Functional Annotation Databases Research Articles

Proteome reorganization and amino acid metabolism during germination and seedling establishment in Lupinus albus.

During germination plants rely entirely on their seed storage compounds to provide energy and precursors for the synthesis of macromolecular structures until the seedling has emerged from the soil and photosynthesis can be established. Lupin seeds use proteins as their major storage compounds, accounting for up to 40% of the seed dry weight. Lupins are therefore a valuable complement to soy as a source of plant protein for human and animal nutrition. The aim of this study was to elucidate how storage protein metabolism is coordinated with other metabolic processes to meet the requirements of the growing seedling. In a quantitative approach, we analyzed seedling growth, as well as alterations in biomass composition, the proteome, and metabolite profiles during germination and seedling establishment in Lupinus albus. The reallocation of nitrogen resources from seed storage proteins to functional seed proteins was mapped based on a manually curated functional protein annotation database. Although classified as a protein crop, Lupinus albus does not use amino acids as a primary substrate for energy metabolism during germination. However, fatty acid and amino acid metabolism may be integrated at the level of malate synthase to combine stored carbon from lipids and proteins into gluconeogenesis.

Predicting Protein Functions Based on Heterogeneous Graph Attention Technique.

In bioinformatics, protein function prediction stands as a fundamental area of research and plays a crucial role in addressing various biological challenges, such as the identification of potential targets for drug discovery and the elucidation of disease mechanisms. However, known functional annotation databases usually provide positive experimental annotations that proteins carry out a given function, and rarely record negative experimental annotations that proteins do not carry out a given function. Therefore, existing computational methods based on deep learning models focus on these positive annotations for prediction and ignore these scarce but informative negative annotations, leading to an underestimation of precision. To address this issue, we introduce a deep learning method that utilizes a heterogeneous graph attention technique. The method first constructs a heterogeneous graph that covers the protein-protein interaction network, ontology structure, and positive and negative annotation information. Then, it learns embedding representations of proteins and ontology terms by using the heterogeneous graph attention technique. Finally, it leverages these learned representations to reconstruct the positive protein-term associations and score unobserved functional annotations. It can enhance the predictive performance by incorporating these known limited negative annotations into the constructed heterogeneous graph. Experimental results on three species (i.e., Human, Mouse, and Arabidopsis) demonstrate that our method can achieve better performance in predicting new protein annotations than state-of-the-art methods.

RSAD2 Is an Effective Target for High-Yield Vaccine Production in MDCK Cells.

Increasingly, attention has focused on improving vaccine production in cells using gene editing technology to specifically modify key virus regulation-related genes to promote virus replication. In this study, we used DIA proteomics analysis technology to compare protein expression differences between two groups of MDCK cells: uninfected and influenza A virus (IAV) H1N1-infected cells 16 h post infection (MOI = 0.01). Initially, 266 differentially expressed proteins were detected after infection, 157 of which were upregulated and 109 were downregulated. We screened these proteins to 23 genes related to antiviral innate immunity regulation based on functional annotation database analysis and verified the mRNA expression of these genes using qPCR. Combining our results with published literature, we focused on the proteins RSAD2, KCNN4, IDO1, and ISG20; we verified their expression using western blot, which was consistent with our proteomics results. Finally, we knocked down RSAD2 using lentiviral shRNA expression vectors and found that RSAD2 inhibition significantly increased IAV NP gene expression, effectively promoting influenza virus replication with no significant effect on cell proliferation. These results indicate that RSAD2 is potentially an effective target for establishing high-yield vaccine MDCK cell lines and will help to fully understand the interaction mechanism between host cells and influenza viruses.

FAVOR: functional annotation of variants online resource and annotator for variation across the human genome.

