Biological macromolecules are dynamic entities and their ability to adopt alternative conformations can be central to their function. Examples include motions that underlie regulation of ligand binding, membrane proteins that act as efficient and selective transporters across biological membranes, or intrinsically disordered proteins that can adapt their shape to binding partners. I will discuss methods and applications for how simulations and experiments can be used synergistically to study protein and RNA dynamics .
Functional protein motions are often described as an exchange between a dominant, ground state structure and one or more minor states. The structural and biophysical properties of these transiently and sparsely populated states are, however, difficult to study, and an atomic-level description of those states is challenging. Using soluble proteins with extensive NMR data available as a test system, we have shown how enhanced sampling simulations can be used to capture accurately complex conformational changes in proteins, and I will discuss such examples [2,3].
Despite recent progress, one may still find that a simulation does not match quantitatively experimental measurements. Then, experiments and simulations may be combined in a very direct fashion to provide a description of the molecular motions that combines the details of atomic simulations with the accuracy afforded by experimental measurements [4,5]. The resulting conformational ensembles may provide novel insight into biomolecular systems that are not obtainable by simulations of experiments alone. I will discuss how this may be achieved, and give examples of the application of such approaches using both NMR and small-angle scattering experiments to refine conformational ensembles of proteins [6–8] and RNA [9,10].
1. Bottaro, S., & Lindorff-Larsen, K. (2018). Biophysical experiments and biomolecular simulations: A perfect match?. Science, 361(6400), 355-360.
2. Wang, Y., Papaleo, E., & Lindorff-Larsen, K. (2016). Mapping transiently formed and sparsely populated conformations on a complex energy landscape. Elife, 5, e17505.
3. Papaleo, E., Sutto, L., Gervasio, F. L., & Lindorff-Larsen, K. (2014). Conformational changes and free energies in a proline isomerase. Journal of chemical theory and computation, 10(9), 4169-4174.
4. Boomsma, W., Ferkinghoff-Borg, J., & Lindorff-Larsen, K. (2014). Combining experiments and simulations using the maximum entropy principle. PLoS computational biology, 10(2), e1003406.
5. Bottaro, S., Bengtsen, T., & Lindorff-Larsen, K. (2018). Integrating Molecular Simulation and Experimental Data: A Bayesian/Maximum Entropy Reweighting Approach. bioRxiv, 457952.
6. Ahmed, M. C., Crehuet, R., & Lindorff-Larsen, K. (2019). Analyzing and comparing the radius of gyration and hydrodynamic radius in conformational ensembles of intrinsically disordered proteins. bioRxiv, 679373.
7. Crehuet, R., Jorro, P. J. B., Lindorff-Larsen, K., & Salvatella, X. (2019). Bayesian-Maximum-Entropy reweighting of IDPs ensembles based on NMR chemical shifts. BioRxiv, 689083.
8. Papaleo, E., Camilloni, C., Teilum, K., Vendruscolo, M., & Lindorff-Larsen, K. (2018). Molecular dynamics ensemble refinement of the heterogeneous native state of NCBD using chemical shifts and NOEs. PeerJ, 6, e5125.
9. Bottaro, S., Bussi, G., Kennedy, S. D., Turner, D. H., & Lindorff-Larsen, K. (2018). Conformational ensembles of RNA oligonucleotides from integrating NMR and molecular simulations. Science advances, 4(5), eaar8521.
10. Bottaro, S., Nichols, P. J., Vogeli, B., Parrinello, M., & Lindorff-Larsen, K. (2019). Molecular Dynamics Simulations, Exact NOE Measurements, and Machine Learning Reveal a Low-populated State of the UUCG RNA Tetraloop. BioRxiv, 690412.