Using Unassigned NMR Chemical Shifts to Model RNA Secondary Structure.

NMR-derived chemical shifts are sensitive probes of RNA structure. However, the need to assign NMR spectra hampers their utility as a direct source of structural information. In this report, we describe a simple method that uses unassigned 2D NMR spectra to model the secondary structure of RNAs. As in the case of assigned chemical shifts, we could use unassigned chemical shift data to reweight conformational libraries such that the highest weighted structure closely resembles their reference NMR structure. Furthermore, the application of our approach to the 3'- and 5'-UTR of the SARS-CoV-2 genome yields structures that are, for the most part, consistent with the secondary structure models derived from chemical probing data. Therefore, we expect the framework we describe here will be useful as a general strategy for rapidly generating preliminary structural RNA models directly from unassigned 2D NMR spectra. As we demonstrated for the 337-nt and 472-nt UTRs of SARS-CoV-2, our approach could be especially valuable for modeling the secondary structures of large RNA.

Navigating Chemical Space by Interfacing Generative Artificial Intelligence and Molecular Docking.

Here, we report the implementation and application of a simple, structure-aware framework to generate target-specific screening libraries. Our approach combines advances in generative artificial intelligence (AI) with conventional molecular docking to explore chemical space conditioned on the unique physicochemical properties of the active site of a biomolecular target. As a demonstration, we used our framework, which we refer to as sample-and-dock, to construct focused libraries for cyclin-dependent kinase type-2 (CDK2) and the active site of the main protease (Mpro) of the SARS-CoV-2 virus. We envision that the sample-and-dock framework could be used to generate theoretical maps of the chemical space specific to a given target and so provide information about its molecular recognition characteristics.

CS-Annotate: A Tool for Using NMR Chemical Shifts to Annotate RNA Structure.

Here, we introduce CS-Annotate, a tool that uses assigned NMR chemical shifts to annotate structural features in RNA. At its core, CS-Annotate is a deployment of a multitask deep learning model that simultaneously classifies the solvent exposure, base-stacking and -pairing status, and conformation of individual RNA residues from their chemical shift fingerprint. Here, we briefly describe how we trained and tested the classifier and demonstrate its application to a model RNA system. CS-Annotate can be accessed via the SMALTR (Structure-based MAchine Learning Tools for RNA) Science Gateway (smaltr.org).

Correction: Paszkiewicz et al. A Peripheral CB1R Antagonist Increases Lipolysis, Oxygen Consumption Rate, and Markers of Beiging in 3T3-L1 Adipocytes Similar to RIM, Suggesting That Central Effects Can Be Avoided. Int. J. Mol. Sci. 2020, 21, 6639.

The authors wish to make the following corrections to this paper [...].

PyShifts: A PyMOL Plugin for Chemical Shift-Based Analysis of Biomolecular Ensembles.

Here, we present PyShifts-a PyMOL plugin for chemical shift-based analysis of biomolecular ensembles. With PyShifts, users can compare and visualize differences between experimentally measured and computationally predicted chemical shifts. When analyzing multiple conformations of a biomolecule with PyShifts, users can also sort a set of conformations based on chemical shift differences and identify the conformers that exhibit the best agreement between measured and predicted chemical shifts. Although we have integrated PyShifts with the chemical shift predictors LARMORD and LARMORCα, PyShifts can read in chemical shifts from any source, and so, users can employ PyShifts to analyze biomolecular structures using chemical shifts computed by any chemical shift predictor. We envision, therefore, that PyShifts (https://github.com/atfrank/PyShifts) will find utility as a general-purpose tool for exploring chemical shift-structure relationships in biomolecular ensembles.

Chemical characterization of marijuana blunt smoke by non-targeted chemical analysis.

