The Community for Technology Leaders
RSS Icon
Issue No.05 - Sept.-Oct. (2012 vol.9)
pp: 1535-1538
Shaini Joseph , Biomed. Inf. Center of Indian Council of Med. Res., Nat. Inst. for Res. in Reproductive Health, Mumbai, India
Shreyas Karnik , Sch. of Inf., Indiana Univ., Marshfield, WI, USA
Pravin Nilawe , Biomed. Inf. Center of Indian Council of Med. Res., Nat. Inst. for Res. in Reproductive Health, Navi Mumbai, India
V. K. Jayaraman , Center for Dev. of Adv. Comput., Pune Univ. Campus, Pune, India
Susan Idicula-Thomas , Biomed. Inf. Center of Indian Council of Med. Res., Nat. Inst. for Res. in Reproductive Health, Mumbai, India
Antimicrobial peptides (AMPs) are gaining popularity as anti-infective agents. Information on sequence features that contribute to target specificity of AMPs will aid in accelerating drug discovery programs involving them. In this study, an algorithm called ClassAMP using Random Forests (RFs) and Support Vector Machines (SVMs) has been developed to predict the propensity of a protein sequence to have antibacterial, antifungal, or antiviral activity. ClassAMP is available at
support vector machines, antibacterial activity, biology computing, drugs, molecular biophysics, proteins, random sequences, antiviral activity, ClassAMP, antimicrobial peptide classification, antiinfective agents, sequence features, drug discovery programs, random forests, support vector machines, SVM, protein sequence, antibacterial activity, antifungal activity, Anti-bacterial, Support vector machines, Anti-fungal, Radio frequency, Peptides, Predictive models, Training, SVM., Antibacterial, antifungal, antimicrobial, antiviral, prediction algorithm, random forests
Shaini Joseph, Shreyas Karnik, Pravin Nilawe, V. K. Jayaraman, Susan Idicula-Thomas, "ClassAMP: A Prediction Tool for Classification of Antimicrobial Peptides", IEEE/ACM Transactions on Computational Biology and Bioinformatics, vol.9, no. 5, pp. 1535-1538, Sept.-Oct. 2012, doi:10.1109/TCBB.2012.89
[1] B.M. Peters, M.E. Shirtiff, and M.A. Jabra-Rizk, "Antimicrobial Peptides: Primeval Molecules or Future Drugs," PLoS Pathogens, vol. 6, no. 10, p. e1001067, Oct. 2010. doi: 10.1371journal.ppat.1001067.
[2] K.A. Brogden, "Antimicrobial Peptides: Pore Formers or Metabolic Inhibitors in Bacteria?" Nature Rev. Microbiology, vol. 3, no. 3, pp. 238-250, Mar. 2005.
[3] M.R. Yeaman and N.Y. Yount, "Mechanisms of Antimicrobial Peptide Action and Resistance," Pharmacological Rev., vol. 55, no. 1, pp. 27-55, Mar. 2003.
[4] H. Jenssen, P. Hamill, and R.E. Hancock, "Peptide Antimicrobial Agents," Clinical Microbiology Rev., vol. 19, no. 3, pp. 491-511, July 2006.
[5] Y. Aboudy, E. Mendelson, I. Shalit, R. Bessalle, and M. Fridkin, "Activity of Two Synthetic Amphiphilic Peptides and Magainin-2 against Herpes Simplex Virus Types 1 and 2," Int'l J. Peptide and Protein Research, vol. 43, no. 6, pp. 573-582, June 1994.
[6] A. Belaid, M. Aouni, R. Khelifa, A. Trabelsi, M. Jemmali, and K. Hani, "In Vitro Antiviral Activity of Dermaseptins against Herpes Simplex Virus Type 1," J. Medical Virology, vol. 66, no. 2, pp. 229-234, Feb. 2002.
[7] W.E. RobinsonJr., B. McDougall, D. Tran, and M.E. Selsted, "Anti-HIV-1 Activity of Indolicidin, an Antimicrobial Peptide from Neutrophils," J. Leukocyte Biology, vol. 63, no. 1, pp. 94-100, Jan. 1998.
[8] S. Lata, N.K. Mishra, and G.P. Raghava, "AntiBP2: Improved Version of Antibacterial Peptide Prediction," BMC Bioinformatics, vol. 11, pp. S1-S19, Jan. 2010.
[9] S. Thomas, S. Karnik, R.S. Barai, V.K Jayaraman, and S. Idicula-Thomas, "CAMP: A Useful Resource for Research on Antimicrobial Peptides," Nucleic Acids Research, vol. 38, pp. D774- D780, Jan. 2010.
[10] G.D. Rose, A.R. Geselowitz, G.J. Lesser, R.H. Lee, and M.H. Zehfus, "Hydrophobicity of Amino Acid Residues in Globular Proteins," Science, vol. 229, no. 4716, pp. 834-838, Aug. 1985.
[11] K. Tomii and M. Kanehisa, "Analysis of Amino Acid Indices and Mutation Matrices for Sequence Comparison and Structure Prediction of Proteins," Protein Eng., vol. 9, no. 1, pp. 27-36, Jan. 1996.
[12] L.R. Murphy, A. Wallqvist, and R.M. Levy, "Simplified Amino Acid Alphabets for Protein Fold Recognition and Implications for Folding," Protein Eng., vol. 13, no. 3, pp. 149-152, Mar. 2000.
