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The neXtProt knowledgebase (https://www.nextprot.org) is an integrative resource providing both data on human protein and the tools to explore these. In order to provide comprehensive and up-to-date data, we evaluate and add new data sets. We describe the incorporation of three new data sets that provide expression, function, protein-protein binary interaction, post-translational modifications (PTM) and variant information. New SPARQL query examples illustrating uses of the new data were added. neXtProt has continued to develop tools for proteomics. We have improved the peptide uniqueness checker and have implemented a new protein digestion tool. Together, these tools make it possible to determine which proteases can be used to identify trypsin-resistant proteins by mass spectrometry. In terms of usability, we have finished revamping our web interface and completely rewritten our API. Our SPARQL endpoint now supports federated queries. All the neXtProt data are available via our user interface, API, SPARQL endpoint and FTP site, including the new PEFF 1.0 format files. Finally, the data on our FTP site is now CC BY 4.0 to promote its reuse.

About 10% of human proteins have no annotated function in protein knowledge bases. A workflow to generate hypotheses for the function of these uncharacterized proteins has been developed, based on predicted and experimental information on protein properties, interactions, tissular expression, subcellular localization, conservation in other organisms, as well as phenotypic data in mutant model organisms. This workflow has been applied to seven uncharacterized human proteins (C6orf118, C7orf25, CXorf58, RSRP1, SMLR1, TMEM53 and TMEM232) in the frame of a course-based undergraduate research experience named Functionathon organized at the University of Geneva to teach undergraduate students how to use biological databases and bioinformatics tools and interpret the results. C6orf118, CXorf58 and TMEM232 were proposed to be involved in cilia-related functions; TMEM53 and SMLR1 were proposed to be involved in lipid metabolism and C7orf25 and RSRP1 were proposed to be involved in RNA metabolism and gene expression. Experimental strategies to test these hypotheses were also discussed. The results of this manual data mining study may contribute to the project recently launched by the Human Proteome Organization (HUPO) Human Proteome Project aiming to fill gaps in the functional annotation of human proteins. Database URL: http://www.nextprot.org

The Cellosaurus is a knowledge resource on cell lines. It aims to describe all cell lines used in biomedical research. Its scope encompasses both vertebrates and invertebrates. Currently, information for >100,000 cell lines is provided. For each cell line, it provides a wealth of information, cross-references, and literature citations. The Cellosaurus is available on the ExPASy server (https://web.expasy.org/cellosaurus/) and can be downloaded in a variety of formats. Among its many uses, the Cellosaurus is a key resource to help researchers identify potentially contaminated/misidentified cell lines, thus contributing to improving the quality of research in the life sciences.

Unambiguous cell line authentication is essential to avoid loss of association between data and cells. The risk for loss of references increases with the rapidity that new human pluripotent stem cell (hPSC) lines are generated, exchanged, and implemented. Ideally, a single name should be used as a generally applied reference for each cell line to access and unify cell-related information across publications, cell banks, cell registries, and databases and to ensure scientific reproducibility. We discuss the needs and requirements for such a unique identifier and implement a standard nomenclature for hPSCs, which can be automatically generated and registered by the human pluripotent stem cell registry (hPSCreg). To avoid ambiguities in PSC-line referencing, we strongly urge publishers to demand registration and use of the standard name when publishing research based on hPSC lines., In this article Kurtz, Seltmann and colleagues propose a standard nomenclature and registry for human pluripotent stem cells. The nomenclature is based on fixed name elements and provides a unique identifier for PSC lines suitable for referencing over diverse data sources, registries, publications, and cell banks. Rigorous application is needed to reduce risks from misidentification and false referencing of lines and data.

Voltage-gated sodium channels are pore-forming transmembrane proteins that selectively allow sodium ions to flow across the plasma membrane according to the electro-chemical gradient thus mediating the rising phase of action potentials in excitable cells and playing key roles in physiological processes such as neurotransmission, skeletal muscle contraction, heart rhythm, and pain sensation. Genetic variations in the nine human genes encoding these channels are known to cause a large range of diseases affecting the nervous and cardiac systems. Understanding the molecular effect of genetic variations is critical for elucidating the pathologic mechanisms of known variations and in predicting the effect of newly discovered ones. To this end, we have created a Web-based tool, the Ion Channels Variants Portal, which compiles all variants characterized functionally in the human sodium channel genes. This portal describes 672 variants each associated with at least one molecular or clinical phenotypic impact, for a total of 4,658 observations extracted from 264 different research articles. These data were captured as structured annotations using standardized vocabularies and ontologies, such as the Gene Ontology and the Ion Channel ElectroPhysiology Ontology. All these data are available to the scientific community via neXtProt at https://www.nextprot.org/portals/navmut.

Central nervous system (CNS), notably brain, metastases are most prevalent in lung cancer (20–56% of patients), breast cancer (5–20%) and melanoma (7–16%). Lesions occur in both the brain parenchyma and the meninges. To mechanistically understand CNS metastasis formation and develop preventive and therapeutic strategies, it is essential to use model systems that, as much as possible, faithfully recapitulate the clinical disease process. Furthermore, the complexities of brain metastases dictate that studies should utilize multiple model systems in various stages of brain metastases progression. To facilitate brain metastasis research, 19 laboratories around the world have compiled comprehensive information on their brain metastasis mouse models. Each lab has provided details on the cell lines that they have generated or characterized as being capable of forming metastatic colonies in the brain, as well as principle methodologies of brain metastasis research. This Brain Metastasis Cell Lines Panel (BrMPanel, https://apps.cnio.es/app/BrainMetastasis/CellLines) represents the first of its class and includes information about each cell line, how tropism to the brain was established, and the behavior of each model in vivo. The BrMPanel is composed of 60 cell lines, derived from patients (32 cell lines, 53%), mouse (27, 45%) or rat (1, 2%), and represent the three main cancer types that result in brain metastasis: breast cancer (38 cell lines, 63%), lung cancer (8, 13%) and melanoma (14, 23%). This resource is intended to assist investigators in choosing the most suitable model for research on brain metastasis, and is available to the entire scientific community. The ultimate goal of this effort is to facilitate research on this unmet clinical need, to improve models through a collaborative environment, and to promote the exchange of information on these valuable resources. We invite other collaborators to contribute their models to the BrMPanel to grow this resource.

