

Volumn 8, Issue 5, 2009, Pages 333-341

  Heider, Dominik ^a   Appelmann, Jessica ^b   Bayro, Tuygun ^b   Dreckmann, Winfried ^b   Held, Andreas ^b   Winkler, Jonas ^b   Barnekow, Angelika ^b   Borschbach, Markus ^c  

^a UNIVERSITY OF DUISBURG ESSEN   (Germany)

^b UNIVERSITY OF MÜNSTER   (Germany)

^c Faculty of Computer Science   (Germany)

Author keywords

Cancer; Classification; Machine learning; Neural networks; Proteins

Indexed keywords

GUANOSINE TRIPHOSPHATASE;

ACCURACY; AMINO ACID SEQUENCE; ARTICLE; CARCINOGENESIS; COMPUTER MODEL; ENZYME STRUCTURE; HYDROPHOBICITY; IN VITRO STUDY; NERVE CELL NETWORK; PREDICTION; PROTEIN ANALYSIS; PROTEIN FUNCTION; PROTEIN STRUCTURE;

AMINO ACID SEQUENCE; COMPUTATIONAL BIOLOGY; HUMANS; MOLECULAR SEQUENCE DATA; MONOMERIC GTP-BINDING PROTEINS; NEURAL NETWORKS (COMPUTER); ROC CURVE;

EID: 70349314467     PISSN: 15330346     EISSN: None     Source Type: Journal    
DOI: 10.1177/153303460900800503     Document Type: Article

References (48)

