Statistical Inference for Ergodic Algorithmic Model (EAM), Applied to Hydrophobic Hydration Processes

• Main Authors: Emilia Fisicaro, Carlotta Compari, Antonio Braibanti
• Format: Article
• Language: English
• Published: MDPI AG 2021-06-01
• Series: Entropy
• Subjects: hydrophobic hydration process; ergodic algorithmic model (EAM); thermal equivalent dilution (TED); binding potential functions; intensity entropy; density entropy
• Online Access: https://www.mdpi.com/1099-4300/23/6/700

The thermodynamic properties of hydrophobic hydration processes can be represented in probability space by a Dual-Structure Partition Function {<i>DS-PF</i>} = {M<i>-PF</i>} · {<i>T-PF</i>}, which is the product of a Motive Partition Function {M<i>-PF</i>} multiplied by a Thermal Partition Function {<i>T-PF</i>}. By development of {<i>DS-PF</i>}, parabolic binding potential functions α) <i>R</i>ln<i>K_dual</i> = (−Δ<i>G°_dual/T</i>) ={<i>f(</i>1/<i>T</i>)<i>*g(T</i>)} and β) <i>RT</i>ln<i>K_dual</i> = (−Δ<i>G°_dual</i>) = {<i>f(T</i>)<i>*g</i>(<i>lnT</i>)} have been calculated. The resulting binding functions are “<i>convoluted</i>” functions dependent on the reciprocal interactions between the primary function <i>f(</i>1/<i>T</i>) or <i>f(T</i>) with the secondary function <i>g(T</i>) or <i>g</i>(<i>lnT</i>), respectively. The binding potential functions carry the essential thermodynamic information elements of each system. The analysis of the binding potential functions experimentally determined at different temperatures by means of the Thermal Equivalent Dilution (TED) principle has made possible the evaluation, for each compound, of the <i>pseudo</i>-stoichiometric coefficient ±<i>ξ</i><i>_w</i>, from the curvature of the binding potential functions. The positive value indicates convex binding functions (Class A), whereas the negative value indicates concave binding function (Class B). All the information elements concern sets of compounds that are very different from one set to another, in molecular dimension, in chemical function, and in aggregation state. Notwithstanding the differences between, surprising equal unitary values of <i>niche</i> (cavity) formation in Class A <Δ<i>h_for</i>>_A = −22.7 ± 0.7 kJ·mol⁻¹<i>·</i><i>ξ</i><i>_w</i>⁻¹ sets with standard deviation σ = ±3.1% and <Δ<i>s_for</i>>_A = −445 ± 3J·K⁻¹·mol⁻¹·<i>ξ</i><i>_w</i>⁻¹J·K⁻¹·mol⁻¹·<i>ξ</i><i>_w</i>⁻¹ with standard deviation σ = ±0.7%. Other surprising similarities have been found, demonstrating that all the data analyzed belong to the same normal statistical population. The Ergodic Algorithmic Model (EAM) has been applied to the analysis of important classes of reactions, such as thermal and chemical denaturation, denaturation of proteins, iceberg formation or reduction, hydrophobic bonding, and null thermal free energy. The statistical analysis of errors has shown that EAM has a general validity, well beyond the limits of our experiments. Specifically, the properties of hydrophobic hydration processes as biphasic systems generating convoluted binding potential functions, with water as the implicit solvent, hold for all biochemical and biological solutions, on the ground that they also are necessarily diluted solutions, statistically validated.