Capturing Data on Antimicrobial Resistance Patterns and Trends in Use in Regions of Asia (CAPTURA).

BACKGROUND: In 2015, the UK government established the Fleming Fund with the aim to address critical gaps in surveillance of antimicrobial resistance (AMR) in low- and middle-income countries in Asia and Africa. Among a large portfolio of grants, the Capturing Data on Antimicrobial Resistance Patterns and Trends in Use in Regions of Asia (CAPTURA) project was awarded with the specific objective of expanding the volume of historical data on AMR, consumption (AMC), and use (AMU) in the human healthcare sector across 12 countries in South and Southeast Asia. METHODS: Starting in early 2019, the CAPTURA consortium began working with local governments and >100 relevant data-holding facilities across the region to identify, assess for quality, prioritize, and subsequently retrieve data on AMR, AMC, and AMU. Relevant and shared data were collated and analyzed to provide local overviews for national stakeholders as well as regional context, wherever possible. RESULTS: From the vast information resource generated on current surveillance capacity and data availability, the project has highlighted gaps and areas for quality improvement and supported comprehensive capacity-building activities to optimize local data-collection and -management practices. CONCLUSIONS: The project has paved the way for expansion of surveillance networks to include both the academic and private sector in several countries and has actively engaged in discussions to promote data sharing at the local, national, and regional levels. This paper describes the overarching approach to, and emerging lessons from, the CAPTURA project, and how it contributes to other ongoing efforts to strengthen national AMR surveillance in the region and globally.

Methodological Approach to Identify and Expand the Volume of Antimicrobial Resistance (AMR) Data in the Human Health Sector in Low- and Middle-Income Countries in Asia: Implications for Local and Regional AMR Surveillance Systems Strengthening.

Antimicrobial resistance (AMR) is a multifaceted global health problem disproportionately affecting low- and middle-income countries (LMICs). The Capturing data on Antimicrobial resistance Patterns and Trends in Use in Regions of Asia (CAPTURA) project was tasked to expand the volume of AMR and antimicrobial use data in Asia. The CAPTURA project used 2 data-collection streams: facility data and project metadata. Project metadata constituted information collected to map out data sources and assess data quality, while facility data referred to the retrospective data collected from healthcare facilities. A down-selection process, labelled "the funnel approach" by the project, was adopted to use the project metadata in prioritizing and selecting laboratories for retrospective AMR data collection. Moreover, the metadata served as a guide for understanding the AMR data once they were collected. The findings from CAPTURA's metadata add to the current discourse on the limitation of AMR data in LMICs. There is generally a low volume of AMR data generated as there is a lack of microbiology laboratories with sufficient antimicrobial susceptibility testing capacity. Many laboratories in Asia are still capturing data on paper, resulting in scattered or unused data not readily accessible or shareable for analyses. There is also a lack of clinical and epidemiological data captured, impeding interpretation and in-depth understanding of the AMR data. CAPTURA's experience in Asia suggests that there is a wide spectrum of capacity and capability of microbiology laboratories within a country and region. As local AMR surveillance is a crucial instrument to inform context-specific measures to combat AMR, it is important to understand and assess current capacity-building needs while implementing activities to enhance surveillance systems.

Rapid Genomic Characterization and Global Surveillance of Klebsiella Using Pathogenwatch.