Background: Marijuana blunts, which are tobacco cigar wrappers filled with marijuana, are commonly smoked in the US as a means of cannabis use. The use of marijuana blunts presents toxicity concerns because the smoke contains both marijuana-related and tobacco-related chemicals. Thus, it is important to understand the chemical composition of mainstream smoke (MSS) from marijuana blunts. This study demonstrates the ability to detect and identify chemical constituents exclusively associated with blunt MSS in contrast to tobacco cigar MSS (designated as 'new exposures') through non-targeted chemical analysis.Methods: Samples collected separately from blunt MSS and tobacco cigar MSS were analyzed using two-dimensional gas chromatography-time-of-flight mass spectrometry (GC × GC-TOFMS).Results and Discussion: Two new exposures, which likely represent only a subset of all new exposures, were identified by evaluating the data from thousands of detected signals and then confirming selected compound identities in analyses using authentic chemical standards. The two confirmed new exposures, mellein and 2-phenyl-2-oxazoline, are not cannabinoids and, to the best of our knowledge, have not been previously reported in association with cannabis, tobacco, or smoke of any kind. In addition, we detected and quantified three phenols (2-, 3-, and 4-ethylphenol) in blunt MSS. Given the toxicity of phenols, quantifying the levels of other phenols could be pursued in future research on blunt MSS.Conclusion: This study shows the power and utility of GC × GC-TOFMS as a methodology for non-targeted chemical analysis to identify new chemical exposures in blunt MSS and to provide data to guide further investigations of blunt MSS.

A Peripheral CB1R Antagonist Increases Lipolysis, Oxygen Consumption Rate, and Markers of Beiging in 3T3-L1 Adipocytes Similar to RIM, Suggesting that Central Effects Can Be Avoided.

With the increased prevalence of obesity and related co-morbidities, such as type 2 diabetes (T2D), worldwide, improvements in pharmacological treatments are necessary. The brain- and peripheral-cannabinoid receptor 1 (CB1R) antagonist rimonabant (RIM) has been shown to induce weight loss and improve glucose homeostasis. We have previously demonstrated that RIM promotes adipose tissue beiging and decreased adipocyte cell size, even during maintenance on a high-fat diet. Given the adverse side-effects of brain-penetrance with RIM, in this study we aimed to determine the site of action for a non-brain-penetrating CB1R antagonist AM6545. By using in vitro assays, we demonstrated the direct effects of this non-brain-penetrating CB1R antagonist on cultured adipocytes. Specifically, we showed, for the first time, that AM6545 significantly increases markers of adipose tissue beiging, mitochondrial biogenesis, and lipolysis in 3T3-L1 adipocytes. In addition, the oxygen consumption rate (OCR), consisting of baseline respiratory rate, proton leak, maximal respiratory capacity, and ATP synthase activity, was greater for cells exposed to AM6545, demonstrating greater mitochondrial uncoupling. Using a lipolysis inhibitor during real-time OCR measurements, we determined that the impact of CB1R antagonism on adipocytes is driven by increased lipolysis. Thus, our data suggest the direct role of CB1R antagonism on adipocytes does not require brain penetrance, supporting the importance of focus on peripheral CB1R antagonism pharmacology for reducing the incidence of obesity and T2D.

Determinants of body fat distribution in humans may provide insight about obesity-related health risks.

Obesity increases the risks of developing cardiovascular and metabolic diseases and degrades quality of life, ultimately increasing the risk of death. However, not all forms of obesity are equally dangerous: some individuals, despite higher percentages of body fat, are at less risk for certain chronic obesity-related complications. Many open questions remain about why this occurs. Data suggest that the physical location of fat and the overall health of fat dramatically influence disease risk; for example, higher concentrations of visceral relative to subcutaneous adipose tissue are associated with greater metabolic risks. As such, understanding the determinants of the location and health of adipose tissue can provide insight about the pathological consequences of obesity and can begin to outline targets for novel therapeutic approaches to combat the obesity epidemic. Although age and sex hormones clearly play roles in fat distribution and location, much remains unknown about gene regulation at the level of adipose tissue or how genetic variants regulate fat distribution. In this review, we discuss what is known about the determinants of body fat distribution, and we highlight the important roles of sex hormones, aging, and genetic variation in the determination of body fat distribution and its contribution to obesity-related comorbidities.

