Víctor A. Lórenz-Fonfría

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Efficient retinal photoisomerization, signal transduction, and amplification contribute to single-photon electrical responses in vertebrates visual cells. However, spontaneous discrete electrical signals arising in the dark, with identical intensity and time profiles as those generated by genuine single photons (dark events), limit the potential capability(More)
Channelrhodopsin-1 from Chlamydomonas augustae (CaChR1) is a light-activated cation channel, which is a promising optogenetic tool. We show by resonance Raman spectroscopy and retinal extraction followed by high pressure liquid chromatography (HPLC) that the isomeric ratio of all-trans to 13-cis of solubilized channelrhodopsin-1 is with 70:30 identical to(More)
The discovery of channelrhodopsins introduced a new class of light-gated ion channels, which when genetically encoded in host cells resulted in the development of optogenetics. Channelrhodopsin-2 from Chlamydomonas reinhardtii, CrChR2, is the most widely used optogenetic tool in neuroscience. To explore the connection between the gating mechanism and the(More)
Monitoring the dynamics of protonation and protein backbone conformation changes during the function of a protein is an essential step towards understanding its mechanism. Protonation and conformational changes affect the vibration pattern of amino acid side chains and of the peptide bond, respectively, both of which can be probed by infrared (IR)(More)
Bacteriorhodopsin has a polar cluster of amino acids surrounding the retinal molecule, which is responsible for light harvesting to fuel proton pumping. From our previous studies, we have shown that threonine 90 is the pivotal amino acid in this polar cluster, both functionally and structurally. In an attempt to perform a phenotype rescue, we have(More)
Estimation of the band parameters of overlapped bands often relies on curve-fitting. It has been demonstrated that curve-fitting provides the maximum likelihood estimation of band parameters under a series of assumptions. One of these assumptions is that the curve-fitting model is correct and any error in the data is random. Under real conditions, we have(More)
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