Large biobank-scale whole genome sequencing (WGS) studies are rapidly identifying a multitude of coding and non-coding variants. They provide an unprecedented resource for illuminating the genetic basis of human diseases. Variant functional annotations play a critical role in WGS analysis, result interpretation, and prioritization of disease- or trait-associated causal variants. Existing functional annotation databases have limited scope to perform online queries and functionally annotate the genotype data of large biobank-scale WGS studies. We develop the Functional Annotation of Variants Online Resources (FAVOR) to meet these pressing needs. FAVOR provides a comprehensive multi-faceted variant functional annotation online portal that summarizesand visualizes findings of all possible nine billion single nucleotide variants (SNVs) across the genome. It allows for rapid variant-, gene-and region-level queries of variant functional annotations. FAVOR integrates variant functional information from multiple sources to describe the functional characteristics of variants and facilitates prioritizing plausible causal variants influencing human phenotypes. Furthermore, we provide a scalable annotation tool, FAVORannotator, to functionally annotate large-scale WGS studies and efficiently store the genotype and their variant functional annotation data in a single file using the annotated Genomic Data Structure (aGDS) format, making downstream analysis more convenient. FAVOR and FAVORannotator are available at https://favor.genohub.org.

ChanFAD: A Functional Annotation Database for Ion Channels.

Ion channels are integral membrane protein complexes critical for regulation of membrane potential, cell volume, and other signaling events. As complex molecular assemblies with many interacting partners, ion channels have multiple structural and functional domains. While channel sequence and functional data are readily available across multiple online resources, there is an unmet need for functional annotation directly relating primary sequence information, 2D interactions, and three-dimensional protein structure. To this end, we present ChanFAD (Channel Functional Annotation Database), to provide the research community with a centralized resource for ion channel structure and functional data. ChanFAD provides functional annotation of PDB structures built on the National Center for Biotechnology Information’s iCn3D structure viewing tool while providing additional information such as primary sequence, organism, and relevant links to other databases. Here we provide a brief tour of ChanFAD functionality while showing example use cases involving drug-channel interactions and structural changes based on mutation. ChanFAD is freely available and can be accessed at https://www.chanfad.org/.

FusionGDB 2.0: fusion gene annotation updates aided by deep learning.

A knowledgebase of the systematic functional annotation of fusion genes is critical for understanding genomic breakage context and developing therapeutic strategies. FusionGDB is a unique functional annotation database of human fusion genes and has been widely used for studies with diverse aims. In this study, we report fusion gene annotation updates aided by deep learning (FusionGDB 2.0) available at https://compbio.uth.edu/FusionGDB2/. FusionGDB 2.0 has substantial updates of contents such as up-to-date human fusion genes, fusion gene breakage tendency score with FusionAI deep learning model based on 20 kb DNA sequence around BP, investigation of overlapping between fusion breakpoints with 44 human genomic features across five cellular role's categories, transcribed chimeric sequence and following open reading frame analysis with coding potential based on deep learning approach with Ribo-seq read features, and rigorous investigation of the protein feature retention of individual fusion partner genes in the protein level. Among ∼102k fusion genes, about 15k kept their ORF as In-frames, which is two times compared to the previous version, FusionGDB. FusionGDB 2.0 will be used as the reference knowledgebase of fusion gene annotations. FusionGDB 2.0 provides eight categories of annotations and it will be helpful for diverse human genomic studies.

Four chromosome scale genomes and a pan-genome annotation to accelerate pecan tree breeding

Genome-enabled biotechnologies have the potential to accelerate breeding efforts in long-lived perennial crop species. Despite the transformative potential of molecular tools in pecan and other outcrossing tree species, highly heterozygous genomes, significant presence–absence gene content variation, and histories of interspecific hybridization have constrained breeding efforts. To overcome these challenges, here, we present diploid genome assemblies and annotations of four outbred pecan genotypes, including a PacBio HiFi chromosome-scale assembly of both haplotypes of the ‘Pawnee’ cultivar. Comparative analysis and pan-genome integration reveal substantial and likely adaptive interspecific genomic introgressions, including an over-retained haplotype introgressed from bitternut hickory into pecan breeding pedigrees. Further, by leveraging our pan-genome presence–absence and functional annotation database among genomes and within the two outbred haplotypes of the ‘Lakota’ genome, we identify candidate genes for pest and pathogen resistance. Combined, these analyses and resources highlight significant progress towards functional and quantitative genomics in highly diverse and outbred crops.

