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Register a new userFRETtranslator: translating FRET traces into RNA structural pathways
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Recent genome and transcriptome sequencing projects have unveiled a plethora of highly structured RNA molecules as central mediators of cellular function. Single molecule Forster Resonance Energy Transfer (smFRET) is a powerful tool for analyzing the temporal evolution of the global structure of individual RNA molecules, in pursuit of understanding their essential structure-dynamics-function relationships. In contrast to enzymatic and chemical footprinting, NMR spectroscopy and X-ray crystallography, smFRET yields temporally resolved, quantitative information about single molecules rather than only time and ensemble averages of entire populations. This enables unique observations of transient and rare conformations under both equilibrium and non-equilibrium conditions.
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Competition to bind microRNAs induces an effective positive crosstalk between their targets, therefore known as `competing endogenous RNAs or ceRNAs. While such an effect is known to play a significant role in specific conditions, estimating its strength from data and, experimentally, in physiological conditions appears to be far from simple. Here we show that the susceptibility of ceRNAs to different types of perturbations affecting their competitors (and hence their tendency to crosstalk) can be encoded in quantities as intuitive and as simple to measure as correlation functions. We confirm this scenario by extensive numerical simulations and validate it by re-analyzing PTENs crosstalk pattern from TCGA breast cancer dataset. These results clarify the links between different quantities used to estimate the intensity of ceRNA crosstalk and provide new keys to analyze transcriptional datasets and effectively probe ceRNA networks in silico.
The effect of polyvalent molecular cations, such as spermine, on the condensation of DNA into very well-defined toroidal shapes have been well studied and understood. However, a great effort has been made trying to obtain similar condensed structures from either ssRNA or dsRNA, which the latter carries similar negative charge density as dsDNA, although it adopts a different helical form. But the analogous condensation of RNA molecules into well-defined structures has so far been elusive. In this work, we show that ssRNA molecules can easily be condensed into nanoring structures on a mice surface, where each nanoring structure is formed mostly by a single RNA molecule. The condensation occurs in a concentration range of different atomic cations, from monovalent to trivalent. The structures of the RNA nanorings on mica surfaces were oberved by atomic force microscopy (AFM). The samples were observed in tapping mode and were prepared by drop evaporation of a solution of RNA in the presence of one type of the different cations used. As far as we know, this is the first time that nanorings or any other well-defined condensed RNA structures have been reported. The RNA nanorings formation can be understood by an energy competition between the hydrogen bonding forming hairpin stems, weakened by the salts, and hairpin loops. This results may have an important biological relevance, since it has been proposed that RNA is the oldest genome coding molecule and the formation of these structures could have given it stability against degradation in primeval times. Even more, the nanoring structures could have the potential to be used as biosensors and functionalized nanodevices.
Global coronavirus disease pandemic (COVID-19) caused by newly identified SARS- CoV-2 coronavirus continues to claim the lives of thousands of people worldwide. The unavailability of specific medications to treat COVID-19 has led to drug repositioning efforts using various approaches, including computational analyses. Such analyses mostly rely on molecular docking and require the 3D structure of the target protein to be available. In this study, we utilized a set of machine learning algorithms and trained them on a dataset of RNA-dependent RNA polymerase (RdRp) inhibitors to run inference analyses on antiviral and anti-inflammatory drugs solely based on the ligand information. We also performed virtual screening analysis of the drug candidates predicted by machine learning models and docked them against the active site of SARS- CoV-2 RdRp, a key component of the virus replication machinery. Based on the ligand information of RdRp inhibitors, the machine learning models were able to identify candidates such as remdesivir and baloxavir marboxil, molecules with documented activity against RdRp of the novel coronavirus. Among the other identified drug candidates were beclabuvir, a non-nucleoside inhibitor of the hepatitis C virus (HCV) RdRp enzyme, and HCV protease inhibitors paritaprevir and faldaprevir. Further analysis of these candidates using molecular docking against the SARS-CoV-2 RdRp revealed low binding energies against the enzyme active site. Our approach also identified anti-inflammatory drugs lupeol, lifitegrast, antrafenine, betulinic acid, and ursolic acid to have potential activity against SARS-CoV-2 RdRp. We propose that the results of this study are considered for further validation as potential therapeutic options against COVID-19.
Signaling pathways serve to communicate information about extracellular conditions into the cell, to both the nucleus and cytoplasmic processes to control cell responses. Genetic mutations in signaling network components are frequently associated with cancer and can result in cells acquiring an ability to divide and grow uncontrollably. Because signaling pathways play such a significant role in cancer initiation and advancement, their constituent proteins are attractive therapeutic targets. In this review, we discuss how signaling pathway modeling can assist with identifying effective drugs for treating diseases, such as cancer. An achievement that would facilitate the use of such models is their ability to identify controlling biochemical parameters in signaling pathways, such as molecular abundances and chemical reaction rates, because this would help determine effective points of attack by therapeutics.
The evolution of the genome has led to very sophisticated and complex regulation. Because of the abundance of non-coding RNA (ncRNA) in the cell, different species will promiscuously associate with each other, suggesting collective dynamics similar to artificial neural networks. Here we present a simple mechanism allowing ncRNA to perform computations equivalent to neural network algorithms such as Boltzmann machines and the Hopfield model. The quantities analogous to the neural couplings are the equilibrium constants between different RNA species. The relatively rapid equilibration of RNA binding and unbinding is regulated by a slower process that degrades and creates new RNA. The model requires that the creation rate for each species be an increasing function of the ratio of total to unbound RNA. Similar mechanisms have already been found to exist experimentally for ncRNA regulation. With the overall concentration of RNA regulated, equilibrium constants can be chosen to store many different patterns, or many different input-output relations. The network is also quite insensitive to random mutations in equilibrium constants. Therefore one expects that this kind of mechanism will have a much higher mutation rate than ones typically regarded as being under evolutionary constraint.
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