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Single particle cryo-EM often yields multiple protein conformations within a single dataset, but experimentally deducing the temporal relationship of these conformers within a conformational trajectory is not trivial. Here, we use thermal titration methods and cryo-EM in an attempt to obtain temporal resolution of the conformational trajectory of the vanilloid receptor TRPV1 with resiniferatoxin (RTx) bound. Based on our cryo-EM ensemble analysis, RTx binding to TRPV1 appears to induce intracellular gate opening first, followed by selectivity filter dilation, then pore loop rearrangement to reach the final open state. This apparent conformational wave likely arises from the concerted, stepwise, additive structural changes of TRPV1 over many subdomains. Greater understanding of the RTx-mediated long-range allostery of TRPV1 could help further the therapeutic potential of RTx, which is a promising drug candidate for pain relief associated with advanced cancer or knee arthritis.
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http://dx.doi.org/10.1038/s41467-022-30602-2 | DOI Listing |
J Safety Res
September 2025
Department of Civil & Environmental Engineering, The University of Tennessee, Knoxville, United States. Electronic address:
Introduction: Pedestrian safety is a growing concern in the United States transportation sector, with around 7,500 pedestrian crash fatalities reported in the United States in recent years. Pedestrians are at an even higher risk of crashes at night due to limited visibility and alcohol impairment of the drivers or pedestrians. The U.
View Article and Find Full Text PDFJ Chem Inf Model
September 2025
Department of Chemistry, Delaware State University, Dover, Delaware 19901, United States.
The calculation of the highest occupied molecular orbital-lowest unoccupied molecular orbital (HOMO-LUMO) gap for chemical molecules is computationally intensive using quantum mechanics (QM) methods, while experimental determination is often costly and time-consuming. Machine Learning (ML) offers a cost-effective and rapid alternative, enabling efficient predictions of HOMO-LUMO gap values across large data sets without the need for extensive QM computations or experiments. ML models facilitate the screening of diverse molecules, providing valuable insights into complex chemical spaces and integrating seamlessly into high-throughput workflows to prioritize candidates for experimental validation.
View Article and Find Full Text PDFPLoS One
September 2025
School of Computer Science, CHART Laboratory, University of Nottingham, Nottingham, United Kingdom.
Background And Objective: Male fertility assessment through sperm morphology analysis remains a critical component of reproductive health evaluation, as abnormal sperm morphology is strongly correlated with reduced fertility rates and poor assisted reproductive technology outcomes. Traditional manual analysis performed by embryologists is time-intensive, subjective, and prone to significant inter-observer variability, with studies reporting up to 40% disagreement between expert evaluators. This research presents a novel deep learning framework combining Convolutional Block Attention Module (CBAM) with ResNet50 architecture and advanced deep feature engineering (DFE) techniques for automated, objective sperm morphology classification.
View Article and Find Full Text PDFSci Adv
September 2025
Laoshan Laboratory, Qingdao, China.
Anthropogenic aerosols are an important driver of historical climate change but the climate response is not fully understood and the climate model simulations suffer large uncertainties. On the basis of a multimodel ensemble of historical aerosol forcing simulation for a period of global aerosol increase during 1965 to 1989, here, we show that the precipitation response shares a common southward displacement of tropical rain bands but the magnitude differs markedly among models, accounting for 76% of the intermodel uncertainty in zonal-mean precipitation change. Our analysis of atmospheric energetics further reveals key mechanisms for magnitude uncertainty: aerosol radiative forcing drives, cloud radiative feedback amplifies, and ocean circulation damps the intermodel uncertainty in cross-equatorial atmospheric energy transport change and the meridional shift of tropical rain bands.
View Article and Find Full Text PDFActa Psychiatr Scand
September 2025
Department of Clinical Medicine, Aarhus University, Aarhus, Denmark.
Introduction: Machine learning studies sometimes include a high number of predictors relative to the number of training cases. This increases the risk of overfitting and poor generalizability. A recent study hypothesized that between-trial heterogeneity precluded generalizable outcome prediction in schizophrenia from being achieved.
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