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Fluctuations of protein three-dimensional structures and large-scale conformational transitions are crucial for the biological function of proteins and their complexes. Experimental studies of such phenomena remain very challenging and therefore molecular modeling can be a good alternative or a valuable supporting tool for the investigation of large molecular systems and long-time events. In this minireview, we present two alternative approaches to the coarse-grained (CG) modeling of dynamic properties of protein systems. We discuss two CG representations of polypeptide chains used for Monte Carlo dynamics simulations of protein local dynamics and conformational transitions, and highly simplified structure-based elastic network models of protein flexibility. In contrast to classical all-atom molecular dynamics, the modeling strategies discussed here allow the quite accurate modeling of much larger systems and longer-time dynamic phenomena. We briefly describe the main features of these models and outline some of their applications, including modeling of near-native structure fluctuations, sampling of large regions of the protein conformational space, or possible support for the structure prediction of large proteins and their complexes.
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This content will become publicly available on March 27, 2027
Fast sampling of protein conformational dynamics
Protein function often depends on dynamic transitions between conformations rather than just static structures. However, our current ability to characterize or predict such dynamics lags behind recent advances in protein structure prediction. Enhanced sampling methods can speed up molecular dynamics simulations to study protein conformational transitions but require prior knowledge of key collective motions involved. Here, we demonstrate for a series of proteins of varying complexity that the required information is encoded in anharmonic low-frequency vibrations. Using recently developed methods, we show that this information can be easily extracted from short dynamics simulations without requiring prior knowledge. Combined with enhanced sampling, we correctly predict conformational transitions in all test proteins and generate highly reproducible free energy landscapes. This allows for the rapid generation of accurate protein conformational ensembles, which is critical to unravel the complex relationship between protein sequence, structure, and dynamics.
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- Award ID(s):
- 2154834
- PAR ID:
- 10681023
- Publisher / Repository:
- American Association for the Advancement of Science
- Date Published:
- Journal Name:
- Science Advances
- Volume:
- 12
- Issue:
- 13
- ISSN:
- 2375-2548
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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