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1.
Can J Physiol Pharmacol ; 95(9): 1058-1063, 2017 Sep.
Artículo en Inglés | MEDLINE | ID: mdl-28654763

RESUMEN

The application of tetraethylammonium (TEA), a blocker of voltage-dependent potassium channels, can induce long-term potentiation (LTP) in the synaptic systems CA3-CA1 and mossy fiber-CA3 pyramidal cells of the hippocampus. In the mossy fibers, the depolarization evoked by extracellular TEA induces a large amount of glutamate and also of zinc release. It is considered that zinc has a neuromodulatory role at the mossy fiber synapses, which can, at least in part, be due to the activation of presynaptic ATP-dependent potassium (KATP) channels. The aim of this work was to study properties of TEA-induced zinc signals, detected at the mossy fiber region, using the permeant form of the zinc indicator Newport Green. The application of TEA caused a depression of those signals that was partially blocked by the KATP channel inhibitor tolbutamide. After the removal of TEA, the signals usually increased to a level above baseline. These results are in agreement with the idea that intense zinc release during strong synaptic events triggers a negative feedback action. The zinc depression, caused by the LTP-evoking chemical stimulation, turns into potentiation after TEA washout, suggesting the existence of a correspondence between the observed zinc potentiation and TEA-evoked mossy fiber LTP.


Asunto(s)
Región CA3 Hipocampal/citología , Fibras Musgosas del Hipocampo/efectos de los fármacos , Transducción de Señal/efectos de los fármacos , Sinapsis/efectos de los fármacos , Tetraetilamonio/farmacología , Tolbutamida/farmacología , Zinc/metabolismo , Animales , Región CA3 Hipocampal/efectos de los fármacos , Femenino , Canales KATP/metabolismo , Potenciación a Largo Plazo/efectos de los fármacos , Bloqueadores de los Canales de Potasio/farmacología , Embarazo , Ratas , Ratas Wistar , Sinapsis/metabolismo
2.
Methods Mol Biol ; 2190: 267-288, 2021.
Artículo en Inglés | MEDLINE | ID: mdl-32804371

RESUMEN

Targeting protein-protein interactions is a challenge and crucial task of the drug discovery process. A good starting point for rational drug design is the identification of hot spots (HS) at protein-protein interfaces, typically conserved residues that contribute most significantly to the binding. In this chapter, we depict point-by-point an in-house pipeline used for HS prediction using only sequence-based features from the well-known SpotOn dataset of soluble proteins (Moreira et al., Sci Rep 7:8007, 2017), through the implementation of a deep neural network. The presented pipeline is divided into three steps: (1) feature extraction, (2) deep learning classification, and (3) model evaluation. We present all the available resources, including code snippets, the main dataset, and the free and open-source modules/packages necessary for full replication of the protocol. The users should be able to develop an HS prediction model with accuracy, precision, recall, and AUROC of 0.96, 0.93, 0.91, and 0.86, respectively.


Asunto(s)
Mapeo de Interacción de Proteínas/métodos , Proteínas/química , Bases de Datos de Proteínas , Aprendizaje Profundo , Redes Neurales de la Computación , Unión Proteica/fisiología
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