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Strauss Vasquez posted an update 6 months ago
RNA offers nearly unlimited potential as a target for small molecule chemical probes and lead medicines. Many RNAs fold into structures that can be selectively targeted with small molecules. This Perspective discusses molecular recognition of RNA by small molecules and highlights key enabling technologies and properties of bioactive interactions. Sequence-based design of ligands targeting RNA has established rules for affecting RNA targets and provided a potentially general platform for the discovery of bioactive small molecules. The RNA targets that contain preferred small molecule binding sites can be identified from sequence, allowing identification of off-targets and prediction of bioactive interactions by nature of ligand recognition of functional sites. Small molecule targeted degradation of RNA targets (ribonuclease-targeted chimeras, RIBOTACs) and direct cleavage by small molecules have also been developed. These growing technologies suggest that the time is right to provide small molecule chemical probes to target functionally relevant RNAs throughout the human transcriptome.The low-frequency vibrational fundamentals of D2h inorganic oxides are readily modeled by heuristic scaling factors at fractions of the computational cost compared to explicit anharmonic frequency computations. Oxygen and the other elements in the present study are abundant in geochemical environments and have the potential to aggregate into minerals in planet-forming regions or in the remnants of supernovae. Explicit quartic force field computations at the CCSD(T)-F12b/cc-pVTZ-F12 level of theory generate scaling factors that accurately predict the anharmonic frequencies with an average error of less than 1.0 cm-1 for both the metal-oxygen stretching frequencies and the torsion and antisymmetric stretching frequencies. Inclusion of hydrogen motions are less absolutely accurate but are similarly relatively predictive. The fundamental vibrational frequencies for the seven tetraatomic inorganic oxides examined presently fall below 875 cm-1, and most for the hydrogenated species do, as well. Additionally, ν3 for the SiO dimer is shown to have an intensity of 562 km mol-1, with each of the other molecules having one or more frequencies with intensities greater than 80 km mol-1 again with most in the low-frequency infrared range. These inten- sities and the frequencies computed in the present study should assist in laboratory characterization and potential interstellar or circumstellar observation.Hypergolic ionic liquids (HILs) are a new kind of green rocket fuels, which are used as potential replacements for toxic hydrazine derivatives in liquid bipropellants. These functional HILs can react with oxidizers and release a large amount of heat in a very short time, finally leading to ignition of the propellant system. Among them, most borohydride-rich HILs were very sensitive to water, but a few special examples displayed good hydrophobicity and remained very stable in air even after a month or more. However, the reasons behind their hydrolytic stability are unclear. In this study, several calculation methods including electrostatic potentials (ESPs), molecular orbital energy gaps, and interaction energy were used to explore the water stability of eight typical borohydride-rich HILs. The obtained results demonstrated that negatively charged anions with high absolute ESP values usually reacted more easily with positively charged water. The large molecular orbital energy gap with BPB-, BCNBCN-, CTB-, and BTB- indicates the high degree of difficulty of interactions between anions and water, leading to a better hydrolytic stability of borohydride-rich anions. During the analyses of interaction energy, the relatively water-sensitive borohydride-rich anions (BH4-, BH3CN-, etc.) generally had lower interaction energy with water than stable anions such as BPB- and BCNBCN-. Studies on their stepwise hydrolysis mechanism demonstrate that, in the case of all the reactions, the first step is the rate-determining step and high energy barrier values of anions correspond to good hydrophobicity. This study will help us understand the hydrolysis of borohydride-rich HILs and provide a guide for the development of new HILs with promising properties.Vibrational sum-frequency generation (SFG) spectroscopy is used to determine the surface pKa of p-methyl benzoic acid (pMBA) at the air-water interface, by monitoring the carbonyl and carboxylate stretching modes over the pH range from 2 to 12. The SFG intensities of pMBA and its conjugate base, p-methyl benzoate (pMBA-), exhibit an anomalously large enhancement over a narrow pH range (~0.5 units) centered at 6.3 near the SFG determined surface pKa, 5.9 ± 0.1. The increase in surface pKa relative to the bulk value of 4.34 is consistent with the trend previously observed for long chain carboxylic acids, in which the surface pKa is higher than the bulk solution pKa. SFG polarization studies help distinguish orientation and number density contributions to this observed anomalous surface phenomenon. The large SFG intensity increase is attributed to an increase in pMBA and pMBA- surface concentrations in this narrow pH range due to a cooperative adsorption effect between pMBA and pMBA-. selleck chemical This cooperativity is manifested only on the 2-D air-water interface, where the interactions between the acid and base are not as dielectrically screened as in the aqueous bulk phase. Surface effects are critical to understanding and controlling the reactivity, solubility and behavior of organic acids at interfaces, and can have an impact for biomedical applications.A proof-of-concept framework for identifying molecules of unknown elemental composition and structure using experimental rotational data and probabilistic deep learning is presented. Using a minimal set of input data determined experimentally, we describe four neural network architectures that yield information to assist in the identification of an unknown molecule. The first architecture translates spectroscopic parameters into Coulomb matrix eigenspectra as a method of recovering chemical and structural information encoded in the rotational spectrum. The eigenspectrum is subsequently used by three deep learning networks to constrain the range of stoichiometries, generate SMILES strings, and predict the most likely functional groups present in the molecule. In each model, we utilize dropout layers as an approximation to Bayesian sampling, which subsequently generates probabilistic predictions from otherwise deterministic models. These models are trained on a modestly sized theoretical dataset comprising ∼83 000 unique organic molecules (between 18 and 180 amu) optimized at the ωB97X-D/6-31+G(d) level of theory, where the theoretical uncertainties of the spectoscopic constants are well-understood and used to further augment training.
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