The Community for Technology Leaders
RSS Icon
Issue No.02 - April-June (2008 vol.5)
pp: 291-300
The HIV-1 genome is highly heterogeneous. This variation affords the virus a wide range of molecular properties, including the ability to infect cell types, such as macrophages and lymphocytes, expressing different chemokine receptors on the cell surface. In particular, R5 HIV-1 viruses use CCR5 as co-receptor for viral entry, X4 viruses use CXCR4, whereas some viral strains, known as R5X4 or D-tropic, have the ability to utilize both co-receptors. X4 and R5X4 viruses are associated with rapid disease progression to AIDS. R5X4 viruses differ in that they have yet to be characterized by the examination of the genetic sequence of HIV-1 alone. In this study, a series of experiments was performed to evaluate different strategies of feature selection and neural network optimization. We demonstrate the use of artificial neural networks trained via evolutionary computation to predict viral co-receptor usage. The results indicate identification of R5X4 viruses with predictive accuracy of 75.5%.
Computational intelligence, evolutionary computation, artificial neural networks, HIV, AIDS, phenotype prediction, tropism, dual-tropic viruses
Susanna L. Lamers, Marco Salemi, Michael S. McGrath, Gary B. Fogel, "Prediction of R5, X4, and R5X4 HIV-1 Coreceptor Usage with Evolved Neural Networks", IEEE/ACM Transactions on Computational Biology and Bioinformatics, vol.5, no. 2, pp. 291-300, April-June 2008, doi:10.1109/TCBB.2007.1074
[1] P.J. Werbos, The Roots of Backpropagation. John Wiley & Sons, 1994.
[2] D.B. Fogel, L.J. Fogel, and V.W. Porto, “Evolving Neural Networks,” Biological Cybernetics, vol. 63, no. 6, pp. 487-493, 1990.
[3] V.W. Porto, D.B. Fogel, and L.J. Fogel, “Alternative Neural Network Training Methods,” IEEE Expert, vol. 10, no. 3, pp. 16-22, 1995.
[4] X. Yao, “Evolving Artificial Neural Networks,” Proc. IEEE, vol. 87, no. 9, pp. 1423-1447, 1999.
[5] D.G. Landavazo, G.B. Fogel, and D.B. Fogel, “Quantitative Structure-Activity Relationships by Evolved Neural Networks for the Inhibition of Dihydrofolate Reductase by Pyrimidines,” BioSystems, vol. 65, pp. 37-47, 2002.
[6] D. Weekes and G.B. Fogel, “Evolutionary Optimization, Backpropagation, and Data Preparation Issues in QSAR Modeling of HIV Inhibition by HEPT Derivatives,” BioSystems, vol. 72, pp. 149-158, 2003.
[7] D.B. Fogel, Blondie24: Playing at the Edge of AI. Morgan Kaufmann, 2002.
[8] D.B. Fogel, T.J. Hays, S.L. Hahn, and J. Quon, “A Self-Learning Evolutionary Chess Program,” Proc. IEEE, vol. 92, pp. 1947-1954, 2004.
[9] E.A. Berger, P.M. Murphy, and J.M. Farber, “Chemokine Receptors as HIV-1 Coreceptors: Roles in Viral Entry, Tropism, and Disease,” Ann. Rev. Immunology, vol. 17, pp. 675-700, 1999.
[10] M.M. Goodenow and R.G. Collman, “HIV-1 Coreceptor Preference Is Distinct from Target Cell Tropism: A Dual-Parameter Nomenclature to Define Viral Phenotypes,” J. Leukocyte Biology, vol. 80, no. 5, pp. 965-972, 2006.
[11] M. Koot, A.B. van't Wout, N.A. Koostra, R.E.Y. Degoede, M. Tersmette, and H. Schitemaker, “Prognostic Value of HIV-1 Syncytium-Inducing Phenotype for Rate of CD4+ Cell Depletion and Progression to AIDS,” Annals of Internal Medicine, vol. 118, pp.681-688.
[12] J.F. Kreisberg, D. Kwa, B. Schramm, V. Trautner, R. Connor, H. Schuitemaker, J.I. Mullins, A.B. van't Wout, and M.A. Goldsmith, “Cytopathicity of Human Immunodeficiency Virus Type 1 Primary Isolates Depends on Coreceptor Usage and Not Patient Disease Status,” J. Virology, vol. 75, no. 18, pp. 8842-8847, 2001.
[13] D.L. Tuttle, C.B. Anders, M.J. Aquino-De Jesus, P.P. Poole, S.L. Lamers, D.R. Briggs, S.M. Pomeroy, L. Alexander, K.W. Peden, W.A. Andiman, J.W. Sleasman, and M.M. Goodenow, “Increased Replication of Non-Syncytium-Inducing HIV Type 1 Isolates in Monocyte-Derived Macrophages Is Linked to Advanced Disease in Infected Children,” AIDS Research and Human Retroviruses, vol. 18, no. 5, pp. 353-362, 2002.
[14] C. Cheng-Mayer, C. Weiss, D. Seto, and J.A. Levy, “Isolates of Human Immunodeficiency Virus Type 1 from the Brain May Constitute a Special Group of the AIDS Virus,” Proc. Nat'l Academy of Sciences USA, vol. 86, no. 21, pp. 8575-8579, 1989.
[15] S.G. Kitchen and J.A. Zack, “CXCR4 Expression during Lymphopoiesis: Implications for Human Immunodeficiency Virus Type 1 Infection of the Thymus,” J. Virology, vol. 71, no. 9, pp. 6928-6934, 1997.
