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Int J Mol Sci. 2016 Oct 11;17(10). pii: E1710.

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

Author information

1
Department of Automatic Control and Systems Engineering, Faculty of Automatic Control and Computers, Politehnica University of Bucharest, Bucharest 060042, Romania. catalin.buiu@acse.pub.ro.
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. mv_putz@yahoo.com.
3
Laboratory of Renewable Energies-Photovoltaics, R&D National Institute for Electrochemistry and Condensed Matter, Timisoara 300569, Romania. mv_putz@yahoo.com.
4
Department of Anatomy, Animal Physiology and Biophysics, Faculty of Biology, University of Bucharest, Bucharest 050095, Romania. speranta.avram@gmail.com.

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.

KEYWORDS:

HIV-1; antibodies; artificial neural network; glycoproteins; neutralization data; regression

PMID:
27727189
PMCID:
PMC5085742
DOI:
10.3390/ijms17101710
[Indexed for MEDLINE]
Free PMC Article

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