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Objective To assess the impact of Florida’s 3-day opioid prescription supply law, effective July 2018, on opioids dispensed for acute pain patients. Methods Pharmacy claims from a health plan serving a large Florida employer from January 2015 through March 2019 were analyzed. We used an interrupted time series study design accounting for autocorrelation of trends before and after policy change. Acute pain patients met inclusion criteria if they had not received any opioid containing medications in the past 180 days. Patients could contribute to additional new use time if subsequent opioid claims occurred ≥180 days since the previous claim. Outcomes included mean number of units dispensed of the initial opioid prescription, mean morphine milligram equivalents (MMEs) per day of initial prescription by month, and mean total MMEs per initial prescription by month. Results A total of 8,375 enrollees had 10,583 unique opioid starts in the given timeframe. Following the policy, there was an immediate significant decrease in the units dispensed per prescription of 4.9 (95% confidence interval [CI] −8.95, −.82 units). Additionally, there was a significant immediate reduction in total MMEs dispensed per prescription of 25.6 (95% CI −44.76, −6.44 MMEs). Conclusions Among a group of privately-insured plan enrollees in Florida, and as a result of the law, there were significant decreases in the number of units dispensed, and total MMEs of opioid prescriptions. The immediate reduction in new opioid utilization following policy implementation suggests effective policy; however, impacts on chronic pain patients were not assessed.

Lumen integrity in vascularization requires fully differentiated endothelial cells (ECs). Here, we report that endothelial-mesenchymal transitions (EndMTs) emerged in ECs of cerebral arteriovenous malformation (AVMs) and caused disruption of the lumen or lumen disorder. We show that excessive Sry-box 2 (Sox2) signaling was responsible for the EndMTs in cerebral AVMs. EC-specific suppression of Sox2 normalized endothelial differentiation and lumen formation and improved the cerebral AVMs. Epigenetic studies showed that induction of Sox2 altered the cerebral-endothelial transcriptional landscape and identified jumonji domain-containing protein 5 (JMJD5) as a direct target of Sox2. Sox2 interacted with JMJD5 to induce EndMTs in cerebral ECs. Furthermore, we utilized a high-throughput system to identify the β-adrenergic antagonist pronethalol as an inhibitor of Sox2 expression. Treatment with pronethalol stabilized endothelial differentiation and lumen formation, which limited the cerebral AVMs.

Describing the dynamics and conformational landscapes of Intrinsically Disordered Proteins (IDPs) is of paramount importance to understanding their functions. Markov State Models (MSMs) are often used to characterize the dynamics of more structured proteins, but models of IDPs built using conventional MSM modelling protocols can be difficult to interpret due to the inherent nature of IDPs, which exhibit fast transitions between disordered microstates. We propose a new method of determining MSM states from all-atom molecular dynamics simulation data of IDPs by using per-residue secondary structure assignments as input features in a MSM model. Because such secondary structure algorithms use a select set of features for assignment (dihedral angles, contact distances, etc.), they represent a knowledge-based refinement of feature sets used for model-building. This method adds interpretability to IDP conformational landscapes, which are increasingly viewed as composed of transient secondary structure, and allows us to readily use MSM analysis tools in this paradigm. We demonstrate the use of our method with the transcription factor p53 c-terminal domain (p53-CTD), a commonly-studied IDP. We are able to characterize the full secondary structure phase space observed for p53-CTD, and describe characteristics of p53-CTD as a network of transient helical and beta-hairpin structures with different network behaviors in different domains of secondary structure. This analysis provides a novel example of how IDPs can be studied and how researchers might better understand a disordered protein conformational landscape.

