Computational Enzyme Stabilization Can Affect Folding Energy Landscapes and Lead to Catalytically Enhanced Domain-Swapped Dimers

RNA sequences fold into their native conformations by means of an adaptive search of their folding energy landscapes. The energy landscape may contain one or more suboptimal attractor conformations, making it possible for an RNA sequence to become trapped in a suboptimal attractor during the folding process. How the probability that an RNA sequence will find a given attractor before it finds another one depends on the relative positions of those attractors on the energy landscape is not well understood. Similarly, there is an inadequate understanding of the mechanisms that underlie differences in the amount of time an RNA sequence spends in a particular state. Elucidation of those mechanisms would contribute to the understanding of constraints operating on RNA folding. This chapter explores the kinetics of RNA folding using theoretical models and experimental data. Discrepancies between experimental predictions and expectations based on prevailing assumptions about the determinants of RNA folding kinetics are highlighted. An analogy between kinetic accessibility and evolutionary accessibility is also discussed.

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Modulation of folding energy landscape by charge–charge interactions: Linking experiments with computational modeling

Proceedings of the National Academy of Sciences ◽

10.1073/pnas.1410424112 ◽

2015 ◽

Vol 112 (3) ◽

pp. E259-E266 ◽

Cited By ~ 31

Author(s):

Franco O. Tzul ◽

Katrina L. Schweiker ◽

George I. Makhatadze

Keyword(s):

Electrostatic Interactions ◽

Simple Structure ◽

Energy Landscape ◽

Folding Energy ◽

Energy Landscapes ◽

Wild Type ◽

Folding Rates ◽

Naturally Occurring ◽

Kinetics Of ◽

Charge Interactions

The kinetics of folding–unfolding of a structurally diverse set of four proteins optimized for thermodynamic stability by rational redesign of surface charge–charge interactions is characterized experimentally. The folding rates are faster for designed variants compared with their wild-type proteins, whereas the unfolding rates are largely unaffected. A simple structure-based computational model, which incorporates the Debye–Hückel formalism for the electrostatics, was used and found to qualitatively recapitulate the experimental results. Analysis of the energy landscapes of the designed versus wild-type proteins indicates the differences in refolding rates may be correlated with the degree of frustration of their respective energy landscapes. Our simulations indicate that naturally occurring wild-type proteins have frustrated folding landscapes due to the surface electrostatics. Optimization of the surface electrostatics seems to remove some of that frustration, leading to enhanced formation of native-like contacts in the transition-state ensembles (TSE) and providing a less frustrated energy landscape between the unfolded and TS ensembles. Macroscopically, this results in faster folding rates. Furthermore, analyses of pairwise distances and radii of gyration suggest that the less frustrated energy landscapes for optimized variants are a result of more compact unfolded and TS ensembles. These findings from our modeling demonstrates that this simple model may be used to: (i) gain a detailed understanding of charge–charge interactions and their effects on modulating the energy landscape of protein folding and (ii) qualitatively predict the kinetic behavior of protein surface electrostatic interactions.

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Characterization of the Folding Energy Landscapes of Computer Generated Proteins Suggests High Folding Free Energy Barriers and Cooperativity may be Consequences of Natural Selection

Journal of Molecular Biology ◽

10.1016/j.jmb.2004.02.055 ◽

2004 ◽

Vol 338 (3) ◽

pp. 573-583 ◽

Cited By ~ 69

Author(s):

Michelle Scalley-Kim ◽

David Baker

Keyword(s):

Free Energy ◽

Natural Selection ◽

Folding Energy ◽

Energy Landscapes ◽

Energy Barriers ◽

Folding Free Energy

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Prediction of Protein–Protein Binding Interactions in Dimeric Coiled Coils by Information Contained in Folding Energy Landscapes

International Journal of Molecular Sciences ◽

10.3390/ijms22031368 ◽

2021 ◽

Vol 22 (3) ◽

pp. 1368

Author(s):

Panagiota S. Georgoulia ◽

Sinisa Bjelic

Keyword(s):

Protein Interactions ◽

Protein Complexes ◽

Coiled Coil ◽

Extensive Study ◽

Coiled Coils ◽

Folding Energy ◽

Energy Landscapes ◽

Binding Interactions ◽

Interacting Protein ◽

Protein Partners

Coiled coils represent the simplest form of a complex formed between two interacting protein partners. Their extensive study has led to the development of various methods aimed towards the investigation and design of complex forming interactions. Despite the progress that has been made to predict the binding affinities for protein complexes, and specifically those tailored towards coiled coils, many challenges still remain. In this work, we explore whether the information contained in dimeric coiled coil folding energy landscapes can be used to predict binding interactions. Using the published SYNZIP dataset, we start from the amino acid sequence, to simultaneously fold and dock approximately 1000 coiled coil dimers. Assessment of the folding energy landscapes showed that a model based on the calculated number of clusters for the lowest energy structures displayed a signal that correlates with the experimentally determined protein interactions. Although the revealed correlation is weak, we show that such correlation exists; however, more work remains to establish whether further improvements can be made to the presented model.

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