1. Malumbres, M., Barbacid, M. RAS oncogenes: the first 30 years. Nat Rev Cancer 3, 7-13 (2003).
2. Karnoub, E. A., Weinberg, R. A. Ras oncogenes: split personalities. Nature Reviews Mol Cell Biol 9, 517-531 (2008).
3. Bos, J. L. The ras gene family and human carcinogenesis. Mutat Res 195, 255-271 (1988).
4. Bos, J. L. Ras oncogenes in human cancer: a review. Cancer Res 49, 4682-4689 (1989).
5. Charvier, P., Goud, B. The role of ARF and Rab GTPases in membrane transport. Curr Opin Cell Biol 11, 466-475 (1999). (Pubitemid 29396793)
6. Wennerberg, K., Rossmann, K. L., Der C.J. The ras superfamily at a glance. J Cell Sci 118, 162-177 (2005).
7. Pfeffer, S. R. Unsolved mysteries in membrane traffic. Annu Rev Biochem 76, 629-645 (2007).
8. Kail, M., Barnekow, A. Identification and characterization of interacting partners of Rab GTPases by yeast two-hybrid analyses. Methods in Mol Biol 440, 111-125 (2008).
9. Olkkonen, V. M., Stenmark, H. Role of Rab GTPases in membrane traffic. Int Rev Cytology 176, 1-85 (1997). (Pubitemid 27395619)
10. Matanis, T., Akhmanova, A., Wulf, P., Nery, E. D., Weide, T., Stepanova, T., Galjart, N., Grosveld, F., Goud, B., Zeeuw, C. D., Barnekow, A., Hoogenraad, C. Bicaudal-D regulates COPI-independent Golgi-ER transport by recruiting the dynein-dynactin motor complex. Nature Cell Biol 4, 986-992 (2002). (Pubitemid 35469430)
11. Grosshans, B. L., Ortiz, D., Novick, P. Rabs and their effectors: Achieving specificity in membrane traffic. Proc Nat Acad Sci USA 103, 11821-11827 (2006). (Pubitemid 44215770)
12. Heider, D., Barnekow, A. DNA-based watermarks using the DNA-Crypt algorithm. BMC Bioinformatics 8, 176 (2007). (Pubitemid 47018648)
13. Heider, D., Barnekow, A. DNA watermarks: A proof of concept. BMC Molecular Biology 9, 40 (2008).
14. Barnekow, A., Thyrock, A., Kessler, D. Rab proteins and their interaction partners. Int Review of Cell and Molecular Biology 274, 235-274 (2009).
15. Pereira-Leal, J. B., Seabra, M. C. Evolution of the Rab family of small GTP-binding proteins. J Mol Biol 313, 889-901 (2001).
16. McConlogue, L., Castellano, F., deWit, C., Schenk, D., Maltese, W. A. Differential effects of a Rab6 mutant on secretory versus amyloidogenic processing of Alzheimer's beta-amyloid precursor protein. J Biol Chem 271, 1343-1348 (1996). (Pubitemid 26028615)
17. Teber, I., Nagano, F., Kremerskothen, J., Bilbilis, K., Goud, B., Barnekow, A. Rab6 interacts with the Mint3 adaptor protein. Biol Chem 386, 671-677 (2005). (Pubitemid 41448169)
18. Scheper, W., Hoozemans, J. J., Hoogenraad, C., Rozemuller, A. J., Eikelenboom, P., Baas, F. Rab6 is increased in Alzheimer's disease brain and correlates with endoplasmic reticulum stress. Neuropathol Appl Neurobiol 33, 523-532 (2007). (Pubitemid 47390081)
19. Ni, X., Ma, Y., Cheng, H., Jiang, M., Guo, L., Ji, C., Gu, S., Cao, Y., Xie, Y., Mao, Y. Molecular cloning and characterization of a novel human Rab (Rab2B) gene. J Hum Genet 47, 548-551 (2002). (Pubitemid 35238972)
20. Cheng, K. W., Lahad, J. P., Gray, J. W., Mills, G. B. Emerging role of Rab GTPases in cancer and human disease. Cancer Res 65, 2516-2519 (2005).
21. Calvo, A., Xiao, N., Kang, J., Best, C. J., Leiva, I., Emmert-Buck, M. R., Jorcyk, C., Green, J.E. Alterations in gene expression profiles during prostate cancer progression: functional correlations to tumorigenicity and down-regulation of selenoprotein-P in mouse and human tumors. Cancer Res 62, 5325-5335 (2002). (Pubitemid 35024609)
22. Catz, S. D. Characterization of Rab27a and JFC1 as constituents of the secretory machinery of prostate-specific antigen in prostate carcinoma cells. Methods in Enzymology 438, 25-40 (2008).
23. He, H., Dai, F., Yu, L., She, X., Zhao, Y., Jiang, J., Chen, X., Zhao, S. Identification and characterization of nine novel human small GTPases showing variable expressions in liver cancer tissues. Gene Expr 10, 231-242 (2002). (Pubitemid 35315385)
24. Shimada, K., Uzawa, K., Kato, M., Endo, Y., Shiiba, M., Bukawa, H., Yokoe, H., Seki, N., Tanzawa, H. Aberrant expression of Rab1A in human tongue cancer. British J. Cancer 92, 1915-1921 (2005).
