Experimental accuracy in protein structure refinement via molecular dynamics simulations

By Lim Heo, Michael Feig. 2018

66 citations recorded by the source corpus.

Significance Protein structures in atomistic detail are essential for a full understanding of biological mechanisms, yet high-resolution structural information remains unavailable for many proteins. Computational structure prediction aims to fill the gap but does not routinely provide models that reach experimental resolution. This study explores the conformational landscape between imperfect structure predictions and correct native experimental structures via molecular dynamics computer simulations. The results presented here demonstrate that structure refinement to experimental accuracy via simulation is indeed possible, but significant challenges are revealed that have to be overcome in practical refinement protocols. Refinement is the last step in protein structure prediction pipelines to convert approximate homology models to experimental accuracy. Protocols based on molecular dynamics (MD) simulations have shown promise, but current methods are limited to moderate levels of consistent refinement. To explore the energy landscape between homology models and native structures and analyze the challenges of MD-based refinement, eight test cases were studied via extensive simulations followed by Markov state modeling. In all cases, native states were found very close to the experimental structures and at the lowest free energies, but refinement was hindered by a rough energy landscape. Transitions from the homology model to the native states require the crossing of significant kinetic barriers on at least microsecond time scales. A significant energetic driving force toward the native state was lacking until its immediate vicinity, and there was significant sampling of off-pathway states competing for productive refinement. The role of recent force field improvements is discussed and transition paths are analyzed in detail to inform which key transitions have to be overcome to achieve successful refinement.

Subfields: Materials Chemistry, Molecular Biology, Spectroscopy

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Citations in the corpus

Improved protein structure refinement guided by deep learning based accuracy estimation (2020, background): “To date the most successful approaches have been with physically based methods that involve large scale search for low energy structures, for example with Rosetta (5) and/or molecular dynamics (6) .”
Driven to Near-Experimental Accuracy by Refinement via Molecular Dynamics Simulations (2019, methodology): “The simulations were performed iteratively and MSMs were built subsequently as described in Heo et al. 23 Detailed information about the additional simulations and the MSMs is given in Table S1.”
Toward the solution of the protein structure prediction problem (2021, methodology): “Nevertheless, molecular dynamics has remained a popular tool to study protein motion (128, 131) and for full-atom re ﬁ nement of protein structures (132 – 134).”