BACKGROUND: Klebsiella species, including the notable pathogen K. pneumoniae, are increasingly associated with antimicrobial resistance (AMR). Genome-based surveillance can inform interventions aimed at controlling AMR. However, its widespread implementation requires tools to streamline bioinformatic analyses and public health reporting. METHODS: We developed the web application Pathogenwatch, which implements analytics tailored to Klebsiella species for integration and visualization of genomic and epidemiological data. We populated Pathogenwatch with 16 537 public Klebsiella genomes to enable contextualization of user genomes. We demonstrated its features with 1636 genomes from 4 low- and middle-income countries (LMICs) participating in the NIHR Global Health Research Unit (GHRU) on AMR. RESULTS: Using Pathogenwatch, we found that GHRU genomes were dominated by a small number of epidemic drug-resistant clones of K. pneumoniae. However, differences in their distribution were observed (eg, ST258/512 dominated in Colombia, ST231 in India, ST307 in Nigeria, ST147 in the Philippines). Phylogenetic analyses including public genomes for contextualization enabled retrospective monitoring of their spread. In particular, we identified hospital outbreaks, detected introductions from abroad, and uncovered clonal expansions associated with resistance and virulence genes. Assessment of loci encoding O-antigens and capsule in K. pneumoniae, which represent possible vaccine candidates, showed that 3 O-types (O1-O3) represented 88.9% of all genomes, whereas capsule types were much more diverse. CONCLUSIONS: Pathogenwatch provides a free, accessible platform for real-time analysis of Klebsiella genomes to aid surveillance at local, national, and global levels. We have improved representation of genomes from GHRU participant countries, further facilitating ongoing surveillance.

Gene3D: Multi-domain annotations for protein sequence and comparative genome analysis.

Gene3D (http://gene3d.biochem.ucl.ac.uk) is a database of protein domain structure annotations for protein sequences. Domains are predicted using a library of profile HMMs from 2738 CATH superfamilies. Gene3D assigns domain annotations to Ensembl and UniProt sequence sets including >6000 cellular genomes and >20 million unique protein sequences. This represents an increase of 45% in the number of protein sequences since our last publication. Thanks to improvements in the underlying data and pipeline, we see large increases in the domain coverage of sequences. We have expanded this coverage by integrating Pfam and SUPERFAMILY domain annotations, and we now resolve domain overlaps to provide highly comprehensive composite multi-domain architectures. To make these data more accessible for comparative genome analyses, we have developed novel search algorithms for searching genomes to identify related multi-domain architectures. In addition to providing domain family annotations, we have now developed a pipeline for 3D homology modelling of domains in Gene3D. This has been applied to the human genome and will be rolled out to other major organisms over the next year.

Evolutionary history of the TBP-domain superfamily.

The TATA binding protein (TBP) is an essential transcription initiation factor in Archaea and Eucarya. Bacteria lack TBP, and instead use sigma factors for transcription initiation. TBP has a symmetric structure comprising two repeated TBP domains. Using sequence, structural and phylogenetic analyses, we examine the distribution and evolutionary history of the TBP domain, a member of the helix-grip fold family. Our analyses reveal a broader distribution than for TBP, with TBP-domains being present across all three domains of life. In contrast to TBP, all other characterized examples of the TBP domain are present as single copies, primarily within multidomain proteins. The presence of the TBP domain in the ubiquitous DNA glycosylases suggests that this fold traces back to the ancestor of all three domains of life. The TBP domain is also found in RNase HIII, and phylogenetic analyses show that RNase HIII has evolved from bacterial RNase HII via TBP-domain fusion. Finally, our comparative genomic screens confirm and extend earlier reports of proteins consisting of a single TBP domain among some Archaea. These monopartite TBP-domain proteins suggest that this domain is functional in its own right, and that the TBP domain could have first evolved as an independent protein, which was later recruited in different contexts.

New functional families (FunFams) in CATH to improve the mapping of conserved functional sites to 3D structures.

CATH version 3.5 (Class, Architecture, Topology, Homology, available at http://www.cathdb.info/) contains 173 536 domains, 2626 homologous superfamilies and 1313 fold groups. When focusing on structural genomics (SG) structures, we observe that the number of new folds for CATH v3.5 is slightly less than for previous releases, and this observation suggests that we may now know the majority of folds that are easily accessible to structure determination. We have improved the accuracy of our functional family (FunFams) sub-classification method and the CATH sequence domain search facility has been extended to provide FunFam annotations for each domain. The CATH website has been redesigned. We have improved the display of functional data and of conserved sequence features associated with FunFams within each CATH superfamily.

