The Proteins API provides searching and programmatic access to protein and associated genomics data such as curated protein sequence positional annotations from UniProtKB, as well as mapped variation and proteomics data from large scale data sources (LSS). Using the coordinates service, researchers are able to retrieve the genomic sequence coordinates for proteins in UniProtKB. This, the LSS genomics and proteomics data for UniProt proteins is programmatically only available through this service. A Swagger UI has been implemented to provide documentation, an interface for users, with little or no programming experience, to ‘talk’ to the services to quickly and easily formulate queries with the services and obtain dynamically generated source code for popular programming languages, such as Java, Perl, Python and Ruby. Search results are returned as standard JSON, XML or GFF data objects. The Proteins API is a scalable, reliable, fast, easy to use RESTful services that provides a broad protein information resource for users to ask questions based upon their field of expertise and allowing them to gain an integrated overview of protein annotations available to aid their knowledge gain on proteins in biological processes. The Proteins API is available at (http://www.ebi.ac.uk/proteins/api/doc).

The building construction industry faces challenges, such as increasing project complexity and scope requirements, but shorter deadlines. Additionally, economic uncertainty and rising business competition with a subsequent decrease in profit margins for the industry demands the development of new approaches to construction management. However, the building construction sector relies on practices based on intuition and experience, overlooking the dynamics of its production system. Furthermore, researchers maintain that the construction industry has no history of the application of mathematical approaches to model and manage production. Much work has been carried out on how manufacturing practices apply to construction projects, mostly lean principles. Nevertheless, there has been little research to understand the fundamental mechanisms of production in construction. This study develops an in-depth literature review to examine the existing knowledge about production models and their characteristics in order to establish a foundation for dynamic production systems management in construction. As a result, a theoretical framework is proposed, which will be instrumental in the future development of mathematical production models aimed at predicting the performance and behaviour of dynamic project-based systems in construction.

In this article, we provide a comprehensive study of the content of the Universal Protein Resource (UniProt) protein data sets for human and mouse. The tryptic search spaces of the UniProtKB (UniProt knowledgebase) complete proteome sets were compared with other data sets from UniProtKB and with the corresponding International Protein Index, reference sequence, Ensembl, and UniRef100 (where UniRef is UniProt reference clusters) organism-specific data sets. All protein forms annotated in UniProtKB (both the canonical sequences and isoforms) were evaluated in this study. In addition, natural and disease-associated amino acid variants annotated in UniProtKB were included in the evaluation. The peptide unicity was also evaluated for each data set. Furthermore, the peptide information in the UniProtKB data sets was also compared against the available peptide-level identifications in the main MS-based proteomics repositories. Identifying the peptides observed in these repositories is an important resource of information for protein databases as they provide supporting evidence for the existence of otherwise predicted proteins. Likewise, the repositories could use the information available in UniProtKB to direct reprocessing efforts on specific sets of peptides/proteins of interest. In summary, we provide comprehensive information about the different organism-specific sequence data sets available from UniProt, together with the pros and cons for each, in terms of search space for MS-based bottom-up proteomics workflows. The aim of the analysis is to provide a clear view of the tryptic search space of UniProt and other protein databases to enable scientists to select those most appropriate for their purposes.

Enzymes are a key part of life processes and are increasingly important for various areas of research such as medicine, biotechnology, bioprocessing and drug research. The goal of the Enzyme Portal is to provide an interface to all European Bioinformatics Institute (EMBL-EBI) data about enzymes (de Matos, P., et al., (2013), BMC Bioinformatics, 14 (1), 103). These data include enzyme function, sequence features and family classification, protein structure, reactions, pathways, small molecules, diseases and the associated literature. The sources of enzyme data are: the UniProt Knowledgebase (UniProtKB) (UniProt Consortium, 2015), the Protein Data Bank in Europe (PDBe), (Valenkar, S., et al., Nucleic Acids Res.2016; 44, D385–D395) Rhea—a database of enzyme-catalysed reactions (Morgat, A., et al., Nucleic Acids Res. 2015; 43, D459-D464), Reactome—a database of biochemical pathways (Fabregat, A., et al., Nucleic Acids Res. 2016; 44, D481–D487), IntEnz—a resource with enzyme nomenclature information (Fleischmann, A., et al., Nucleic Acids Res. 2004 32, D434–D437) and ChEBI (Hastings, J., et al., Nucleic Acids Res. 2013) and ChEMBL (Bento, A. P., et al., Nucleic Acids Res. 201442, 1083–1090)—resources which contain information about small-molecule chemistry and bioactivity. This article describes the redesign of Enzyme Portal and the increased functionality added to maximise integration and interpretation of these data. Use case examples of the Enzyme Portal and the versatile workflows its supports are illustrated. We welcome the suggestion of new resources for integration.