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Hydrogen-deuterium eXchange mass spectrometry (HDX-MS) is increasingly used in drug development to locate binding sites and to identify allosteric effects in drug/target interactions. However, the potential of this technique to quantitatively analyze drug candidate libraries remains largely unexplored. Here, a collection of 13 WDR5-targeting small molecules with surface plasmon resonance (SPR) dissociation coefficients ( K D ) ranging from 20 nM to ∼116 μM were characterized using differential HDX-MS (ΔHDX-MS). Conventional qualitative analysis of the ΔHDX-MS data set revealed the binding interfaces for all compounds and allosteric effects where present. We then demonstrated that ΔHDX-MS signal-to-noise (S/N) not only can rank library-relative affinity but also can accurately predict K D from a calibration curve constructed from high-quality SPR data. Three methods for S/N calculation are explored, each suitable for libraries with different characteristics. Our results demonstrate the potential for ΔHDX-MS use in drug candidate library affinity validation and/or determination while simultaneously characterizing structure.

Helicases have emerged as promising targets for the development of antiviral drugs; however, the family remains largely undrugged. To support the focused development of viral helicase inhibitors we identified, collected, and integrated all chemogenomics data for all available helicases from the ChEMBL database. After thoroughly curating and enriching the data with relevant annotations we have created a derivative database of helicase inhibitors which we dubbed Heli-SMACC (Helicase-targeting SMAll Molecule Compound Collection). The current version of Heli-SMACC contains 20,432 bioactivity entries for viral, human, and bacterial helicases. We have selected 30 compounds with promising viral helicase activity and tested them in a SARS-CoV-2 NSP13 ATPase assay. Twelve compounds demonstrated ATPase inhibition and a consistent dose-response curve. The Heli-SMACC database may serve as a reference for virologists and medicinal chemists working on the development of novel helicase inhibitors. Heli-SMACC is publicly available at https://smacc.mml.unc.edu., Competing Interests: Conflict of interest AT and ENM are co-founders of Predictive, LLC, which develops novel alternative methods and software for toxicity prediction. All other authors have nothing to disclose.

We herewith applied a priori a generic hit identification method (POEM) for difficult targets of known three-dimensional structure, relying on the simple knowledge of physicochemical and topological properties of a user-selected cavity. Searching for local similarity to a set of fragment-bound protein microenvironments of known structure, a point cloud registration algorithm is first applied to align known subpockets to the target cavity. The resulting alignment then permits us to directly pose the corresponding seed fragments in a target cavity space not typically amenable to classical docking approaches. Last, linking potentially connectable atoms by a deep generative linker enables full ligand enumeration. When applied to the WD40 repeat (WDR) central cavity of leucine-rich repeat kinase 2 (LRRK2), an unprecedented binding site, POEM was able to quickly propose 94 potential hits, five of which were subsequently confirmed to bind in vitro to LRRK2-WDR.

Breast cancer linked with BRCA1/2 mutations commonly recur and resist current therapies, including PARP inhibitors. Given the lack of effective targeted therapies for BRCA1-mutant cancers, we sought to identify novel targets to selectively kill these cancers. Here, we report that loss of RNF8 significantly protects Brca1-mutant mice against mammary tumorigenesis. RNF8 deficiency in human BRCA1-mutant breast cancer cells was found to promote R-loop accumulation and replication fork instability, leading to increased DNA damage, senescence, and synthetic lethality. Mechanistically, RNF8 interacts with XRN2, which is crucial for transcription termination and R-loop resolution. We report that RNF8 ubiquitylates XRN2 to facilitate its recruitment to R-loop-prone genomic loci and that RNF8 deficiency in BRCA1-mutant breast cancer cells decreases XRN2 occupancy at R-loop-prone sites, thereby promoting R-loop accumulation, transcription-replication collisions, excessive genomic instability, and cancer cell death. Collectively, our work identifies a synthetic lethal interaction between RNF8 and BRCA1, which is mediated by a pathological accumulation of R-loops., (© The Author(s) 2023. Published by Oxford University Press on behalf of Nucleic Acids Research.)

