Physiological temperatures reduce dimerization of dengue and Zika virus recombinant envelope proteins.

The spread of dengue (DENV) and Zika virus (ZIKV) is a major public health concern. The primary target of antibodies that neutralize DENV and ZIKV is the envelope (E) glycoprotein, and there is interest in using soluble recombinant E (sRecE) proteins as subunit vaccines. However, the most potent neutralizing antibodies against DENV and ZIKV recognize epitopes on the virion surface that span two or more E proteins. Therefore, to create effective DENV and ZIKV vaccines, presentation of these quaternary epitopes may be necessary. The sRecE proteins from DENV and ZIKV crystallize as native-like dimers, but studies in solution suggest that these dimers are marginally stable. To better understand the challenges associated with creating stable sRecE dimers, we characterized the thermostability of sRecE proteins from ZIKV and three DENV serotypes, DENV2-4. All four proteins irreversibly unfolded at moderate temperatures (46-53 °C). At 23 °C and low micromolar concentrations, DENV2 and ZIKV were primarily dimeric, and DENV3-4 were primarily monomeric, whereas at 37 °C, all four proteins were predominantly monomeric. We further show that the dissociation constant for DENV2 dimerization is very temperature-sensitive, ranging from <1 µm at 25 °C to 50 µm at 41 °C, due to a large exothermic enthalpy of binding of -79 kcal/mol. We also found that quaternary epitope antibody binding to DENV2-4 and ZIKV sRecE is reduced at 37 °C. Our observation of reduced sRecE dimerization at physiological temperature highlights the need for stabilizing the dimer as part of its development as a subunit vaccine.

Machine-learned molecular mechanics force fields from large-scale quantum chemical data.

The development of reliable and extensible molecular mechanics (MM) force fields-fast, empirical models characterizing the potential energy surface of molecular systems-is indispensable for biomolecular simulation and computer-aided drug design. Here, we introduce a generalized and extensible machine-learned MM force field, espaloma-0.3, and an end-to-end differentiable framework using graph neural networks to overcome the limitations of traditional rule-based methods. Trained in a single GPU-day to fit a large and diverse quantum chemical dataset of over 1.1 M energy and force calculations, espaloma-0.3 reproduces quantum chemical energetic properties of chemical domains highly relevant to drug discovery, including small molecules, peptides, and nucleic acids. Moreover, this force field maintains the quantum chemical energy-minimized geometries of small molecules and preserves the condensed phase properties of peptides and folded proteins, self-consistently parametrizing proteins and ligands to produce stable simulations leading to highly accurate predictions of binding free energies. This methodology demonstrates significant promise as a path forward for systematically building more accurate force fields that are easily extensible to new chemical domains of interest.