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Manimekalai, M.
- Literature Survey on the Prediction of Secondary Structure of Proteins Using Radial Basis Function Neural Networks (RBFNN) and Support Vector Machines (SVM)
Authors
1 Bharathidasan University, Tiruchirappalli, Tamil Nadu, IN
2 Department of MCA, Shrimati Indira Gandhi College, Tiruchirappalli, Tamil Nadu, IN
Source
International Journal of Information and Computation Technology, Vol 3, No 1 (2013), Pagination: 33-43Abstract
The Protein structure prediction has been an active research area for the last 40 years or so. The technical progress in computational Molecular Biology during the last decades has contributed significantly to the progress we see today. The major goal of predicting Protein structures underpins the correct assumption that three dimensional structures confer protein function. The linear Amino Acid sequences must transform to nonlinear Secondary Structures and then to Tertiary and Quaternary Structures that are responsible for biological functions. Biological functions may remain similar or change in the related organisms through the evolutionary process. By considering the importance of the prediction of secondary structure of protein a detailed literature study of the same using Radial Basis Function Neural Networks (RBFNN) AND Support Vector Machines (SVM) has been reviewed in this paper.Keywords
Radial Basis Function Neural Networks, Support Vector Machines, Secondary Structures, Tertiary Structure And Quaternary StructuresReferences
- David T. Jones, “Protein Secondary Structure Prediction Based on Positionspecific Scoring Matrices, ” University of Warwick, Coventry CV4 7AL, United Kingdom, 1999.
- Wootton, J.C. and Federhen, S., “Statistics of local complexity in amino acid sequences and sequence databases”, Computers and Chemistry 17, 149-163 1993.
- Stephen F. Altschul, Thomas L. Madden, Alejandro A. Schäffer1, Jinghui Zhang, “Gapped BLAST and PSI-BLAST: a new generation of protein database search programs”, Received June 20, 1997; Revised and Accepted July 16
- L., Leopold, J.L., Frank, R.L. and Maglia, A.M., "Protein Secondary Structure Prediction Using Rule Induction from Coverings", Proceedings of IEEE Symposium on Computational Intelligence in Bioinformatics and Computational Biology (part of IEEE Symposium Series on Computational Intelligence 2009), Nashville, Tennessee, USA, pp. 79-86, 2009.
- Rost, B., “Rising accuracy of protein secondary structure prediction”, D.Chasman, Ed., “Protein structure determination, analysis, and modeling for drug discovery”, New York: Dekker, pp. 207-249, 2003.
- Kabsh, W. and Sander, C., “How good are predictions of protein secondary structure, ” FEBS Letters, 155, pp. 179-182, 1983.
- Kloczkowski, A., Taner Z. Sen, “Combining the GOR V algorithm with evolutionary information for protein secondary structure prediction from amino acid sequence”, Proteins, 49, 154-166, 2002.
- Garnier, J.J., Gibrat, J.F. and Robson, “GOR method for predicting protein secondary structure from amino acid sequence, Methods Enzymol”, 266, 540-550, 1996.
- Sung-Joon Park, “A Study of Fragment-Based Protein Structure Prediction: Biased Fragment Replacement for Searching Low-Energy Conformation”, 104-115, 2005.
- Karplus, K., Karchin, R., Draper, J., Casper, J., Mandel-Gutfreund, Y., Diekhans, M. and Hughey, R., “Combining local-structure, fold-recognition, and new fold methods for protein structure prediction”, Proteins, 53:491-496, 2003
- Kolodny, R., Koehl, R., Guibas, L. and Levitt, M., “Small libraries of protein fragments model native protein structures accurately”, J. Mol. Biol., 323:297-307, 2002.
- Pearson, W.R. and Lipman, D.J. Improved tools for biological sequence comparison proc. Natl. Acad. Sci. U.S.A., 85, 2444-2448, 1998.
- Söding, J, Biegert, A. and Lupas, A.N., "The HHpred interactive server for protein homology detection and structure prediction, ” Nucleic Acids Research 33 ((Web Server issue)): W244-248, 2005.
