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Water is a unique solvent that is ubiquitous in biology and present in a variety of solutions, mixtures, and materials settings. It therefore forms the basis for all molecular dynamics simulations of biological phenomena, as well as for many chemical, industrial, and materials investigations. Over the years, many water models have been developed, and it remains a challenge to find a single water model that accurately reproduces all experimental properties of water simultaneously. Here, we report a comprehensive comparison of structural and dynamic properties of 30 commonly used 3-point, 4-point, 5-point, and polarizable water models simulated using consistent settings and analysis methods. For the properties of density, coordination number, surface tension, dielectric constant, self-diffusion coefficient, and solvation free energy of methane, models published within the past two decades consistently show better agreement with experimental values compared to models published earlier, albeit with some notable exceptions. However, no single model reproduced all experimental values exactly, highlighting the need to carefully choose a water model for a particular study, depending on the phenomena of interest. Finally, machine learning algorithms quantified the relationship between the water model force field parameters and the resulting bulk properties, providing insight into the parameter−property relationship and illustrating the challenges of developing a water model that can accurately reproduce all properties of water simultaneously.

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Organic photovoltaic (OPV) materials are promising candidates for cheap, printable solar cells. However, there are a very large number of potential donors and acceptors, making selection of the best materials difficult. Here, we show that machine-learning approaches can leverage computationally expensive DFT calculations to estimate important OPV materials properties quickly and accurately. We generate quantitative relationships between simple and interpretable chemical signature and one-hot descriptors and OPV power conversion efficiency (PCE), open circuit potential (Voc), short circuit density (Jsc), highest occupied molecular orbital (HOMO) energy, lowest unoccupied molecular orbital (LUMO) energy, and the HOMO–LUMO gap. The most robust and predictive models could predict PCE (computed by DFT) with a standard error of ±0.5 for percentage PCE for both the training and test set. This model is useful for pre-screening potential donor and acceptor materials for OPV applications, accelerating design of these devices for green energy applications.

BECN1/Beclin 1 is a critical protein in the initiation of autophagosome formation. Recent studies have shown that phosphorylation of BECN1 by STK4/MST1 at threonine 108 (T108) within its BH3 domain blocks macroautophagy/autophagy by increasing BECN1 affinity for its negative regulators, the antiapoptotic proteins BCL2/Bcl-2 and BCL2L1/Bcl-x L . It was proposed that this increased binding is due to formation of an electrostatic interaction with a conserved histidine residue on the anti-apoptotic molecules. Here, we performed biophysical studies which demonstrated that a peptide corresponding to the BECN1 BH3 domain in which T108 is phosphorylated (p-T108) does show increased affinity for anti-apoptotic proteins that is significant, though only minor (<2-fold). We also determined X-ray crystal structures of BCL2 and BCL2L1 with T108-modified BECN1 BH3 peptides, but only showed evidence of an interaction between the BH3 peptide and the conserved histidine residue when the histidine flexibility was restrained due to crystal contacts. These data, together with molecular dynamics studies, indicate that the histidine is highly flexible, even when complexed with BECN1 BH3. Binding studies also showed that detergent can increase the affinity of the interaction. Although this increase was similar for both the phosphorylated and non-phosphorylated peptides, it suggests factors such as membranes could impact on the interaction between BECN1 and BCL2 proteins, and therefore, on the regulation of autophagy. Hence, we propose that phosphorylation of BECN1 by STK4/MST1 can increase the affinity of the interaction between BECN1 and anti-apoptotic proteins and this interaction can be stabilized by local environmental factors.