Conformational Changes of G Protein‐Coupled Receptors During Their Activation by Agonist Binding

Abstract

The superfamily of G protein‐coupled receptors (GPCRs) is the largest and most diverse group of transmembrane proteins involved in signal transduction. Many of the over 1000 human GPCRs represent important pharmaceutical targets. However, despite high interest in this receptor family, no high‐resolution structure of a human GPCR has been resolved yet. This is mainly due to difficulties in obtaining large quantities of pure and active protein. Until now, only a high‐resolution x‐ray structure of an inactive state of bovine rhodopsin is available. Since no structure of an active state has been solved, information of the GPCR activation process can be gained only by biophysical techniques. In this review, we first describe what is known about the ground state of GPCRs to then address questions about the nature of the conformational changes taking place during receptor activation and the mechanism controlling the transition from the resting to the active state. Finally, we will also address the question to what extent information about the three‐dimensional GPCR structure can be included into pharmaceutical drug design programs.     

THRONE: A New Approach for Accurate Prediction of Human RNA N7-Methylguanosine Sites

N⁷-methylguanosine (m7G) is an essential, ubiquitous, and positively charged modification at the 5′ cap of eukaryotic mRNA, modulating its export, translation, and splicing processes. Although several machine learning (ML)-based computational predictors for m7G have been developed, all utilized specific computational framework. This study is the first instance we explored four different computational frameworks and identified the best approach. Based on that we developed a novel predictor, THRONE (A three-layer ensemble predictor for identifying human RNA N7-methylguanosine sites) to accurately identify m7G sites from the human genome. THRONE employs a wide range of sequence-based features inputted to several ML classifiers and combines these models through ensemble learning. The three-step ensemble learning is as follows: 54 baseline models were constructed in the first layer and the predicted probability of m7G was considered as a new feature vector for the sequential step. Subsequently, six meta-models were created using the new feature vector and their predicted probability was yet again considered as novel features. Finally, random forest was deemed as the best super classifier learner for the final prediction using a systematic approach incorporated with novel features. Interestingly, THRONE outperformed other existing methods in the prediction of m7G sites on both cross-validation analysis and independent evaluation. The proposed method is publicly accessible at: http://thegleelab.org/THRONE/ and expects to help the scientific community identify the putative m7G sites and formulate a novel testable biological hypothesis.     

Demographic resilience may sustain significant coral populations in a 2°C-warmer world

Projections of coral reefs under climate change have important policy implications, but most analyses have focused on the intensification of climate-related physical stress rather than explicitly modelling how coral populations respond to stressors. Here, we analyse the future of the Great Barrier Reef (GBR) under multiple, spatially realistic drivers which allows less impacted sites to facilitate recovery. Under a Representative Concentration Pathway (RCP) 2.6 CMIP5 climate ensemble, where warming is capped at ~2°C, GBR mean coral cover declined mid-century but approached present-day levels towards 2100. This is considerably more optimistic than most analyses. However, under RCP4.5, mean coral cover declined by >80% by late-century, and reached near zero under RCP ≥6.0. While these models do not allow for adaptation, they significantly extend past studies by revealing demographic resilience of coral populations to low levels of additional warming, though more pessimistic outcomes might be expected under CMIP6. Substantive coral populations under RCP2.6 would facilitate long-term genetic adaptation, adding value to ambitious greenhouse emissions mitigation.     

Ensemble clustering for step data via binning

This paper considers the clustering problem of physical step count data recorded on wearable devices. Clustering step data give an insight into an individual's activity status and further provide the groundwork for health‐related policies. However, classical methods, such as K‐means clustering and hierarchical clustering, are not suitable for step count data that are typically high‐dimensional and zero‐inflated. This paper presents a new clustering method for step data based on a novel combination of ensemble clustering and binning. We first construct multiple sets of binned data by changing the size and starting position of the bin, and then merge the clustering results from the binned data using a voting method. The advantage of binning, as a critical component, is that it substantially reduces the dimension of the original data while preserving the essential characteristics of the data. As a result, combining clustering results from multiple binned data can provide an improved clustering result that reflects both local and global structures of the data. Simulation studies and real data analysis were carried out to evaluate the empirical performance of the proposed method and demonstrate its general utility.     