Spread of cancer to the brain remains an unmet clinical need in spite of the increasing number of cases among patients with lung, breast cancer, and melanoma most notably. Although research on brain metastasis was considered a minor aspect in the past due to its untreatable nature and invariable lethality, nowadays, limited but encouraging examples have questioned this statement, making it more attractive for basic and clinical researchers. Evidences of its own biological identity (i.e., specific microenvironment) and particular therapeutic requirements (i.e., presence of blood–brain barrier, blood–tumor barrier, molecular differences with the primary tumor) are thought to be critical aspects that must be functionally exploited using preclinical models. We present the coordinated effort of 19 laboratories to compile comprehensive information related to brain metastasis experimental models. Each laboratory has provided details on the cancer cell lines they have generated or characterized as being capable of forming metastatic colonies in the brain, as well as principle methodologies of brain metastasis research. The Brain Metastasis Cell Lines Panel (BrMPanel) represents the first of its class and includes information about the cell line, how tropism to the brain was established, and the behavior of each model in vivo. These and other aspects described are intended to assist investigators in choosing the most suitable cell line for research on brain metastasis. The main goal of this effort is to facilitate research on this unmet clinical need, to improve models through a collaborative environment, and to promote the exchange of information on these valuable resources.

Despite an increased awareness of the problematic of cell line cross-contamination and misidentification, it remains nowadays a major source of erroneous experimental results in biomedical research. To prevent it, researchers are expected to frequently test the authenticity of the cell lines they are working on. STR profiling was selected as the international reference method to perform cell line authentication. While the experimental protocols and manipulations for generating a STR profile are well described, the available tools and workflows to analyze such data are lacking. The Cellosaurus knowledge resource aimed to improve the situation by compiling all the publicly available STR profiles from the literature and other databases. As a result, it grew to become the largest database in terms of human STR profiles, with 6,474 distinct cell lines having an associated STR profile (release July 31, 2019). Here we present CLASTR, the Cellosaurus STR similarity search tool enabling users to compare one or more STR profiles with those available in the Cellosaurus cell line knowledge resource. It aims to help researchers in the process of cell line authentication by providing numerous functionalities. The tool is publicly accessible on the SIB ExPASy server (https://web.expasy.org/cellosaurus-str-search) and its source code is available on GitHub under the GPL-3.0 license.

The ABCD (for AntiBodies Chemically Defined) database is a repository of sequenced antibodies, integrating curated information about the antibody and its antigen with cross-links to standardized databases of chemical and protein entities. It is freely available to the academic community, accessible through the ExPASy server (https://web.expasy.org/abcd/). The ABCD database aims at helping to improve reproducibility in academic research by providing a unique, unambiguous identifier associated to each antibody sequence. It also allows to determine rapidly if a sequenced antibody is available for a given antigen.

© The Author(s) 2020, The Human Proteome Organization (HUPO) launched the Human Proteome Project (HPP) in 2010, creating an international framework for global collaboration, data sharing, quality assurance and enhancing accurate annotation of the genome-encoded proteome. During the subsequent decade, the HPP established collaborations, developed guidelines and metrics, and undertook reanalysis of previously deposited community data, continuously increasing the coverage of the human proteome. On the occasion of the HPP’s tenth anniversary, we here report a 90.4% complete high-stringency human proteome blueprint. This knowledge is essential for discerning molecular processes in health and disease, as we demonstrate by highlighting potential roles the human proteome plays in our understanding, diagnosis and treatment of cancers, cardiovascular and infectious diseases., Parts of this work were supported by grants to ProteoRed PRB3-ISCIII, PT17/0019/0001 Comunidad de Madrid Grant B2017/BMD-3817 (F.J.C.); Korean Ministry of Health and Welfare HI13C2098 and HI16C0257 (Y.K.P.); NIH grants P30ES017885 and U24CA210967 (G.S.O.), 5U01HL-13104204, PADOM-SPO11347 and PARYB-SPO112285 (M.P.S.); NCI CPTAC U24CA210985 and NCI EDRN U24CA115102 (D.W.C.); NIH National Institute of General Medical Sciences R01GM087221 (E.W.D./R.L.M.) and R24GM127667 (E.W.D.); NIH National Institute on Aging U19AG023122 (R.L.M.); NSF DBI-1933311 (E.W.D.); CIHR COVID-19 Rapid Research Funding (F20-01013), CIHR Foundation Grant FDN:14840 and Canada Research Chair (C.M.O.); Investissement d’Avenir Infrastructures Nationales en Biologie et Santé ANR-10-INBS-08 (Proteomics French Infrastructure ProFI (Y.V.); Wellcome Trust WT101477MA and 208391/Z/17/Z (J.A.V.); Knut and Alice Wallenberg Foundation (M.U., C.L., J.M.S., E.L.); Brazilian CAPES 88887.130697, CNPq 440613/2016-7, FAPERJ E-26/210.173/2018 (G.B.D.) and FAPERJ E-26/202.650/2018 (F.C.S.N.), Australian Commonwealth NCRIS (M.S.B.); NHMRC 1010303 (M.S.B., E.C.N.); Cancer Council NSW RG19-04 (M.S.B., S.B.A., E.C.N.); Cancer Institute NSW Fellowship 15/ECF/1-38 (S.B.A.), Sydney Vital CINSW Translational Cancer Research Centre grant (M.S.B., S.B.A., S.A.), ‘Fight on the Beaches’ (M.S.B., S.B.A., E.C.N., S.A.) funding and an International Macquarie Research Excellence Scholarship (S.A.). M.S.B. thanks the Faculty of Medicine, Stanford University for a sabbatical visiting professorship.