[16] M. Salemi, S.L. Lamers, S. Yu, T. de Oliveira, W.M. Fitch, and M.S. McGrath, “HIV-1 Phylodynamic Analysis in Distinct Brain Compartments Provides a Model for the Neuropathogenesis of AIDS,” J. Virology, vol. 79, pp. 11343-11352, 2005.
[17] A.B. van't Wout, N.A. Kootstra, G.A. Mulder-Kampinga, N. Albrecht-van Lent, H.J. Scherpbier, J. Veenstra, K. Boer, R.A. Coutinho, F. Miedema, and H. Schuitemaker, “Macrophage-Tropic Variants Initiate Human Immunodeficiency Virus Type 1 Infection after Sexual, Parenteral, and Vertical Transmission,” J.Clinical Investigation, vol. 94, no. 5, pp. 2060-2067, 1994.
[18] M. Markowitz, H. Mohri, S. Mehandru, A. Shet, L. Berry, R. Kalyanaraman, A. Kim, C. Chung, P. Jean-Pierre, A. Horowitz, M. La Mar, T. Wrin, N. Parkin, M. Poles, C. Petropoulos, M. Mullen, D. Boden, and D.D. Ho, “Infection with Multidrug Resistant, Dual-Tropic HIV-1 and Rapid Progression to AIDS: A Case Report,” Lancet, vol. 365, no. 9464, pp. 1031-1038, 2005.
[19] W. Resch, N. Hoffman, and R. Swanstrom, “Improved Success of Phenotype Prediction of the Human Immunodeficiency Virus Type 1 from Envelope Variable Loop 3 Sequence Using Neural Networks,” J. Virology, vol. 76, pp. 3852-3864, 2001.
[20] J.A. Loannidis, T.A. Trikalinos, and M. Law, “HIV Lipodystrophy Case Definition Using Artificial Neural Network Modeling,” Antiviral Therapy, vol. 8, pp. 435-441, 2003.
[21] D. Wang and B. Larder, “Enhanced Prediction of Lopinavir Resistance from Genotype by Use of Artificial Neural Networks,” J. Infectious Diseases, vol. 188, pp. 653-660, 2003.
[22] Z.L. Brumme, W.W.Y. Dong, B. Yip, B. Wynhoven, N.G. Hoffman, R. Swanstrom, M.A. Jensen, J.I. Mullins, R.S. Hogg, J.S.G. Montaner, and P.R. Harrigan, “Clinical and Immunological Impact of HIV Envelope V3 Sequence Variation after Starting Initial Triple Antiretroviral Therapy,” AIDS, vol. 18, pp. F1-F9, 2004.
[23] L. Milich, B. Margolin, and R. Swanstrom, “V3 Loop of the Human Immunodeficiency Virus Type 1 Env Protein: Interpreting Sequence Variability,” J. Virology, vol. 67, no. 9, pp. 5623-5634, 1993.
[24] R.A. Foucier, M. Brouwer, S.M. Broersen, and H. Schuitemaker, “Determination of Human Immunodeficiency Virus Type 1 Syncytium-Inducing V3 Genotype by PCR,” J. Clinical Microbiology, vol. 33, no. 4, pp. 906-911, 1995.
[25] M.A. Jensen, F.S. Li, A.B. van 't Wout, D.C. Nickle, D. Shriner, H.X. He, S. McLaughlin, R. Shankarappa, J.B. Margolick, and J.I. Mullins, “Improved Coreceptor Usage Prediction and Genotypic Monitoring of R5-to-X4 Transition by Motif Analysis of Human Immunodeficiency Virus Type 1 env V3 Loop Sequences,” J.Virology, vol. 77, no. 24, pp. 13376-13388, 2003.
[26] M. Nielsen, C. Lundegaard, P. Worning, S.L. Lauemoller, K. Lamberth, S. Buus, S. Brunak, and O. Lund, “Reliable Prediction of T-Cell Epitopes Using Neural Networks with Novel Sequence Representations,” Protein Science, vol. 12, pp. 1007-1017, 2003.
[27] S.K. Pillai, B. Good, S.K. Pond, J.K. Wong, M.C. Strain, D.D. Richmand, and D.M. Smith, “Semen-Specific Genetic Characteristics of Human Immunodeficiency Virus Type 1 env,” J. Virology, vol. 39, pp. 1734-1742, 2005.
[28] M. Salemi, M.M. Goodenow, and S.L. Lamers, Inferring Correct Positional Homology in Human Immunodeficiency Virus Type 1 Envelope V1-V2 Hypervariable Domains by Motif-Based Alignment: Consequence for Phylogenetic and Selection Pressure Analyses, submitted, 2006.
[29] S. Lamers, S. Beason, L. Dunlap, R. Compton, and M. Salemi, “HIVbase: A PC/Windows-Based Software Offering Storage and Querying Power for Locally Held HIV-1 Genetic, Experimental and Clinical Data,” Bioinformatics, vol. 20, pp. 436-438, 2002.
[30] T.E. Creighton, Proteins. W.H. Freeman, 1993.
[31] M. Cayabyab, D. Rohne, G. Pollakis, C. Mische, T. Messele, A. Abebe, B. Etemad-Moghadam, P. Yang, S. Henson, M. Axthelm, J. Goudsmit, N.L. Letvin, and J. Sodroski, “Rapid CD4+ T-Lymphocyte Depletion in Rhesus Monkeys Infected with a Simian-Human Immunodeficiency Virus Expressing the Envelope Glycoproteins of a Primary Dual-Tropic Ethiopian Clade C HIV Type 1 Isolate,” AIDS Research and Human Retroviruses, vol. 20, no. 1, pp. 27-40, 2004.
3 ms
(Ver 2.0)

Marketing Automation Platform Marketing Automation Tool