Voltage-dependent translocation of a series of cationic rhodamine B derivatives differing in n-alkyl chain length (ethyl, butyl, octyl, dodecyl, octadecyl) from one lipid monolayer to another was studied by measuring electrical current relaxation after a voltage jump on a planar bilayer phosphatidylcholine (PC) membrane. The rate of the translocation decreased in the following series of lipids: diphytanyl-PC > dioleyl-PC > diphytanoyl-PC > dierucoyl-PC. For all the lipids studied, the rate increased with lengthening of the hydrocarbon chain of the rhodamine derivatives, with the increase being most pronounced for the compounds having a short alkyl chain. The results could be well explained by involvement of molecule reorientations in the process of transmembrane flip-flop of the hydrophobic membrane-bound compounds. However, an impact of membrane dipole potential on the translocation rate could not be excluded, because the dipole potential could contribute to the energy barrier for translocation of the compounds located at different depths in the water-membrane interface. Based on the data obtained, a difference in the dipole potential of ester diphytanoyl-PC membranes with respect to ether diphytanyl-PC was estimated to be 108 mV, highlighting the contribution of a layer of oriented carbonyl groups of the lipids to the membrane dipole potential.
Download full-text PDF |
Source |
---|---|
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC6084523 | PMC |
http://dx.doi.org/10.1016/j.bpj.2018.07.001 | DOI Listing |
J Phys Chem B
January 2025
Institut für Physik, Universität Augsburg, 86159 Augsburg, Germany.
The alignment of permanent dipole moments and the resulting spontaneous orientation polarization (SOP) are commonly observed in evaporated neat films of polar organic molecules and lead to a so-called giant surface potential. In the case of mixed films, often enhanced molecular orientation is observed, i.e.
View Article and Find Full Text PDFJ Comput Chem
January 2025
Universidade de São Paulo, Instituto de Química, Departamento de Química Fundamental, São Paulo, Brazil.
Seventeen electronic states of the dication VH were characterized by the SA-CASSCF/icMRCI methodology using very extended basis sets; 11 were described for the first time. Potential energy curves were constructed and the associated spectroscopic parameters evaluated. Triplet and quintet states correlating with the V + H channel are thermodynamic stable.
View Article and Find Full Text PDFBMC Chem
January 2025
Department of Biochemistry, Faculty of Pharmacy, Adıyaman University, Adıyaman, 02000, Türkiye.
This study investigates the phenolic compounds (PC), volatile compounds (VC), and fatty acids (FA) of extra virgin olive oil (EVOO) derived from the Turkish olive variety "Sarı Ulak", along with ADMET, DFT, molecular docking, and gene network analyses of significant molecules identified within the EVOO. Chromatographic methods (GC-FID, HPLC) were employed to characterize FA, PC, and VC profiles, while quality parameters, antioxidant activities (TAC, ABTS, DPPH) were assessed via spectrophotometry. The analysis revealed a complex composition of 40 volatile compounds, with estragole, 7-hydroxyheptene-1, and 3-methoxycinnamaldehyde as the primary components.
View Article and Find Full Text PDFNeuroimage
January 2025
Dept. of Electrical and Computer Engineering, Worcester Polytechnic Institute, Worcester, MA, USA.
A fast BEM (boundary element method) based approach is developed to solve an EEG/MEG forward problem for a modern high-resolution head model. The method utilizes a charge-based BEM accelerated by the fast multipole method (BEM-FMM) with an adaptive mesh pre-refinement method (called b-refinement) close to the singular dipole source(s). No costly matrix-filling or direct solution steps typical for the standard BEM are required; the method generates on-skin voltages as well as MEG magnetic fields for high-resolution head models within 90 seconds after initial model assembly using a regular workstation.
View Article and Find Full Text PDFJ Phys Chem A
January 2025
Liaoning Key Laboratory of Manufacturing System and Logistics Optimization, Shenyang 110819, China.
Artificial intelligence technology has introduced a new research paradigm into the fields of quantum chemistry and materials science, leading to numerous studies that utilize machine learning methods to predict molecular properties. We contend that an exemplary deep learning model should not only achieve high-precision predictions of molecular properties but also incorporate guidance from physical mechanisms. Here, we propose a framework for predicting molecular properties based on data-driven electron density images, referred to as D3-ImgNet.
View Article and Find Full Text PDFEnter search terms and have AI summaries delivered each week - change queries or unsubscribe any time!