The Southern Ocean upper-layer freshwater balance exerts a global climatic influence by modulating density stratification and biological productivity, and hence the exchange of heat and carbon between the atmosphere and the ocean interior. It is thus important to understand and quantify the time-varying freshwater inputs, which is challenging from measurements of salinity alone. Here we use seawater oxygen isotopes from samples collected between 2016 and 2021 along a transect spanning the Scotia and northern Weddell Seas to separate the freshwater contributions from sea ice and meteoric sources. The unprecedented retreat of sea ice in 2016 is evidenced as a strong increase in sea ice melt across the northern Weddell Sea, with surface values increasing approximately two percentage points between 2016 and 2018 and column inventories increasing approximately 1 to 2 m. Surface meteoric water concentrations exceeded 4% in early 2021 close to South Georgia due to meltwater from the A68 megaberg; smaller icebergs may influence meteoric water at other times also. Both these inputs highlight the importance of a changing cryosphere for upper-ocean freshening; potential future sea ice retreats and increases in iceberg calving would enhance the impacts of these freshwater sources on the ocean and climate. This article is part of a discussion meeting issue 'Heat and carbon uptake in the Southern Ocean: the state of the art and future priorities'.

Oceanographic changes adjacent to Antarctica have global climatic and ecological impacts. However, this is the most challenging place in the world to obtain marine data due to its remoteness and inhospitable nature, especially in winter. Here, we present more than 2000 Conductivity-Temperature-Depth (CTD) profiles and associated water sample data collected with (almost uniquely) full year-round coverage from the British Antarctic Survey Rothera Research Station at the west Antarctic Peninsula. Sampling is conducted from a small boat or a sled, depending on the sea ice conditions. When conditions allow, sampling is twice weekly in summer and weekly in winter, with profiling to nominally 500 m and with discrete water samples taken at 15 m water depth. Daily observations are made of the sea ice conditions in the area. This paper presents the first 20 years of data collection, 1997-2017. This time series represents a unique and valuable resource for investigations of the high-latitude ocean's role in climate change, ocean/ice interactions, and marine biogeochemistry and carbon drawdown., (© 2023. The Author(s).)

RAS-mediated human cell transformation requires inhibition of the tumor suppressor protein phosphatase 2A (PP2A). However, the phosphoprotein targets and cellular processes in which RAS and PP2A activities converge in human cancers have not been systematically analyzed. Here, we discover that phosphosites co-regulated by RAS and PP2A are enriched on proteins involved in epigenetic gene regulation. As examples, RAS and PP2A co-regulate the same phosphorylation sites on HDAC1/2, KDM1A, MTA1/2, RNF168, and TP53BP1. We validate RAS- and PP2A-elicited regulation of HDAC1/2 chromatin recruitment, of RNF168-TP53BP1 interaction, and of gene expression. Consistent with their known synergistic effects in cancer, RAS activation and PP2A inhibition resulted in epigenetic reporter derepression and activation of oncogenic transcription. Transcriptional derepression by PP2A inhibition was associated with an increase in euchromatin and a decrease in global DNA methylation. Collectively, the results indicate that epigenetic protein complexes constitute a significant point of convergence for RAS hyperactivity and PP2A inhibition in cancer. Furthermore, the work provides an important resource for future studies focusing on phosphoregulation of epigenetic gene regulation in cancer and in other RAS/PP2A-regulated cellular processes., (© 2023 Aakula et al.)

Biallelic inactivation of the tumour suppressor gene Von Hippel-Lindau (VHL) occurs in the vast majority of clear cell renal cell carcinoma (ccRCC) instances, disrupting cellular oxygen-sensing mechanisms to yield a state of persistent pseudo-hypoxia, defined as a continued hypoxic response despite the presence of adequate oxygen levels. However, loss of VHL alone is often insufficient to drive oncogenesis. Results from genomic studies have shown that co-deletions of VHL with one (or more) of three genes encoding proteins involved in chromatin modification and remodelling, polybromo-1 gene (PBRM1), BRCA1-associated protein 1 (BAP1) and SET domain-containing 2 (SETD2), are common and important co-drivers of tumorigenesis. These genes are all located near VHL on chromosome 3p and are often altered following cytogenetic rearrangements that lead to 3p loss and precede the establishment of ccRCC. These three proteins have multiple roles in the regulation of crucial cancer-related pathways, including protection of genomic stability, antagonism of polycomb group (PcG) complexes to maintain a permissive transcriptional landscape in physiological conditions, and regulation of genes that mediate responses to immune checkpoint inhibitor therapy. An improved understanding of these mechanisms will bring new insights regarding cellular drivers of ccRCC growth and therapy response and, ultimately, will support the development of novel translational therapeutics., (© 2022. Springer Nature Limited.)

