A semantic similarity based methodology for predicting protein-protein interactions: Evaluation with P53-interacting kinases.

Biomedical literature contains unstructured, rich information regarding proteins, ligands, diseases as well as biological pathways in which they are involved. Systematically analyzing such textual corpus has the potential for biomedical discovery of new protein-protein interactions and hidden drug indications. For this purpose, we have investigated a methodology that is based on a well-established text mining tool, Word2Vec, for the analysis of PubMed full text articles to derive word embeddings, and the use of a simple semantic similarity comparison either by itself or in conjunction with k-Nearest Neighbor (kNN) technique for the prediction of new relationships. To test this methodology, three lines of retrospective analyses of a dataset with known P53-interacting proteins have been conducted. First, we demonstrated that Word2Vec semantic similarity can infer functional relatedness among all kinases known to interact with P53. Second, in a series of time-split experiments, we demonstrated that both a simple similarity comparison and kNN models built with papers published up to a certain year were able to discover P53 interactors described in later publications. Third, in a different scenario of time-split experiments, we examined the predictions of P53-interacting proteins based on the kNN models built on data prior to a certain split year for different time ranges past that year, and found that the cumulative number of correct predictions was indeed increasing with time. We conclude that text mining of research papers in the PubMed literature based on Word2Vec analysis followed by a simple similarity comparison or kNN modeling affords excellent predictions of protein-protein interactions between P53 and kinases, and should have wide applications in translational biomedical studies such as repurposing of existing drugs, drug-drug interaction, and elucidation of mechanisms of action for drugs.

An integrated chemical environment with tools for chemical safety testing.

Moving toward species-relevant chemical safety assessments and away from animal testing requires access to reliable data to develop and build confidence in new approaches. The Integrated Chemical Environment (ICE) provides tools and curated data centered around chemical safety assessment. This article describes updates to ICE, including improved accessibility and interpretability of in vitro data via mechanistic target mapping and enhanced interactive tools for in vitro to in vivo extrapolation (IVIVE). Mapping of in vitro assay targets to toxicity endpoints of regulatory importance uses literature-based mode-of-action information and controlled terminology from existing knowledge organization systems to support data interoperability with external resources. The most recent ICE update includes Tox21 high-throughput screening data curated using analytical chemistry data and assay-specific parameters to eliminate potential artifacts or unreliable activity. Also included are physicochemical/ADME parameters for over 800,000 chemicals predicted by quantitative structure-activity relationship models. These parameters are used by the new ICE IVIVE tool in combination with the U.S. Environmental Protection Agency's httk R package to estimate in vivo exposures corresponding to in vitro bioactivity concentrations from stored or user-defined assay data. These new ICE features allow users to explore the applications of an expanded data space and facilitate building confidence in non-animal approaches.

RNA-Seq Analysis of Developing Pecan (Carya illinoinensis) Embryos Reveals Parallel Expression Patterns among Allergen and Lipid Metabolism Genes.

The pecan nut is a nutrient-rich part of a healthy diet full of beneficial fatty acids and antioxidants, but can also cause allergic reactions in people suffering from food allergy to the nuts. The transcriptome of a developing pecan nut was characterized to identify the gene expression occurring during the process of nut development and to highlight those genes involved in fatty acid metabolism and those that commonly act as food allergens. Pecan samples were collected at several time points during the embryo development process including the water, gel, dough, and mature nut stages. Library preparation and sequencing were performed using Illumina-based mRNA HiSeq with RNA from four time points during the growing season during August and September 2012. Sequence analysis with Trinotate software following the Trinity protocol identified 133,000 unigenes with 52,267 named transcripts and 45,882 annotated genes. A total of 27,312 genes were defined by GO annotation. Gene expression clustering analysis identified 12 different gene expression profiles, each containing a number of genes. Three pecan seed storage proteins that commonly act as allergens, Car i 1, Car i 2, and Car i 4, were significantly up-regulated during the time course. Up-regulated fatty acid metabolism genes that were identified included acyl-[ACP] desaturase and omega-6 desaturase genes involved in oleic and linoleic acid metabolism. Notably, a few of the up-regulated acyl-[ACP] desaturase and omega-6 desaturase genes that were identified have expression patterns similar to the allergen genes based upon gene expression clustering and qPCR analysis. These findings suggest the possibility of coordinated accumulation of lipids and allergens during pecan nut embryogenesis.