Features of CPB: a Poisson-Boltzmann solver that uses an adaptive Cartesian grid.

The capabilities of an adaptive Cartesian grid (ACG)-based Poisson-Boltzmann (PB) solver (CPB) are demonstrated. CPB solves various PB equations with an ACG, built from a hierarchical octree decomposition of the computational domain. This procedure decreases the number of points required, thereby reducing computational demands. Inside the molecule, CPB solves for the reaction-field component (ϕrf ) of the electrostatic potential (ϕ), eliminating the charge-induced singularities in ϕ. CPB can also use a least-squares reconstruction method to improve estimates of ϕ at the molecular surface. All surfaces, which include solvent excluded, Gaussians, and others, are created analytically, eliminating errors associated with triangulated surfaces. These features allow CPB to produce detailed surface maps of ϕ and compute polar solvation and binding free energies for large biomolecular assemblies, such as ribosomes and viruses, with reduced computational demands compared to other Poisson-Boltzmann equation solvers. The reader is referred to http://www.continuum-dynamics.com/solution-mm.html for how to obtain the CPB software.

Drivers of mortality in COVID ARDS depend on patient sub-type.

The most common cause of death in people with COVID-19 is Acute Respiratory Distress Syndrome (ARDS). Prior studies have demonstrated that ARDS is a heterogeneous syndrome and have identified ARDS sub-types (phenoclusters). However, non-COVID-19 ARDS phenoclusters do not clearly apply to COVID-19 ARDS patients. In this retrospective cohort study, we implemented an iterative approach, combining supervised and unsupervised machine learning methodologies, to identify clinically relevant COVID-19 ARDS phenoclusters, as well as characteristics that are predictive of the outcome for each phenocluster. To this end, we applied a supervised model to identify risk factors for hospital mortality for each phenocluster and compared these between phenoclusters and the entire cohort. We trained the models using a comprehensive, preprocessed dataset of 2,864 hospitalized COVID-19 ARDS patients. Our research demonstrates that the risk factors predicting mortality in the overall cohort of COVID-19 ARDS may not necessarily apply to specific phenoclusters. Additionally, some risk factors increase the risk of hospital mortality in some phenoclusters but decrease mortality in others. These phenocluster-specific risk factors would not have been observed with a single predictive model. Heterogeneity in phenoclusters of COVID-19 ARDS as well as the drivers of mortality may partially explain challenges in finding effective treatments for all patients with ARDS.

Temperature Dependence of Static Structure Factor Peak Intensities for a Pyrrolidinium-Based Ionic Liquid.

Static structure factors ( S( q)) for many ionic liquids show low-wavenumber peaks whose intensities increase with increasing temperature. The greater peak intensities might seem to imply increasing intermediate-range order with increasing temperature. Molecular dynamics (MD) simulations for a representative ionic liquid, 1-butyl-1-methylpyrrolidinium bis(trifluoromethylsulfonyl)imide (C4C1pyrrTFSI), were used to calculate S( q) and partial S( q) (cation-cation, anion-anion, and cation-anion) at 298, 363, and 500 K. S( q) and partial S( q) were further decomposed into positive and negative components (which each indicate structural ordering) by separately summing positive and negative Fourier transform summands. Increasing temperature causes the negative components of each partial S( q) to decrease in magnitude more than the positive components, causing the total S( q) to increase in magnitude. Thus, structural ordering with periodicities corresponding to observed peaks in S( q) does not increase but instead decoheres with increasing temperature, even though S( q) peak heights increase. Fourier transform summands also show where in real space the positive and negative component contributions to S( q) change when the temperature increases. This new, detailed analysis based on Fourier transform summands comprising S( q) argues for great caution when interpreting S( q) intensities and highlights the value of simulations as a complement to X-ray (or neutron) scattering experiments.

Problems of robustness in Poisson-Boltzmann binding free energies.

Although models based on the Poisson­Boltzmann (PB) equation have been fairly successful at predicting some experimental quantities, such as solvation free energies (ΔG), these models have not been consistently successful at predicting binding free energies (ΔΔG). Here we found that ranking a set of protein­protein complexes by the electrostatic component (ΔΔGel) of ΔΔG was more difficult than ranking the same molecules by the electrostatic component (ΔGel) of ΔG. This finding was unexpected because ΔΔGel can be calculated by combining estimates of ΔGel for the complex and its components with estimates of the ΔΔGel in vacuum. One might therefore expect that if a theory gave reliable estimates of ΔGel, then its estimates of ΔΔGel would be reliable. However, ΔΔGel for these complexes were orders of magnitude smaller than ΔGel, so although estimates of ΔGel obtained with different force fields and surface definitions were highly correlated, similar estimates of ΔΔGel were often not correlated.