[13] P. Chakrabarti and D. Pal, "The Interrelationships of Side-Chain and Main-Chain Conformations in Proteins," Progress in Biophysics and Molecular Biology, vol. 76, nos. 1/2, pp. 1-102, 2001.
[14] Z.R. Li, H.H Lin, L.Y. Han, L. Jiang, X. Chen, and Y.Z. Chen, "PROFEAT: A Web Server for Computing Structural and Physicochemical Features of Proteins and Peptides from Amino Acid Sequence," Nucleic Acids Research, vol. 34, pp. W32-37, July 2006.
[15] I. Dubchak, I. Muchnik, S.R. Holbrook, and S.H. Kim, "Prediction of Protein Folding Class Using Global Description of Amino Acid Sequence," Proc. Nat'l Academy of Sciences of USA, vol. 92, no. 19, pp. 8700-8704, Sept. 1995.
[16] I. Dubchak, I. Muchnik, C. Mayor, I. Dralyuk, and S.H. Kim, "Recognition of a Protein Fold in the Context of the SCOP Classification," Proteins, vol. 35, no. 4, pp. 401-407, June 1999.
[17] V.L. Ravich, M. Masso, and I.I. Vaisman, "A Combined Sequence-Structure Approach for Predicting Resistance to the Non-Nucleoside HIV-1 Reverse Transcriptase Inhibitor Nevirapine," Biophysical Chemistry, vol. 153, nos. 2/3, pp. 168-172, Jan. 2011.
[18] G. Riddick, H. Song, S. Ahn, J. Walling, D. Borges-Rivera, W. Zhang, and H.A. Fine, "Predicting in Vitro Drug Sensitivity Using Random Forests," Bioinformatics, vol. 27, no. 2, pp. 220-224, Jan. 2011.
[19] L. Breiman, "Random Forests," Machine Learning, vol. 35, no. 1, pp. 5-32, Oct. 2001, doi: 10.1023A:1010933404324.
[20] A. Liaw and M. Wiener, "Classification and Regression by Random Forest," R News, vol. 2, pp. 18-22, Dec. 2002.
[21] V. Vapnik, Statistical Learning Theory. Wiley and Sons, 1998.
[22] S.R. Gunn, "Support Vector Machines for Classification and Regression," http://www.svms.orgtutorials/, 2012.
[23] S. Idicula-Thomas, A.J. Kulkarni, B.D. Kulkarni, V.K. Jayaraman, and P.V. Balaji, "A Support Vector Machine-Based Method for Predicting the Propensity of a Protein to be Soluble or to form Inclusion Body on Overexpression in Escherichia Coli," Bioinformatics, vol. 22, no. 3, pp. 278-284, Feb. 2006.
[24] V. Vapnik, The Nature of Statistical Learning Theory, first ed. Springer, 1995.
[25] K.R. Muller, S. Mika, G. Ratsch, K. Tsuda, and B. Scholkopf, "An Introduction to Kernel-Based Learning Algorithms," IEEE Trans. Neural Networks, vol. 12, pp. 181-201, Mar. 2001.
[26] R Development Core Team, "R: A Language and Environment for Statistical Computing," R Foundation for Statistical Computing, 2009.
[27] A. Kulkarni, B.D. Kulkarni, and V.K. Jayaraman, "Support Vector Classification with Parameter Tuning Assisted by Agent-Based Technique," Computers and Chemical Eng., vol. 28, pp. 311-318, Mar. 2004.
[28] , 2012.
[29] I. Guyon, J. Weston, S. Barnhill, and V. Vapnik, "Gene Selection for Cancer Classification Using Support Vector Machines," Machine Learning, vol. 46, no. 1, pp. 389-422, 2002.
[30] P. Baldi, S. Brunak, Y. Chauvin, C.A. Andersen, and H. Nielsen, "Assessing the Accuracy of Prediction Algorithms for Classification: An Overview," Bioinformatics, vol. 16, no. 5, pp. 412-424, May 2000.
[31] M. Charton and B.I. Charton, "The Dependence of the Chou-Fasman Parameters on Amino Acid Side Chain Structure," J. Theoretical Biology, vol. 102, no. 1, pp. 121-134, May 1983.
[32] Z. Wang and G. Wang, "APD: The Antimicrobial Peptide Database," Nucleic Acids Research, vol. 32, pp. D590-D592, Jan. 2004.
[33] A. Cherkasov and B. Jankovic, "Application of 'Inductive' QSAR Descriptors for Quantification of Antibacterial Activity of Cationic Polypeptides," Molecules, vol. 9, no. 12, pp. 1034-1052, Dec. 2004.
[34] C. Polanco and J.L. Samaniego, "Detection of Selective Cationic Amphipatic Antibacterial Peptides by Hidden Markov Models," Acta Biochimica Polonica, vol. 56, no. 1, pp. 167-176, Mar. 2009.
[35] S. Soltani, K. Keymanesh, and S. Sardari, "Evaluation of Structural Features of Membrane Acting Antifungal Peptides by Artificial Neural Network," J. Biological Sciences, vol. 8, no. 5, pp. 834-845, 2008, doi:10.3923jbs. 2008.834.845.
55 ms
(Ver 2.0)

Marketing Automation Platform Marketing Automation Tool