Fusion peptides (FPs) in spike proteins are key players mediating early events in cell-to-cell fusion, vital for intercellular viral spread. A proline residue located at the central FP region has often been suggested to have a distinctive role in this fusion event. The spike glycoprotein from strain RSA59 (PP) of mouse hepatitis virus (MHV) contains two central, consecutive prolines in the FP. Here, we report that deletion of one of these proline residues, resulting in RSA59 (P), significantly affected neural cell syncytia formation and viral titers postinfection in vitro. Transcranial inoculation of C57Bl/6 mice with RSA59 (PP) or RSA59 (P) yielded similar degrees of necrotizing hepatitis and meningitis, but only RSA59 (PP) produced widespread encephalitis that extended deeply into the brain parenchyma. By day 6 postinfection, both virus variants were mostly cleared from the brain. Interestingly, inoculation with the RSA59 (P)–carrying MHV significantly reduced demyelination at the chronic stage. We also found that the presence of two consecutive prolines in FP promotes a more ordered, compact, and rigid structure in the spike protein. These effects on FP structure were due to proline's unique stereochemical properties intrinsic to its secondary amino acid structure, revealed by molecular dynamics and NMR experiments. We therefore propose that the differences in the severity of encephalitis and demyelination between RSA59 (PP) and RSA59 (P) arise from the presence or absence, respectively, of the two consecutive prolines in FP. Our studies define a structural determinant of MHV entry in the brain parenchyma important for altered neuropathogenesis.

Molecular dynamics (MD) simulations have become an integral part of biomolecular study. Most MD software suites do not include analysis tools and those which do create very basic visualizations. Molecular Dynamics Data Visualizer (MD DaVis) is a python package developed to facilitate quick comparative analysis of MD trajectories of similar proteins or the same protein under different conditions. MD DaVis can quickly generate interactive visualization from molecular dynamics trajectories with a few simple steps. Interactive plots eliminate the need to make multiple plots for comparison, improving productivity and saving time. MD DaVis is an open-source Python 3 package (https://pypi.org/project/md-davis/) distributed under MIT license. The source code is available at https://github.com/djmaity/md-davis or https://doi.org/10.5281/zenodo.6227047. Supplementary data are available at Bioinformatics online.

Allostery refers to the activity regulation of biological macromolecules originating from the binding of an effector molecule at the allosteric site that is distant from the active site. The few existing allosteric data sets have not been updated with recent discoveries of allosteric proteins and are challenging to use for data-intensive tasks. Instead of providing another data set bound to become outdated, we present the AlloBench pipeline to create high-quality data sets of biomolecules with allosteric and active site information suitable for computational and data-driven studies of protein allostery. The pipeline produces a data set of 2141 allosteric sites from 2034 protein structures with 418 unique protein chains by integrating information from AlloSteric Database, UniProt, Mechanism and Catalytic Site Atlas, and Protein Data Bank. Furthermore, we use a subset of 100 proteins from the AlloBench data set to quantitatively compare the performance of currently available allosteric site prediction tools: APOP, PASSer, Ohm, ALLO, Allosite, STRESS, and AlloPred. Such a large-scale benchmarking of these programs has not been undertaken on a common test set. The results show a significant need for improvement, as the accuracy for all programs is well below 60%, with PASSer (Ensemble) outperforming the rest. The AlloBench pipeline will not only promote the development of improved allosteric site prediction tools but also serve as a reference for studying allostery in general.