Access to healthcare, including physiotherapy, is increasingly occurring through virtual formats. At-home adherence to physical therapy programs is often poor and few tools exist to objectively measure participation. The aim of this study was to develop and evaluate the potential for performing automatic, unsupervised video-based monitoring of at-home low-back and shoulder physiotherapy exercises using a mobile phone camera. Joint locations were extracted from the videos of healthy subjects performing low-back and shoulder physiotherapy exercises using an open source pose detection framework. A convolutional neural network was trained to classify physiotherapy exercises based on the segments of keypoint time series data. The model's performance as a function of input keypoint combinations was studied in addition to its robustness to variation in the camera angle. The CNN model achieved optimal performance using a total of 12 pose estimation landmarks from the upper and lower body (low-back exercise classification: 0.995 ± 0.009; shoulder exercise classification: 0.963 ± 0.020). Training the CNN on a variety of angles was found to be effective in making the model robust to variations in video filming angle. This study demonstrates the feasibility of using a smartphone camera and a supervised machine learning model to effectively classify at-home physiotherapy participation and could provide a low-cost, scalable method for tracking adherence to physical therapy exercise programs in a variety of settings.

Background: Physiotherapy is a critical element in the successful conservative management of low back pain (LBP). A gold standard for quantitatively measuring physiotherapy participation is crucial to understanding physiotherapy adherence in managing recovery from LBP., Objective: This study aimed to develop and evaluate a system with wearable inertial sensors to objectively detect the performance of unsupervised exercises for LBP comprising movement in multiple planes and sitting postures., Methods: A quantitative classification design was used within a machine learning framework to detect exercise performance and posture in a cohort of healthy participants. A set of 8 inertial sensors were placed on the participants, and data were acquired as they performed 7 McKenzie low back exercises and 3 sitting posture positions. Engineered time series features were extracted from the data and used to train 9 models by using a 6-fold cross-validation approach, from which the best 2 models were selected for further study. In addition, a convolutional neural network was trained directly on the time series data. A feature importance analysis was performed to identify sensor locations and channels that contributed the most to the models. Finally, a subset of sensor locations and channels was included in a hyperparameter grid search to identify the optimal sensor configuration and best performing algorithms for exercise and posture classification. The final models were evaluated using the F 1 score in a 10-fold cross-validation approach., Results: In total, 19 healthy adults with no history of LBP each completed at least one full session of exercises and postures. Random forest and XGBoost (extreme gradient boosting) models performed the best out of the initial set of 9 engineered feature models. The optimal hardware configuration was identified as a 3-sensor setup-lower back, left thigh, and right ankle sensors with acceleration, gyroscope, and magnetometer channels. The XGBoost model achieved the highest exercise (F 1 score: mean 0.94, SD 0.03) and posture (F 1 score: mean 0.90, SD 0.11) classification scores. The convolutional neural network achieved similar results with the same sensor locations, using only the accelerometer and gyroscope channels for exercise classification (F 1 score: mean 0.94, SD 0.02) and the accelerometer channel alone for posture classification (F 1 score: mean 0.88, SD 0.07)., Conclusions: This study demonstrates the potential of a 3-sensor lower body wearable solution (eg, smart pants) that can identify exercises in multiple planes and proper sitting postures, which is suitable for the treatment of LBP. This technology has the potential to improve the effectiveness of LBP rehabilitation by facilitating quantitative feedback, early problem diagnosis, and possible remote monitoring., (©Abdalrahman Alfakir, Colin Arrowsmith, David Burns, Helen Razmjou, Michael Hardisty, Cari Whyne. Originally published in JMIR Rehabilitation and Assistive Technology (https://rehab.jmir.org), 23.08.2022.)

A significant challenge for a supervised learning approach to inertial human activity recognition is the heterogeneity of data generated by individual users, resulting in very poor performance for some subjects. We present an approach to personalized activity recognition based on deep feature representation derived from a convolutional neural network (CNN). We experiment with both categorical cross-entropy loss and triplet loss for training, and describe a novel loss function based on subject triplets. We evaluate these methods on three publicly available inertial human activity recognition datasets (MHEALTH, WISDM, and SPAR) comparing classification accuracy, out-of-distribution activity detection, and generalization to new activity classes. The proposed triplet algorithm achieved an average 96.7% classification accuracy across tested datasets versus the 87.5% achieved by the baseline CNN algorithm. We demonstrate that personalized algorithms, and, in particular, the proposed novel triplet loss algorithms, are more robust to inter-subject variability and thus exhibit better performance on classification and out-of-distribution detection tasks.