Excipient exchange in the comparison of preparations of the same biologic made by different manufacturing processes: An exploratory study with recombinant human growth hormone (rhGH)

Demonstrations of bio-similarity between subsequent entry (follow-on) biologics and innovator’s formulated drug products may depend upon methods that either remove excipients completely or allow the exchange of excipients to give equivalent formulations. Excipient exchange through dialysis is perhaps the simplest of such methods but its use has been hotly debated. This debate, in the absence of published data, has relied largely on theoretical considerations. This study presents data that indicate that excipient exchange can allow comparisons of different formulations of the same therapeutic protein. The use of excipient exchange to and from one concentration of mannitol to another or to a mixture of glycine and mannitol was reproducibly demonstrated for recombinant human growth hormone (rhGH). We show that marketed rhGH products from several different manufacturers exhibit differences in conformational stability when compared directly. These differences, however, are shown to be the result of differences in formulation rather than in the drug substance itself and were removed through excipient exchange. The data presented, therefore, also indicate that failure to assure a common excipient background can lead to erroneous conclusions about the similarities and differences in the physico-chemical properties of two preparations of the same therapeutic protein made by different manufacturing processes.     

Flexible species distribution modelling methods perform well on spatially separated testing data

Aim

To assess whether flexible species distribution models that perform well at nearby testing locations still perform strongly when evaluated on spatially separated testing data.

Location

Australian Wet Tropics (AWT), Ontario, Canada (CAN), north-east New South Wales, Australia (NSW), New Zealand (NZ), five countries of South America (SA), and Switzerland (SWI).

Time period

Most species data were collected between 1950 and 2000.

Major taxa studied

Birds, mammals, plants and reptiles.

Methods

We compared 10 species distribution modelling methods with varying flexibility in terms of the allowed complexity of their fitted functions [boosted regression trees (BRT), generalized additive model (GAM), multivariate adaptive regression splines (MARS), maximum entropy (MaxEnt), support vector machine (SVM), variants of generalized linear model (GLM) and random forest (RF), and an Ensemble model]. We used established practices for model selection to avoid overfitting, including parameter tuning in learning methods. Models were trained on presence–background data for 171 species and tested on presence–absence data. Training and testing data were separated using both random and spatial partitioning, the latter based on 75-km blocks. We calculated the average performance and mean rank of the methods (focussing on the area under the receiver operating characteristic and precision-recall gain curves, and correlation) and assessed the statistical significance of the differences between them.

Results

The ranking of methods did not change when evaluated on spatially separated testing data. Methods with the strongest predictive performance were nonparametric methods known to be flexible. An ensemble formed by averaging predictions of five pre-selected modelling methods was the best model in both random and spatial partitioning, followed by MaxEnt and a variant of random forest.

Main conclusions

Whilst some modellers expect methods limited to simple smooth functions to predict better spatially separated data, we found no evidence of that using blocks of 75 km. We conclude that flexible models that are tuned well enough to avoid overfitting are effective at predicting to spatially distinct areas.     

400 MHz NMR study on the C-terminal fragment 21–28 of vasoactive intestinal peptide

A conformational study of the C-terminal fragment 21–28 of vasoactive intestinal peptide (VIP) was carried out by high resolution NMR spectroscopy. All spectral data were recorded with a Brüker 400 MHz spectrometer. The correct assignment of peaks was determined by specific homonuclear decoupling and by titration. The chemical shifts of amide protons were measured as a function of temperature in order to detect the presence of intramolecular hydrogen bonds. A tridimensional structure is proposed for the peptide.     

Probing the conformational switch of I-motif DNA using tunable resistive pulse sensing

I-motif DNA, which can fold and unfold reversibly in various environments, plays a significant role in DNA nanotechnology and biological functions. Thus, it is of fundamental importance to identify the different conformations of i-motif DNA. Here, we demonstrate that distinct structures of i-motif DNA conjugated to polystyrene spheres can be distinguished through tunable resistive pulse sensing technique. When dispersed in acidic buffer, i-motif DNA coating on polystyrene spheres would fold into quadruplex structure and subsequently induce an apparent increase in the translocation duration time upon passing through a nanopore due to the shielding effect of the surface charge of the nanospheres. However, if the DNA strands don't have conformational changes in acidic buffer, little shift can be observed in the translocation duration time of the DNA functionalized polystyrene spheres. A before-and-after assay was also performed to illustrate the fast speed of i-motif DNA folding using this technique. The successful implementation of tunable resistive pulse sensing to monitor the conformational transition of i-motif DNA provides a potential tool to detect the structural changes of DNA and an alternative approach to study the function of DNA structures.