Spotlights Cite This: J. Phys. Chem. Lett. 2018, 9, 5890−5890
pubs.acs.org/JPCL
Spotlights: Volume 9, Issue 19
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J. Phys. Chem. Lett. 2018.9:5890-5890. Downloaded from pubs.acs.org by 5.189.202.145 on 10/12/18. For personal use only.
PHYSICAL CHEMISTRY AND MODERN MEDICINE The wonders of modern medicine are endless, and physical chemistry plays no small role in the ongoing advancements. Scientific research being conducted in physical chemistry laboratories around the world remains crucial to many medical innovations, from improving drug delivery to mapping the human genome to explaining physiological phenomena to detecting disease and beyond. There are several examples of the role of physical chemistry in the world of medicine in this issue of The Journal of Physical Chemistry Letters.
can detect and identify the presence of different biomarkers corresponding to hypo- and hypermethylation within a limited set of dictionary signals. Although the method has not yet been tested on experimental traces, Sarathy et al. believe that it is general enough to be expanded to include an exhaustive set of current signatures for various epigenetic markers calculated from different sensing materials, possibly enabling the development of a multipurpose technology for early disease detection.
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EFFECTS OF PREFERENTIAL COUNTERION INTERACTIONS ON THE SPECIFICITY OF RNA FOLDING RNA molecules must fold into well-defined three-dimensional structures to activate many RNA-dependent biological events, but it has been difficult to study how interactions between condensed counterions and RNA contribute to the early stages of the folding mechanism. In part, this difficulty is because the initial collapse of the polynucleotide chain simultaneously depends on the effects of electrostatic neutralization of the RNA charge, the intrinsic stability of RNA−RNA interactions, and the energy barriers to diffusion of intrachain interactions. Using submillisecond stopped-flow small-angle X-ray scattering, Roh et al. (10.1021/acs.jpclett.8b02086) investigated the initial collapse transition of the Azoarcus group I ribozyme in the presence of seven different counterions. They found that the fast, native-like folding population was smallest in Mg2+ ions despite the fact that Mg2+ efficiently stabilized the final folded state. Conversely, monovalent ions inefficiently stabilized the folded RNA, yet a larger fraction of the RNA population achieved a compact state in the first 1 ms of the folding reaction in Na+ or K+. The authors found that Mg2+ ions were associated with smaller preferential interaction coefficients with the early RNA folding intermediates than monovalent and even some trivalent ions. In addition, they show that Mg2+ ions produced more rigid structures after 1 ms than monovalent ions, suggesting that stable ion−RNA interactions can impede the search for native RNA structures.
FAST AND ACCURATE MOLECULAR PROPERTY PREDICTION: LEARNING ATOMIC INTERACTIONS AND POTENTIALS WITH NEURAL NETWORKS In the quest for new and effective drugs, researchers seek to discover molecules with specific properties. Deep neural networks (DNNs) have been applied to quantum chemistry to accelerate such discoveries using machine learning, but the DNNs have required chemical descriptors and multiple learning parameters to model atomic interactions. Tsubaki and Mizoguchi (10.1021/acs.jpclett.8b01837) propose a more compact model that does not depend on descriptors and incorporates additional neural networks to model the interactions and potentials among all of the atoms in a molecular structure. In the model, the interactions and potentials are characterized by using the global molecular structure, which is a function of the depth of the neural networks, leading to the implicit or indirect consideration of “many-body” interactions and potentials within the neural networks. The authors evaluated their model using the QM9 data set and found that they achieved fast and accurate prediction performances for most of the quantum chemical properties.
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CLASSIFICATION OF EPIGENETIC BIOMARKERS WITH ATOMICALLY THIN NANOPORES The ability to decode the human genome and epigenome is one of the most significant scientific advances of the new century, and attention is now focused on finding methods that reduce cost and increase speed without sacrificing accuracy and reliability. Conventional biochemical sequencing processes may not be up to the task, so alternatives are being sought. One possible alternative uses a very thin membrane with a nanoscale pore through which DNA molecules are threaded to identify not only the nucleotide sequence but also traits of DNA such as methylation. In their Letter, Sarathy et al. (10.1021/acs.jpclett.8b02200) introduce a versatile sensing technology for the classification of epigenetic biomarkers. They used electrically active, two-dimensional, atomically thin materials as a membrane to detect methylated DNA threaded through a nanopore by variation of the electronic current across the membrane. The biomarker identification is based on a classical molecular dynamics simulation combined with device modeling for electron transport and statistical signal processing algorithms. The authors found that their approach © 2018 American Chemical Society
Published: October 4, 2018 5890
DOI: 10.1021/acs.jpclett.8b02958 J. Phys. Chem. Lett. 2018, 9, 5890−5890