- Battey, J.N., Kopp, J., Bordoli, L., Read, R.J., Clarke, N.D. and Schwede, T., "Automated server predictions in CASP7, ” Proteins 69 (S): 68-82, 2007.
- Wang Z-X. “Assessing the accuracy of secondary structure”. NatStruct Biol., 3:145-146. 1994.
- Luciano Brocchieri and Samuel Karlin, “How are close residues of protein structures distributed in primary sequence”, Proc. Natl. Acad. Sci. USA Vol. 92, 12136-12140, December 1995.
- Henrick, K. and Thornton, J.M., “PQS: a protein quaternary structure file server”, Trends. Biochem. Sci. 23:358-361, 1998.
- Fischer, D., Rychlewski, L., Elofsson, A., Pazos, F., Valencia, A., Rost, B., Ortiz, A.R. and Dunbrack, R.L.J., “Proteins”, Suppl. 5, 171-183, 2001.
- Richard O. Day, Gary B. Lamont and Ruth Pachter, “Protein Structure Prediction by Applying an Evolutionary Algorithm”, in December 2001.
- Zhen Zhang and Nan Jing, “Radial basis function method for prediction of protein secondary structure, ” International Conference on Machine Learning and Cybernetics, vol. 3, pp. 1379-1383, 2008.
- Senapati, M.R., Vijaya, I. and Dash, P.K., “Rule Extraction from Radial Basis Functional Neural Networks by Using Particle Swarm Optimization”, Journal of Computer Science, 3(8): 592-599, 2007.
- Karayiannis, N.B. and Mi, G.W., “Growing radial basis neural networks: Merging supervised and unsupervised learning with network growth techniques, ” IEEE Trans. Neural Networks, Vol. 8, 1492-1506, 1997.
- Susan C. White, “NN3 Time Series Forecasting with Radial Basis Function Networks”.
- Bishop, C., “Improving the Generalization Properties of Radial Basis Function Neural Networks, ” Neural Computation, 3, 579 - 588, 1991.
- Kenneth J. McGarry, John Tait, Stefan Wermter and John Macintyre, “Rule- Extraction from Radial Basis Function Networks”, IEE, 613-618, 1999.
- Park, J. and Sandberg, I.W., “Approximation and radial-basis-function networks, ” Neural Comput., Vol. 5, 305-316, 1993.
- Cai, Y.D., Liu, X.J., Xu, X.B. and Zhou, G.P., “Support vector machines for predicting protein structural class”, BMC Bioinformatics, 15, 2001.
- Minh Ngoc Nguyen and Jagath C. Rajapakse, “Two-stage support vector machines for protein secondary structure prediction, ” Neural, Parallel & Scientific Computations, vol. 11, No. 2, 1-18, 2003.
- Hu, H.-J. P.C. Tai, J. He, R. Harrison, and Y. Pan, “Protein secondary structure prediction using support vector machine with a PSSM profile and an advanced tertiary classifier, ” IEEE Computational Systems Bioinformatics Conference. 213-214, 2005.
- Lukasz A. Kurgan, Mandana Rahbari and Leila Homaeian, “Impact of thePredicted Protein Structural Content on Prediction of Structural Classes for the Twilight Zone Proteins”, Proceedings of the 5th International Conference on Machine Learning and Applications, IEEE.180-186, 2006.
- Kristin K. Koretke, Zaida Luthey-Schulten and Peter G. Wolynes, “Selfconsistently optimized energy functions for protein structure prediction by molecul
- Jian-xiong Dong, Adam Krzyzak and Ching Y. Suen, "A Fast SVM Training Algorithm", S.-W. Lee and A. Verri (Eds.): SVM 2002, LNCS 2388. 53-67, 2002.
- Chao Chen, Lixuan Chen, Xiaoyong Zou and Peixiang Cai, “Prediction of Protein Secondary Structure Content by Using the Concept of Chou’s Pseudo Amino Acid Composition and Support Vector Machine”, Protein & Peptide Letters, 16, 27-31, 2009.D.