Summary The Feature-Viewer is a lightweight library for the visualization of biological data mapped to a protein or nucleotide sequence. It is designed for ease of use while allowing for a full customization. The library is already used by several biological data resources and allows intuitive visual mapping of a full spectra of sequence features for different usages. Availability and implementation The Feature-Viewer is open source, compatible with state-of-the-art development technologies and responsive, also for mobile viewing. Documentation and usage examples are available online.

Research in toxicology relies on in vitro models such as cell lines. These living models are prone to change and may be described in publications with insufficient information or quality control testing. This article sets out recommendations to improve the reliability of cell-based research.

The neXtProt human protein knowledgebase (https://www.nextprot.org) continues to add new content and tools, with a focus on proteomics and genetic variation data. neXtProt now has proteomics data for over 85% of the human proteins, as well as new tools tailored to the proteomics community. Moreover, the neXtProt release 2016-08-25 includes over 8000 phenotypic observations for over 4000 variations in a number of genes involved in hereditary cancers and channelopathies. These changes are presented in the current neXtProt update. All of the neXtProt data are available via our user interface and FTP site. We also provide an API access and a SPARQL endpoint for more technical applications.

The use of misidentified and contaminated cell lines continues to be a problem in biomedical research. Research Resource Identifiers (RRIDs) should reduce the prevalence of misidentified and contaminated cell lines in the literature by alerting researchers to cell lines that are on the list of problematic cell lines, which is maintained by the International Cell Line Authentication Committee (ICLAC) and the Cellosaurus database. To test this assertion, we text-mined the methods sections of about two million papers in PubMed Central, identifying 305,161 unique cell-line names in 150,459 articles. We estimate that 8.6% of these cell lines were on the list of problematic cell lines, whereas only 3.3% of the cell lines in the 634 papers that included RRIDs were on the problematic list. This suggests that the use of RRIDs is associated with a lower reported use of problematic cell lines.

The practice of data sharing in the proteomics field took off and quickly spread in recent years as a result of collective effort. Nowadays, most journal editors mandate the submission of the original raw mass spectra to one of the databases of the ProteomeXchange consortium. With the exception of large institutional initiatives such as PeptideAtlas or the GPMDB, few new studies are however based on the reanalysis of mass spectrometry data. A wealth of information is thus left unexploited in public databases and repositories. Here, we present the large-scale reanalysis of 41 publicly available data sets corresponding to experiments carried out on the HeLa cancer cell line using a custom workflow. In addition to the search of new post-translational modification sites and "missing proteins", our main goal is to identify single amino acid variants and evaluate their impact on protein expression and stability through the spectral counting quantification approach. The X!Tandem software was selected to perform the search of a total of 56 363 701 tandem mass spectra against a customized variant protein database, compiled by the application of the in-house MzVar tool on HeLa-specific somatic and genomic variants retrieved from the COSMIC cell line project. After filtering the resulting identifications with a 1% FDR threshold computed at the protein level, 49 466 unique peptides were identified in 7266 protein entries, allowing the validation of 5576 protein entries in accordance with the HPP guidelines version 2.1. A new "missing protein" was observed (FRAT2, NX_O75474, chromosome 10), and 189 new phosphorylation and 392 new protein N-terminal acetylation sites could be identified. Twenty-four variant peptides were also identified, corresponding to 21 variants in 21 proteins. For three of the nine heterozygous cases where both the variant peptide and its wild-type counterpart were detected, the application of a two-tailed sign test showed a significant difference in the abundance of the two peptide versions.

International audience; The high throughput characterization of protein N-termini is becoming an emerging challenge in the proteomics and proteogenomics fields. The present study describes the free N-terminome analysis of human mitochondria-enriched samples using trimethoxyphenyl phosphonium (TMPP) labelling approaches. Owing to the extent of protein import and cleavage for mitochondrial proteins, determining the new N-termini generated after translocation/processing events for mitochondrial proteins is crucial to understand the transformation of precursors to mature proteins. The doublet N-terminal oriented proteomics (dN-TOP) strategy based on a double light/heavy TMPP labelling has been optimized in order to improve and automate the workflow for efficient, fast and reliable high throughput N-terminome analysis. A total of 2714 proteins were identified and 897 N-terminal peptides were characterized (424 N-α-acetylated and 473 TMPP-labelled peptides). These results allowed the precise identification of the N-terminus of 693 unique proteins corresponding to 26% of all identified proteins. Overall, 120 already annotated processing cleavage sites were confirmed while 302 new cleavage sites were characterized. The accumulation of experimental evidence of mature N-termini should allow increasing the knowledge of processing mechanisms and consequently also enhance cleavage sites prediction algorithms. Complete datasets have been deposited to the ProteomeXchange Consortium with identifiers PXD001521, PXD001522 and PXD001523 (http://proteomecentral.proteomexchange.org/dataset/PXD001521, http://proteomecentral.proteomexchange.org/dataset/PXD0001522 and http://proteomecentral.proteomexchange.org/dataset/PXD001523, respectively).

Mammalian mitochondria may contain up to 1,500 different proteins, and many of them have neither been confidently identified nor characterized. In this study, we demonstrated that C11orf83, which was lacking experimental characterization, is a mitochondrial inner membrane protein facing the intermembrane space. This protein is specifically associated with the bc1 complex of the electron transport chain and involved in the early stages of its assembly by stabilizing the bc1 core complex. C11orf83 displays some overlapping functions with Cbp4p, a yeast bc1 complex assembly factor. Therefore, we suggest that C11orf83, now called UQCC3, is the functional human equivalent of Cbp4p. In addition, C11orf83 depletion in HeLa cells caused abnormal crista morphology, higher sensitivity to apoptosis, a decreased ATP level due to impaired respiration and subtle, but significant, changes in cardiolipin composition. We showed that C11orf83 binds to cardiolipin by its α-helices 2 and 3 and is involved in the stabilization of bc1 complex-containing supercomplexes, especially the III2/IV supercomplex. We also demonstrated that the OMA1 metalloprotease cleaves C11orf83 in response to mitochondrial depolarization, suggesting a role in the selection of cells with damaged mitochondria for their subsequent elimination by apoptosis, as previously described for OPA1.

