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A Comparative Study on Filtering Protein Secondary Structure Prediction
May-June 2012 (vol. 9 no. 3)
pp. 731-739
G. Christodoulou, Dept. of Comput. Sci., Univ. of Cyprus, Nicosia, Cyprus
V. J. Promponas, Dept. of Biol. Sci., Univ. of Cyprus, Nicosia, Cyprus
M. Agathocleous, Dept. of Comput. Sci., Univ. of Cyprus, Nicosia, Cyprus
P. Kountouris, Dept. of Comput. Sci., Univ. of Cyprus, Nicosia, Cyprus
S. Hadjicostas, Dept. of Comput. Sci., Univ. of Cyprus, Nicosia, Cyprus
V. Vassiliades, Dept. of Comput. Sci., Univ. of Cyprus, Nicosia, Cyprus
C. Christodoulou, Dept. of Comput. Sci., Univ. of Cyprus, Nicosia, Cyprus
Filtering of Protein Secondary Structure Prediction (PSSP) aims to provide physicochemically realistic results, while it usually improves the predictive performance. We performed a comparative study on this challenging problem, utilizing both machine learning techniques and empirical rules and we found that combinations of the two lead to the highest improvement.

[1] N. Qian and T.J. Sejnowski, "Predicting the Secondary Structure of Globular Proteins Using Neural Network Models," J. Molecular Biology, vol. 202, no. 4, pp. 865-884, 1988.
[2] B. Rost and C. Sander, "Prediction of Protein Secondary Structure at Better than 70 percent Accuracy," J. Molecular Biology, vol. 232, no. 2, pp. 584-599, 1993.
[3] D.T. Jones, "Protein Secondary Structure Prediction Based on Position-Specific Scoring Matrices." J. Molecular Biology, vol. 292, no. 2, pp. 195-202, 1999.
[4] P. Baldi, S. Brunak, P. Frasconi, G. Soda, and G. Pollastri, "Exploiting the Past and the Future in Protein Secondary Structure Prediction," Bioinformatics, vol. 15, no. 11, pp. 937-946, 1999.
[5] G. Pollastri, D. Przybylski, B. Rost, and P. Baldi, "Improving the Prediction of Protein Secondary Structure in Three and Eight Classes Using Recurrent Neural Networks and Profiles," Proteins, vol. 47, no. 2, pp. 228-235, 2002.
[6] G. Pollastri and A. McLysaght, "Porter: A New, Accurate Server for Protein Secondary Structure Prediction," Bioinformatics, vol. 21, no. 8, pp. 1719-1720, 2005.
[7] M.J. Wood and J.D. Hirst, "Protein Secondary Structure Prediction with Dihedral Angles," Proteins, vol. 59, no. 3, pp. 476-481, 2005.
[8] S. Hua and Z. Sun, "A Novel Method of Protein Secondary Structure Prediction with High Segment Overlap Measure: Support Vector Machine Approach," J. Molecular Biology, vol. 308, no. 2, pp. 397-407, 2001.
[9] G. Karypis, "YASSPP: Better Kernels and Coding Schemes Lead to Improvements in Protein Secondary Structure Prediction," Proteins, vol. 64, no. 3, pp. 575-586, 2006.
[10] P. Kountouris and J.D. Hirst, "Prediction of Backbone Dihedral Angles and Protein Secondary Structure Using Support Vector Machines," BMC Bioinformatics, vol. 10, no. 1,article 437, 2009.
[11] K. Karplus, C. Barrett, M. Cline, M. Diekhans, L. Grate, and R. Hughey, "Predicting Protein Structure Using Only Sequence Information," Proteins, vol. 37, pp. 121-125, 1999.
[12] K. Lin, V.A. Simossis, W.R. Taylor, and J. Heringa, "A Simple and Fast Secondary Structure Prediction Method using Hidden Neural Networks," Bioinformatics, vol. 21, no. 2, pp. 152-159, 2005.