Genome3D: a UK collaborative project to annotate genomic sequences with predicted 3D structures based on SCOP and CATH domains.

Genome3D, available at http://www.genome3d.eu, is a new collaborative project that integrates UK-based structural resources to provide a unique perspective on sequence-structure-function relationships. Leading structure prediction resources (DomSerf, FUGUE, Gene3D, pDomTHREADER, Phyre and SUPERFAMILY) provide annotations for UniProt sequences to indicate the locations of structural domains (structural annotations) and their 3D structures (structural models). Structural annotations and 3D model predictions are currently available for three model genomes (Homo sapiens, E. coli and baker's yeast), and the project will extend to other genomes in the near future. As these resources exploit different strategies for predicting structures, the main aim of Genome3D is to enable comparisons between all the resources so that biologists can see where predictions agree and are therefore more trusted. Furthermore, as these methods differ in whether they build their predictions using CATH or SCOP, Genome3D also contains the first official mapping between these two databases. This has identified pairs of similar superfamilies from the two resources at various degrees of consensus (532 bronze pairs, 527 silver pairs and 370 gold pairs).

Crystal structures of the human Dysferlin inner DysF domain.

BACKGROUND: Mutations in dysferlin, the first protein linked with the cell membrane repair mechanism, causes a group of muscular dystrophies called dysferlinopathies. Dysferlin is a type two-anchored membrane protein, with a single C terminal trans-membrane helix, and most of the protein lying in cytoplasm. Dysferlin contains several C2 domains and two DysF domains which are nested one inside the other. Many pathogenic point mutations fall in the DysF domain region. RESULTS: We describe the crystal structure of the human dysferlin inner DysF domain with a resolution of 1.9 Ångstroms. Most of the pathogenic mutations are part of aromatic/arginine stacks that hold the domain in a folded conformation. The high resolution of the structure show that these interactions are a mixture of parallel ring/guanadinium stacking, perpendicular H bond stacking and aliphatic chain packing. CONCLUSIONS: The high resolution structure of the Dysferlin DysF domain gives a template on which to interpret in detail the pathogenic mutations that lead to disease.

Gene3D: a domain-based resource for comparative genomics, functional annotation and protein network analysis.

Gene3D http://gene3d.biochem.ucl.ac.uk is a comprehensive database of protein domain assignments for sequences from the major sequence databases. Domains are directly mapped from structures in the CATH database or predicted using a library of representative profile HMMs derived from CATH superfamilies. As previously described, Gene3D integrates many other protein family and function databases. These facilitate complex associations of molecular function, structure and evolution. Gene3D now includes a domain functional family (FunFam) level below the homologous superfamily level assignments. Additions have also been made to the interaction data. More significantly, to help with the visualization and interpretation of multi-genome scale data sets, we have developed a new, revamped website. Searching has been simplified with more sophisticated filtering of results, along with new tools based on Cytoscape Web, for visualizing protein-protein interaction networks, differences in domain composition between genomes and the taxonomic distribution of individual superfamilies.

InterPro in 2011: new developments in the family and domain prediction database.

InterPro (http://www.ebi.ac.uk/interpro/) is a database that integrates diverse information about protein families, domains and functional sites, and makes it freely available to the public via Web-based interfaces and services. Central to the database are diagnostic models, known as signatures, against which protein sequences can be searched to determine their potential function. InterPro has utility in the large-scale analysis of whole genomes and meta-genomes, as well as in characterizing individual protein sequences. Herein we give an overview of new developments in the database and its associated software since 2009, including updates to database content, curation processes and Web and programmatic interfaces.

Antibiotic resistance determination using Enterococcus faecium whole-genome sequences: a diagnostic accuracy study using genotypic and phenotypic data.

