This content will become publicly available on July 1, 2024
- Award ID(s):
- NSF-PAR ID:
- Date Published:
- Journal Name:
- Biophysical Journal
- Page Range / eLocation ID:
- 2988 to 2995
- Medium: X
- Sponsoring Org:
- National Science Foundation
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Prediction of RNA structure is an important problem in understanding biological processes in living organism. Computational models have been created to study the processes with the aim of unravelling the RNA structure. In this work, a novel formalism for formal analysis of RNA structure prediction is described. A graph rewriting system is formalized to represent structural dynamics of RNA structure under uncertainty. Probabilistic model checking is performed on queries seeking structural properties in RNA. Experiments were conducted to evaluate the computational feasibility of the model.more » « less
Understanding how proteins fold has remained a problem of great interest in biophysical research. Atomistic computer simulations using physics-based force fields can provide important insights on the interplay of different interactions and energetics and their roles in governing the folding thermodynamics and mechanism. In particular, generalized Born (GB)-based implicit solvent force fields can be optimized to provide an appropriate balance between solvation and intramolecular interactions and successfully recapitulate experimental conformational equilibria for a set of helical and β-hairpin peptides. Here, we further demonstrate that key thermodynamic properties and their temperature dependence obtained from replica exchange molecular dynamics simulations of these peptides are in quantitative agreement with experimental results. Useful lessons can be learned on how the interplay of entropy and sequentially long-range interactions governs the mechanism and cooperativity of folding. These results highlight the great potential of high-quality implicit solvent force fields for studying protein folding and large-scale conformational transitions.
Protein (un)folding rates depend on the free-energy barrier separating the native and unfolded states and a prefactor term, which sets the timescale for crossing such barrier or folding speed limit. Because extricating these two factors is usually unfeasible, it has been common to assume a constant prefactor and assign all rate variability to the barrier. However, theory and simulations postulate a protein-specific prefactor that contains key mechanistic information. Here, we exploit the special properties of fast-folding proteins to experimentally resolve the folding rate prefactor and investigate how much it varies among structural homologs. We measure the ultrafast (un)folding kinetics of five natural WW domains using nanosecond laser-induced temperature jumps. All five WW domains fold in microseconds, but with a 10-fold difference between fastest and slowest. Interestingly, they all produce biphasic kinetics in which the slower phase corresponds to reequilibration over the small barrier (<3
RT) and the faster phase to the downhill relaxation of the minor population residing at the barrier top [transition state ensemble (TSE)]. The fast rate recapitulates the 10-fold range, demonstrating that the folding speed limit of even the simplest all-β fold strongly depends on the amino acid sequence. Given this fold’s simplicity, the most plausible source for such prefactor differences is the presence of nonnative interactions that stabilize the TSE but need to break up before folding resumes. Our results confirm long-standing theoretical predictions and bring into focus the rate prefactor as an essential element for understanding the mechanisms of folding.
Flat, organic microstructures that can self‐fold into 3D microstructures are promising for tissue regeneration, for being capable of distributing living cells in 3D while forming highly complex, biomimetic architectures to assist cells in performing regeneration. However, the design of self‐folding microstructures is difficult due to a lack of understanding of the underlying formation mechanisms. This study helps bridge this gap by deciphering the dynamics of the self‐folding using a mass‐spring model. This numerical study reveals that self‐folding procedure is multi‐modal, which can become random and unpredictable by involving the interplays between internal stresses, external stimulation, imperfection, and self‐hindrance of the folding body. To verify the numerical results, bilayered, hydrogel‐based micropatterns capable of self‐folding are fabricated using inkjet‐printing and tested. The experimental and numerical results are consistent with each other. The above knowledge is applied to designing and fabricating self‐folding microstructures for tissue‐engineering, which successfully creates 3D, cell‐scaled, and biomimetic microstructures, such as microtubes, branched microtubes, and hollow spheres. Embedded in self‐folded microtubes, human mesenchymal stem cells proliferate and form linear cell‐organization mimicking the cell morphology in muscles and tendons. The above knowledge and study platforms can greatly contribute to the research on self‐folding microstructures and applications to tissue regeneration.