A Deeply Glimpse into Protein Fold Recognition

A Deeply Glimpse into Protein Fold Recognition

Loading document ...
Loading page ...


Author(s): Marwa Mohammed M. Ghareeb, Ahmed Sharaf Eldin, Taysir Hassan A. Soliman, Mohammed Ebrahim Marie

Download Full PDF Read Complete Article

653 1047 24-33 Volume 2 - Jun 2013


The rapid growth in genomic and proteomic data causes a lot of challenges that are raised up and need powerful solutions. It is worth noting that UniProtKB/TrEMBL database Release 28-Nov-2012 contains 28,395,832 protein sequence entries, while the number of stored protein structures in Protein Data Bank (PDB, 4-12-2012) is 65,643. Thus, the need of extracting structural information through computational analysis of protein sequences has become very important, especially, the prediction of the fold of a query protein from its primary sequence has become very challenging. The traditional computational methods are not powerful enough to address theses challenges. Researchers have examined the use of a lot of techniques such as neural networks, Monte Carlo, support vector machine and data mining techniques. This paper puts a spot on this growing field and covers the main approaches and perspectives to handle this problem.


Protein fold recognition, Neural network, Evolutionary algorithms, Meta Servers.


  1. Raymond Chang. Chemistry. McGraw Hill, 4 edition, 1991
  2. Thomas E. Creighton. Proteins, Structures and molecular properties.W. H. Freeman and Company, New York, 2nd edition, 1993
  3. G. N. Ramachandran and V. Sasiskharan. Conformation of polypeptides and proteins. Adv. Protein Chem., 23:283-437, 1968
  4. J. E. Wampler. Tutorial on peptide and protein structure. http://bmbiris.bmb.uga.edu/wampler/tutorial/
  5. Carl Branden and John Tooze. Introduction to protein structure. Garland Publishing, Inc., New York and London, 1991
  6. Robert Matthew MacCallum. “Computational Analysis of Protein Sequence and Structure,” Ph.D. thesis, university College London, September 1997
  7. Eaton E. Lattman. CASP4. Proteins: Structure, Function, and Genetics, 44(4):399, 2001
  8. A. Bairoch and R. Apweiler. The SWISS-PROT protein sequence database and its supplement TrEMBL in 2000. Nucleic Acids Research, 28:45-48, 2000
  9. Richard Bonneau and David Baker. Ab initio protein structure prediction: progress and prospects. Annual Review Biophysics Biomolecular Structure, 30:173{89, 200
  10. Richard H. Lathrop et al. Computational Methods in Molecular Biology, chapter 12, pages 227-283. Elsevier Press, Amsterdam, 1998
  11. An-Suei Yang and Barry Honig. Sequence to structure alignment in comparative modeling using PrISM. PROTEINS: Structure, Function and Genetics Supplement, 3:66-72, 1999
  12. David Baker. A surprising simplicity to protein folding. Nature, 405:39-42, 2000
  13. Ken A Dill, S Banu Ozkan, M Scott Shell, and Thomas R Weikl, “The protein folding problem,” Annual review of biophysics, 37:289–316, 2008
  14. Apweiler, R., Bairoch, A., Wu, C.H., Barker, W.C., Boeckmann, B., Ferro, S., Gasteiger, E., Huang, H., Lopez, R., Magrane, M., Martin, M.J., Natale, D.A., O’Donovan, C., Redaschi, N. and Yeh, L.L. ‘UniProt: the universal protein knowledge base’ Nucleic Acids Research, Vol. 32, pp.D115–D119, 2004
  15. Berman, H.M., Westbrook, J., Feng, Z., Gilliland, G., Bhat, T.N., Weissig, H., Shindyalov, I.N. and Bourne, P.E. ‘The Protein Data Bank’, Nucleic Acids Research, Vol. 28, pp.235–242, 2000
  16. [16] Akutsu, T. and S. Miyano, On the approximation of protein threading. Theoret. Comput. Sci., 210: 261-275. DOI: 10.1016/S0304-3975(98)00089-9, 1999.
  17. [17] Lathrop, R., The protein threading problem with sequence amino acid interaction preferences is NP-complete. Protein Eng. Des. Select., 7: 1059-1068, 1994.
  18. [18] Jones, D.T., “GenTHREADER: An efficient and reliable protein fold recognition method for genomic sequences,” J. Mol. Biol., vol. 287, pp. 797-815, 1999.
  19. [19] Lin, K., A.C.W. May and W.R. Taylor, “Threading Using Neural NETwork (TUNE): The measure of protein sequence-structure compatibility,” Bioinformatics, vol. 18, pp. 1350-1357, 2002.
  20. Mcguffin, L.J. and D.T. Jones, “Improvement of the GenTHREADER method for genomic fold recognition,” Bioinformatics, vol. 19, pp. 874-881, 2003
  21. Jiang, N., W.X. Wu and I. Mitchell, “Protein fold recognition using neural networks and support vector machines,” Proceeding of the 6th International Conference on Intelligent Data Engineering and Automated Learning-IDEAL, July 6-8, 2005