BACKGROUND: DNA sequencing could become an alternative to in vitro antibiotic susceptibility testing (AST) methods for determining antibiotic resistance by detecting genetic determinants associated with decreased antibiotic susceptibility. Here, we aimed to assess and improve the accuracy of antibiotic resistance determination from Enterococcus faecium genomes for diagnosis and surveillance purposes. METHODS: In this retrospective diagnostic accuracy study, we first conducted a literature search in PubMed on Jan 14, 2021, to compile a catalogue of genes and mutations predictive of antibiotic resistance in E faecium. We then evaluated the diagnostic accuracy of this database to determine susceptibility to 12 different, clinically relevant antibiotics using a diverse population of 4382 E faecium isolates with available whole-genome sequences and in vitro culture-based AST phenotypes. Isolates were obtained from various sources in 11 countries worldwide between 2000 and 2018. We included isolates tested with broth microdilution, Vitek 2, and disc diffusion, and antibiotics with at least 50 susceptible and 50 resistant isolates. Phenotypic resistance was derived from raw minimum inhibitory concentrations and measured inhibition diameters, and harmonised primarily using the breakpoints set by the European Committee on Antimicrobial Susceptibility Testing. A bioinformatics pipeline was developed to process raw sequencing reads, identify antibiotic resistance genetic determinants, and report genotypic resistance. We used our curated database, as well as ResFinder, AMRFinderPlus, and LRE-Finder, to assess the accuracy of genotypic predictions against phenotypic resistance. FINDINGS: We curated a catalogue of 228 genetic markers involved in resistance to 12 antibiotics in E faecium. Very accurate genotypic predictions were obtained for ampicillin (sensitivity 99·7% [95% CI 99·5-99·9] and specificity 97·9% [95·8-99·0]), ciprofloxacin (98·0% [96·4-98·9] and 98·8% [95·9-99·7]), vancomycin (98·8% [98·3-99·2] and 98·8% [98·0-99·3]), and linezolid resistance (after re-testing false negatives: 100·0% [90·8-100·0] and 98·3% [97·8-98·7]). High sensitivity was obtained for tetracycline (99·5% [99·1-99·7]), teicoplanin (98·9% [98·4-99·3]), and high-level resistance to aminoglycosides (97·7% [96·6-98·4] for streptomycin and 96·8% [95·8-97·5] for gentamicin), although at lower specificity (60-90%). Sensitivity was expectedly low for daptomycin (73·6% [65·1-80·6]) and tigecycline (38·3% [27·1-51·0]), for which the genetic basis of resistance is not fully characterised. Compared with other antibiotic resistance databases and bioinformatic tools, our curated database was similarly accurate at detecting resistance to ciprofloxacin and linezolid and high-level resistance to streptomycin and gentamicin, but had better sensitivity for detecting resistance to ampicillin, tigecycline, daptomycin, and quinupristin-dalfopristin, and better specificity for ampicillin, vancomycin, teicoplanin, and tetracycline resistance. In a validation dataset of 382 isolates, similar or improved diagnostic accuracies were also achieved. INTERPRETATION: To our knowledge, this work represents the largest published evaluation to date of the accuracy of antibiotic susceptibility predictions from E faecium genomes. The results and resources will facilitate the adoption of whole-genome sequencing as a tool for the diagnosis and surveillance of antimicrobial resistance in E faecium. A complete characterisation of the genetic basis of resistance to last-line antibiotics, and the mechanisms mediating antibiotic resistance silencing, are needed to close the remaining sensitivity and specificity gaps in genotypic predictions. FUNDING: Wellcome Trust, UK Department of Health, British Society for Antimicrobial Chemotherapy, Academy of Medical Sciences and the Health Foundation, Medical Research Council Newton Fund, Vietnamese Ministry of Science and Technology, and European Society of Clinical Microbiology and Infectious Disease.

The Gene3D Web Services: a platform for identifying, annotating and comparing structural domains in protein sequences.

