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55 results for “Binding Energy”
Figure 2 from: Okechukwu P, Sharma M, Tan WH, Chan HK, Chirara K, Gaurav A, Al-Nema M (2020) In-vitro antidiabetic activity and in-silico studies of the binding energies of palmatine and the standard compounds with the three receptors of alpha amylase, alpha glucosidase, and DPP-IV enzyme. Pharmacia 67(4): 363-371. https://doi.org/10.3897/pharmacia.67.e58392
Figure 2 Alpha-glucosidase enzyme inhibting activity ((IC50(µM) of palmatine, glimepiride, metformin and standard drug(acarbose). The result are shown as Mean ± standard deviation in triplicates (n = 3). The data were statistically analyzed by One-way ANOVA followed by Dunnet's post hoc test. Graph showed that acarbose(a) vs. glimepiride = p < 0.0001 = d; acarbose vs. metformin = p < 0.0001 = d; acarbose vs. palmatine = p < 0.0001 = d. Glimepiride vs. metformin = No significant = d, glimepiride vs. palmatine = No significant = d and metformin vs. palmatine = No significant = d.
Figure 1 from: Okechukwu P, Sharma M, Tan WH, Chan HK, Chirara K, Gaurav A, Al-Nema M (2020) In-vitro antidiabetic activity and in-silico studies of the binding energies of palmatine and the standard compounds with the three receptors of alpha amylase, alpha glucosidase, and DPP-IV enzyme. Pharmacia 67(4): 363-371. https://doi.org/10.3897/pharmacia.67.e58392
Figure 1 Alpha-amylase enzyme inhibting activity (IC50(µM) of palmatine, glimepiride, metformin and standard drug(acarbose). The result are shown as Mean ± standard deviation in triplicates (n = 3). The data were statistically analyzed by One-way ANOVA followed by Dunnet's post hoc test. Graph showed that acarbose(a) vs. glimepiride = p < 0.001 = c; acarbose vs. metformin = p < 0.01 = b; acarbose vs. palmatine = p < 0.0001 = d. Glimepiride vs. Metformin = p < 0.001 = c, glimepiride vs. palmatine = p < 0.0001 = d, metformin vs. palmatine = p < 0.0001 = d.
Figure 9 from: Okechukwu P, Sharma M, Tan WH, Chan HK, Chirara K, Gaurav A, Al-Nema M (2020) In-vitro antidiabetic activity and in-silico studies of the binding energies of palmatine and the standard compounds with the three receptors of alpha amylase, alpha glucosidase, and DPP-IV enzyme. Pharmacia 67(4): 363-371. https://doi.org/10.3897/pharmacia.67.e58392
Figure 9 Docking interaction and binding mode of palmatine and DPP-IV enzyme. The hydrogen bonds are presented in green dotted lines, the π-π staked interactions are presented in magenta dotted lines and the π-alkyl interactions are presented in pink dotted lines.
Figure 3 from: Okechukwu P, Sharma M, Tan WH, Chan HK, Chirara K, Gaurav A, Al-Nema M (2020) In-vitro antidiabetic activity and in-silico studies of the binding energies of palmatine and the standard compounds with the three receptors of alpha amylase, alpha glucosidase, and DPP-IV enzyme. Pharmacia 67(4): 363-371. https://doi.org/10.3897/pharmacia.67.e58392
Figure 3 DPP-IV enzyme inhibting activity ((IC50(µM) of glimepiride, metformin, palmatine and standard drug(sitagliptin). The result are shown as Mean ± standard deviation in triplicates (n = 3). The data were statistically analyzed by One-way ANOVA followed by Dunnet's post hoc test. Graph showed that sitagliptin (a) vs. palmatine = p < 0.0001 = d. Glimepiride and metformin = NA = No activity.
Figure 6 from: Okechukwu P, Sharma M, Tan WH, Chan HK, Chirara K, Gaurav A, Al-Nema M (2020) In-vitro antidiabetic activity and in-silico studies of the binding energies of palmatine and the standard compounds with the three receptors of alpha amylase, alpha glucosidase, and DPP-IV enzyme. Pharmacia 67(4): 363-371. https://doi.org/10.3897/pharmacia.67.e58392
Figure 6 Docking interaction and binding mode of acarbose and alpha-glucosidase enzyme. The hydrogen bonds are presented in green dotted lines and the π-alkyl interactions are presented in pink dotted lines.
Data from: Experimental measurements of water molecule binding energies for the second and third solvation shells of [Ca(H2O)n]2+ complexes
Further understanding of the biological role of the Ca2+ ion in an aqueous environment requires quantitative measurements of both the short- and long-range interactions experienced by the ion in an aqueous medium. Here, we present experimental measurements of binding energies for water molecules occupying the second and, quite possibly, the third solvation shell surrounding a central Ca2+ ion in [Ca(H2O)n]2+ complexes. Results for these large, previously inaccessible, complexes have come from the application of finite heat bath theory to kinetic energy measurements following unimolecular decay. Even at n = 20, the results show water molecules to be more strongly bound to Ca2+ than would be expected just from the presence of an extended network of hydrogen bonds. For n > 10, there is very good agreement between the experimental binding energies and recently published density functional theory calculations. Comparisons are made with similar data recorded for [Ca(NH3)n]2+ and [Ca(CH3OH)n]2+ complexes.
An accurate binding free energy method from end-state MD simulations (ANI_LIE test files)
<p>Required files are added.</p>
"Methodological and force field effects in the molecular dynamics-based prediction of binding free energies of host-guest systems"
Open the record for dataset details and reuse information.
LED Binding Energy Calculations for POCB/PEI Polymer Hydrate Clathrates
<p>Raw data used to calculate the binding energies of POCB and PEI clathrate hydrates</p>
Data from: Experimental measurements of water molecule binding energies for the second and third solvation shells of [Ca(H2O)n]2+ complexes
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Comprehensive, high-resolution binding energy landscapes reveal context dependencies of transcription factor binding
GEO Series GSE111936. synthetic construct. 14 samples. Type: Other.
A Systems Approach to Measuring the Binding Energy Landscapes of Transcription Factors
<p>A major goal of systems biology is to predict the function of biological networks. Although network topologies have been successfully determined in many cases, the quantitative parameters governing these networks generally have not. Measuring affinities of molecular interactions in high-throughput format remains problematic, especially for transient and low-affinity interactions. We describe a high-throughput microfluidic platform that measures such properties on the basis of mechanical trapping of molecular interactions. With this platform we characterized DNA binding energy landscapes for four eukaryotic transcription factors; these landscapes were used to test basic assumptions about transcription factor binding and to predict their in vivo function.</p>
Cap-proximal nucleotides via differential eIF4E binding and alternative promoter usage mediate translational response to energy stress
GEO Series GSE93981. Mus musculus. 12 samples. Type: Other.
De novo inference of thermodynamic binding energies using deep learning models of in vivo transcription factor binding
GEO Series GSE207001. Saccharomyces cerevisiae. 4 samples. Type: Genome binding/occupancy profiling by high throughput sequencing.
Translation regulation of specific mRNAs by RPS26 C-terminal RNA-binding tail integrates energy metabolism and AMPK-mTOR signaling
GEO Series GSE217180. Homo sapiens. 24 samples. Type: Expression profiling by high throughput sequencing.
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