Genome-wide Protein-chemical Interaction Prediction

The analysis of protein-chemical reactions on a large scale is critical to understanding the complex interrelated mechanisms that govern biological life at the cellular level. Chemical proteomics is a new research area aimed at genome-wide screening of such chemical-protein interactions. Traditional approaches to such screening involve in vivo or in vitro experimentation, which while becoming faster with the application of high-throughput screening technologies, remains costly and time-consuming compared to in silico methods. Early in silico methods are dependant on knowing 3D protein structures (docking) or knowing binding information for many chemicals (ligand-based approaches). Typical machine learning approaches follow a global classification approach where a single predictive model is trained for an entire data set, but such an approach is unlikely to generalize well to the protein-chemical interaction space considering its diversity and heterogeneous distribution. In response to the global approach, work on local models has recently emerged to improve generalization across the interaction space by training a series of independant models localized to each predict a single interaction. This work examines current approaches to genome-wide protein-chemical interaction prediction and explores new computational methods based on modifications to the boosting framework for ensemble learning. The methods are described and compared to several competing classification methods. Genome-wide chemical-protein interaction data sets are acquired from publicly available resources, and a series of experimental studies are performed in order to compare the the performance of each method under a variety of conditions

Macroeconomic Indicator Forecasting with Deep Neural Networks

Resumen de la comunicación[EN] Economic policymaking relies upon accurate forecasts of economic conditions. Current methods for unconditional forecasting are dominated by inherently linear models that exhibit model dependence and have high data demands. We explore deep neural networks as an opportunity to improve upon forecast accurac y with limited data and while remaining agnostic as to functional form. We focus on predicting civilian unemployment using models based on four different neural network architectures. Each of these models outperforms benchmark models at short time horizons. One model, based on an Encoder Decoder architecture outperforms benchmark models at every forecast horizon (up to four quarters).Cook, T.; Smalter Hall, A. (2018). Macroeconomic Indicator Forecasting with Deep Neural Networks. En 2nd International Conference on Advanced Reserach Methods and Analytics (CARMA 2018). Editorial Universitat Politècnica de València. 261-261. https://doi.org/10.4995/CARMA2018.2018.8571OCS26126

GPD: A Graph Pattern Diffusion Kernel for Accurate Graph Classification with Applications in Cheminformatics

Graph data mining is an active research area. Graphs are general modeling tools to organize information from heterogeneous sources and have been applied in many scientific, engineering, and business fields. With the fast accumulation of graph data, building highly accurate predictive models for graph data emerges as a new challenge that has not been fully explored in the data mining community. In this paper, we demonstrate a novel technique called graph pattern diffusion (GPD) kernel. Our idea is to leverage existing frequent pattern discovery methods and to explore the application of kernel classifier (e.g., support vector machine) in building highly accurate graph classification. In our method, we first identify all frequent patterns from a graph database. We then map subgraphs to graphs in the graph database and use a process we call “pattern diffusion” to label nodes in the graphs. Finally, we designed a graph alignment algorithm to compute the inner product of two graphs. We have tested our algorithm using a number of chemical structure data. The experimental results demonstrate that our method is significantly better than competing methods such as those kernel functions based on paths, cycles, and subgraphs

Production, purification, and characterization of recombinant hFSH glycoforms for functional studies

Previously, our laboratory demonstrated the existence of a β-subunit glycosylation-deficient human FSH glycoform, hFSH21. A third variant, hFSH18, has recently been detected in FSH glycoforms isolated from purified pituitary hLH preparations. Human FSH21 abundance in individual female pituitaries progressively decreased with increasing age. Hypo-glycosylated glycoform preparations are significantly more active than fully-glycosylated hFSH preparations. The purpose of this study was to produce, purify and chemically characterize both glycoform variants expressed by a mammalian cell line. Recombinant hFSH was expressed in a stable GH3 cell line and isolated from serum-free cell culture medium by sequential, hydrophobic and immunoaffinity chromatography. FSH glycoform fractions were separated by Superdex 75 gel-filtration. Western blot analysis revealed the presence of both hFSH18 and hFSH21 glycoforms in the low molecular weight fraction, however, their electrophoretic mobilities differed from those associated with the corresponding pituitary hFSH variants. Edman degradation of FSH21/18 -derived β-subunit before and after peptide-N-glycanase F digestion confirmed that it possessed a mixture of both mono-glycosylated FSHβ subunits, as both Asn7 and Asn24 were partially glycosylated. FSH receptor-binding assays confirmed our previous observations that hFSH21/18 exhibits greater receptor-binding affinity and occupies more FSH binding sites when compared to fully-glycosylated hFSH24. Thus, the age-related reduction in hypo-glycosylated hFSH significantly reduces circulating levels of FSH biological activity that may further compromise reproductive function. Taken together, the ability to express and isolate recombinant hFSH glycoforms opens the way to study functional differences between them both in vivo and in vitro

Molecular Dynamics of Multivalent Soluble Antigen Arrays Support a Two-Signal Co-delivery Mechanism in the Treatment of Experimental Autoimmune Encephalomyelitis

Many current therapies for autoimmune diseases such as multiple sclerosis (MS) result in global immunosuppression, rendering insufficient efficacy with increased risk of adverse side effects. Multivalent soluble antigen arrays, nanomaterials presenting both autoantigen and secondary inhibitory signals on a flexible polymer backbone, are hypothesized to shift the immune response toward selective autoantigenic tolerance to repress autoimmune disease. Two-signal co-delivery of both autoantigen and secondary signal were deemed necessary for therapeutic efficacy against experimental autoimmune encephalomyelitis, a murine model of MS. Dynamic light scattering and in silico molecular dynamics simulations complemented these studies to illuminate the role of two-signal co-delivery in determining therapeutic potential. Physicochemical characteristics such as particle size and molecular affinity for intermolecular interactions and chain entanglement likely facilitated cotransport of two signals to produce efficacy. These findings elucidate potential mechanisms whereby soluble antigen arrays enact their therapeutic effect and help to guide the development of future multivalent antigen-specific immunotherapies