96 research outputs found

    Identification of small molecule inhibitors of Interleukin-18

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    Interleukin-18 (IL-18) is a pleiotropic pro-inflammatory cytokine belonging to the IL-1 superfamily. IL-18 plays an important role in host innate and adaptive immune defense but its aberrant activities are also associated with inflammatory diseases such as rheumatoid arthritis and Crohn's disease. IL-18 activity is modulated in vivo by its naturally occurring antagonist, IL-18 Binding Protein (IL-18BP). Recent crystal structures of human IL-18 (hIL-18) in complex with its antagonists or cognate receptor(s) have revealed a conserved binding interface on hIL-18. Through virtual screening of the National Cancer Institute Diversity Set II and in vitro competitive ELISA we have identified three compounds (NSC201631, NSC80734, and NSC61610) that disrupt hIL-18 binding to the ectromelia virus IL-18BP. Through cell-based bioassay, we show that NSC80734 inhibits IL-18-induced production of IFN-γ in a dose-dependent manner with an EC50 of ~250 nM. Our results and methodology presented here demonstrate the feasibility of developing small molecule inhibitors that specifically target the rather large interface of IL-18 that is involved in extensive protein-protein interactions with both IL-18BP and its cognate receptor(s). Our data therefore provide the basis for an approach by which small molecules can be identified that modulate IL-18 activity

    Uncertainty Guided Adaptive Warping for Robust and Efficient Stereo Matching

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    Correlation based stereo matching has achieved outstanding performance, which pursues cost volume between two feature maps. Unfortunately, current methods with a fixed model do not work uniformly well across various datasets, greatly limiting their real-world applicability. To tackle this issue, this paper proposes a new perspective to dynamically calculate correlation for robust stereo matching. A novel Uncertainty Guided Adaptive Correlation (UGAC) module is introduced to robustly adapt the same model for different scenarios. Specifically, a variance-based uncertainty estimation is employed to adaptively adjust the sampling area during warping operation. Additionally, we improve the traditional non-parametric warping with learnable parameters, such that the position-specific weights can be learned. We show that by empowering the recurrent network with the UGAC module, stereo matching can be exploited more robustly and effectively. Extensive experiments demonstrate that our method achieves state-of-the-art performance over the ETH3D, KITTI, and Middlebury datasets when employing the same fixed model over these datasets without any retraining procedure. To target real-time applications, we further design a lightweight model based on UGAC, which also outperforms other methods over KITTI benchmarks with only 0.6 M parameters.Comment: Accepted by ICCV202

    Structure-guided design of an Hsp90â N-terminal isoform-selective inhibitor

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    The 90 kDa heat shock protein (Hsp90) is a molecular chaperone responsible for folding proteins that are directly associated with cancer progression. Consequently, inhibition of the Hsp90 protein folding machinery results in a combinatorial attack on numerous oncogenic pathways. Seventeen small-molecule inhibitors of Hsp90 have entered clinical trials, all of which bind the Hsp90 N-terminus and exhibit pan-inhibitory activity against all four Hsp90 isoforms. pan-Inhibition of Hsp90 appears to be detrimental as toxicities have been reported alongside induction of the pro-survival heat shock response. The development of Hsp90 isoform-selective inhibitors represents an alternative approach towards the treatment of cancer that may limit some of the detriments. Described herein is a structure-based approach to design isoform-selective inhibitors of Hsp90β, which induces the degradation of select Hsp90 clients without concomitant induction of Hsp90 levels. Together, these initial studies support the development of Hsp90β-selective inhibitors as a method to overcome the detriments associated with pan-inhibition

    Structure of the unique SEFIR domain from human interleukin 17 receptor A reveals a composite ligand-binding site containing a conserved a-helix for Act1 binding and IL-17 signaling

