The all-atom additive CHARMM36 protein force field is widely used in molecular modeling and simulations. We present its refinement, CHARMM36m (http://mackerell.umaryland.edu/charmm_ff.shtml), with improved accuracy in generating polypeptide backbone conformational ensembles for intrinsically disordered peptides and proteins.
Computer simulations are widely used to study molecular systems, especially in biology. As simulations have greatly increased in scale reaching cellular levels there are now significant challenges in managing, analyzing, and interpreting such data in comparison with experiments that are being discussed. Management challenges revolve around storing and sharing terabyte to petabyte scale data sets whereas the analysis of simulations of highly complex systems will increasingly require automated machine learning and artificial intelligence approaches. The comparison between simulations and experiments is furthermore complicated not just by the complexity of the data but also by difficulties in interpreting experiments for highly heterogeneous systems. As an example, the interpretation of NMR relaxation measurements and comparison with simulations for highly crowded systems is discussed.
For a long time the effect of crowded cellular environment on protein dynamics has been largely ignored. Recent experiments indicate that proteins diffuse much slower in a living cell than in a diluted solution and further studies suggest that the diffusion depends on the local surrounding. Here, detailed insight into how diffusion depends on protein-protein contacts is presented based on extensive all-atom molecular dynamics simulations of concentrated villin head piece solutions. After force field adjustments in the form of increased protein-water interactions to reproduce experimental data, translational and rotational diffusion was analyzed in detail. While internal protein dynamics remained largely unaltered, rotational diffusion was found to slow down more significantly than translational diffusion as the protein concentration increased. The decrease in diffusion is interpreted in terms of a transient formation of protein clusters. These clusters persist on sub-microsecond time scales and follow distributions that increasingly shift toward larger cluster size with increasing protein concentrations. Weighting diffusion coefficients estimated for different clusters extracted from the simulations with the distribution of clusters largely reproduces the overall observed diffusion rates, suggesting that transient cluster formation is a primary cause for a slow-down in diffusion upon crowding with other proteins.
The
effects of crowding in biological environments on biomolecular
structure, dynamics, and function remain not well understood. Computer
simulations of atomistic models of concentrated peptide and protein
systems at different levels of complexity are beginning to provide
new insights. Crowding, weak interactions with other macromolecules
and metabolites, and altered solvent properties within cellular environments
appear to remodel the energy landscape of peptides and proteins in
significant ways including the possibility of native state destabilization.
Crowding is also seen to affect dynamic properties, both conformational
dynamics and diffusional properties of macromolecules. Recent simulations
that address these questions are reviewed here and discussed in the
context of relevant experiments.
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