25. Kotsch, M., Sieuwerts, A. M., Grosser, M., Fuessel, A. M. S., van Gelder, M. E. M., Smid, M., Schmitt, M., Baretton, G., Luther, T., Magdolen, V., Foekens, J. A. Urokinase receptor splice variant uPAR-del4/5-associated gene expression in breast cancer: identification of rab31 as an independent prognostic factor. Breast Cancer Res Treat 111, 229-240 (2008).
26. Gromiha, M. M., Yabuki, Y. Functional discrimination of membrane proteins using machine learning techniques. BMC Bioinformatics 9, 135 (2008).
27. Hoff, K. J., Tech, M., Lingner, T., Daniel, R., Morgenstern, B., Meinicke, P. Gene prediction in metagenomic fragments: A large scale machine learning approach. BMC Bioinformatics 9, 217 (2008). (Pubitemid 351785135)
28. Karchin, R., Karplus, K., Haussler, D. Classifying G-protein coupled receptors with support vector machines. Bioinformatics 18, 147-150 (2002). (Pubitemid 34145043)
29. Cai, C. Z., Han, L. Y., Ji, Z. L., Chen, X., Chen, Y. Z. SVM-Prot: web-based support vector machine software for functional classification of a protein from its primary sequence. Nucleic Acids Research 31, 459-462 (2003). (Pubitemid 37442225)
30. Statnikov, A., Wang, L., Aliferis, C. F. A comprehensive comparison of random forests and support vector machines for microarray-based cancer classification. BMC Bioinformatics 9, 319 (2008).
31. Kumar, M., Raghava, G. P. S. Prediction of nuclear proteins using SVM and HMM models. BMC Bioinformatics 10, 22 (2009).
32. Fernandez, L., Caballero, J., Abreu, J. I., Fernandez, M. Amino acid sequence autocorrelation vectors and bayesian-regularized genetic neural networks for modeling protein conformational stability: Gene V protein mutants. PROTEINS: Structure, Function, and Bioinformatics 67, 834-852 (2007). (Pubitemid 46753933)
33. Fernandez, M., Caballero, J., Fernandez, L., Abreu, J. I., Acosta, G. Classification of conformational stability of protein mutants from 3D pseudo-folding graph representation of protein sequences using support vector machines. Proteins 70, 167-175 (2008).
34. Chen, K., Kurgan, L. A. PFRES: protein fold classification by using evolutionary information and predicted secondary structure. Bioinformatics 23(21), 2843-2850 (2007). (Pubitemid 350162889)
35. Ong, S. A. K., Lin, H. H., Chen, Y. Z., Li, Z. R., Cao, Z. Efficacy of different protein descriptors in predicting protein functional families. BMC Bioinformatics 8, 300 (2007). (Pubitemid 47520444)
36. Kernytsky, A., Rost, B. Using genetic algorithms to select most predictive protein features. Proteins 75, 75-88 (2009).
37. Kyte, J., Doolittle, R. F. A Simple Method for Displaying the Hydropathic Character of a Protein. J Mol Biol 157, 105-132 (1982).
38. Frishman, D., Argos, P. Incorporation of long-distance interactions into a secondary structure prediction algorithm. Protein Engineering 9, 133-142 (1996). (Pubitemid 26119294)
39. Demuth, H., Beagle, M., Hagan, M. Neural Network Toolbox 5 User's Guide. The MathWorks, (2007).
40. Rumelhart, D. E., Hinton, G.. E., Williams, R. J. Learning representations by back-propagating errors. Nature 323, 533-536 (1986).
41. Riedmiller, M., Braun, H. RPROP - A fast adaptive learning algorithm. In Proceedings of ISCIS VII, (1992).
42. Nguyen, D., Widrow, B. Improving the learning speed of 2-layer neural networks by choosing initial values of the adaptive weights. In Proc Intl Joint Conf on Neural Networks, 21-26 (1990).
43. Jolliffe, I. T. Principal Component Analysis. Springer Series in Statistics, 2nd Ed., Springer, NY, (2002).
44. Fawcett, T. An introduction to ROC analysis. Pattern Recognition Letter 27, 861 (2006).
45. Herbrich, R. Learning Kernel Classifiers: Theory and Algorithms, MIT Press, (2001).
46. Pao, G. M., Johnson, K. D., Chrispeels, M. J., Sweet, G., Sandal, N. N., Wu, L. F., Saier, M. H., Jr. Hofte, H. Evolution of the MIP family of integral membrane transport proteins. Mol Microbiol 5(1), 33-37 (1991). (Pubitemid 21895938)
47. Chrispeels, M. J., Agre, P. Aquaporins: water channel proteins of plant and animal cells. Trends Biochem Sci 19, 421-425 (1994).
48. Thompson, J. D., Higgins, D. G., Gibson, T. J. CLUSTAL W: improving the sensitivity of progressive multiple sequence alignment through sequence weighting, position-specific gap penalties and weight matrix choice. Nucleic Acids Res 22, 4673-4680 (1994).