Comparing ANI-2x, ANI-1ccx neural networks, force field, and DFT methods for predicting conformational potential energy of organic molecules.

In this study, the conformational potential energy surfaces of Amylmetacresol, Benzocaine, Dopamine, Betazole, and Betahistine molecules were scanned and analyzed using the neural network architecture ANI-2 × and ANI-1ccx, the force field method OPLS, and density functional theory with the exchange-correlation functional B3LYP and the basis set 6-31G(d). The ANI-1ccx and ANI-2 × methods demonstrated the highest accuracy in predicting torsional energy profiles, effectively capturing the minimum and maximum values of these profiles. Conformational potential energy values calculated by B3LYP and the OPLS force field method differ from those calculated by ANI-1ccx and ANI-2x, which account for non-bonded intramolecular interactions, since the B3LYP functional and OPLS force field weakly consider van der Waals and other intramolecular forces in torsional energy profiles. For a more comprehensive analysis, electronic parameters such as dipole moment, HOMO, and LUMO energies for different torsional angles were calculated at two levels of theory, B3LYP/6-31G(d) and ωB97X/6-31G(d). These calculations confirmed that ANI predictions are more accurate than density functional theory calculations with B3LYP functional and OPLS force field for determining potential energy surfaces. This research successfully addressed the challenges in determining conformational potential energy levels and shows how machine learning and deep neural networks offer a more accurate, cost-effective, and rapid alternative for predicting torsional energy profiles.

Impacts of Mechanical Stiffness of Bacteriophage-Loaded Hydrogels on Their Antibacterial Activity.

The elaboration of efficient hydrogel-based materials with antimicrobial properties requires a refined control of defining their physicochemical features, which includes mechanical stiffness, so as to properly mediate their antibacterial activity. In this work, we design hydrogels consisting of polyelectrolyte multilayer films for the loading of T4 and φX174 bacteria-killing viruses, also called bacteriophages. We investigate the antiadhesion and bactericidal performances of this biomaterial against Escherichia coli, with a specific focus on the effects of chemical cross-linking of the hydrogel matrix, which, in turn, mediates the hydrogel stiffness. Depending on the latter and on phage replication features, it is found that the hydrogels loaded with the bacteria-killing viruses make both contact killing (targeted bacteria are those adhered at the hydrogel surface) and release killing (planktonic bacteria are the targets) possible with ca. 20-80% efficiency after only 4 h of incubation at 25 °C as compared to cases where hydrogels are free of viruses. We further demonstrate the lack of dependence of virus diffusion within the hydrogel and of the maximal viral storage capacity on the hydrogel mechanical properties. In addition to the evidenced bacteriolytic activity of the phages loaded in the hydrogels, the antimicrobial property of the phage-loaded materials is shown to be partly controlled by the chemistry of the hydrogel skeleton and, more specifically, by the mobility of the peripheral free polycationic components, known for their ability to weaken and permeabilize membranes of bacteria, the latter then becoming "easier" targets for the viruses.