DbNSFP v4: a comprehensive database of transcript-specific functional predictions and annotations for human nonsynonymous and splice-site SNVs

Whole exome sequencing has been increasingly used in human disease studies. Prioritization based on appropriate functional annotations has been used as an indispensable step to select candidate variants. Here we present the latest updates to dbNSFP (version 4.1), a database designed to facilitate this step by providing deleteriousness prediction and functional annotation for all potential nonsynonymous and splice-site SNVs (a total of 84,013,093) in the human genome. The current version compiled 36 deleteriousness prediction scores, including 12 transcript-specific scores, and other variant and gene-level functional annotations. The database is available at http://database.liulab.science/dbNSFP with a downloadable version and a web-service.

Bisphenol A biodegradation differs between mudflat and mangrove forest sediments

Bisphenol A (BPA) is one of the widely detected endocrine disrupting chemicals in coastal sediment. Biodegradation is a vital pathway of BPA elimination in sediment. However, the impact of vegetation on BPA degradation in coastal sediment is still unclear. In this study, the differences of BPA biodegradation and the functional microbial community and metabolic pathway were explored between mangrove forest and mudflat sediments. A nearly complete BPA attenuation was detected in 4 days in mudflat sediment but 8 days in forest sediment. Bacterial abundance varied greatly in different sediment types. Bacterial community structure changed with BPA biodegradation, dependent on sediment type. During the degradation, the proportions of Alphaproteobacteria and Gammaproteobacteria were higher in BPA amended microcosms than in un-amended microcosms. With BPA biodegradation, a substantial increase in Novosphingobium and Croceicoccus occurred in forest sediment and mudflat sediment, respectively. Additionally, two divergent BPA biodegradation pathways were proposed based on functional annotation and KEGG pathway database. The abundance of functional genes also varied with BPA biodegradation, dependent on sediment type. Gene pcaGH decreased, while genes ligK and pcaD increased in both sediment types. Gene pcaB showed a remarkable increase in forest sediment but a decrease in mudflat sediment. Therefore, BPA degradation and the associated microbial community and metabolic pathway differed between mudflat and mangrove forest sediments.

ClusterMine: A knowledge-integrated clustering approach based on expression profiles of gene sets.

Clustering analysis of gene expression data is essential for understanding complex biological data, and is widely used in important biological applications such as the identification of cell subpopulations and disease subtypes. In commonly used methods such as hierarchical clustering (HC) and consensus clustering (CC), holistic expression profiles of all genes are often used to assess the similarity between samples for clustering. While these methods have been proven successful in identifying sample clusters in many areas, they do not provide information about which gene sets (functions) contribute most to the clustering, thus limiting the interpretability of the resulting cluster. We hypothesize that integrating prior knowledge of annotated gene sets would not only achieve satisfactory clustering performance but also, more importantly, enable potential biological interpretation of clusters. Here we report ClusterMine, an approach that identifies clusters by assessing functional similarity between samples through integrating known annotated gene sets in functional annotation databases such as Gene Ontology. In addition to the cluster membership of each sample as provided by conventional approaches, it also outputs gene sets that most likely contribute to the clustering, thus facilitating biological interpretation. We compare ClusterMine with conventional approaches on nine real-world experimental datasets that represent different application scenarios in biology. We find that ClusterMine achieves better performances and that the gene sets prioritized by our method are biologically meaningful. ClusterMine is implemented as an R package and is freely available at: www.genemine.org/clustermine.php.