  22. W. Thomas, C Igel, Jutta Gebert, “Protein Fold Class Prediction Using Neural Networks with Tailored Early-Stopping,” in International Joint Conference on Neural Networks IJCNN, 2004
  23. Raval, A., Z. Ghahramani and D.L. Wild, “ABayesian network model for protein fold and remote homologue recognition,” Bioinformatics, vol.18, pp.788-801. 200
  24. Taylor, W.R. and I. Jonassen, “A structural pattern-based method for protein fold recognition. Proteins,” Struct. Funct. Bioinformatics, vol. 56, pp. 222-234. 2004
  25. Xu, J., “Fold recognition by predicted alignment accuracy,” IEEE/ACM Trans. Comput. Biol. Bioinform., 2: 157-165, 2005
  26. Sangjo Han, Byung-chul Lee, Seung Taek Yu, Chan-seok Jeong, Soyoung Lee and Dongsup Kim, “Fold recognition by combining profile-profile alignment and support vector machine,” Bioinformatics, vol.21, pp. 2667-2673. 2005
  27. W. Chmielnicki, K. Stapor, "Protein fold recognition with combined SVM-RDA classifier," in: Proceedings of the HAIS 2010, Part I, LNAI, vol. 6076, pp. 162-169. 2010
  28. Dandekar, T. and P. Argos, “Potential of genetic algorithms in protein folding and protein engineering simulations,” Protein Eng. Des. Select., vol.5, pp. 637-645. 1992
  29. Yanev, N. and R. Andonoy, “Solving the protein threading problem in parallel,” Proceedings of the 17th International Symposium on Parallel and Distributed Processing, Apr. 22-26, IEEE Computer Society, Washington, DC., USA., pp: 157, 2003
  30. Yadgari, J., A. Amir and R. Uunger, “Genetic threading,” Constraints, 6: 271-292, 2001
  31. Unger, R., “The genetic algorithm approach to protein structure prediction,” Struct. Bond., vol.110, pp.153-175, 2004
  32. Judy, M.V. and K.S. Ravichandran. “A solution to protein folding problem using a genetic algorithm with modified keep best reproduction strategy,”Proceeding of IEEE Congress on Evolution Computation, Sep. 25-28, IEEE Xplore Press, Singapore, pp. 4776-4780, 2007
  33. Liang, F. and W.H. Wong, “Evolutionary monte carlo for protein folding simulations,” J.Chemi. Phy., vol. 115, pp. 3374-3380, 2001
  34. Carpio, C.A.D., Sasaki, S.I., L. Baranyi and H. Okada, “A parallel hybrid GA for peptide 3D structure prediction,” Proceedings of the Workshop on Genome Informatics, Dec. 11-12, Universal Academy Press, Tokyo, 1995
  35. Nguyen, D.H., Yoshihara, I. Yamamori, K. and Yasunaga, M. “Aligning multiple protein sequences by parallel hybrid genetic algorithm,” Genome Inform., vol.13, pp. 123-132, 2002
  36. Day, R.O., G.B. Lamont and R. Pachter, “Protein structure prediction by applying an evolutionary algorithm,” Proceedings of the International Symposium on Parallel and Distributed Processing, Nice, France, pp. 155-162, Apr. 22-26, 2003
  37. Islam, R. and A. Ngom, “Parallel evolution strategy for protein threading,” Proceedings of the 25th International Conference on Chilean Computer Science Society, IEEE Computer Society, Washington, DC., USA., pp. 74, Nov, 07-11, 2005
  38. Alione, N., “Parallel evolution strategy on grids for the protein threading problem,” J. Parallel Distributed Computing, vol. 66, pp. 1489-1502, 2006
  39. Thomas, S. and N.M. Amato, “Parallel protein folding with STAPL,” Proceedings of the 18th International Parallel and Distributed Processing Symposium, IEEE Computer Society, Washington, DC., USA., pp. 189, Apr. 26-30, 2004
  40. Wiese, K.C and A. Hendriks, “A detailed analysis of parallel speedup in P-RnaPredict-an evolutionary algorithm for RNA secondary structure prediction,” Proceeding of the IEEE Congress on Evolutionary Computation, IEEE Computer Society, Washington, DC., USA., pp. 2323-2330. July 16-21, 2006
  41. Lundstrom, J., Rychlewski, L., Bujnicki, J. & Elofsson, A., “Pcons: a neural network based consensus predictor that improves fold recognition,” Protein Sci.,vol.10, pp. 2354–2362, 2001
  42. Xu J, Yu L, Li M,”Consensus fold recognition by predicted model quality,” Asian-Pacific Bioinformatics Conference (APBC) , 105-116. 200
  43. Riccardo Lovsey,”Development of an Enhanced Fold Recognition Ensemble System for Protein Structure Prediction,” Ph.D. thesis, University of London, London, England, September 2006

Cite this Article:

  • BibTex
  • RIS
  • APA
  • Harvard
  • IEEE
  • MLA
  • Vancouver
  • Chicago

International Journal of Sciences is Open Access Journal.
This article is licensed under a Creative Commons Attribution 4.0 International (CC BY 4.0) License.
Author(s) retain the copyrights of this article, though, publication rights are with Alkhaer Publications.

Search Articles

Issue September 2020

Volume 9, September 2020

Table of Contents

World-wide Delivery is FREE

Share this Issue with Friends:

Submit your Paper