A rapid solvent accessible surface area estimator for coarse grained molecular simulations.

The rapid and accurate calculation of solvent accessible surface area (SASA) is extremely useful in the energetic analysis of biomolecules. For example, SASA models can be used to estimate the transfer free energy associated with biophysical processes, and when combined with coarse-grained simulations, can be particularly useful for accounting for solvation effects within the framework of implicit solvent models. In such cases, a fast and accurate, residue-wise SASA predictor is highly desirable. Here, we develop a predictive model that estimates SASAs based on Cα-only protein structures. Through an extensive comparison between this method and a comparable method, POPS-R, we demonstrate that our new method, Protein-C α Solvent Accessibilities or PCASA, shows better performance, especially for unfolded conformations of proteins. We anticipate that this model will be quite useful in the efficient inclusion of SASA-based solvent free energy estimations in coarse-grained protein folding simulations. PCASA is made freely available to the academic community at https://github.com/atfrank/PCASA. © 2017 Wiley Periodicals, Inc.

Sex, Gender, and Transgender: Metabolic Impact of Cross Hormone Therapy.

Most preclinical and clinical, animal, and human research has been biased with respect to sex and even more so with respect to gender. In fact, little is known about the impact of sex and even less about the influence of gender on overall metabolic processes. The National Institutes of Health has recognized this gap in scientific knowledge and now mandates that studies be conducted in both sexes and to include gender as variables influencing physiological processes such as metabolism. It is therefore critical to understand and appreciate how to incorporate sex and gender in preclinical and clinical research in order to enhance our understanding of the mechanisms by which metabolic processes differ by sex and gender. In this chapter, we define sex and gender and discuss when sex and gender are not aligned, such as that which occurs in transgender individuals, and how this impacts metabolic processes. We discuss the importance of understanding the influence and interactions between sex hormones and sex chromosomes rather than focusing on their relative contributions to metabolism in isolation. This knowledge will optimize therapies specific for individuals which need to encompass sex and gender.

Dealing with femtorisks in international relations.

The contemporary global community is increasingly interdependent and confronted with systemic risks posed by the actions and interactions of actors existing beneath the level of formal institutions, often operating outside effective governance structures. Frequently, these actors are human agents, such as rogue traders or aggressive financial innovators, terrorists, groups of dissidents, or unauthorized sources of sensitive or secret information about government or private sector activities. In other instances, influential "actors" take the form of climate change, communications technologies, or socioeconomic globalization. Although these individual forces may be small relative to state governments or international institutions, or may operate on long time scales, the changes they catalyze can pose significant challenges to the analysis and practice of international relations through the operation of complex feedbacks and interactions of individual agents and interconnected systems. We call these challenges "femtorisks," and emphasize their importance for two reasons. First, in isolation, they may be inconsequential and semiautonomous; but when embedded in complex adaptive systems, characterized by individual agents able to change, learn from experience, and pursue their own agendas, the strategic interaction between actors can propel systems down paths of increasing, even global, instability. Second, because their influence stems from complex interactions at interfaces of multiple systems (e.g., social, financial, political, technological, ecological, etc.), femtorisks challenge standard approaches to risk assessment, as higher-order consequences cascade across the boundaries of socially constructed complex systems. We argue that new approaches to assessing and managing systemic risk in international relations are required, inspired by principles of evolutionary theory and development of resilient ecological systems.

Can Holo NMR Chemical Shifts be Directly Used to Resolve RNA-Ligand Poses?

Using a set of machine learning based predictors that are capable of predicting ligand-induced shielding effects on (1)H and (13)C nonexchangeable nuclei, it was discovered that holo NMR chemical shifts can be used to resolve RNA-ligand poses. This was accomplished by quantitatively comparing measured and predicted holo chemical shifts in conformationally diverse "decoy" pools for three test cases and then, for each, comparing the native pose to the pose in the decoy pool that exhibited the lowest error. For three test cases, the poses in the decoy pools that exhibited the best agreement between measured and predicted holo chemical shifts were within 0.28, 1.12, and 2.38 Å of the native poses. Interestingly, the predictors used in this study were trained on a database containing, only, apo RNA data. The agreement between the chemical shift-selected poses and the native NMR poses suggests that the predictors used in this study were able to "learn" general chemical shift-structure relationships from apo RNA data that could be used to account for ligand-induced shielding effects on RNA nuclei for the test cases studied.