Cross-population analysis for functional characterization of type II diabetes variants

BackgroundAs Genome-Wide Association Studies (GWAS) have been increasingly used with data from various populations, it has been observed that data from different populations reveal different sets of Single Nucleotide Polymorphisms (SNPs) that are associated with the same disease. Using Type II Diabetes (T2D) as a test case, we develop measures and methods to characterize the functional overlap of SNPs associated with the same disease across populations.ResultsWe introduce the notion of an Overlap Matrix as a general means of characterizing the functional overlap between different SNP sets at different genomic and functional granularities. Using SNP-to-gene mapping, functional annotation databases, and functional association networks, we assess the degree of functional overlap across nine populations from Asian and European ethnic origins. We further assess the generalizability of the method by applying it to a dataset for another complex disease – Prostate Cancer. Our results show that more overlap is captured as more functional data is incorporated as we go through the pipeline, starting from SNPs and ending at network overlap analyses. We hypothesize that these observed differences in the functional mechanisms of T2D across populations can also explain the common use of different prescription drugs in different populations. We show that this hypothesis is concordant with the literature on the functional mechanisms of prescription drugs.ConclusionOur results show that although the etiology of a complex disease can be associated with distinct processes that are affected in different populations, network-based annotations can capture more functional overlap across populations. These results support the notion that it can be useful to take ethnicity into account in making personalized treatment decisions for complex diseases.

The MACADAM database: a MetAboliC pAthways DAtabase for Microbial taxonomic groups for mining potential metabolic capacities of archaeal and bacterial taxonomic groups.

Progress in genome sequencing and bioinformatics opens up new possibilities, including that of correlating genome annotations with functional information such as metabolic pathways. Thanks to the development of functional annotation databases, scientists are able to link genome annotations with functional annotations. We present MetAboliC pAthways DAtabase for Microbial taxonomic groups (MACADAM) here, a user-friendly database that makes it possible to find presence/absence/completeness statistics for metabolic pathways at a given microbial taxonomic position. For each prokaryotic ‘RefSeq complete genome’, MACADAM builds a pathway genome database (PGDB) using Pathway Tools software based on MetaCyc data that includes metabolic pathways as well as associated metabolites, reactions and enzymes. To ensure the highest quality of the genome functional annotation data, MACADAM also contains MicroCyc, a manually curated collection of PGDBs; Functional Annotation of Prokaryotic Taxa (FAPROTAX), a manually curated functional annotation database; and the IJSEM phenotypic database. The MACADAM database contains 13 509 PGDBs (13 195 bacterial and 314 archaeal), 1260 unique metabolic pathways, completed with 82 functional annotations from FAPROTAX and 16 from the IJSEM phenotypic database. MACADAM contains a total of 7921 metabolites, 592 enzymatic reactions, 2134 EC numbers and 7440 enzymes. MACADAM can be queried at any rank of the NCBI taxonomy (from phyla to species). It provides the possibility to explore functional information completed with metabolites, enzymes, enzymatic reactions and EC numbers. MACADAM returns a tabulated file containing a list of pathways with two scores (pathway score and pathway frequency score) that are present in the queried taxa. The file also contains the names of the organisms in which the pathways are found and the metabolic hierarchy associated with the pathways. Finally, MACADAM can be downloaded as a single file and queried with SQLite or python command lines or explored through a web interface.

Mammalian Annotation Database for improved annotation and functional classification of Omics datasets from less well-annotated organisms