Germline pathogenic variants in the breast cancer type 1 susceptibility gene BRCA1 are associated with a 60% lifetime risk for breast and ovarian cancer. This overall risk estimate is for all BRCA1 variants; obviously, not all variants confer the same risk of developing a disease. In cancer patients, loss of BRCA1 function in tumor tissue has been associated with an increased sensitivity to platinum agents and to poly-(ADP-ribose) polymerase (PARP) inhibitors. For clinical management of both at-risk individuals and cancer patients, it would be important that each identified genetic variant be associated with clinical significance. Unfortunately for the vast majority of variants, the clinical impact is unknown. The availability of results from studies assessing the impact of variants on protein function may provide insight of crucial importance. We have collected, curated, and structured the molecular and cellular phenotypic impact of 3654 distinct BRCA1 variants. The data was modeled in triple format, using the variant as a subject, the studied function as the object, and a predicate describing the relation between the two. Each annotation is supported by a fully traceable evidence. The data was captured using standard ontologies to ensure consistency, and enhance searchability and interoperability. We have assessed the extent to which functional defects at the molecular and cellular levels correlate with the clinical interpretation of variants by ClinVar submitters. Approximately 30% of the ClinVar BRCA1 missense variants have some molecular or cellular assay available in the literature. Pathogenic variants (as assigned by ClinVar) have at least some significant functional defect in 94% of testable cases. For benign variants, 77% of ClinVar benign variants, for which neXtProt Cancer variant portal has data, shows either no or mild experimental functional defects. While this does not provide evidence for clinical interpretation of variants, it may provide some guidance for variants of unknown significance, in the absence of more reliable data. The neXtProt Cancer variant portal ( https://www.nextprot.org/portals/breast-cancer ) contains over 6300 observations at the molecular and/or cellular level for BRCA1 variants.

20,230 protein-coding genes have been predicted from the analysis of the human genome (neXtProt release 2018-01-17), and about 10% of them are still lacking functional annotation, either predicted by bioinformatics tools or captured from experimental reports. A systematic exploration of the available literature on uncharacterized human genes/proteins led to proposal of functional annotations for 113 proteins and to consolidation of a list of 1,862 uncharacterized human proteins. The advanced search functionality of neXtProt was used extensively in order to examine the landscape of the uncharacterized human proteome in terms of subcellular locations, protein-protein interactions, tissue expression, association with diseases, and 3D structure. Finally, a deep data mining in various publicly available resources allowed building functional hypotheses for 26 uncharacterized human proteins validated at protein level (uPE1). These hypotheses cover the fields of cilia biology, male reproduction, metabolism, nervous system, immunity, inflammation, RNA metabolism, and chromatin biology. They will require experimental validation before they can be considered for annotation. Despite technological progresses, the pace of human protein characterization studies is still slow. It could be accelerated by a better integration of existing knowledge resources and by initiating large collaborative projects involving specialists of different biology fields. We hope that our analysis will contribute to set up the ground for such collaborative approaches and will be exploited by the HUPO Human Proteome Project teams committed to characterize uPE1 proteins.

Protein kinases are a large family of enzymes catalyzing protein phosphorylation. The human genome contains 518 protein kinase genes, 478 of which belong to the classical protein kinase family and 40 are atypical protein kinases [...].

neXtProt (http://www.nextprot.org) is a human protein-centric knowledgebase developed at the SIB Swiss Institute of Bioinformatics. Focused solely on human proteins, neXtProt aims to provide a state of the art resource for the representation of human biology by capturing a wide range of data, precise annotations, fully traceable data provenance and a web interface which enables researchers to find and view information in a comprehensive manner. Since the introductory neXtProt publication, significant advances have been made on three main aspects: the representation of proteomics data, an extended representation of human variants and the development of an advanced search capability built around semantic technologies. These changes are presented in the current neXtProt update.

The development of Linked Data provides the opportunity for databases to supply extensive volumes of biological data, information, and knowledge in a machine interpretable format to make previously isolated data silos interoperable. To increase ease of use, often databases incorporate annotations from several different resources. Linked Data can overcome many formatting and identifier issues that prevent data interoperability, but the extensive cross incorporation of annotations between databases makes the tracking of provenance in open, decentralized systems especially important. With the diversity of published data, provenance information becomes critical to providing reliable and trustworthy services to scientists. The nanopublication system addresses many of these challenges. We have developed the neXtProt Linked Data by serializing in RDF/XML annotations specific to neXtProt and started employing the nanopublication model to give appropriate attribution to all data. Specifically, a use case demonstrates the handling of post-translational modification (PTM) data modeled as nanopublications to illustrate the how the different levels of provenance and data quality thresholds can be captured in this model.

The PROSITE database consists of biologically significant patterns and profiles formulated in such a way that with appropriate computational tools it can help to determine to which known family of proteins (if any) a new sequence belongs or which known domain(s) it contains.

neXtProt (http://www.nextprot.org/) is a new human protein-centric knowledge platform. Developed at the Swiss Institute of Bioinformatics (SIB), it aims to help researchers answer questions relevant to human proteins. To achieve this goal, neXtProt is built on a corpus containing both curated knowledge originating from the UniProtKB/Swiss-Prot knowledgebase and carefully selected and filtered high-throughput data pertinent to human proteins. This article presents an overview of the database and the data integration process. We also lay out the key future directions of neXtProt that we consider the necessary steps to make neXtProt the one-stop-shop for all research projects focusing on human proteins.

The SWISS-2DPAGE database (http: //www.expasy.ch/ch2d/ch2d-top.html ) consists of two-dimensional polyacrylamide gel electrophoresis images, as well as textual descriptions of the proteins that have been identified on them. The current release contains 15 reference maps from human biological samples, as well as from Saccharomyces cerevisiae , Escherichia coli and Dictyostelium discoideum origin. These reference maps have 2088 identified spots, corresponding to 410 separate protein entries in the database, in addition to virtual entries for each SWISS-PROT sequence.