Slowdown of Interhelical Motions Induces a Glass Transition in RNA.

RNA function depends crucially on the details of its dynamics. The simplest RNA dynamical unit is a two-way interhelical junction. Here, for such a unit--the transactivation response RNA element--we present evidence from molecular dynamics simulations, supported by nuclear magnetic resonance relaxation experiments, for a dynamical transition near 230 K. This glass transition arises from the freezing out of collective interhelical motional modes. The motions, resolved with site-specificity, are dynamically heterogeneous and exhibit non-Arrhenius relaxation. The microscopic origin of the glass transition is a low-dimensional, slow manifold consisting largely of the Euler angles describing interhelical reorientation. Principal component analysis over a range of temperatures covering the glass transition shows that the abrupt slowdown of motion finds its explanation in a localization transition that traps probability density into several disconnected conformational pools over the low-dimensional energy landscape. Upon temperature increase, the probability density pools then flood a larger basin, akin to a lakes-to-sea transition. Simulations on transactivation response RNA are also used to backcalculate inelastic neutron scattering data that match previous inelastic neutron scattering measurements on larger and more complex RNA structures and which, upon normalization, give temperature-dependent fluctuation profiles that overlap onto a glass transition curve that is quasi-universal over a range of systems and techniques.

Maternal high-fat diet is associated with impaired fetal lung development.

Maternal nutrition has a profound long-term impact on infant health. Poor maternal nutrition influences placental development and fetal growth, resulting in low birth weight, which is strongly associated with the risk of developing chronic diseases, including heart disease, hypertension, asthma, and type 2 diabetes, later in life. Few studies have delineated the mechanisms by which maternal nutrition affects fetal lung development. Here, we report that maternal exposure to a diet high in fat (HFD) causes placental inflammation, resulting in placental insufficiency, fetal growth restriction (FGR), and inhibition of fetal lung development. Notably, pre- and postnatal exposure to maternal HFD also results in persistent alveolar simplification in the postnatal period. Our novel findings provide a strong association between maternal diet and fetal lung development.

The role of hypothalamic estrogen receptors in metabolic regulation.

Estrogens regulate key features of metabolism, including food intake, body weight, energy expenditure, insulin sensitivity, leptin sensitivity, and body fat distribution. There are two 'classical' estrogen receptors (ERs): estrogen receptor alpha (ERS1) and estrogen receptor beta (ERS2). Human and murine data indicate ERS1 contributes to metabolic regulation more so than ESR2. For example, there are human inactivating mutations of ERS1 which recapitulate aspects of the metabolic syndrome in both men and women. Much of our understanding of the metabolic roles of ERS1 was initially uncovered in estrogen receptor α-null mice (ERS1(-/-)); these mice display aspects of the metabolic syndrome, including increased body weight, increased visceral fat deposition and dysregulated glucose intolerance. Recent data further implicate ERS1 in specific tissues and neuronal populations as being critical for regulating food intake, energy expenditure, body fat distribution and adipose tissue function. This review will focus predominantly on the role of hypothalamic ERs and their critical role in regulating all aspects of energy homeostasis and metabolism.

PCASSO: a fast and efficient Cα-based method for accurately assigning protein secondary structure elements.

Proteins are often characterized in terms of their primary, secondary, tertiary, and quaternary structure. Algorithms such as define secondary structure of proteins (DSSP) can automatically assign protein secondary structure based on the backbone hydrogen-bonding pattern. However, the assignment of secondary structure elements (SSEs) becomes a challenge when only the Cα coordinates are available. In this work, we present protein C-alpha secondary structure output (PCASSO), a fast and accurate program for assigning protein SSEs using only the Cα positions. PCASSO achieves ∼95% accuracy with respect to DSSP and takes ∼0.1 s using a single processor to analyze a 1000 residue system with multiple chains. Our approach was compared with current state-of-the-art Cα-based methods and was found to outperform all of them in both speed and accuracy. A practical application is also presented and discussed.