The Gene3D structural domain database provides domain annotations for 7 million proteins, based on the manually curated structural domain superfamilies in CATH. These annotations are integrated with functional, genomic and molecular information from external resources, such as GO, EC, UniProt and the NCBI Taxonomy database. We have constructed a set of web services that provide programmatic access to this integrated database, as well as the Gene3D domain recognition tool (Gene3DScan) and protein sequence annotation pipeline for analysing novel protein sequences. Example queries include retrieving all curated GO terms for a domain superfamily or all the multi-domain architectures for the human genome. The services can be accessed using simple HTTP calls and are able to return results in a range of formats for quick downloading and easy parsing, graphical rendering and data storage. Hence, they provide a simple, but flexible means of integrating domain annotations and associated data sets into locally run pipelines and analysis software. The services can be found at http://gene3d.biochem.ucl.ac.uk/WebServices/.

Unlocking the Potential of Genomic Data to Inform Typhoid Fever Control Policy: Supportive Resources for Genomic Data Generation, Analysis, and Visualization.

The global response to the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) pandemic demonstrated the value of timely and open sharing of genomic data with standardized metadata to facilitate monitoring of the emergence and spread of new variants. Here, we make the case for the value of Salmonella Typhi (S. Typhi) genomic data and demonstrate the utility of freely available platforms and services that support the generation, analysis, and visualization of S. Typhi genomic data on the African continent and more broadly by introducing the Africa Centres for Disease Control and Prevention's Pathogen Genomics Initiative, SEQAFRICA, Typhi Pathogenwatch, TyphiNET, and the Global Typhoid Genomics Consortium.

Comparing genomic variant identification protocols for Candida auris.

Genomic analyses are widely applied to epidemiological, population genetic and experimental studies of pathogenic fungi. A wide range of methods are employed to carry out these analyses, typically without including controls that gauge the accuracy of variant prediction. The importance of tracking outbreaks at a global scale has raised the urgency of establishing high-accuracy pipelines that generate consistent results between research groups. To evaluate currently employed methods for whole-genome variant detection and elaborate best practices for fungal pathogens, we compared how 14 independent variant calling pipelines performed across 35 Candida auris isolates from 4 distinct clades and evaluated the performance of variant calling, single-nucleotide polymorphism (SNP) counts and phylogenetic inference results. Although these pipelines used different variant callers and filtering criteria, we found high overall agreement of SNPs from each pipeline. This concordance correlated with site quality, as SNPs discovered by a few pipelines tended to show lower mapping quality scores and depth of coverage than those recovered by all pipelines. We observed that the major differences between pipelines were due to variation in read trimming strategies, SNP calling methods and parameters, and downstream filtration criteria. We calculated specificity and sensitivity for each pipeline by aligning three isolates with chromosomal level assemblies and found that the GATK-based pipelines were well balanced between these metrics. Selection of trimming methods had a greater impact on SAMtools-based pipelines than those using GATK. Phylogenetic trees inferred by each pipeline showed high consistency at the clade level, but there was more variability between isolates from a single outbreak, with pipelines that used more stringent cutoffs having lower resolution. This project generated two truth datasets useful for routine benchmarking of C. auris variant calling, a consensus VCF of genotypes discovered by 10 or more pipelines across these 35 diverse isolates and variants for 2 samples identified from whole-genome alignments. This study provides a foundation for evaluating SNP calling pipelines and developing best practices for future fungal genomic studies.

Low-density lipoprotein receptor gene familial hypercholesterolemia variant database: update and pathological assessment.

