Kinesthetic and visual scaffolding for understanding oxygen delivery and reading hemoglobin oxygen curves

I describe a kinesthetic activity about oxygen handling by hemoglobin with two specific goals: 1) to help students gain a better understanding of how hemoglobin properties affect oxygen delivery and 2) to improve the ability of the students to actually read the hemoglobin oxygen-binding curve. The activity makes understanding oxygen delivery more intuitive, provides a kinesthetic analog to delivery of oxygen, and provides data to plot for the hemoglobin-oxygen curve.

A phosphorylation-dependent switch in the disordered p53 transactivation domain regulates DNA binding

The tumor-suppressor p53 is a critical regulator of the cellular response to DNA damage and is tightly regulated by posttranslational modifications. Thr55 in the AD2 interaction motif of the N-terminal transactivation domain functions as a phosphorylation-dependent regulatory switch that modulates p53 activity. Thr55 is constitutively phosphorylated, becomes dephosphorylated upon DNA damage, and is subsequently rephosphorylated to facilitate dissociation of p53 from promoters and inactivate p53-mediated transcription. Using NMR and fluorescence spectroscopy, we show that Thr55 phosphorylation inhibits DNA-binding by enhancing competitive interactions between the disordered AD2 motif and the structured DNA-binding domain (DBD). Nonphosphorylated p53 exhibits positive cooperativity in binding DNA as a tetramer. Upon phosphorylation of Thr55, cooperativity is abolished and p53 binds initially to cognate DNA sites as a dimer. As the concentration of phosphorylated p53 is further increased, a second dimer binds and causes p53 to dissociate from the DNA, resulting in a bell-shaped binding curve. This autoinhibition is driven by favorable interactions between the DNA-binding surface of the DBD and the multiple phosphorylated AD2 motifs within the tetramer. These interactions are augmented by additional phosphorylation of Ser46 and are fine-tuned by the proline-rich domain (PRD). Removal of the PRD strengthens the AD2–DBD interaction and leads to autoinhibition of DNA binding even in the absence of Thr55 phosphorylation. This study reveals the molecular mechanism by which the phosphorylation status of Thr55 modulates DNA binding and controls both activation and termination of p53-mediated transcriptional programs at different stages of the cellular DNA damage response.

Architecture of Allosteric Structure. Rate Equations, Rate Constants, and Equilibrium Constants for Reaction of: Hb4 with O2 and (HbO2)4 with Dithionate, in the Presence of 2,3-Bisphosphoglycerate

Three unknown quantities are all that is required to describe the O2-equilibrium binding curve for fractional saturation of human hemoglobin in red blood cells, under standard conditions: Kα, the O2-binding constant of equivalent α-chains; KC, the equilibrium constant for the T →R conformation change; Kβ, the O2-binding constant of equivalent β-chains. The model for formulation of the equation of state is a 3-stage ordered sequence of reactions. The values of were established by determination of rate constants for the oxygenation reaction and the dithionite-mediated de oxygenation reaction. The rate law for the forward reaction in the presence of excess O2 yields The same rate law yields for the dithionite-mediated de-oxygenation reaction. The rate constants for binding O2 are pseudo-first-order. The rate constants for release of O2 are first-order. Reactions involving O2, are 2-step ordered sequences of equivalent subunits. Progress curves for a 2-step ordered sequence of equivalent chains collapse to a first order reaction. Progress curves for both oxygenation and dithionite-mediated de-oxygenation reactions return is 0.0580 for the oxygenation reaction and 0.0358 for the dithionite-mediated de-oxygenation reaction. The corresponding values from the O2-equilibrium binding curve are: and = 0.02602. Values of determined from rate constants of progress curves for oxygenation and dithionite-mediated de-oxygenation reactions are close to values of determined by analysis of the O2-equilibrium binding curves for whole blood, by the Perutz/Adair equation.<br>

Architecture of Allosteric Structure. Rate Equations, Rate Constants, and Equilibrium Constants for Reaction of: Hb4 with O2 and (HbO2)4 with Dithionate, in the Presence of 2,3-Bisphosphoglycerate

Three unknown quantities are all that is required to describe the O2-equilibrium binding curve for fractional saturation of human hemoglobin in red blood cells, under standard conditions: Kα, the O2-binding constant of equivalent α-chains; KC, the equilibrium constant for the T →R conformation change; Kβ, the O2-binding constant of equivalent β-chains. The model for formulation of the equation of state is a 3-stage ordered sequence of reactions. The values of were established by determination of rate constants for the oxygenation reaction and the dithionite-mediated de oxygenation reaction. The rate law for the forward reaction in the presence of excess O2 yields The same rate law yields for the dithionite-mediated de-oxygenation reaction. The rate constants for binding O2 are pseudo-first-order. The rate constants for release of O2 are first-order. Reactions involving O2, are 2-step ordered sequences of equivalent subunits. Progress curves for a 2-step ordered sequence of equivalent chains collapse to a first order reaction. Progress curves for both oxygenation and dithionite-mediated de-oxygenation reactions return is 0.0580 for the oxygenation reaction and 0.0358 for the dithionite-mediated de-oxygenation reaction. The corresponding values from the O2-equilibrium binding curve are: and = 0.02602. Values of determined from rate constants of progress curves for oxygenation and dithionite-mediated de-oxygenation reactions are close to values of determined by analysis of the O2-equilibrium binding curves for whole blood, by the Perutz/Adair equation.<br>