Next-generation sequencing technologies and the availability of an increasing number of mammalian and other genomes allow gene expression studies, particularly RNA sequencing, in many non-model organisms. However, incomplete genome annotation and assignments of genes to functional annotation databases can lead to a substantial loss of information in downstream data analysis. To overcome this, we developed Mammalian Annotation Database tool (MAdb, https://madb.ethz.ch) to conveniently provide homologous gene information for selected mammalian species. The assignment between species is performed in three steps: (i) matching official gene symbols, (ii) using ortholog information contained in Ensembl Compara and (iii) pairwise BLAST comparisons of all transcripts. In addition, we developed a new tool (AnnOverlappeR) for the reliable assignment of the National Center for Biotechnology Information (NCBI) and Ensembl gene IDs. The gene lists translated to gene IDs of well-annotated species such as a human can be used for improved functional annotation with relevant tools based on Gene Ontology and molecular pathway information. We tested the MAdb on a published RNA-seq data set for the pig and showed clearly improved overrepresentation analysis results based on the assigned human homologous gene identifiers. Using the MAdb revealed a similar list of human homologous genes and functional annotation results regardless of whether starting with gene IDs from NCBI or Ensembl. The MAdb database is accessible via a web interface and a Galaxy application.

Identification of Genes Related to Cell Wall Metabolism and Fruit Ripening in Ziziphus jujube Using RNA-seq and Expression Analysis

The transcriptome of Chinese jujube (Ziziphus jujube Mill.) fruit was characterized to determine which genes were involved in cell wall metabolism and ripening. Based on RNA-seq of fruit pulp, 61.243 contigs (mean length 440 nt) were assembled and 31.725 genes were identified (mean length 842 nt), of which 25.257 were annotated using a non-redundant protein sequence database (Nr). Finally, 8.409 of these genes were classified using the Gene Ontology (GO) functional annotation database into three major categories: cell component, molecular function, and biology process. Comparison of young and mature fruit transcripts showed that 1.206 genes were upregulated while 1.256 genes were downregulated. The 19 candidate genes related to cell wall metabolism and fruit ripening were selected from the differentially expressed genes (DEGs) for further study; qRT-PCR analysis showed that gene expression levels were up- or downregulated significantly at the stages of young and ripe fruit when compared to plant leaves.

SDADB: a functional annotation database of protein structural domains.

Annotating functional terms with individual domains is essential for understanding the functions of full-length proteins. We describe SDADB, a functional annotation database for structural domains. SDADB provides associations between gene ontology (GO) terms and SCOP domains calculated with an integrated framework. GO annotations are assigned probabilities of being correct, which are estimated with a Bayesian network by taking advantage of structural neighborhood mappings, SCOP-InterPro domain mapping information, position-specific scoring matrices (PSSMs) and sequence homolog features, with the most substantial contribution coming from high-coverage structure-based domain-protein mappings. The domain-protein mappings are computed using large-scale structure alignment. SDADB contains ontological terms with probabilistic scores for more than 214 000 distinct SCOP domains. It also provides additional features include 3D structure alignment visualization, GO hierarchical tree view, search, browse and download options.Database URL: http://sda.denglab.org

Identification of susceptible genes for complex chronic diseases based on disease risk functional SNPs and interaction networks

Complex chronic diseases are caused by the effects of genetic and environmental factors. Single nucleotide polymorphisms (SNPs), one common type of genetic variations, played vital roles in diseases. We hypothesized that disease risk functional SNPs in coding regions and protein interaction network modules were more likely to contribute to the identification of disease susceptible genes for complex chronic diseases. This could help to further reveal the pathogenesis of complex chronic diseases. Disease risk SNPs were first recognized from public SNP data for coronary heart disease (CHD), hypertension (HT) and type 2 diabetes (T2D). SNPs in coding regions that were classified into nonsense and missense by integrating several SNP functional annotation databases were treated as functional SNPs. Then, regions significantly associated with each disease were screened using random permutations for disease risk functional SNPs. Corresponding to these regions, 155, 169 and 173 potential disease susceptible genes were identified for CHD, HT and T2D, respectively. A disease-related gene product interaction network in environmental context was constructed for interacting gene products of both disease genes and potential disease susceptible genes for these diseases. After functional enrichment analysis for disease associated modules, 5 CHD susceptible genes, 7 HT susceptible genes and 3 T2D susceptible genes were finally identified, some of which had pleiotropic effects. Most of these genes were verified to be related to these diseases in literature. This was similar for disease genes identified from another method proposed by Lee et al. from a different aspect. This research could provide novel perspectives for diagnosis and treatment of complex chronic diseases and susceptible genes identification for other diseases.