The ENZYME data bank is a repository of information relative to the nomenclature of enzymes. The current version (October 1995) contains information relevant to 3594 enzymes. It is available from a variety of file and ftp servers as well as through the ExPASy World Wide Web server (http://expasy.hcuge.ch/).

SWISS-2DPAGE (http://www.expasy.ch/ch2d/) is an annotated two-dimensional polyacrylamide gel electrophoresis (2-D PAGE) database established in 1993. The current release contains 21 reference maps from human and mouse biological samples, as well as from Saccharomyces cerevisiae, Escherichia coli and Dictyostelium discoideum origin. These reference maps now have 2480 identified spots, corresponding to 528 separate protein entries in the database, in addition to virtual entries for each SWISS-PROT sequence. During the last year, the SWISS-2DPAGE has undergone major changes. Six new maps have been added, and new functions to access the data have been provided through the ExPASy server. Finally, an important change concerns the database funding source.

The InterPro database (http://www.ebi.ac.uk/interpro/) integrates together predictive models or 'signatures' representing protein domains, families and functional sites from multiple, diverse source databases: Gene3D, PANTHER, Pfam, PIRSF, PRINTS, ProDom, PROSITE, SMART, SUPERFAMILY and TIGRFAMs. Integration is performed manually and approximately half of the total approximately 58,000 signatures available in the source databases belong to an InterPro entry. Recently, we have started to also display the remaining un-integrated signatures via our web interface. Other developments include the provision of non-signature data, such as structural data, in new XML files on our FTP site, as well as the inclusion of matchless UniProtKB proteins in the existing match XML files. The web interface has been extended and now links out to the ADAN predicted protein-protein interaction database and the SPICE and Dasty viewers. The latest public release (v18.0) covers 79.8% of UniProtKB (v14.1) and consists of 16 549 entries. InterPro data may be accessed either via the web address above, via web services, by downloading files by anonymous FTP or by using the InterProScan search software (http://www.ebi.ac.uk/Tools/InterProScan/).

The Universal Protein Resource (UniProt) provides a central resource on protein sequences and functional annotation with three database components, each addressing a key need in protein bioinformatics. The UniProt Knowledgebase (UniProtKB), comprising the manually annotated UniProtKB/Swiss-Prot section and the automatically annotated UniProtKB/TrEMBL section, is the preeminent storehouse of protein annotation. The extensive cross-references, functional and feature annotations and literature-based evidence attribution enable scientists to analyse proteins and query across databases. The UniProt Reference Clusters (UniRef) speed similarity searches via sequence space compression by merging sequences that are 100% (UniRef100), 90% (UniRef90) or 50% (UniRef50) identical. Finally, the UniProt Archive (UniParc) stores all publicly available protein sequences, containing the history of sequence data with links to the source databases. UniProt databases continue to grow in size and in availability of information. Recent and upcoming changes to database contents, formats, controlled vocabularies and services are described. New download availability includes all major releases of UniProtKB, sequence collections by taxonomic division and complete proteomes. A bibliography mapping service has been added, and an ID mapping service will be available soon. UniProt databases can be accessed online at http://www.uniprot.org or downloaded at ftp://ftp.uniprot.org/pub/databases/.

SWISS-PROT is a curated protein sequence database which strives to provide a high level of annotation (such as the description of the function of a protein, its domain structure, post-translational modifications, variants, etc), a minimal level of redundancy and a high level of integration with other databases. Recent developments of the database include: an increase in the number and scope of model organisms; cross-references to seven additional databases; a variety of new documentation files; the creation of TREMBL, and unannotated supplement to SWISS-PROT. This supplement consists of entries in SWISS-PROT-like format derived from the translation of all coding sequences (CDS) in the EMBL nucleotide sequence database, except CDS already included in SWISS-PROT.

Deoxyribose-phosphate aldolase (EC 4.1.2.4), which converts 2-deoxy-d-ribose-5-phosphate into glyceraldehyde-3-phosphate and acetaldehyde, belongs to the core metabolism of living organisms. It was previously shown that human cells harbor deoxyribose phosphate aldolase activity but the protein responsible of this activity has never been formally identified. This study provides the first experimental evidence that DERA, which is mainly expressed in lung, liver and colon, is the human deoxyribose phosphate aldolase. Among human cell lines, the highest DERA mRNA level and deoxyribose phosphate aldolase activity were observed in liver-derived Huh-7 cells. DERA was shown to interact with the known stress granule component YBX1 and to be recruited to stress granules after oxidative or mitochondrial stress. In addition, cells in which DERA expression was down-regulated using shRNA formed fewer stress granules and were more prone to apoptosis after clotrimazole stress, suggesting the importance of DERA for stress granule formation. Furthermore, the expression of DERA was shown to permit cells in which mitochondrial ATP production was abolished to make use of extracellular deoxyinosine to maintain ATP levels. This study unraveled a previously undescribed pathway which may allow cells with high deoxyribose-phosphate aldolase activity, such as liver cells, to minimize or delay stress-induced damage by producing energy through deoxynucleoside degradation.

