Abstract
Dynamics can provide deep insights into the functional mechanisms of proteins and protein complexes. For large protein complexes such as GroEL/GroES with more than 8,000 residues, obtaining a fine-grained all-atom description of its normal mode motions can be computationally prohibitive and is often unnecessary. For this reason, coarse-grained models have been used successfully. However, most existing coarse-grained models use extremely simple potentials to represent the interactions within the coarse-grained structures and as a result, the dynamics obtained for the coarse-grained structures may not always be fully realistic. There is a gap between the quality of the dynamics of the coarse-grained structures given by all-atom models and that by coarse-grained models. In this work, we resolve an important question in protein dynamics computations—how can we efficiently construct coarse-grained models whose description of the dynamics of the coarse-grained structures remains as accurate as that given by all-atom models? Our method takes advantage of the sparseness of the Hessian matrix and achieves a high efficiency with a novel iterative matrix projection approach. The result is highly significant since it can provide descriptions of normal mode motions at an all-atom level of accuracy even for the largest biomolecular complexes. The application of our method to GroEL/GroES offers new insights into the mechanism of this biologically important chaperonin, such as that the conformational transitions of this protein complex in its functional cycle are even more strongly connected to the first few lowest frequency modes than with other coarse-grained models.
Highlights
Protein dynamics plays a key role in describing the function of most proteins and protein complexes
We solve an important problem in protein dynamics studies: how to preserve the atomic-level accuracy in describing molecular motions while using coarse-grained models? We accomplish this by developing a novel iterative matrix projection method that dramatically speeds up the computations
This method is significant since it promises accurate descriptions of protein motions approaching an all-atom level even for the largest biomolecular complexes
Summary
Protein dynamics plays a key role in describing the function of most proteins and protein complexes. The importance of protein dynamics studies has been increasingly recognized alongside the importance of the structures themselves. Normal mode analysis (NMA) is another popular and powerful tool for studying protein dynamics and was first applied to proteins in the early 80’s [9,10,11]. To apply NMA, a structure is first energetically minimized. The minimized structure is used to construct the Hessian matrix, from which normal modes can be obtained from its eigenvectors and eigen-frequencies. This method poses a huge demand on computational resources, especially memory, since some large supramolecules may have hundreds of thousands of atoms.
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