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Open AccessReview

Gap Junction Channels of Innexins and Connexins: Relations and Computational Perspectives

1
Facultad de Ciencias, Universidad Autónoma de Baja California, Ensenada, Baja California 22860, Mexico
2
Facultad of Ingeniería, Arquitectura y Diseño, Universidad Autónoma de Baja California, Ensenada, Baja California 22860, Mexico
3
Department of Computer Science, Institute for Genomics and Bioinformatics, and Center for Machine Learning and Intelligent Systems, University of California, Irvine, CA 92697, USA
*
Authors to whom correspondence should be addressed.
Int. J. Mol. Sci. 2019, 20(10), 2476; https://doi.org/10.3390/ijms20102476
Received: 30 March 2019 / Revised: 4 May 2019 / Accepted: 14 May 2019 / Published: 19 May 2019
(This article belongs to the Section Biochemistry)
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Abstract

Gap junction (GJ) channels in invertebrates have been used to understand cell-to-cell communication in vertebrates. GJs are a common form of intercellular communication channels which connect the cytoplasm of adjacent cells. Dysregulation and structural alteration of the gap junction-mediated communication have been proven to be associated with a myriad of symptoms and tissue-specific pathologies. Animal models relying on the invertebrate nervous system have exposed a relationship between GJs and the formation of electrical synapses during embryogenesis and adulthood. The modulation of GJs as a therapeutic and clinical tool may eventually provide an alternative for treating tissue formation-related diseases and cell propagation. This review concerns the similarities between Hirudo medicinalis innexins and human connexins from nucleotide and protein sequence level perspectives. It also sets forth evidence of computational techniques applied to the study of proteins, sequences, and molecular dynamics. Furthermore, we propose machine learning techniques as a method that could be used to study protein structure, gap junction inhibition, metabolism, and drug development. View Full-Text
Keywords: connexin; innexin; gap junctions; leech; central nervous system; machine learning connexin; innexin; gap junctions; leech; central nervous system; machine learning
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Sánchez, A.; Castro, C.; Flores, D.-L.; Gutiérrez, E.; Baldi, P. Gap Junction Channels of Innexins and Connexins: Relations and Computational Perspectives. Int. J. Mol. Sci. 2019, 20, 2476.

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