Supramolecular protein assemblies including molecular motors, cytoskeletal filaments, chaperones, and ribosomes play a central role in a broad array of cellular functions ranging from cell division and motility to RNA and protein synthesis and folding. Single-particle reconstructions of such assemblies have been growing rapidly in recent years, providing increasingly high resolution structural information under native conditions. While the static structure of these assemblies provides essential insight into their mechanism of biological function, their dynamical motions provide additional important information that cannot be inferred from structure alone. Here we present an unsupervised computational framework for the analysis of high molecular weight assemblies and use it to analyze the conformational dynamics of structures deposited in the Electron Microscopy Data Bank. Protein assemblies are modeled using a recently introduced coarse-grained modeling framework based on the finite element method, which is used to compute equilibrium thermal fluctuations, elastic strain energy distributions associated with specific conformational transitions, and dynamical correlations in distant molecular domains. Results are presented in detail for the ribosome-bound termination factor RF2 from Escherichia coli, the nuclear pore complex from Dictyostelium discoideum, and the chaperonin GroEL from E. coli. Elastic strain energy distributions reveal hinge-regions associated with specific conformational change pathways, and correlations in collective molecular motions reveal dynamical coupling between distant molecular domains that suggest new, as well as confirm existing, allosteric mechanisms. Results are publically available for use in further investigation and interpretation of biological function including cooperative transitions, allosteric communication, and molecular mechanics, as well as in further classification and refinement of electron microscopy based structures.
set of recurring motion patterns. The complete set of these patterns, which we tentatively call the dynasome, spans a high-dimensional space whose axes, the dynasome descriptors, characterize different aspects of protein dynamics. Methodology: The unique dynamic fingerprint of each protein is represented as a vector in the basis of this dynasome space. The difference between any two vectors, consequently, gives a reliable measure of dynamics similarity. From extended molecular dynamics simulations of 100 representatively chosen soluble proteins, dynamics fingerprints were obtained, which served to characterize in detail the statistical properties of the dynasome. Conclusions: 1. We find that proteins do not fall into natural, well separated dynamics classes. 2. Four collective dynamics descriptors obtained from PCA are sufficient to characterize the dynasome. 3. For the majority of proteins we observe strong correlation between structure and dynamics. Exceptions are convergent and divergent dynamics, respectively, where minor structural differences yield major dynamics differences and vice versa. 4. Proteins with similar function carry out similar dynamics. Combination of structural and dynamics data yields superior predictions of protein function.
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