N1-Methylpseudouridine and pseudouridine modifications modulate mRNA decoding during translation.

The ribosome utilizes hydrogen bonding between mRNA codons and aminoacyl-tRNAs to ensure rapid and accurate protein production. Chemical modification of mRNA nucleobases can adjust the strength and pattern of this hydrogen bonding to alter protein synthesis. We investigate how the N1-methylpseudouridine (m1Ψ) modification, commonly incorporated into therapeutic and vaccine mRNA sequences, influences the speed and fidelity of translation. We find that m1Ψ does not substantially change the rate constants for amino acid addition by cognate tRNAs or termination by release factors. However, we also find that m1Ψ can subtly modulate the fidelity of amino acid incorporation in a codon-position and tRNA dependent manner in vitro and in human cells. Our computational modeling shows that altered energetics of mRNA:tRNA interactions largely account for the context dependence of the low levels of miscoding we observe on Ψ and m1Ψ containing codons. The outcome of translation on modified mRNA bases is thus governed by the sequence context in which they occur.

Disease related changes in ATAC-seq of iPSC-derived motor neuron lines from ALS patients and controls.

Amyotrophic Lateral Sclerosis (ALS), like many other neurodegenerative diseases, is highly heritable, but with only a small fraction of cases explained by monogenic disease alleles. To better understand sporadic ALS, we report epigenomic profiles, as measured by ATAC-seq, of motor neuron cultures derived from a diverse group of 380 ALS patients and 80 healthy controls. We find that chromatin accessibility is heavily influenced by sex, the iPSC cell type of origin, ancestry, and the inherent variance arising from sequencing. Once these covariates are corrected for, we are able to identify ALS-specific signals in the data. Additionally, we find that the ATAC-seq data is able to predict ALS disease progression rates with similar accuracy to methods based on biomarkers and clinical status. These results suggest that iPSC-derived motor neurons recapitulate important disease-relevant epigenomic changes.

Discovery of selective bioactive small molecules by targeting an RNA dynamic ensemble.

Current approaches used to identify protein-binding small molecules are not suited for identifying small molecules that can bind emerging RNA drug targets. By docking small molecules onto an RNA dynamic ensemble constructed by combining NMR spectroscopy and computational molecular dynamics, we virtually screened small molecules that target the entire structure landscape of the transactivation response element (TAR) from HIV type 1 (HIV-1). We quantitatively predict binding energies for small molecules that bind different RNA conformations and report the de novo discovery of six compounds that bind TAR with high affinity and inhibit its interaction with a Tat peptide in vitro (K(i) values of 710 nM-169 µM). One compound binds HIV-1 TAR with marked selectivity and inhibits Tat-mediated activation of the HIV-1 long terminal repeat by 81% in T-cell lines and HIV replication in an HIV-1 indicator cell line (IC(50) ∼23.1 µM).

In Silico Discovery of Group II Intron RNA Splicing Inhibitors.

Here, we describe the discovery of compounds that inhibit self-splicing in group II introns. Using docking calculations, we targeted the catalytic active site within the Oceanobacillus iheyensis group IIC intron and virtually screened a library of lead-like compounds. From this initial virtual screen, we identified three unique scaffolds that inhibit splicing in vitro. Additional tests revealed that an analog of the lead scaffold inhibits splicing in an intron-dependent manner. Furthermore, this analog exhibited activity against the group II intron from a different class: the yeast ai5γ IIB intron. The splicing inhibitors we identified could serve as chemical tools for developing group II intron-targeted antifungals, and, more broadly, our results highlight the potential of in silico techniques for identifying bioactive hits against structured and functionally complex RNAs.