[13] X.M. Pan, "Multiple Linear Regression for Protein Secondary Structure Prediction," Proteins, vol. 43, no. 3, pp. 256-259, 2001.
[14] S. Qin, Y. He, and X.-M. Pan, "Predicting Protein Secondary Structure and Solvent Accessibility with an Improved Multiple Linear Regression Method," Proteins, vol. 61, no. 3, pp. 473-480, 2005.
[15] J.R. Green, M.J. Korenberg, and M.O. Aboul-Magd, "PCI-SS: MISO Dynamic Nonlinear Protein Secondary Structure Prediction," BMC Bioinformatics, vol. 10, article 222, 2009.
[16] J.A. Cuff, M.E. Clamp, A.S. Siddiqui, M. Finlay, and G.J. Barton, "JPred: A Consensus Secondary Structure Prediction Server," Bioinformatics, vol. 14, no. 10, pp. 892-893, 1998.
[17] S. Montgomerie, S. Sundararaj, W.J. Gallin, and D.S. Wishart, "Improving the Accuracy of Protein Secondary Structure Prediction Using Structural Alignment," BMC Bioinformatics, vol. 7, article 301, 2006.
[18] K.P. Wu, H.N. Lin, J.M. Chang, T.Y. Sung, and W.L. Hsu, "HYPROSP: A Hybrid Protein Secondary Structure Prediction Algorithm-a Knowledge-Based Approach," Nucleic Acids Research, vol. 32, no. 17, pp. 5059-5065, 2004.
[19] C. Mooney and G. Pollastri, "Beyond the Twilight Zone: Automated Prediction of Structural Properties of Proteins by Recursive Neural Networks and Remote Homology Information," Proteins, vol. 77, no. 1, pp. 181-190, 2009.
[20] A. Zemla, C. Venclovas, K. Fidelis, and B. Rost, "A Modified Definition of Sov, a Segment-Based Measure for Protein Secondary Structure Prediction Assessment," Proteins, vol. 34, no. 2, pp. 220-223, 1999.
[21] H. Zhang, T. Zhang, K. Chen, K.D. Kedarisetti, M.J. Mizianty, Q. Bao, W. Stach, and L. Kurgan, "Critical Assessment of High-Throughput Standalone Methods for Secondary Structure Prediction," Briefings in Bioinformatics, vol. 12, pp. 672-688, 2011.
[22] J. Chen and N. Chaudhari, "Cascaded Bidirectional Recurrent Neural Networks for Protein Secondary Structure Prediction," IEEE/ACM Trans Computational Biology and Bioinformatics, vol. 4, no. 4, pp. 572-582, Oct.-Dec. 2007.
[23] A.A. Salamov and V.V. Solovyev, "Prediction of Protein Secondary Structure by Combining Nearest-Neighbor Algorithms and Multiple Sequence Alignments," J. Molecular Biology, vol. 247, no. 1, pp. 11-15, 1995.
[24] M. Agathocleous, G. Christodoulou, V. Promponas, C. Christodoulou, V. Vassiliades, and A. Antoniou, "Protein Secondary Structure Prediction with Bidirectional Recurrent Neural Nets: Can Weight Updating for each Residue Enhance Performance?," AIAI (Artificial Intelligence Applications and Innovations) 2010, IFIP Int'l Federation for Information Processing AICT, H. Papadopoulos, A. S. Andreou and M. Bramer, eds., vol. 339, pp. 128-137, Springer-Verlag, 2010.
[25] J.A. Cuff and G.J. Barton, "Evaluation and Improvement of Multiple Sequence Methods for Protein Secondary Structure Prediction," Proteins, vol. 34, no. 4, pp. 508-519, 1999.
[26] U. Hobohm, M. Scharf, R. Schneider, and C. Sander, "Selection of Representative Protein Data Sets," Protein Science, vol. 1, no. 3, pp. 409-417, 1992.
