New enumeration algorithm for protein structure comparison and classification.

BACKGROUND: Protein structure comparison and classification is an effective method for exploring protein structure-function relations. This problem is computationally challenging. Many different computational approaches for protein structure comparison apply the secondary structure elements (SSEs) representation of protein structures. RESULTS: We study the complexity of the protein structure comparison problem based on a mixed-graph model with respect to different computational frameworks. We develop an effective approach for protein structure comparison based on a novel independent set enumeration algorithm. Our approach (named: ePC, efficient enumeration-based Protein structure Comparison) is tested for general purpose protein structure comparison as well as for specific protein examples. Compared with other graph-based approaches for protein structure comparison, the theoretical running-time O(1.47 rnn2) of our approach ePC is significantly better, where n is the smaller number of SSEs of the two proteins, r is a parameter of small value. CONCLUSION: Through the enumeration algorithm, our approach can identify different substructures from a list of high-scoring solutions of biological interest. Our approach is flexible to conduct protein structure comparison with the SSEs in sequential and non-sequential order as well. Supplementary data of additional testing and the source of ePC will be available at http://bioinformatics.astate.edu/.

Role of progesterone in TLR4-MyD88-dependent signaling pathway in pre-eclampsia.

The role of progesterone in the Toll-like receptor 4 (TLR4)-MyD88-dependent signaling pathway in pre-eclampsia was studied. Peripheral blood mononuclear cells (PBMCs) from pre-eclampsia (PE) patients were subjected to primary culture, and stimulated with different concentrations of progesterone (0, 10(-8), 10(-6), and 10(-4) mol/L). The mRNA expression of TLR4, MyD88 and nuclear factor-kappaB (NF-κB) was detected by using real-time PCR. The Ikappa-B protein expression was detected by using Western blotting. The expression of tumor necrosis factor-α (TNF-α) and interleukin-6 (IL-6) in the supernatant was determined by using ELISA. With the concentrations of progesterone increasing, the mRNA expression levels of TLR4, MyD88 and NF-κB in 2(-ΔΔCT) value were significantly decreased, and the IkappaB protein expression levels were significantly increased. The TNF-α and IL-6 expression showed a downward trend when the progesterone concentration increased, and there were significant differences among all of the groups (P<0.05). It was suggested that progesterone can inhibit the TLR4-MyD88-dependent signaling pathway in PE significantly and benefit for the pregnancy.

RBoost: Label Noise-Robust Boosting Algorithm Based on a Nonconvex Loss Function and the Numerically Stable Base Learners.

AdaBoost has attracted much attention in the machine learning community because of its excellent performance in combining weak classifiers into strong classifiers. However, AdaBoost tends to overfit to the noisy data in many applications. Accordingly, improving the antinoise ability of AdaBoost plays an important role in many applications. The sensitiveness to the noisy data of AdaBoost stems from the exponential loss function, which puts unrestricted penalties to the misclassified samples with very large margins. In this paper, we propose two boosting algorithms, referred to as RBoost1 and RBoost2, which are more robust to the noisy data compared with AdaBoost. RBoost1 and RBoost2 optimize a nonconvex loss function of the classification margin. Because the penalties to the misclassified samples are restricted to an amount less than one, RBoost1 and RBoost2 do not overfocus on the samples that are always misclassified by the previous base learners. Besides the loss function, at each boosting iteration, RBoost1 and RBoost2 use numerically stable ways to compute the base learners. These two improvements contribute to the robustness of the proposed algorithms to the noisy training and testing samples. Experimental results on the synthetic Gaussian data set, the UCI data sets, and a real malware behavior data set illustrate that the proposed RBoost1 and RBoost2 algorithms perform better when the training data sets contain noisy data.

JSI-124 (Cucurbitacin I) inhibits tumor angiogenesis of human breast cancer through reduction of STAT3 phosphorylation.

Breast cancer (BC) is the most frequently diagnosed type of cancer all over the world. Angiogenesis, a physiological or pathological process characterized by the sprouting of new blood vessels from existing vessels, plays a vital role in tumor nutrition. In this work, we used JSI-124 (Cucurbitacin I), a selective JAK/STAT3 signaling pathway inhibitor, to investigate the role of STAT3 in tumor angiogenesis of a human BC cell line in vitro. JSI-124 inhibited cell viability, proliferation, adhesion, migration and tube formation of a human BC cell line MDA-MB-468. After transfection with pMXs-Stat3C, a dominant active mutant, the inhibitory effects of JSI-124 on MDA-MB-468 were abolished. Furthermore, JSI-124 reduced the phosphorylation of STAT3. These results suggested that JSI-124 inhibited tumor angiogenesis of the human BC cell line in vitro through the reduction of STAT3 phosphorylation. In addition, JSI-124 could reduce VEGF transcription and secretion, suggesting that JSI-124 is also involved in the inhibition of the VEGF autocrine loop in the tumor microenvironment.

Click Reaction-Mediated Functionalization of Near-Infrared Pyrrolopyrrole Cyanine Dyes for Biological Imaging Applications.

A clickable pyrrolopyrrole cyanine (PPCy) dye was synthesized by incorporating an alkyne moiety, followed by click reaction with azide-functionalized molecules of different polarities. The clickable dyes are readily amenable to labelling diverse molecules and exhibit an exceptionally high photostability and an impressive fluorescence quantum yield.