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    Interleukin 17 (IL-17) cytokines play a crucial role in mediating inflammatory and autoimmune diseases. A unique intracellular signaling domain termed SEFIR is found within all IL-17 receptors (IL-17Rs) as well as the key adaptor protein Act1. SEFIR-mediated protein–protein interaction is a crucial step in IL-17 cytokine signaling. Here, the 2.3 Å resolution crystal structure of the SEFIR domain of IL-17RA, the most commonly shared receptor for IL-17 cytokine signaling, is reported. The structure includes the complete SEFIR domain and an additional α-helical C-terminal extension, which pack tightly together to form a compact unit. Structural comparison between the SEFIR domains of IL-17RA and IL-17RB reveals substantial differences in protein topology and folding. The uniquely long insertion between strand βC and helix αC in IL-17RA SEFIR is mostly well ordered, displaying a helix (αCC′ins) and a flexible loop (CC′). The DD′ loop in the IL-17RA SEFIR structure is much shorter; it rotates nearly 90° with respect to the counterpart in the IL-17RB SEFIR structure and shifts about 12 Å to accommodate the αCC′ins helix without forming any knots. Helix αC was identified as critical for its interaction with Act1 and IL-17-stimulated gene expression. The data suggest that the heterotypic SEFIR–SEFIR association via helix αC is a conserved and signature mechanism specific for IL-17 signaling. The structure also suggests that the downstream motif of IL-17RA SEFIR together with helix αC could provide a composite ligand-binding surface for recruiting Act1 during IL-17 signaling.Peer reviewedBiochemistry and Molecular Biolog

    Large expert-curated database for benchmarking document similarity detection in biomedical literature search

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    Document recommendation systems for locating relevant literature have mostly relied on methods developed a decade ago. This is largely due to the lack of a large offline gold-standard benchmark of relevant documents that cover a variety of research fields such that newly developed literature search techniques can be compared, improved and translated into practice. To overcome this bottleneck, we have established the RElevant LIterature SearcH consortium consisting of more than 1500 scientists from 84 countries, who have collectively annotated the relevance of over 180 000 PubMed-listed articles with regard to their respective seed (input) article/s. The majority of annotations were contributed by highly experienced, original authors of the seed articles. The collected data cover 76% of all unique PubMed Medical Subject Headings descriptors. No systematic biases were observed across different experience levels, research fields or time spent on annotations. More importantly, annotations of the same document pairs contributed by different scientists were highly concordant. We further show that the three representative baseline methods used to generate recommended articles for evaluation (Okapi Best Matching 25, Term Frequency-Inverse Document Frequency and PubMed Related Articles) had similar overall performances. Additionally, we found that these methods each tend to produce distinct collections of recommended articles, suggesting that a hybrid method may be required to completely capture all relevant articles. The established database server located at https://relishdb.ict.griffith.edu.au is freely available for the downloading of annotation data and the blind testing of new methods. We expect that this benchmark will be useful for stimulating the development of new powerful techniques for title and title/abstract-based search engines for relevant articles in biomedical research.Peer reviewe

    Bionic Design Method of a Non-Uniform Lattice Structure for a Landing Footpad

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    Due to its excellent performance and high design freedom, the lattice structure has shown excellent capabilities and considerable potential in aerospace and other fields. Inspired by the bamboo structure, a lattice cell configuration namely BCC4IZ is designed and a lattice alternative layout is obtained. Then, a design and modeling method for non-uniform lattice structures is proposed. Four designs of the landing footpad with different kinds of lattice cells are developed. A series of dynamic explicit finite element simulations were conducted to evaluate and compare the energy absorption and capacity of resisting impact deformation performance of different designs. The results show that the combination of the bionic design and the lattice structure can effectively improve the performance of the lattice-filled footpad. This study proves the feasibility and potential of application for bionic design in lattice structure

    Preparation and Mechanical Properties of Aligned Discontinuous Carbon Fiber Composites

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    Aligned discontinuous carbon fiber composites were fabricated from aligned discontinuous carbon fiber prepreg, which was prepared from continuous carbon fiber prepreg via mechanical high-frequency cutting. The internal quality and mechanical properties were characterized and compared with continuous carbon fiber composites. The results show that the internal quality of the aligned discontinuous carbon fiber composites is fine and the mechanical properties have high retention rate after the fibers were cut compared with continuous carbon fiber composites. The minimum retention rate of 0° tension strength is 63%, and the modulus is basically without chang. The minimum retention rates of 0° flexural strength and modulus are 85% and 78% respectively. The mechanical properties of aligned discontinuous carbon fiber composites are much better than that of the random oriented carbon fiber composites
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