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Structural and Functional Consequences of the Weak Binding of Chlorin e6 to Bovine Rhodopsin
摘要： The chlorophyll-derivative chlorin e6 (Ce6) identified in the retinas of deep-sea ocean fish is proposed to play a functional role in red bioluminescence detection. Fluorescence and 1H NMR spectroscopy studies with the bovine dim-light photoreceptor, rhodopsin, indicate that Ce6 weakly binds to it with μM affinity. Absorbance spectra prove that red light sensitivity enhancement is not brought about by a shift in the absorbance maximum of rhodopsin. 19F NMR experiments with samples where 19F labels are either placed at the cytoplasmic binding site or incorporated as fluorinated retinal, indicate that the cytoplasmic domain is highly perturbed by binding, while little to no changes are detected near the retinal. Binding of Ce6 also inhibits G protein activation. Chemical shift changes in 1H,15N NMR spectroscopy of 15N-Trp labeled bovine rhodopsin reveal that Ce6 binding perturbs the entire structure. These results provide experimental evidence that Ce6 is an allosteric modulator of rhodopsin.
关键词： G protein coupled receptor,chlorophyll-derivative,porphyrin,night vision,light activation,photosensitization,bovine rhodopsin,Allosteric modulator
更新于2025-09-23 15:22:29
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[IEEE 2017 2nd International Conference On Emerging Computation and Information Technologies (ICECIT) - Tumakuru, India (2017.12.15-2017.12.16)] 2017 2nd International Conference On Emerging Computation and Information Technologies (ICECIT) - Image Processing Approaches for Autonomous Navigation of Terrestrial Vehicles in Low Illumination
摘要： Computer vision can be used as an integral part of any autonomous systems. Visual input and processing enables faster and early decisions. An important challenge in computer vision is detection and recognition of objects. This challenge is more pronounced in low illumination. In this paper, we are proposing a detection and recognition model for road warning signs with voice noti?cation system for both autonomous and usual vehicles considering varied level of illumination. Real-time video from the vehicles was analysed using opencv. The noise from the video was removed using ?lters. Detection was based on Haar-cascades and training was done with sample positive and negative images. Text recognition was based on pattern matching. Voice noti?cation was done using string to voice converters. The night vision was lightened considering the glare of vehicles headlight.
关键词： Haar-cascade,night vision,opencv
更新于2025-09-10 09:29:36