Analysis on protein structures using statistical and bioinformatical methods

This PhD dissertation mainly focuses on the statistical analysis for the protein structure data. The first research project focuses on data mining and prediction for side chain orientation in protein structure. Through this study, we find that the general side chain orientation can be viewed as a manifestation of hydrophobic force. Along with this study, we also developed the software for visualizing the general side chain vector and applied statistical machine learning methods to fit several models for predicting general side chain orientation. In the second project, we studied the motion of partially assembled ribosome 30S subunit using the coarse-grained elastic network model. Besides our studies on ribosome motion, using 176 NMR structure ensembles, we applied principal component analysis to analyze the essential conformational changes to validate the motion generated by the elastic network model. Furthermore, we also studied the effects of different superposition methods on the correspondence between the conformational changes and the simulated motion. Principal component shaving method is often used to cluster gene with the similar gene expression pattern in micro-array data analysis. In the third research project, we applied this method to cluster the structures within a NMR structure ensemble and demonstrated that this method could be used to find the similar structure cluster

Necessary and sufficient conditions for oscillations of delay equations with impulses

AbstractIn this paper, a necessary and sufficient condition for oscillation of a first-order delay differential equation with impulses x′(t)+∑i=1npix(t−τi)=0, t≠tk,x(tk+)−x(tk=bkx(tk), k=1,2,… is established

Oscillation properties of nonlinear impulsive delay differential equations and applications to population models

AbstractComparison theorem and explicit sufficient conditions are obtained for oscillation and nonoscillation of solutions of nonlinear impulsive delay differential equations which can be utilized to population dynamic models. Our results in this paper generalize and improve several known results

Double-Stranded RNA-Dependent Protein Kinase (PKR) is Downregulated by Phorbol Ester

The double-stranded RNA-dependent protein kinase (PKR) is one of the key mediators of interferon (IFN) action against certain viruses. PKR also plays an important role in signal transduction and immunomodulation. Understanding the regulation of PKR activity is important for the use of PKR as a tool to discover and develop novel therapeutics for viral infections, cancer and immune dysfunction. We found that phorbol 12-myristate 13-acetate (PMA), a potent activator of protein kinase C (PKC), decreased the level of autophosphorylated PKR in a dose- and time-dependent manner in IFN-treated mouse fibroblast cells. Polyinosinic–polycytidylic acid (poly I:C) treatment enhanced the activity of PKR induced by IFN, but did not overcome the PMA-induced reduction of PKR autophosphorylation. Western blot analysis with a monoclonal antibody to mouse PKR revealed that the decrease of PKR autophosphorylation in cells by PMA was a result of PKR protein degradation. Selective PKC inhibitors blocked the degradation of PKR stimulated by PMA, indicating that PKC activity was required for the effect. Furthermore, we also found that proteasome inhibitors prevented PMA-induced down regulation of PKR, indicating that an active proteasome is required. Our results identify a novel mechanism for the post-translational regulation of PKR

Distinct features of nucleolus-associated domains in mouse embryonic stem cells [preprint]

Background Heterochromatin in eukaryotic interphase cells frequently localizes to the nucleolar periphery (nucleolus-associated domains, NADs) and the nuclear lamina (lamina-associated domains, LADs). Gene expression in somatic cell NADs is generally low, but NADs have not been characterized in mammalian stem cells. Results Here, we generated the first genome-wide map of NADs in mouse embryonic stem cells (mESCs) via deep sequencing of chromatin associated with biochemically-purified nucleoli. As we had observed in mouse embryonic fibroblasts (MEFs), the large Type I subset of NADs overlaps with constitutive LADs and is enriched for features of constitutive heterochromatin, including late replication timing and low gene density and expression levels. Conversely, the Type II NAD subset overlaps with loci that are not lamina-associated, but in mESCs, Type II NADs are much less abundant than in MEFs. mESC NADs are also much less enriched in H3K27me3 modified regions than are NADs in MEFs. Additionally, comparision of MEF and mESC NADs revealed enrichment of developmentally regulated genes in cell type-specific NADs. Together, these data indicate that NADs are a developmentally dynamic component of heterochromatin. Conclusions These studies implicate association with the nucleolar periphery as a mechanism for developmentally-regulated gene silencing, and will facilitate future studies of NADs during mESC differentiation

BP Neural Network Algorithms for Fault Diagnosis of Microwave Components

Intelligent diagnosis is the main trend of modern fault diagnosis technology. The emergence of artificial neural network technology provides a new way for this kind of intellectualization. Aiming at the problem of microwave module fault diagnosis, an intelligent fault diagnosis method based on BP(Back Propagation) neural network is proposed in this paper. In this paper, the process of determining the neural network model and the operation flow of BP algorithm are introduced, and the network is trained with training samples. By applying the neural network model to an AQ module for testing, the feasibility, accuracy and efficiency of the fault diagnosis of the microwave module are verified, which provides a new method for intelligent fault diagnosis of this kind of microwave module

Double-Stranded RNA-Dependent Protein Kinase (PKR) is Downregulated by Phorbol Ester

The double-stranded RNA-dependent protein kinase (PKR) is one of the key mediators of interferon (IFN) action against certain viruses. PKR also plays an important role in signal transduction and immunomodulation. Understanding the regulation of PKR activity is important for the use of PKR as a tool to discover and develop novel therapeutics for viral infections, cancer and immune dysfunction. We found that phorbol 12-myristate 13-acetate (PMA), a potent activator of protein kinase C (PKC), decreased the level of autophosphorylated PKR in a dose- and time-dependent manner in IFN-treated mouse fibroblast cells. Polyinosinic–polycytidylic acid (poly I:C) treatment enhanced the activity of PKR induced by IFN, but did not overcome the PMA-induced reduction of PKR autophosphorylation. Western blot analysis with a monoclonal antibody to mouse PKR revealed that the decrease of PKR autophosphorylation in cells by PMA was a result of PKR protein degradation. Selective PKC inhibitors blocked the degradation of PKR stimulated by PMA, indicating that PKC activity was required for the effect. Furthermore, we also found that proteasome inhibitors prevented PMA-induced down regulation of PKR, indicating that an active proteasome is required. Our results identify a novel mechanism for the post-translational regulation of PKR