Familial hypercholesterolemia (FH) is caused predominately by variants in the low-density lipoprotein receptor gene (LDLR). We report here an update of the UCL LDLR variant database to include variants reported in the literature and in-house between 2008 and 2010, transfer of the database to LOVDv.2.0 platform (https://grenada.lumc.nl/LOVD2/UCL-Heart/home.php?select_db=LDLR) and pathogenicity analysis. The database now contains over 1288 different variants reported in FH patients: 55% exonic substitutions, 22% exonic small rearrangements (<100 bp), 11% large rearrangements (>100 bp), 2% promoter variants, 10% intronic variants and 1 variant in the 3' untranslated sequence. The distribution and type of newly reported variants closely matches that of the 2008 database, and we have used these variants (n= 223) as a representative sample to assess the utility of standard open access software (PolyPhen, SIFT, refined SIFT, Neural Network Splice Site Prediction Tool, SplicePort and NetGene2) and additional analyses (Single Amino Acid Polymorphism database, analysis of conservation and structure and Mutation Taster) for pathogenicity prediction. In combination, these techniques have enabled us to assign with confidence pathogenic predictions to 8/8 in-frame small rearrangements and 8/9 missense substitutions with previously discordant results from PolyPhen and SIFT analysis. Overall, we conclude that 79% of the reported variants are likely to be disease causing.

Gene3D: merging structure and function for a Thousand genomes.

Over the last 2 years the Gene3D resource has been significantly improved, and is now more accurate and with a much richer interactive display via the Gene3D website (http://gene3d.biochem.ucl.ac.uk/). Gene3D provides accurate structural domain family assignments for over 1100 genomes and nearly 10,000,000 proteins. A hidden Markov model library, constructed from the manually curated CATH structural domain hierarchy, is used to search UniProt, RefSeq and Ensembl protein sequences. The resulting matches are refined into simple multi-domain architectures using a recently developed in-house algorithm, DomainFinder 3 (available at: ftp://ftp.biochem.ucl.ac.uk/pub/gene3d_data/DomainFinder3/). The domain assignments are integrated with multiple external protein function descriptions (e.g. Gene Ontology and KEGG), structural annotations (e.g. coiled coils, disordered regions and sequence polymorphisms) and family resources (e.g. Pfam and eggNog) and displayed on the Gene3D website. The website allows users to view descriptions for both single proteins and genes and large protein sets, such as superfamilies or genomes. Subsets can then be selected for detailed investigation or associated functions and interactions can be used to expand explorations to new proteins. Gene3D also provides a set of services, including an interactive genome coverage graph visualizer, DAS annotation resources, sequence search facilities and SOAP services.

Europe-wide expansion and eradication of multidrug-resistant Neisseria gonorrhoeae lineages: a genomic surveillance study.

BACKGROUND: Genomic surveillance using quality-assured whole-genome sequencing (WGS) together with epidemiological and antimicrobial resistance (AMR) data is essential to characterise the circulating Neisseria gonorrhoeae lineages and their association to patient groups (defined by demographic and epidemiological factors). In 2013, the European gonococcal population was characterised genomically for the first time. We describe the European gonococcal population in 2018 and identify emerging or vanishing lineages associated with AMR and epidemiological characteristics of patients, to elucidate recent changes in AMR and gonorrhoea epidemiology in Europe. METHODS: We did WGS on 2375 gonococcal isolates from 2018 (mainly Sept 1-Nov 30) in 26 EU and EEA countries. Molecular typing and AMR determinants were extracted from quality-checked genomic data. Association analyses identified links between genomic lineages, AMR, and epidemiological data. FINDINGS: Azithromycin-resistant N gonorrhoeae (8·0% [191/2375] in 2018) is rising in Europe due to the introduction or emergence and subsequent expansion of a novel N gonorrhoeae multi-antigen sequence typing (NG-MAST) genogroup, G12302 (132 [5·6%] of 2375; N gonorrhoeae sequence typing for antimicrobial resistance [NG-STAR] clonal complex [CC]168/63), carrying a mosaic mtrR promoter and mtrD sequence and found in 24 countries in 2018. CC63 was associated with pharyngeal infections in men who have sex with men. Susceptibility to ceftriaxone and cefixime is increasing, as the resistance-associated lineage, NG-MAST G1407 (51 [2·1%] of 2375), is progressively vanishing since 2009-10. INTERPRETATION: Enhanced gonococcal AMR surveillance is imperative worldwide. WGS, linked to epidemiological and AMR data, is essential to elucidate the dynamics in gonorrhoea epidemiology and gonococcal populations as well as to predict AMR. When feasible, WGS should supplement the national and international AMR surveillance programmes to elucidate AMR changes over time. In the EU and EEA, increasing low-level azithromycin resistance could threaten the recommended ceftriaxone-azithromycin dual therapy, and an evidence-based clinical azithromycin resistance breakpoint is needed. Nevertheless, increasing ceftriaxone susceptibility, declining cefixime resistance, and absence of known resistance mutations for new treatments (zoliflodacin, gepotidacin) are promising. FUNDING: European Centre for Disease Prevention and Control, Centre for Genomic Pathogen Surveillance, Örebro University Hospital, Wellcome.