The μ Opioid Receptor (μOR) is a G-Protein Coupled Receptor (GPCR) that mediates pain and is a key target for clinically administered analgesics. The current generation of prescribed opiates – drugs that bind to μOR – engender dangerous side effects such as respiratory depression and addiction in part by stabilizing off-target conformations of the receptor. To determine both the key conformations of μOR to atomic resolution as well as the transitions between them, long timescale molecular dynamics (MD) simulations were conducted and analyzed. These simulations predict new and potentially druggable metastable states that have not been observed by crystallography. We applied cutting edge algorithms (e.g., tICA and Transfer Entropy) to guide our analysis and distill the key events and conformations from simulation, presenting a transferrable and systematic analysis scheme. Our approach provides a complete, predictive model of the dynamics, structure of states, and structure–ligand relationships of μOR with broad applicability to GPCR biophysics and medicinal chemistry.

Crystallographic structures and experimental assays of human CXC chemokine receptor type 4 (CXCR4) provide strong evidence for the capacity to homodimerize, potentially as a means of allosteric regulation. Even so, how this homodimer forms and its biological significance has yet to be fully characterized. By applying principles from network analysis, sequence-based approaches such as statistical coupling analysis to determine coevolutionary residues, can be used in conjunction with molecular dynamics simulations to identify residues relevant to dimerization. Here, the predominant coevolution sector lies along the observed dimer interface, suggesting functional relevance. Furthermore, coevolution scoring provides a basis for determining significant nodes, termed hubs, in the network formed by residues found along the interface of the homodimer. These node residues coincide with hotspots indicating potential druggability. Drug design efforts targeting such key residues could potentially result in modulation of binding and therapeutic benefits for disease states, such as lung cancers, lymphomas and latent HIV-1 infection. Furthermore, this method may be applied to any protein–protein interaction.

Often the analysis of time-dependent chemical and biophysical systems produces high-dimensional time-series data for which it can be difficult to interpret which individual features are most salient. While recent work from our group and others has demonstrated the utility of time-lagged co-variate models to study such systems, linearity assumptions can limit the compression of inherently nonlinear dynamics into just a few characteristic components. Recent work in the field of deep learning has led to the development of variational autoencoders (VAE), which are able to compress complex datasets into simpler manifolds. We present the use of a time-lagged VAE, or variational dynamics encoder (VDE), to reduce complex, nonlinear processes to a single embedding with high fidelity to the underlying dynamics. We demonstrate how the VDE is able to capture nontrivial dynamics in a variety of examples, including Brownian dynamics and atomistic protein folding. Additionally, we demonstrate a method for analyzing the VDE model, inspired by saliency mapping, to determine what features are selected by the VDE model to describe dynamics. The VDE presents an important step in applying techniques from deep learning to more accurately model and interpret complex biophysics., Comment: Fixed typos and added references

Summary: MDTraj is a modern, lightweight and efficient software package for analyzing molecular dynamics simulations. MDTraj reads trajectory data from a wide variety of commonly used formats. It provides a large number of trajectory analysis capabilities including RMSD, DSSP secondary structure assignment and the extraction of common order parameters. The package has a strong focus on interoperability with the wider scientific Python ecosystem, bridging the gap between molecular dynamics data and the rapidly-growing collection of industry-standard statistical analysis and visualization tools in Python. Availability: Package downloads, detailed examples and full documentation are available at http://mdtraj.org. The source code is distributed under the GNU Lesser General Public License at https://github.com/simtk/mdtraj.

Crystallographic structures and experimental assays of human CXC chemokine receptor type 4 (CXCR4) provide strong evidence for the capacity to homodimerize, potentially as a means of allosteric regulation. Even so, how this homodimer forms and its biological significance has yet to be fully characterized. By applying principles from network analysis, sequence-based approaches such as statistical coupling analysis to determine coevolutionary residues, can be used in conjunction with molecular dynamics simulations to identify residues relevant to dimerization. Here, the predominant coevolution sector lies along the observed dimer interface, suggesting functional relevance. Furthermore, coevolution scoring provides a basis for determining significant nodes, termed hubs, in the network formed by residues found along the interface of the homodimer. These node residues coincide with hotspots indicating potential druggability. Drug design efforts targeting such key residues could potentially result in modulation of binding and therapeutic benefits for disease states, such as lung cancers, lymphomas and latent HIV-1 infection. Furthermore, this method may be applied to any protein–protein interaction.