Framework for a Protein Ontology

Biomedical ontologies are emerging as critical tools in genomic and proteomic research, where complex data in disparate resources need to be integrated. A number of ontologies describe properties that can be attributed to proteins. For example, protein functions are described by the Gene Ontology (GO) and human diseases by SNOMED CT or ICD10. There is, however, a gap in the current set of ontologies – one that describes the protein entities themselves and their relationships. We have designed the PRotein Ontology (PRO) to facilitate protein annotation and to guide new experiments. The components of PRO extend from the classification of proteins on the basis of evolutionary relationships to the representation of the multiple protein forms of a gene (products generated by genetic variation, alternative splicing, proteolytic cleavage, and other post-translational modifications). PRO will allow the specification of relationships between PRO, GO and other ontologies in the OBO Foundry. Here we describe the initial development of PRO, illustrated using human and mouse proteins involved in the transforming growth factor-beta and bone morphogenetic protein signaling pathways

From protein sequences to 3D-structures and beyond: the example of the UniProt Knowledgebase

With the dramatic increase in the volume of experimental results in every domain of life sciences, assembling pertinent data and combining information from different fields has become a challenge. Information is dispersed over numerous specialized databases and is presented in many different formats. Rapid access to experiment-based information about well-characterized proteins helps predict the function of uncharacterized proteins identified by large-scale sequencing. In this context, universal knowledgebases play essential roles in providing access to data from complementary types of experiments and serving as hubs with cross-references to many specialized databases. This review outlines how the value of experimental data is optimized by combining high-quality protein sequences with complementary experimental results, including information derived from protein 3D-structures, using as an example the UniProt knowledgebase (UniProtKB) and the tools and links provided on its website (http://www.uniprot.org/). It also evokes precautions that are necessary for successful predictions and extrapolations

Manufacturing and Supply Chain Flexibility: Building an Integrative Conceptual Model Through Systematic Literature Review and Bibliometric Analysis

The purpose of this study is twofold: first, to establish the current themes on the topic of manufacturing and supply chain flexibility (MSCF), assess their level of maturity in relation to each other, identify the emerging ones and reflect on how they can inform each other, and second, to develop a conceptual model of MSCF that links different themes connect and highlight future research opportunities. The study builds on a sample of 222 articles published from 1996 to 2018 in international, peer-reviewed journals. The analysis of the sample involves two complementary approaches: the co-word technique to identify the thematic clusters as well as their relative standing and a critical reflection on the papers to explain the intellectual content of these thematic clusters. The results of the co-word analysis show that MSCF is a dynamic topic with a rich and complex structure that comprises five thematic clusters. The value chain, capability and volatility clusters showed research topics that were taking a central role in the discussion on MSCF but were not mature yet. The SC purchasing practices and SC planning clusters involved work that was more focused and could be considered more mature. These clusters were then integrated in a framework that built on the competence–capability perspective and identified the major structural and infrastructural elements of MSCF as well as its antecedents and consequences. This paper proposes an integrative framework helping managers keep track the various decisions they need to make to increase flexibility from the viewpoint of the entire value chain

Reproducible isolation of distinct, overlapping segments of the phosphoproteome

The ability to routinely analyze and quantitatively measure changes in protein phosphorylation on a proteome-wide scale is essential for biological and clinical research. We assessed the ability of three common phosphopeptide isolation methods (phosphoramidate chemistry (PAC), immobilized metal affinity chromatography (IMAC) and titanium dioxide) to reproducibly, specifically and comprehensively isolate phosphopeptides from complex mixtures. Phosphopeptides were isolated from aliquots of a tryptic digest of the cytosolic fraction of Drosophila melanogaster Kc167 cells and analyzed by liquid chromatography-electrospray ionization tandem mass spectrometry. Each method reproducibly isolated phosphopeptides. The methods, however, differed in their specificity of isolation and, notably, in the set of phosphopeptides isolated. The results suggest that the three methods detect different, partially overlapping segments of the phosphoproteome and that, at present, no single method is sufficient for a comprehensive phosphoproteome analysis

Incorporation of Noncanonical Amino Acids into Rosetta and Use in Computational Protein-Peptide Interface Design

<div><p>Noncanonical amino acids (NCAAs) can be used in a variety of protein design contexts. For example, they can be used in place of the canonical amino acids (CAAs) to improve the biophysical properties of peptides that target protein interfaces. We describe the incorporation of 114 NCAAs into the protein-modeling suite Rosetta. We describe our methods for building backbone dependent rotamer libraries and the parameterization and construction of a scoring function that can be used to score NCAA containing peptides and proteins. We validate these additions to Rosetta and our NCAA-rotamer libraries by showing that we can improve the binding of a calpastatin derived peptides to calpain-1 by substituting NCAAs for native amino acids using Rosetta. Rosetta (executables and source), auxiliary scripts and code, and documentation can be found at (<a href="http://www.rosettacommons.org/">http://www.rosettacommons.org/</a>).</p> </div

The HUPO PSI's Molecular Interaction format—a community standard for the representation of protein interaction data

A major goal of proteomics is the complete description of the protein interaction network underlying cell physiology. A large number of small scale and, more recently, large-scale experiments have contributed to expanding our understanding of the nature of the interaction network. However, the necessary data integration across experiments is currently hampered by the fragmentation of publicly available protein interaction data, which exists in different formats in databases, on authors' websites or sometimes only in print publications. Here, we propose a community standard data model for the representation and exchange of protein interaction data. This data model has been jointly developed by members of the Proteomics Standards Initiative (PSI), a work group of the Human Proteome Organization (HUPO), and is supported by major protein interaction data providers, in particular the Biomolecular Interaction Network Database (BIND), Cellzome (Heidelberg, Germany), the Database of Interacting Proteins (DIP), Dana Farber Cancer Institute (Boston, MA, USA), the Human Protein Reference Database (HPRD), Hybrigenics (Paris, France), the European Bioinformatics Institute's (EMBL-EBI, Hinxton, UK) IntAct, the Molecular Interactions (MINT, Rome, Italy) database, the Protein-Protein Interaction Database (PPID, Edinburgh, UK) and the Search Tool for the Retrieval of Interacting Genes/Proteins (STRING, EMBL, Heidelberg, Germany)