A fast and automated solution for accurately resolving protein domain architectures.

MOTIVATION: Accurate prediction of the domain content and arrangement in multi-domain proteins (which make up >65% of the large-scale protein databases) provides a valuable tool for function prediction, comparative genomics and studies of molecular evolution. However, scanning a multi-domain protein against a database of domain sequence profiles can often produce conflicting and overlapping matches. We have developed a novel method that employs heaviest weighted clique-finding (HCF), which we show significantly outperforms standard published approaches based on successively assigning the best non-overlapping match (Best Match Cascade, BMC). RESULTS: We created benchmark data set of structural domain assignments in the CATH database and a corresponding set of Hidden Markov Model-based domain predictions. Using these, we demonstrate that by considering all possible combinations of matches using the HCF approach, we achieve much higher prediction accuracy than the standard BMC method. We also show that it is essential to allow overlapping domain matches to a query in order to identify correct domain assignments. Furthermore, we introduce a straightforward and effective protocol for resolving any overlapping assignments, and producing a single set of non-overlapping predicted domains. AVAILABILITY AND IMPLEMENTATION: The new approach will be used to determine MDAs for UniProt and Ensembl, and made available via the Gene3D website: http://gene3d.biochem.ucl.ac.uk/Gene3D/. The software has been implemented in C++ and compiled for Linux: source code and binaries can be found at: ftp://ftp.biochem.ucl.ac.uk/pub/gene3d_data/DomainFinder3/ CONTACT: yeats@biochem.ucl.ac.uk SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

Finding the "dark matter" in human and yeast protein network prediction and modelling.

Accurate modelling of biological systems requires a deeper and more complete knowledge about the molecular components and their functional associations than we currently have. Traditionally, new knowledge on protein associations generated by experiments has played a central role in systems modelling, in contrast to generally less trusted bio-computational predictions. However, we will not achieve realistic modelling of complex molecular systems if the current experimental designs lead to biased screenings of real protein networks and leave large, functionally important areas poorly characterised. To assess the likelihood of this, we have built comprehensive network models of the yeast and human proteomes by using a meta-statistical integration of diverse computationally predicted protein association datasets. We have compared these predicted networks against combined experimental datasets from seven biological resources at different level of statistical significance. These eukaryotic predicted networks resemble all the topological and noise features of the experimentally inferred networks in both species, and we also show that this observation is not due to random behaviour. In addition, the topology of the predicted networks contains information on true protein associations, beyond the constitutive first order binary predictions. We also observe that most of the reliable predicted protein associations are experimentally uncharacterised in our models, constituting the hidden or "dark matter" of networks by analogy to astronomical systems. Some of this dark matter shows enrichment of particular functions and contains key functional elements of protein networks, such as hubs associated with important functional areas like the regulation of Ras protein signal transduction in human cells. Thus, characterising this large and functionally important dark matter, elusive to established experimental designs, may be crucial for modelling biological systems. In any case, these predictions provide a valuable guide to these experimentally elusive regions.

InterPro: the integrative protein signature database.

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/).