Controllable Molecular Motors Engineered from Myosin and RNA

Omabegho, Tosan; Gurel, Pinar S.; Cheng, Clarence Yu; Kim, Laura Y.; Ruijgrok, Paul V.; Das, Rhiju; Alushin, Gregory M.; Bryant, Zev

doi:10.1016/j.bpj.2016.11.050

Cited by 3 publications

(3 citation statements)

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“…A systematic study of different frameshifting elements may reveal their effect on the energy landscape of frameshifting delineated here as well as distinctive frameshifting pathways used at other stages of translation. A deep understanding of frameshifting mechanisms may permit its use in artificial regulation of gene expression at the translation level through external cues such as temperature or oligonucleotide signals (43).…”

Section: Model Of the Frame Maintenance And Rna Structure Unfolding B...mentioning

confidence: 99%

The energy landscape of −1 ribosomal frameshifting

et al. 2020

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Maintenance of translational reading frame ensures the fidelity of information transfer during protein synthesis. Yet, programmed ribosomal frameshifting sequences within the coding region promote a high rate of reading frame change at predetermined sites thus enriching genomic information density. Frameshifting is typically stimulated by the presence of 3′ messenger RNA (mRNA) structures, but how these mRNA structures enhance −1 frameshifting remains debatable. Here, we apply single-molecule and ensemble approaches to formulate a mechanistic model of ribosomal −1 frameshifting. Our model suggests that the ribosome is intrinsically susceptible to frameshift before its translocation and this transient state is prolonged by the presence of a precisely positioned downstream mRNA structure. We challenged this model using temperature variation in vivo, which followed the prediction made based on in vitro results. Our results provide a quantitative framework for analyzing other frameshifting enhancers and a potential approach to control gene expression dynamically using programmed frameshifting.

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Section: Model Of the Frame Maintenance And Rna Structure Unfolding B...mentioning

confidence: 99%

The energy landscape of −1 ribosomal frameshifting

et al. 2020

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“…While high-throughput methods have been developed for measuring such interactions in vitro, it is unlikely that in the near future we will obtain quantitative experimental data for the binding landscapes of the thousands of RBPs relevant to human biology to all biologically important RNA sequences; computationally predicted protein binding energies may allow initial expansion of RNA structure prediction to include the effects of protein binding. These predictions may also be useful for a variety of applications such as RNP redesign to alter specificity, de novo design of RNA-protein complexes, or design of membraneless RNA-protein bodies (43,44). Notably, the Rosetta-Vienna RNP-ΔΔG method appears to underpredict the effects of some deleterious sequence mutations, suggesting that these predictions could effectively be used as part of an initial computational filtering procedure before experimentally testing binding.…”

Section: Discussionmentioning

confidence: 99%

Blind tests of RNA–protein binding affinity prediction

Kappel

Jarmoskaite

Vaidyanathan

et al. 2019

Proc. Natl. Acad. Sci. U.S.A.

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Interactions between RNA and proteins are pervasive in biology, driving fundamental processes such as protein translation and participating in the regulation of gene expression. Modeling the energies of RNA-protein interactions is therefore critical for understanding and repurposing living systems but has been hindered by complexities unique to RNA-protein binding. Here, we bring together several advances to complete a calculation framework for RNA-protein binding affinities, including a unified free energy function for bound complexes, automated Rosetta modeling of mutations, and use of secondary structure-based energetic calculations to model unbound RNA states. The resulting Rosetta-Vienna RNP-ΔΔG method achieves root-mean-squared errors (RMSEs) of 1.3 kcal/mol on highthroughput MS2 coat protein-RNA measurements and 1.5 kcal/mol on an independent test set involving the signal recognition particle, human U1A, PUM1, and FOX-1. As a stringent test, the method achieves RMSE accuracy of 1.4 kcal/mol in blind predictions of hundreds of human PUM2-RNA relative binding affinities. Overall, these RMSE accuracies are significantly better than those attained by prior structure-based approaches applied to the same systems. Importantly, Rosetta-Vienna RNP-ΔΔG establishes a framework for further improvements in modeling RNA-protein binding that can be tested by prospective high-throughput measurements on new systems.

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“…Finally, a hybrid molecular motor integrating a myosin motor and RNA lever arms was engineered. 235 In this hybrid motor, conformational changes in the myosin were amplified by the RNA arm to generate a larger stroke. The motor has the ability to move filaments over 2 μm at speeds of ∼20 nm/s (Figure 8b).…”

Section: Hybrid Motors Built With Artificial and Biomolecular Motors ...mentioning

confidence: 99%

Synthetic Systems Powered by Biological Molecular Motors

2019

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Biological molecular motors (or biomolecular motors for short) are nature’s solution to the efficient conversion of chemical energy to mechanical movement. In biological systems, these fascinating molecules are responsible for movement of molecules, organelles, cells, and whole animals. In engineered systems, these motors can potentially be used to power actuators and engines, shuttle cargo to sensors, and enable new computing paradigms. Here, we review the progress in the past decade in the integration of biomolecular motors into hybrid nanosystems. After briefly introducing the motor proteins kinesin and myosin and their associated cytoskeletal filaments, we review recent work aiming for the integration of these biomolecular motors into actuators, sensors, and computing devices. In some systems, the creation of mechanical work and the processing of information become intertwined at the molecular scale, creating a fascinating type of “active matter”. We discuss efforts to optimize biomolecular motor performance, construct new motors combining artificial and biological components, and contrast biomolecular motors with current artificial molecular motors. A recurrent theme in the work of the past decade was the induction and utilization of collective behavior between motile systems powered by biomolecular motors, and we discuss these advances. The exertion of external control over the motile structures powered by biomolecular motors has remained a topic of many studies describing exciting progress. Finally, we review the current limitations and challenges for the construction of hybrid systems powered by biomolecular motors and try to ascertain if there are theoretical performance limits. Engineering with biomolecular motors has the potential to yield commercially viable devices, but it also sharpens our understanding of the design problems solved by evolution in nature. This increased understanding is valuable for synthetic biology and potentially also for medicine.

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Controllable Molecular Motors Engineered from Myosin and RNA

Cited by 3 publications

References 0 publications

The energy landscape of −1 ribosomal frameshifting

The energy landscape of −1 ribosomal frameshifting

Blind tests of RNA–protein binding affinity prediction

Synthetic Systems Powered by Biological Molecular Motors

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