Ontology-based Knowledge Representation for Protein Data

The advances in information and communication technologies coupled with increased knowledge about genes and proteins have opened new perspectives for study of protein complexes. There is a growing need to integrate the knowledge about various protein complexes for effective disease prevention mechanisms, individualized medicines and treatments and other accepts of healthcare. In this paper we propose a protein ontology that handles the following computational challenges in the area proteomics and systems biology in general: (1) it provides more accurate interpretations and associations as conclusions are based on data and semantics. (2) It makes it possible to study relationships among proteins, protein folding, behaviour of protein under various environments, and most importantly cellular function of protein. This protein ontology is a unified terminology description integrating various protein database schemas and provides a easier way to predict and understand proteins

Protein ontology development using OWL

To efficiently represent the protein annotation framework and to integrate all the existing data representations into a standardized protein data specification for the bioinformatics community, the protein ontology need to be represented in a format that not enforce semantic constraints on protein data, but can also facilitate reasoning tasks on protein data using semantic query algebra. This motivates the representation of Protein Ontology (PO) Model in Web Ontology Language (OWL). In this paper we briefly discuss the usage of OWL in achieving the objectives of Protein Ontology Project. We provide a brief overview of Protein Ontology (PO) to start with. In the later sections discuss why OWL was an ideal choice for PO Development

Protein Ontology Project: 2006 updates

Protein Ontology (PO) is a means of formalizing protein data and knowledge; protein ontology includes concepts or terms relevant to the domain, definitions of concepts, and defined relationships between the concepts. PO integrates protein data formats and provides a structured and unified vocabulary to represent protein synthesis concepts. PO provides integration of heterogeneous protein and biological data sources. This paper discusses the updates that happened to the Protein Ontology Project since it was last presented at the Data Mining 2005 Conference

Protein ontology: Vocabulary for protein data

These Huge amounts of Protein Structure Data make it difficult to create explanatory and predictive models that are consistent with huge volume of data. Difficulty increase when large variety of heterogeneous approaches gathers data from multiple perspectives. In order to facilitate computational processing data, it is especially critical to develop standardized structured data representation model formats for proteomics data. In this paper we describe a Protein Ontology Model for integrating protein databases and deduce a structured vocabulary for understanding process of protein synthesis completely. Proposed Protein Ontology Model provides biologists and scientists with a description of sequence, structure and functions of protein and also provides interpretation of various factors on final protein structure conformation. The Structured Vocabulary for Protein Data, describing Protein Ontology is composed of various Type Definitions for Protein Entry Details, Sequence and Structural Information of Proteins, Structural Domain Family of Protein, Cellular Function of Protein, Chemical Bonds present in the Protein, and External Constraints deciding final protein conformation. The Proposed Ontology Model will provide easier ways to predict and understand proteins

OWL, proteins and data integration

In this paper, we propose an approach to integrate protein information from various data sources by defining a Protein Ontology. Protein Ontology provides the technical and scientific infrastructure and knowledge to allow description and analysis of relationships between various proteins. Protein Ontology uses relevant protein data sources of information like PDB, SCOP, and OMIM. Protein Ontology describes: Protein Sequence and Structure Information, Protein Folding Process, Cellular Functions of Proteins, Molecular Bindings internal and external to Proteins, and Constraints affecting the Final Protein Conformation. Details about Protein Ontology are available online at http://www.proteinontology.info/

Ceramide remodeling and risk of cardiovascular events and mortality

BackgroundRecent studies suggest that circulating concentrations of specific ceramide species may be associated with coronary risk and mortality. We sought to determine the relations between the most abundant plasma ceramide species of differing acyl chain lengths and the risk of coronary heart disease (CHD) and mortality in community‐based samples. Methods and ResultsWe developed a liquid chromatography/mass spectrometry assay to quantify plasma C24:0, C22:0, and C16:0 ceramides and ratios of these very–long‐chain/long‐chain ceramides in 2642 FHS (Framingham Heart Study) participants and in 3134 SHIP (Study of Health in Pomerania) participants. Over a mean follow‐up of 6 years in FHS, there were 88 CHD and 90 heart failure (HF) events and 239 deaths. Over a median follow‐up time in SHIP of 5.75 years for CHD and HF and 8.24 years for mortality, there were 209 CHD and 146 HF events and 377 deaths. In meta‐analysis of the 2 cohorts and adjusting for standard CHD risk factors, C24:0/C16:0 ceramide ratios were inversely associated with incident CHD (hazard ratio per average SD increment, 0.79; 95% confidence interval, 0.71–0.89; P<0.0001) and inversely associated with incident HF (hazard ratio, 0.78; 95% confidence interval, 0.61–1.00; P=0.046). Moreover, the C24:0/C16:0 and C22:0/C16:0 ceramide ratios were inversely associated with all‐cause mortality (C24:0/C16:0: hazard ratio, 0.60; 95% confidence interval, 0.56–0.65; P<0.0001; C22:0/C16:0: hazard ratio, 0.65; 95% confidence interval, 0.60–0.70; P<0.0001). ConclusionsThe ratio of C24:0/C16:0 ceramides in blood may be a valuable new biomarker of CHD risk, HF risk, and all‐cause mortality in the community

Highly multiplexed and quantitative cell-surface protein profiling using genetically barcoded antibodies.

Human cells express thousands of different surface proteins that can be used for cell classification, or to distinguish healthy and disease conditions. A method capable of profiling a substantial fraction of the surface proteome simultaneously and inexpensively would enable more accurate and complete classification of cell states. We present a highly multiplexed and quantitative surface proteomic method using genetically barcoded antibodies called phage-antibody next-generation sequencing (PhaNGS). Using 144 preselected antibodies displayed on filamentous phage (Fab-phage) against 44 receptor targets, we assess changes in B cell surface proteins after the development of drug resistance in a patient with acute lymphoblastic leukemia (ALL) and in adaptation to oncogene expression in a Myc-inducible Burkitt lymphoma model. We further show PhaNGS can be applied at the single-cell level. Our results reveal that a common set of proteins including FLT3, NCR3LG1, and ROR1 dominate the response to similar oncogenic perturbations in B cells. Linking high-affinity, selective, genetically encoded binders to NGS enables direct and highly multiplexed protein detection, comparable to RNA-sequencing for mRNA. PhaNGS has the potential to profile a substantial fraction of the surface proteome simultaneously and inexpensively to enable more accurate and complete classification of cell states