Structure-Based Predictive Models for Allosteric Hot Spots

In allostery, a binding event at one site in a protein modulates the behavior of a distant site. Identifying residues that relay the signal between sites remains a challenge. We have developed predictive models using support-vector machines, a widely used machine-learning method. The training data set consisted of residues classified as either hotspots or non-hotspots based on experimental characterization of point mutations from a diverse set of allosteric proteins. Each residue had an associated set of calculated features. Two sets of features were used, one consisting of dynamical, structural, network, and informatic measures, and another of structural measures defined by Daily and Gray [1]. The resulting models performed well on an independent data set consisting of hotspots and non-hotspots from five allosteric proteins. For the independent data set, our top 10 models using Feature Set 1 recalled 68–81% of known hotspots, and among total hotspot predictions, 58–67% were actual hotspots. Hence, these models have precision P = 58–67% and recall R = 68–81%. The corresponding models for Feature Set 2 had P = 55–59% and R = 81–92%. We combined the features from each set that produced models with optimal predictive performance. The top 10 models using this hybrid feature set had R = 73–81% and P = 64–71%, the best overall performance of any of the sets of models. Our methods identified hotspots in structural regions of known allosteric significance. Moreover, our predicted hotspots form a network of contiguous residues in the interior of the structures, in agreement with previous work. In conclusion, we have developed models that discriminate between known allosteric hotspots and non-hotspots with high accuracy and sensitivity. Moreover, the pattern of predicted hotspots corresponds to known functional motifs implicated in allostery, and is consistent with previous work describing sparse networks of allosterically important residues

t10c12 Conjugated Linoleic Acid Suppresses HER2 Protein and Enhances Apoptosis in SKBr3 Breast Cancer Cells: Possible Role of COX2

BACKGROUND: HER2-targeted therapy with the monoclonal antibody trastuzumab (Herceptin) has improved disease-free survival for women diagnosed with HER2-positive breast cancers; however, treatment resistance and disease progression are not uncommon. Current data suggest that resistance to treatment in HER2 cancers may be a consequence of NF-kappaB overexpression and increased COX2-derived prostaglandin E2 (PGE(2)). Conjugated linoleic acid (CLA) has been shown to have anti-tumor properties and to inhibit NF-kappaB activity and COX2. METHODS: In this study, HER2-overexpressing SKBr3 breast cancer cells were treated with t10c12 CLA. Protein expression of the HER2 receptor, nuclear NF-kappaB p65, and total and phosphorylated IkappaB were examined by western blot and immunofluorescence. PGE(2) levels were determined by ELISA. Proliferation was measured by metabolism of 3-(4, 5-Dimethylthiazol-2-yl)-2, 5-diphenyltetrazolium bromide (MTT), and apoptosis was measured by FITC-conjugated Annexin V staining and flow cytometry. RESULTS/CONCLUSIONS: We observed a significant decrease in HER2 protein expression on western blot following treatment with 40 and 80 microM t10c12 CLA (p<0.01 and 0.001, respectively) and loss of HER2 protein in cells using immunoflourescence that was most pronounced at 80 microM. Protein levels of nuclear NF-kappaB p65 were also significantly reduced at the 80 microM dose. This was accompanied by a significant decrease in PGE(2) levels (p = 0.05). Pretreatment with t10c12 CLA significantly enhanced TNFalpha-induced apoptosis and the anti-proliferative action of trastuzumab (p = 0.05 and 0.001, respectively). These data add to previous reports of an anti-tumor effect of t10c12 CLA and suggest an effect on the HER2 oncogene that may be through CLA mediated downregulation of COX2-derived PGE(2)

Determination of the ratio of b-quark fragmentation fractions fs/fd in pp collisions at √s = 7 TeV with the ATLAS Detector

With an integrated luminosity of 2.47  fb−1 recorded by the ATLAS experiment at the LHC, the exclusive decays B 0s→J/ψϕ and B0d→J/ψK*0 of B mesons produced in pp collisions at √s=7  TeV are used to determine the ratio of fragmentation fractions fs/fd. From the observed B0s→J/ψϕ and B0d→J/ψK*0 yields, the quantity (fs/fd)[B(B0s→J/ψϕ)/B(B 0d→J/ψK*0)] is measured to be 0.199±0.004(stat)±0.008(syst). Using a recent theory prediction for [B(B0s→J/ψϕ)/B(B0d→J/ψK*0)] yields (fs/fd)=0.240±0.004(stat)±0.010(syst)±0.017(th). This result is based on a new approach that provides a significant improvement of the world average

The burden of heat-related mortality attributable to recent human-induced climate change

Medical Research Council-UK (Grant ID: MR/M022625/1); Natural Environment Research Council UK (Grant ID: NE/R009384/1); European Union’s Horizon 2020 Project Exhaustion (Grant ID: 820655); N. Scovronick was supported by the NIEHS-funded HERCULES Center (P30ES019776); Y. Honda was supported by the Environment Research and Technology Development Fund of the Environmental Restoration and Conservation Agency, Japan (JPMEERF15S11412); J. Jaakkola was supported by Academy of Finland (Grant No. 310372); V. Huber was supported by the Spanish Ministry of Economy, Industry and Competitiveness (Grant ID: PCIN-2017-046) and the German Federal Ministry of Education and Research (Grant ID: 01LS1201A2); J Kysely and A. Urban were supported by the Czech Science Foundation (Grant ID: 20-28560S); J. Madureira was supported by the Fundação para a Ciência e a Tecnologia (FCT) (SFRH/BPD/115112/2016); S. Rao and F. di Ruscio were supported by European Union’s Horizon 2020 Project EXHAUSTION (Grant ID: 820655); M. Hashizume was supported by the Japan Science and Technology Agency (JST) as part of SICORP, Grant Number JPMJSC20E4; Y. Guo was supported by the Career Development Fellowship of the Australian National Health and Medical Research Council (#APP1163693); S. Lee was support by the Early Career Fellowship of the Australian National Health and Medical Research Council (#APP1109193)