- DeCoste and Scholkopf, B., “Training invariant support vector machines”, Machine Learning, 46(1-3):161-190, 2002.
- Comparision of Prediction of Structure of Protein of Soy Beans Using Radial Basis Function Neural Networks with other Methods for Rs126 and PDB Data Sets
Authors
1 Bharathidasan University, Tiruchirappalli, Tamil Nadu, IN
2 Department of MCA, Shrimati Indira Gandhi College, Tiruchirappalli, Tamil Nadu, IN
Source
Journal of Computational Intelligence in Bioinformatics, Vol 6, No 1 (2013), Pagination: 49-57Abstract
In this paper Prediction of structure of Protein of Soy Beans using Radial Basis Function Neural Networks for RS126 Data set and PDB Data set has been made and compared with other traditional methods namely Chou-Fasman, GOR, APSSP, PHD, Prospect and SSpro.The training and testing sets for both have been taken into consideration to train and test the networks respectively. The major parameter for finding the accuracy of the protein secondary structure prediction is the per-residue prediction accuracy, Q3, which gives the percentage of all correctly predicted residues within the three-state (H, E, C) classes, and has also been employed for assessment of prediction approaches. The performance of the RBFNN protein secondary structure prediction models is evaluated based on their prediction accuracy . The accuracy of the developed approach is compared with other traditional methods to explore the performance of the proposed approach. It is found that the proposed techniques provide a prediction accuracy of about 81% which is very significant. The accuracy for different width of sliding windows. It clearly shows that, with the increase in the sliding window width the accuracy also increases.Keywords
RBFNN, Prediction Accuracy, Training Set, Test Set, Sliding WindowReferences
- Boscott, P.E., Barton, G.J. and Richards, W.G., “Secondary Structure Prediction for Modelling by Homology”, PEDS, Vol. 6, Issue 3, pp.261–266, January 1993.
- Marti-Renom et al., “Comparative protein structure modeling of genes and genomes,” Rev. Biophys. Biomol, Struct, 29:291-325, 2000.
- Solovyev, V.V. and Salamov, A.A., “Predicting alpha-helix and betastrand segments of globular proteins”. Computer Applications in the Biosciences, 10, 661-669, 1994.
- Pollastri, G., Przybylski, D., Rost, B. and Baldi, P., “Improving the prediction of protein secondary structure in three and eight classes using recurrent neural networks and profiles. Proteins”, 47(2), 228–235, 2002.
- Karlin, S., Bucher, P., “Correlation analysis of amino acid usage in protein classes”, Proc Natl Acad Sci, USA 1992.
- Chou, P.Y., Fasman, G.D., “Conformational parameters for amino acids in helical, beta-sheet, and random coil regions calculated from proteins”. Biochemistry 13(2):211-22, 1974.
- Garnier, J.J., Gibrat, J.F. and Robson, “GOR method for predicting protein secondary structure from amino acid sequence, Methods Enzymol”, 266, 540– 550, 1996.
- Marti-Renom et al., “Comparative protein structure modeling of genes and genomes,” Rev. Biophys. Biomol, Struct, 29:291-325, 2000
- Rost, B., “Rising accuracy of protein secondary structure prediction”, D.Chasman, Ed., “Protein structure determination, analysis, and modeling for drug discovery”, New York: Dekker, pp. 207–249, 2003.
