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Int. J. Mol. Sci. 2016, 17(10), 1710; doi:10.3390/ijms17101710

Learning the Relationship between the Primary Structure of HIV Envelope Glycoproteins and Neutralization Activity of Particular Antibodies by Using Artificial Neural Networks

1
Department of Automatic Control and Systems Engineering, Faculty of Automatic Control and Computers, Politehnica University of Bucharest, Bucharest 060042, Romania
2
Laboratory of Structural and Computational Physical-Chemistry for Nanosciences and QSAR, Biology-Chemistry Department, Faculty of Chemistry-Biology-Geography, West University of Timisoara, Timisoara 300115, Romania
3
Laboratory of Renewable Energies-Photovoltaics, R&D National Institute for Electrochemistry and Condensed Matter, Timisoara 300569, Romania
4
Department of Anatomy, Animal Physiology and Biophysics, Faculty of Biology, University of Bucharest, Bucharest 050095, Romania
*
Authors to whom correspondence should be addressed.
Academic Editor: Jesus Vicente De Julián Ortiz
Received: 13 August 2016 / Revised: 26 September 2016 / Accepted: 3 October 2016 / Published: 11 October 2016
(This article belongs to the Special Issue Chemical Bond and Bonding 2016)
View Full-Text   |   Download PDF [2319 KB, uploaded 11 October 2016]   |  

Abstract

The dependency between the primary structure of HIV envelope glycoproteins (ENV) and the neutralization data for given antibodies is very complicated and depends on a large number of factors, such as the binding affinity of a given antibody for a given ENV protein, and the intrinsic infection kinetics of the viral strain. This paper presents a first approach to learning these dependencies using an artificial feedforward neural network which is trained to learn from experimental data. The results presented here demonstrate that the trained neural network is able to generalize on new viral strains and to predict reliable values of neutralizing activities of given antibodies against HIV-1. View Full-Text
Keywords: HIV-1; glycoproteins; antibodies; neutralization data; artificial neural network; regression HIV-1; glycoproteins; antibodies; neutralization data; artificial neural network; regression
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Buiu, C.; Putz, M.V.; Avram, S. Learning the Relationship between the Primary Structure of HIV Envelope Glycoproteins and Neutralization Activity of Particular Antibodies by Using Artificial Neural Networks. Int. J. Mol. Sci. 2016, 17, 1710.

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