Comparing enzyme activity modifier equations through the development of global data fitting templates in Excel

The classical way of defining enzyme inhibition has obscured the distinction between inhibitory effect and the inhibitor binding constant. This article examines the relationship between the simple binding curve used to define biomolecular interactions and the standard inhibitory term (1 + ([I]∕Ki)). By understanding how this term relates to binding curves which are ubiquitously used to describe biological processes, a modifier equation which distinguishes between inhibitor binding and the inhibitory effect, is examined. This modifier equation which can describe both activation and inhibition is compared to standard inhibitory equations with the development of global data fitting templates in Excel and via the global fitting of these equations to simulated and previously published datasets. In both cases, this modifier equation was able to match or outperform the other equations by providing superior fits to the datasets. The ability of this single equation to outperform the other equations suggests an over-complication of the field. This equation and the template developed in this article should prove to be useful tools in the study of enzyme inhibition and activation.

Comparing enzyme activity modifier equations through the development of global data fitting templates in Excel

The classical way of defining enzyme inhibition has obscured the distinction between inhibitory effect and the inhibitor binding constant. This article examines the relationship between the simple binding curve used to define biomolecular interactions and the standard inhibitory term (1+([I]/Ki)). By understanding how this term relates to binding curves which are ubiquitously used to describe biological processes, a modifier equation which distinguishes between inhibitor binding and the inhibitory effect, is examined. This modifier equation which can describe both activation and inhibition is compared to standard inhibitory equations with the development of global data fitting templates in Excel and via the global fitting of these equations to simulated and previously published datasets. In both cases, this modifier equation was able to match or outperform the other equations by providing superior fits to the datasets. The ability of this single equation to outperform the other equations suggests an over-complication of the field. This equation and the template developed in this article should prove to be useful tools in the study of enzyme inhibition and activation.

Comparing enzyme activity modifier equations through the development of global data fitting templates in Excel

The classical way of defining enzyme inhibition has obscured the distinction between inhibitory effect and the inhibitor binding constant. This article examines the relationship between the simple binding curve used to define biomolecular interactions and the standard inhibitory term (1+([I]/Ki)). By understanding how this term relates to binding curves which are ubiquitously used to describe biological processes, a modifier equation which distinguishes between inhibitor binding and the inhibitory effect, is examined. This modifier equation which can describe both activation and inhibition is compared to standard inhibitory equations with the development of global data fitting templates in Excel and via the global fitting of these equations to simulated and previously published datasets. In both cases, this modifier equation was able to match or outperform the other equations by providing superior fits to the datasets. The ability of this single equation to outperform the other equations suggests an over-complication of the field. This equation and the template developed in this article should prove to be useful tools in the study of enzyme inhibition and activation.

Measurement of binding strength between prey proteins interacting with Toxoplasma gondii SAG1 and SAG2 using isothermal titration calorimetry (ITC)

Following the outcome from a previously performed yeast two-hybrid experiment, the binding strength between T. gondii SAG1 and SAG2 and their respective prey proteins were further confirmed in this study. The sag1, sag2 and their prey genes were amplified and cloned into a pGEMT vector. To express the recombinant proteins, the fragments were then subcloned into a pRSETA vector and transformed into E. coli BL21 (DE3) cells. The recombinant proteins were expressed optimally at 37°C and 1mM of IPTG. The 6X His-tag fusion proteins were purified, dialyzed and concentrated. To confirm the expressed proteins, the recombinant proteins were analysed by SDS-PAGE and Western blot. As expected, the size of SAG1, SAG2, HLY and HZF protein were 32, 23, 28 and 37 kDa, respectively. The purified proteins were loaded onto a MicroCal Auto-iTC200 calorimeter from MicroCal™ to quantify binding strength. ITC results indicated there was a typical binding curve for interactions between SAG1 and HLY protein. However, there was an atypical binding curve obtained for interactions between SAG2 and HZF protein. By observing the data obtained from the ITC assay, both of the human proteins (HLY and HZF) were demonstrated to bind to their respective SAG1 and SAG2 proteins.

Comparing enzyme activity modifiers equations through the development of global data fitting templates in Excel

The classical way of defining enzyme inhibition has obscured the distinction between inhibitory effect and the inhibitor binding constant. This article examines the relationship between the simple binding curve used to define biomolecular interactions and the standard inhibitory term (1+([I]/Ki)). By understanding how this term relates to binding curves which are ubiquitously used to describe biological processes, a modifier equation which distinguishes between inhibitor binding and the inhibitory effect, is examined. This modifier equation which can describe both activation and inhibition is compared to standard inhibitory equations with the development of global data fitting templates in Excel, and via the global fitting of these equations to previously reported enzyme kinetic data. This equation and the template developed in this article should prove to be useful tools in the study of enzyme inhibition and activation.