- Histological Differentiation along Turtle Ductus Epididymidis, with a Note on Secretion of Seminal Proteins as Discrete Granules
Authors
1 Department of Animal Science, School of Life Sciences, Bharathidasan University, Tiruchirappalli - 620 024, IN
2 Department of Zoology, Periyar EVR College, Tiruchirappalli - 620 023, IN
Source
Journal of Endocrinology and Reproduction, Vol 3, No 1&2 (1999), Pagination: 36-46Abstract
Histological analysis of the male reproductive tract of the peninsular flap-shelled turtle Lissemys p. punctata revealed that several minute ductuli efferentes reach the epididymis to form into a large thin-walled duct which probably, forms a temporary storage region of sperm arriving from the testis. Originating from this duct, the single long ductus epididymidis takes a highly tortuous course when it differentiates along its length, in terms of diameter and epithelial organization, into four regions, viz., the initial segment, caput, corpus and cauda. Turtle ductus epididymidis differs from that of lizards in the extensive pattern of folding of the epithelium of caput, corpus and cauda regions. The differentiation along the ductus epididymidis of the turtle signifies different functional attributes to the different regions. The epithelium of the initial segment of the turtle epididymis secretes large glycoprotein granules (4-8 μm) and that of the caput secretes minute granules (1-2 μm). The large granules possess a central core and a peripheral coat, whereas the minute granules are uniformly dense. Thus, turtle ductus epididymidis differentiates into initial segment, caput, corpus and cauda regions, and the initial segment and caput secrete seminal proteins in the lacertilian pattern as discrete granules.Keywords
Epididymis, Glycoprotein Granules, Seminal Protein.- Prediction of Secondary Structure of Using Neural Networks and Machine Learning Techniques
Authors
Source
Biometrics and Bioinformatics, Vol 4, No 1 (2012), Pagination: 46-51Abstract
One of the most significant problems in biomedical research today is the prediction of protein structure from knowledge of the primary amino acid sequence. Secondary Structure Prediction (SSP) is a very typical problem in the field of bioinformatics.Prediction of secondary structure of Proteins can be done from the Protein sequence. In the Protein structure prediction, the Amino Acid sequence of a Protein, the so-called primary structure, can be easily determined from the sequence on the Gene that codes for it. This primary structure exclusively determines a structure in its native environment. Thus primary structure plays a key role in understanding the function of the Protein. Majority of the previous research have ignored the influence of residue conformational preference on structure prediction of proteins. The primary focus of this research is to investigate a variety of approaches for employing ANN and Machine Learning techniques in order to predict the secondary structure of proteins in soybeans.
Keywords
Protein Structure Prediction, RBFNN, MELM, SVM, Amino Acid, Soybeans.- Protein Structure Prediction in Soybeans Using Neural Networks
Authors
1 Shrimati Indira Gandhi College, Trichy, IN
2 Dept. of IT & Applications, Shrimati Indira Gandhi College, Trichy, IN
Source
Artificial Intelligent Systems and Machine Learning, Vol 2, No 2 (2010), Pagination: 43-47Abstract
Proteins are a definite kind of biological macromolecules that is present in all biological organisms. Amino acids are the building blocks of proteins. They are primary structure, secondary structure, tertiary structure and quaternary structure. Most of the existing algorithms for predicting the content of the protein secondary structure elements have been based on the conventional amino acid composition, where no sequence coupling effects are taken into consideration. Prediction of three dimensional structure, secondary structure, and functional sites of proteins from primary structure are the three major problems in structural bioinformatics. More than a few different approaches have been previously used in these kind of predictions among which, artificial neural networks have been of great interest due to their capability of learning from observations and prediction of the structures for non classified instances. This paper proposes a technique for prediction of protein structure in soybeans using neural networks. This paper uses RBFNN in order to predict the secondary structure. In our approach, genetic encoding scheme is used to generate the centers and widths of radial basis function. In our approach, genetic encoding scheme is used to generate the centers and widths of radial basis function. The neural network architecture used in our approach is a feed forward and fully connected neural network whose Gaussian centers are optimized by genetic algorithm. Experimental are carried on dataset obtained from Protein Data Bank (PDB) to predict the structure of the protein present in it.Keywords
Amino Acids (AA), Bioinformatics, Protein Structure Prediction, Secondary Structure Content, Neural Networks, Radial Basis Function Neural Networks (RBFNN), Genetic Algorithm (GA), Protein Data Bank (PDB).- A Study of Nachiyar Koil Lamp Manufacturers- Business Model
Authors
1 School of Management, SASTRA University, Thanjavur - 613401, Tamil Nadu, IN