Today, molecular biology databases are the cornerstone of knowledge sharing for life and health sciences. The curation and maintenance of these resources are labour intensive. Although text mining is gaining impetus among curators, its integration in curation workflow has not yet been widely adopted. The Swiss Institute of Bioinformatics Text Mining and CALIPHO groups joined forces to design a new curation support system named nextA5. In this report, we explore the integration of novel triage services to support the curation of two types of biological data: protein–protein interactions (PPIs) and post-translational modifications (PTMs). The recognition of PPIs and PTMs poses a special challenge, as it not only requires the identification of biological entities (proteins or residues), but also that of particular relationships (e.g. binding or position). These relationships cannot be described with onto-terminological descriptors such as the Gene Ontology for molecular functions, which makes the triage task more challenging. Prioritizing papers for these tasks thus requires the development of different approaches. In this report, we propose a new method to prioritize articles containing information specific to PPIs and PTMs. The new resources (RESTful APIs, semantically annotated MEDLINE library) enrich the neXtA5 platform. We tuned the article prioritization model on a set of 100 proteins previously annotated by the CALIPHO group. The effectiveness of the triage service was tested with a dataset of 200 annotated proteins. We defined two sets of descriptors to support automatic triage: the first set to enrich for papers with PPI data, and the second for PTMs. All occurrences of these descriptors were marked-up in MEDLINE and indexed, thus constituting a semantically annotated version of MEDLINE. These annotations were then used to estimate the relevance of a particular article with respect to the chosen annotation type. This relevance score was combined with a local vector-space search engine to generate a ranked list of PMIDs. We also evaluated a query refinement strategy, which adds specific keywords (such as ‘binds’ or ‘interacts’) to the original query. Compared to PubMed, the search effectiveness of the nextA5 triage service is improved by 190% for the prioritization of papers with PPIs information and by 260% for papers with PTMs information. Combining advanced retrieval and query refinement strategies with automatically enriched MEDLINE contents is effective to improve triage in complex curation tasks such as the curation of protein PPIs and PTMs. Database URL: http://candy.hesge.ch/nextA5

Within the C-HPP, the Swiss and French teams are responsible for the annotation of proteins from chromosomes 2 and 14, respectively. neXtProt currently reports 1231 entries on chromosome 2 and 624 entries on chromosome 14; of these, 134 and 93 entries are still not experimentally validated and are thus considered as "missing proteins" (PE2-4), respectively. Among these entries, some may never be validated by conventional MS/MS approaches because of incompatible biochemical features. Others have already been validated but are still awaiting annotation. On the basis of information retrieved from the literature and from three of the main C-HPP resources (Human Protein Atlas, PeptideAtlas, and neXtProt), a subset of 40 theoretically detectable missing proteins (25 on chromosome 2 and 15 on chromosome 14) was defined for upcoming targeted studies in sperm samples. This list is proposed as a roadmap for the French and Swiss teams in the near future.

We report progress assembling the parts list for chromosome 17 and illustrate the various processes that we have developed to integrate available data from diverse genomic and proteomic knowledge bases. As primary resources, we have used GPMDB, neXtProt, PeptideAtlas, Human Protein Atlas (HPA), and GeneCards. All sites share the common resource of Ensembl for the genome modeling information. We have defined the chromosome 17 parts list with the following information: 1169 protein-coding genes, the numbers of proteins confidently identified by various experimental approaches as documented in GPMDB, neXtProt, PeptideAtlas, and HPA, examples of typical data sets obtained by RNASeq and proteomic studies of epithelial derived tumor cell lines (disease proteome) and a normal proteome (peripheral mononuclear cells), reported evidence of post-translational modifications, and examples of alternative splice variants (ASVs). We have constructed a list of the 59 "missing" proteins as well as 201 proteins that have inconclusive mass spectrometric (MS) identifications. In this report we have defined a process to establish a baseline for the incorporation of new evidence on protein identification and characterization as well as related information from transcriptome analyses. This initial list of "missing" proteins that will guide the selection of appropriate samples for discovery studies as well as antibody reagents. Also we have illustrated the significant diversity of protein variants (including post-translational modifications, PTMs) using regions on chromosome 17 that contain important oncogenes. We emphasize the need for mandated deposition of proteomics data in public databases, the further development of improved PTM, ASV, and single nucleotide variant (SNV) databases, and the construction of Web sites that can integrate and regularly update such information. In addition, we describe the distribution of both clustered and scattered sets of protein families on the chromosome. Since chromosome 17 is rich in cancer-associated genes, we have focused the clustering of cancer-associated genes in such genomic regions and have used the ERBB2 amplicon as an example of the value of a proteogenomic approach in which one integrates transcriptomic with proteomic information and captures evidence of coexpression through coordinated regulation.

About 5000 (25%) of the ~20400 human protein-coding genes currently lack any experimental evidence at the protein level. For many others, there is only little information relative to their abundance, distribution, subcellular localization, interactions, or cellular functions. The aim of the HUPO Human Proteome Project (HPP, www.thehpp.org ) is to collect this information for every human protein. HPP is based on three major pillars: mass spectrometry (MS), antibody/affinity capture reagents (Ab), and bioinformatics-driven knowledge base (KB). To meet this objective, the Chromosome-Centric Human Proteome Project (C-HPP) proposes to build this catalog chromosome-by-chromosome ( www.c-hpp.org ) by focusing primarily on proteins that currently lack MS evidence or Ab detection. These are termed "missing proteins" by the HPP consortium. The lack of observation of a protein can be due to various factors including incorrect and incomplete gene annotation, low or restricted expression, or instability. neXtProt ( www.nextprot.org ) is a new web-based knowledge platform specific for human proteins that aims to complement UniProtKB/Swiss-Prot ( www.uniprot.org ) with detailed information obtained from carefully selected high-throughput experiments on genomic variation, post-translational modifications, as well as protein expression in tissues and cells. This article describes how neXtProt contributes to prioritize C-HPP efforts and integrates C-HPP results with other research efforts to create a complete human proteome catalog.

Mass spectrometry has evolved and matured to a level where it is able to assess the complexity of the human proteome. We discuss some of the expected challenges ahead and promising strategies for success.