De novo transcriptome analysis of Fraxinus velutina using Illumina platform and development of EST-SSR markers

To facilitate functional genomic analysis and molecular breeding of velvet ash (Fraxinus velutina Torr), the de novo sequencing was carried out by Illumina sequencing technology. The cDNA samples were prepared from eleven different tissues of velvet ash and sequenced by using the Illumina genome analyzer. Subsequently, de novo assemebly, functional annotation databases, and the screening of expressed sequence tag-simple sequence repeats (EST-SSRs) were performed by comparing with corresponding databases using BLASTx and software tools. We obtained 51 698 unigenes with an average length of 661 bp and an N50 length of 980 bp. Among all these unigenes, 41 267 (79.8 %) were annotated in the NCBI non-redundant protein database and 25 236 (48.8 %) were annotated in the Swiss-Prot database. A total of 31 546 (61.0 %) and 13 281 (25.7 %) unigenes were successfully categorized to 59 and 25 functional groups, respectively, by gene ontology categories and clusters of orthologous group categories. A total of 22 323 (43.2 %) unigenes were assigned to 128 pathways using the Kyoto encyclopedia of genes and genomes pathway database. Additionally, 3 249 EST-SSRs markers were detected in 51 698 unigenes from velvet ash. Based on 3 249 EST-SSRs markers, 1 800 primer pairs were successfully designed using Primer 3. In the 50 randomly selected primers, 48 successfully amplified fragments, and 42 showed polymorphisms. We completed a successful application of the Illumina platform to de novo transcriptome assembly of velvet ash, which has the potential to be used for discovering novel genes and further characterization of gene expression profiles.

TFBSbank: a platform to dissect the big data of protein-DNA interaction in human and model species.

Genome-wide transcription factors (TFs) binding data has been extensively generated in the past few years, which poses a great challenge to data interpretation. Therefore, comprehensive and dedicated functional annotation databases for TF–DNA interaction are in great demands to manage, explore and utilize those invaluable data resources. Here, we constructed a platform ‘TFBSbank’ which houses the annotation of 1870 chromatin immunoprecipitation (ChIP) datasets of 585 TFs in five species (human, mouse, fly, worm and yeast). There are mainly five functional modules in TFBSbank aimed at characterizing ChIP peaks, identifying putative targets, predicting TF responsive enhancers, revealing potential cofactors/collaborators and discovering enriched TF motifs. TFBSbank has two distinctive features compared to the existing databases. Firstly, we provided putative cofactors/collaborators analysis (for Drosophila melanogaster), as they are crucial for the in vivo functions of TFs. Additionally, this database predicted the enrichment of both known and de novo motifs based on ChIP data. TFBSbank is freely accessible at http://tfbsbank.co.uk

Genome-wide association studies in oesophageal adenocarcinoma and Barrett's oesophagus: a large-scale meta-analysis

