The use of computer-aided methods have continued to propel accelerated drug discovery across various disease models, interestingly allowing the specific inhibition of pathogenic targets. Chloride Intracellular Channel Protein 4 (CLIC4) is a novel class of intracellular ion channel highly implicated in tumor and vascular biology. It regulates cell proliferation, apoptosis and angiogenesis; and is involved in multiple pathologic signaling pathways. Absence of specific inhibitors however impedes its advancement to translational research. Here, we integrate structural bioinformatics and experimental research approaches for the discovery and validation of small-molecule inhibitors of CLIC4. High-affinity allosteric binders were identified from a library of 1615 Food and Drug Administration (FDA)-approved drugs via a high-performance computing-powered blind-docking approach, resulting in the selection of amphotericin B and rapamycin. NMR assays confirmed the binding and conformational disruptive effects of both drugs while they also reversed stress-induced membrane translocation of CLIC4 and inhibited endothelial cell migration. Structural and dynamics simulation studies further revealed that the inhibitory mechanisms of these compounds were hinged on the allosteric modulation of the catalytic glutathione (GSH)-like site loop and the extended catalytic β loop which may elicit interference with the catalytic activities of CLIC4. Structure-based insights from this study provide the basis for the selective targeting of CLIC4 to treat the associated pathologies.

Claudins (Cldns) define a family of transmembrane proteins that are the major determinants of the tight junction integrity and tissue selectivity. They promote the formation of either barriers or ion-selective channels at the interface between two facing cells, across the paracellular space. Multiple Cldn subunits form complexes that include cis- (intracellular) interactions along the membrane of a single cell and trans- (intercellular) interactions across adjacent cells. The first description of Cldn assemblies was provided by electron microscopy, while electrophysiology, mutagenesis and cell biology experiments addressed the functional role of different Cldn homologs. However, the investigation of the molecular details of Cldn subunits and complexes are hampered by the lack of experimental native structures, currently limited to Cldn15. The recent implementation of computer-based techniques greatly contributed to the elucidation of Cldn properties. Molecular dynamics simulations and docking calculations were extensively used to refine the first Cldn multimeric model postulated from the crystal structure of Cldn15, and contributed to the introduction of a novel, alternative, arrangement. While both these multimeric assemblies were found to account for the physiological properties of some family members, they gave conflicting results for others. In this review, we illustrate the major findings on Cldn-based systems that were achieved by using state-of-the-art computational methodologies. The information provided by these results could be useful to improve the characterization of the Cldn properties and help the design of new efficient strategies to control the paracellular transport of drugs or other molecules.

Poor efficacy of some anthelmintics and rising concerns about the widespread drug resistance have highlighted the need for new drug discovery. The parasitic nematode Haemonchus contortus is an important model organism widely used for studies of drug resistance and drug screening with the current gold standard being the motility assay. We applied a deep learning approach Mask R-CNN for analysing motility videos containing varying rates of motile worms and compared it to other commonly used algorithms with different levels of complexity, namely the Wiggle Index and the Wide Field-of-View Nematode Tracking Platform. Mask R-CNN consistently outperformed the other algorithms in terms of the detection of worms as well as the precision of motility forecasts, having a mean absolute percentage error of 7.6% and a mean absolute error of 5.6% for the detection and motility forecasts, respectively. Using Mask R-CNN for motility assays confirmed the common problem with algorithms that use non-maximum suppression in detecting overlapping objects, which negatively impacts the overall precision. The use of intersect over union as a measure of the classification of motile / non-motile instances had an overall accuracy of 89%, indicating that it is a viable alternative to previously used methods based on movement characteristics, such as body bends. In comparison to the existing methods evaluated here, Mask R-CNN performed better and we anticipate that this method will broaden the number of possible approaches to video analysis of worm motility.

UNASSIGNED: Both Hip Dysplasia(DDH) and Femoro-acetabular-Impingement(FAI) are complex three-dimensional hip pathologies causing hip pain and osteoarthritis in young patients. 3D-MRI-based models were used for radiation-free computer-assisted surgical planning. Automatic segmentation of MRI-based 3D-models are preferred because manual segmentation is time-consuming.To investigate(1) the difference and(2) the correlation for femoral head coverage(FHC) between automatic MR-based and manual CT-based 3D-models and (3) feasibility of preoperative planning in symptomatic patients with hip diseases.
UNASSIGNED: We performed an IRB-approved comparative, retrospective study of 31 hips(26 symptomatic patients with hip dysplasia or FAI). 3D MRI sequences and CT scans of the hip were acquired. Preoperative MRI included axial-oblique T1 VIBE sequence(0.8 mm3 isovoxel) of the hip joint. Manual segmentation of MRI and CT scans were performed. Automatic segmentation of MRI-based 3D-models was performed using deep learning.
UNASSIGNED: (1)The difference between automatic and manual segmentation of MRI-based 3D hip joint models was below 1 mm(proximal femur 0.2 ± 0.1 mm and acetabulum 0.3 ± 0.5 mm). Dice coefficients of the proximal femur and the acetabulum were 98 % and 97 %, respectively. (2)The correlation for total FHC was excellent and significant(r = 0.975, p < 0.001) between automatic MRI-based and manual CT-based 3D-models. Correlation for total FHC (r = 0.979, p < 0.001) between automatic and manual MR-based 3D models was excellent.(3)Preoperative planning and simulation of periacetabular osteotomy was feasible in all patients(100 %) with hip dysplasia or acetabular retroversion.
UNASSIGNED: Automatic segmentation of MRI-based 3D-models using deep learning is as accurate as CT-based 3D-models for patients with hip diseases of childbearing age. This allows radiation-free and patient-specific preoperative simulation and surgical planning of periacetabular osteotomy for patients with DDH.