[27] W. Kabsch and C. Sander, "Dictionary of Protein Secondary Structure: Pattern Recognition of Hydrogen-Bonded and Geometrical Features," Biopolymers, vol. 22, no. 12, pp. 2577-2637, 1983.
[28] B. Rost and V.A. Eyrich, "EVA: Large-Scale Analysis of Secondary Structure Prediction," Proteins, vol. 5, pp. 192-199, 2001.
[29] S.F. Altschul, T.L. Madden, A.A. Schäffer, J. Zhang, Z. Zhang, W. Miller, and D.J. Lipman, "Gapped BLAST and PSI-BLAST: A New Generation of Protein Database Search Programs," Nucleic Acids Research, vol. 25, no. 17, pp. 3389-3402, 1997.
[30] S. Henikoff and J.G. Henikoff, "Amino Acid Substitution Matrices from Protein Blocks," Proc Nat'l Academy of Sciences USA, vol. 89, no. 22, pp. 10915-10919, 1992.
[31] D.T. Jones and M.B. Swindells, "Getting the Most from PSI-BLAST," Trends in Biochemical Sciences, vol. 27, no. 3, pp. 161-164, 2002.
[32] I.H. Witten and E. Frank, Data Mining: Practical Machine Learning Tools and Techniques, second ed. Morgan Kaufmann, 2005.
[33] S. le Cessie and J. van Houwelingen, "Ridge Estimators in Logistic Regression," Applied Statistics, vol. 41, no. 1, pp. 191-201, 1992.
[34] C.C. Chang and C.J. Lin, "LIBSVM: A Library for Support Vector Machines," http://www.csie.ntu.edu.tw/~cjlinlibsvm, accessed in Oct. 2011.
[35] A. Viterbi, "Error Bounds for Convolutional Codes and an Asymptotically Optimum Decoding Algorithm," IEEE Trans Information Theory, vol. 13, no. 2, pp. 260-269, 1967.
[36] X. Wu, V. Kumar, J. Ross Quinlan, J. Ghosh, Q. Yang, H. Motoda, G.J. McLachlan, A. Ng, B. Liu, P.S. Yu, Z.-H. Zhou, M. Steinbach, D.J. Hand, and D. Steinberg, "Top 10 Algorithms in Data Mining," Knowledge and Information Systems, vol. 14, pp. 1-37, 2007.
[37] A.G. Murzin, S.E. Brenner, T. Hubbard, and C. Chothia, "SCOP: A Structural Classification of Proteins Database for the Investigation of Sequences and Structures," J. Molecular Biology, vol. 247, pp. 536-540, 1995.
[38] B.W. Matthews, "Comparison of the Predicted and Observed Secondary Structure of T4 Phage Lysozyme," Biochimica et Biophysica Acta, vol. 405, no. 2, pp. 442-451, 1975.
[39] B. Rost, "Review: Protein Secondary Structure Prediction Continues to Rise," J. Structural Biology, vol. 134, nos. 2/3, pp. 204-218, 2001.
[40] A.J. Shepherd, D. Gorse, and J.M. Thornton, "Prediction of the Location and Type of $\beta$ -Turns in Proteins Using Neural Networks," Protein Science, vol. 8, no. 5, pp. 1045-1055, 1999.

Index Terms:
proteins,bioinformatics,filtering theory,learning (artificial intelligence),molecular biophysics,molecular configurations,empirical rule,protein secondary structure prediction filtering,machine learning technique,Accuracy,Proteins,Filtering,Machine learning algorithms,Logistics,Training,Machine learning,bidirectional recurrent neural networks.,Protein secondary structure prediction,filtering,machine learning,structural bioinformatics
Citation:
G. Christodoulou, V. J. Promponas, M. Agathocleous, P. Kountouris, S. Hadjicostas, V. Vassiliades, C. Christodoulou, "A Comparative Study on Filtering Protein Secondary Structure Prediction," IEEE/ACM Transactions on Computational Biology and Bioinformatics, vol. 9, no. 3, pp. 731-739, May-June 2012, doi:10.1109/TCBB.2012.22
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