OBJECTIVE: The human aldehyde dehydrogenase (ALDH) gene superfamily consists of 19 genes encoding enzymes critical for NAD(P)-dependent oxidation of endogenous and exogenous aldehydes, including drugs and environmental toxicants. Mutations in ALDH genes are the molecular basis of several disease states (e.g. Sjogren-Larsson syndrome, pyridoxine-dependent seizures, and type II hyperprolinemia) and may contribute to the etiology of complex diseases such as cancer and Alzheimer's disease. The aim of this nomenclature update was to identify splice transcriptional variants principally for the human ALDH genes. METHODS: Data-mining methods were used to retrieve all human ALDH sequences. Alternatively spliced transcriptional variants were determined based on (i) criteria for sequence integrity and genomic alignment; (ii) evidence of multiple independent cDNA sequences corresponding to a variant sequence; and (iii) if available, empirical evidence of variants from the literature. RESULTS AND CONCLUSION: Alternatively spliced transcriptional variants and their encoded proteins exist for most of the human ALDH genes; however, their function and significance remain to be established. When compared with the human genome, rat and mouse include an additional gene, Aldh1a7, in the ALDH1A subfamily. To avoid confusion when identifying splice variants in various genomes, nomenclature guidelines for the naming of such alternative transcriptional variants and proteins are recommended herein. In addition, a web database (www.aldh.org) has been developed to provide up-to-date information and nomenclature guidelines for the ALDH superfamily.

PROSITE consists of documentation entries describing protein domains, families and functional sites, as well as associated patterns and profiles to identify them. It is complemented by ProRule, a collection of rules based on profiles and patterns, which increases the discriminatory power of these profiles and patterns by providing additional information about functionally and/or structurally critical amino acids. PROSITE is largely used for the annotation of domain features of UniProtKB/Swiss-Prot entries. Among the 983 (DNA-binding) domains, repeats and zinc fingers present in Swiss-Prot (release 57.8 of 22 September 2009), 696 ( approximately 70%) are annotated with PROSITE descriptors using information from ProRule. In order to allow better functional characterization of domains, PROSITE developments focus on subfamily specific profiles and a new profile building method giving more weight to functionally important residues. Here, we describe AMSA, an annotated multiple sequence alignment format used to build a new generation of generalized profiles, the migration of ScanProsite to Vital-IT, a cluster of 633 CPUs, and the adoption of the Distributed Annotation System (DAS) to facilitate PROSITE data integration and interchange with other sources. The latest version of PROSITE (release 20.54, of 22 September 2009) contains 1308 patterns, 863 profiles and 869 ProRules. PROSITE is accessible at: http://www.expasy.org/prosite/.

The UniProt knowledgebase, UniProtKB, is the main product of the UniProt consortium. It consists of two sections, UniProtKB/Swiss-Prot, the manually curated section, and UniProtKB/TrEMBL, the computer translation of the EMBL/GenBank/DDBJ nucleotide sequence database. Taken together, these two sections cover all the proteins characterized or inferred from all publicly available nucleotide sequences. The Plant Proteome Annotation Program (PPAP) of UniProtKB/Swiss-Prot focuses on the manual annotation of plant-specific proteins and protein families. Our major effort is currently directed towards the two model plants Arabidopsis thaliana and Oryza sativa. In UniProtKB/Swiss-Prot, redundancy is minimized by merging all data from different sources in a single entry. The proposed protein sequence is frequently modified after comparison with ESTs, full length transcripts or homologous proteins from other species. The information present in manually curated entries allows the reconstruction of all described isoforms. The annotation also includes proteomics data such as PTM and protein identification MS experimental results. UniProtKB and the other products of the UniProt consortium are accessible online at www.uniprot.org.

The growth in the number of completely sequenced microbial genomes (bacterial and archaeal) has generated a need for a procedure that provides UniProtKB/Swiss-Prot-quality annotation to as many protein sequences as possible. We have devised a semi-automated system, HAMAP (High-quality Automated and Manual Annotation of microbial Proteomes), that uses manually built annotation templates for protein families to propagate annotation to all members of manually defined protein families, using very strict criteria. The HAMAP system is composed of two databases, the proteome database and the family database, and of an automatic annotation pipeline. The proteome database comprises biological and sequence information for each completely sequenced microbial proteome, and it offers several tools for CDS searches, BLAST options and retrieval of specific sets of proteins. The family database currently comprises more than 1500 manually curated protein families and their annotation templates that are used to annotate proteins that belong to one of the HAMAP families. On the HAMAP website, individual sequences as well as whole genomes can be scanned against all HAMAP families. The system provides warnings for the absence of conserved amino acid residues, unusual sequence length, etc. Thanks to the implementation of HAMAP, more than 200,000 microbial proteins have been fully annotated in UniProtKB/Swiss-Prot (HAMAP website: http://www.expasy.org/sprot/hamap).

The potency of the immune response has still to be harnessed effectively to combat human cancers. However, the discovery of T-cell targets in melanomas and other tumors has raised the possibility that cancer vaccines can be used to induce a therapeutically effective immune response against cancer. The targets, cancer-testis (CT) antigens, are immunogenic proteins preferentially expressed in normal gametogenic tissues and different histological types of tumors. Therapeutic cancer vaccines directed against CT antigens are currently in late-stage clinical trials testing whether they can delay or prevent recurrence of lung cancer and melanoma following surgical removal of primary tumors. CT antigens constitute a large, but ill-defined, family of proteins that exhibit a remarkably restricted expression. Currently, there is a considerable amount of information about these proteins, but the data are scattered through the literature and in several bioinformatic databases. The database presented here, CTdatabase (http://www.cta.lncc.br), unifies this knowledge to facilitate both the mining of the existing deluge of data, and the identification of proteins alleged to be CT antigens, but that do not have their characteristic restricted expression pattern. CTdatabase is more than a repository of CT antigen data, since all the available information was carefully curated and annotated with most data being specifically processed for CT antigens and stored locally. Starting from a compilation of known CT antigens, CTdatabase provides basic information including gene names and aliases, RefSeq accession numbers, genomic location, known splicing variants, gene duplications and additional family members. Gene expression at the mRNA level in normal and tumor tissues has been collated from publicly available data obtained by several different technologies. Manually curated data related to mRNA and protein expression, and antigen-specific immune responses in cancer patients are also available, together with links to PubMed for relevant CT antigen articles.