SummaryBackgroundOesophageal adenocarcinoma represents one of the fastest rising cancers in high-income countries. Barrett's oesophagus is the premalignant precursor of oesophageal adenocarcinoma. However, only a few patients with Barrett's oesophagus develop adenocarcinoma, which complicates clinical management in the absence of valid predictors. Within an international consortium investigating the genetics of Barrett's oesophagus and oesophageal adenocarcinoma, we aimed to identify novel genetic risk variants for the development of Barrett's oesophagus and oesophageal adenocarcinoma.MethodsWe did a meta-analysis of all genome-wide association studies of Barrett's oesophagus and oesophageal adenocarcinoma available in PubMed up to Feb 29, 2016; all patients were of European ancestry and disease was confirmed histopathologically. All participants were from four separate studies within Europe, North America, and Australia and were genotyped on high-density single nucleotide polymorphism (SNP) arrays. Meta-analysis was done with a fixed-effects inverse variance-weighting approach and with a standard genome-wide significance threshold (p<5 × 10−8). We also did an association analysis after reweighting of loci with an approach that investigates annotation enrichment among genome-wide significant loci. Furthermore, the entire dataset was analysed with bioinformatics approaches—including functional annotation databases and gene-based and pathway-based methods—to identify pathophysiologically relevant cellular mechanisms.FindingsOur sample comprised 6167 patients with Barrett's oesophagus and 4112 individuals with oesophageal adenocarcinoma, in addition to 17 159 representative controls from four genome-wide association studies in Europe, North America, and Australia. We identified eight new risk loci associated with either Barrett's oesophagus or oesophageal adenocarcinoma, within or near the genes CFTR (rs17451754; p=4·8 × 10−10), MSRA (rs17749155; p=5·2 × 10−10), LINC00208 and BLK (rs10108511; p=2·1 × 10−9), KHDRBS2 (rs62423175; p=3·0 × 10−9), TPPP and CEP72 (rs9918259; p=3·2 × 10−9), TMOD1 (rs7852462; p=1·5 × 10−8), SATB2 (rs139606545; p=2·0 × 10−8), and HTR3C and ABCC5 (rs9823696; p=1·6 × 10−8). The locus identified near HTR3C and ABCC5 (rs9823696) was associated specifically with oesophageal adenocarcinoma (p=1·6 × 10−8) and was independent of Barrett's oesophagus development (p=0·45). A ninth novel risk locus was identified within the gene LPA (rs12207195; posterior probability 0·925) after reweighting with significantly enriched annotations. The strongest disease pathways identified (p<10−6) belonged to muscle cell differentiation and to mesenchyme development and differentiation.InterpretationOur meta-analysis of genome-wide association studies doubled the number of known risk loci for Barrett's oesophagus and oesophageal adenocarcinoma and revealed new insights into causes of these diseases. Furthermore, the specific association between oesophageal adenocarcinoma and the locus near HTR3C and ABCC5 might constitute a novel genetic marker for prediction of the transition from Barrett's oesophagus to oesophageal adenocarcinoma. Fine-mapping and functional studies of new risk loci could lead to identification of key molecules in the development of Barrett's oesophagus and oesophageal adenocarcinoma, which might encourage development of advanced prevention and intervention strategies.FundingUS National Cancer Institute, US National Institutes of Health, National Health and Medical Research Council of Australia, Swedish Cancer Society, Medical Research Council UK, Cambridge NIHR Biomedical Research Centre, Cambridge Experimental Cancer Medicine Centre, Else Kröner Fresenius Stiftung, Wellcome Trust, Cancer Research UK, AstraZeneca UK, University Hospitals of Leicester, University of Oxford, Australian Research Council.

Structural database resources for biological macromolecules.

This Briefing reviews the widely used, currently active, up-to-date databases derived from the worldwide Protein Data Bank (PDB) to facilitate browsing, finding and exploring its entries. These databases contain visualization and analysis tools tailored to specific kinds of molecules and interactions, often including also complex metrics precomputed by experts or external programs, and connections to sequence and functional annotation databases. Importantly, updates of most of these databases involves steps of curation and error checks based on specific expertise about the subject molecules or interactions, and removal of sequence redundancy, both leading to better data sets for mining studies compared with the full list of raw PDB entries. The article presents the databases in groups such as those aimed to facilitate browsing through PDB entries, their molecules and their general information, those built to link protein structure with sequence and dynamics, those specific for transmembrane proteins, nucleic acids, interactions of biomacromolecules with each other and with small molecules or metal ions, and those concerning specific structural features or specific protein families. A few webservers directly connected to active databases, and a few databases that have been discontinued but would be important to have back, are also briefly commented on. Along the Briefing, sample cases where these databases have been used to aid structural studies or advance our knowledge about biological macromolecules are referenced. A few specific examples are also given where using these databases is easier and more informative than using raw PDB data.