PROSITE consists of documentation entries describing protein domains, families and functional sites, as well as associated patterns and profiles to identify them. It is complemented by ProRule, a collection of rules based on profiles and patterns, which increases the discriminatory power of profiles and patterns by providing additional information about functionally and/or structurally critical amino acids. In this article, we describe the implementation of a new method to assign a status to pattern matches, the new PROSITE web page and a new approach to improve the specificity and sensitivity of PROSITE methods. The latest version of PROSITE (release 20.19 of 11 September 2007) contains 1319 patterns, 745 profiles and 764 ProRules. Over the past 2 years, about 200 domains have been added, and now 53% of UniProtKB/Swiss-Prot entries (release 54.2 of 11 September 2007) have a PROSITE match. PROSITE is available on the web at: http://www.expasy.org/prosite/.

InterPro is an integrated resource for protein families, domains and functional sites, which integrates the following protein signature databases: PROSITE, PRINTS, ProDom, Pfam, SMART, TIGRFAMs, PIRSF, SUPERFAMILY, Gene3D and PANTHER. The latter two new member databases have been integrated since the last publication in this journal. There have been several new developments in InterPro, including an additional reading field, new database links, extensions to the web interface and additional match XML files. InterPro has always provided matches to UniProtKB proteins on the website and in the match XML file on the FTP site. Additional matches to proteins in UniParc (UniProt archive) are now available for download in the new match XML files only. The latest InterPro release (13.0) contains more than 13 000 entries, covering over 78% of all proteins in UniProtKB. The database is available for text- and sequence-based searches via a webserver (http://www.ebi.ac.uk/interpro), and for download by anonymous FTP (ftp://ftp.ebi.ac.uk/pub/databases/interpro). The InterProScan search tool is now also available via a web service at http://www.ebi.ac.uk/Tools/webservices/WSInterProScan.html.

Contains fulltext : 153246.pdf (Publisher’s version ) (Open Access) As the volume of data relating to proteins increases, researchers rely more and more on the analysis of published data, thus increasing the importance of good access to these data that vary from the supplemental material of individual articles, all the way to major reference databases with professional staff and long-term funding. Specialist protein resources fill an important middle ground, providing interactive web interfaces to their databases for a focused topic or family of proteins, using specialized approaches that are not feasible in the major reference databases. Many are labors of love, run by a single lab with little or no dedicated funding and there are many challenges to building and maintaining them. This perspective arose from a meeting of several specialist protein resources and major reference databases held at the Wellcome Trust Genome Campus (Cambridge, UK) on August 11 and 12, 2014. During this meeting some common key challenges involved in creating and maintaining such resources were discussed, along with various approaches to address them. In laying out these challenges, we aim to inform users about how these issues impact our resources and illustrate ways in which our working together could enhance their accuracy, currency, and overall value. Proteins 2015; 83:1005-1013. (c) 2015 The Authors. Proteins: Structure, Function, and Bioinformatics Published by Wiley Periodicals, Inc.

ScanProsite--http://www.expasy.org/tools/scanprosite/--is a new and improved version of the web-based tool for detecting PROSITE signature matches in protein sequences. For a number of PROSITE profiles, the tool now makes use of ProRules--context-dependent annotation templates--to detect functional and structural intra-domain residues. The detection of those features enhances the power of function prediction based on profiles. Both user-defined sequences and sequences from the UniProt Knowledgebase can be matched against custom patterns, or against PROSITE signatures. To improve response times, matches of sequences from UniProtKB against PROSITE signatures are now retrieved from a pre-computed match database. Several output modes are available including simple text views and a rich mode providing an interactive match and feature viewer with a graphical representation of results.

The PROSITE database consists of a large collection of biologically meaningful signatures that are described as patterns or profiles. Each signature is linked to a documentation that provides useful biological information on the protein family, domain or functional site identified by the signature. The PROSITE database is now complemented by a series of rules that can give more precise information about specific residues. During the last 2 years, the documentation and the ScanProsite web pages were redesigned to add more functionalities. The latest version of PROSITE (release 19.11 of September 27, 2005) contains 1329 patterns and 552 profile entries. Over the past 2 years more than 200 domains have been added, and now 52% of UniProtKB/Swiss-Prot entries (release 48.1 of September 27, 2005) have a cross-reference to a PROSITE entry. The database is accessible at http://www.expasy.org/prosite/.

We all know that the dogma 'one gene, one protein' is obsolete. A functional protein and, likewise, a protein's ultimate function depend not only on the underlying genetic information but also on the ongoing conditions of the cellular system. Frequently the transcript, like the polypeptide, is processed in multiple ways, but only one or a few out of a multitude of possible variants are produced at a time. An overview on processes that can lead to sequence variety and structural diversity in eukaryotes is given. The UniProtKB/Swiss-Prot protein knowledgebase provides a wealth of information regarding protein variety, function and associated disorders. Examples for such annotation are shown and further ones are available at http://www.expasy.org/sprot/tutorial/examples_CRB.

The Tox-Prot program was initiated in order to provide the scientific community a summary of the current knowledge on animal protein toxins. The aim of this program is to systematically annotate all proteins which act as toxins and are produced by venomous and poisonous animals. Venomous animals such as snakes, scorpions, spiders, jellyfish, insects, cone snails, sea anemones, lizards, some fish, and platypus are equipped with a specialized organ to inject venom in their prey. In contrast, poisonous animals such as some fish or worms, lack such organs. Each toxin is annotated according to the quality standards of Swiss-Prot. This means providing a wealth of information that includes the description of the function, domain structure, subcellular location, tissue specificity, variants, similarities to other proteins, keywords, etc. In the framework of this program, particular care has been made to capture what is known on the function and mode of action, posttranslational modifications and 3D structural data which are all relatively abundant in the field of protein toxins. Researchers are welcome to contribute their knowledge to the scientific community by submitting relevant findings to Swiss-Prot concerning toxins at Tox-Prot@isb-sib.ch. More information on Tox-Prot can be found at http://www.expasy.org/sprot/tox-prot.