UniProt蛋白质数据库简介

UniProt(https://www.uniprot.org/)是国际知名蛋白质数据库,主要包括UniProtKB知识库、UniParc归档库和UniRef参考序列集三部分。UniProtKB知识库是UniProt的核心,除蛋白质序列数据外,还包括大量注释信息。UniProtKB知识库分Swiss-Prot和TrEMBL两个子库。Swiss-Prot子库中50多万条序列均由人工审阅和注释,而TrEMBL子库中1.4亿多条序列是由核酸序列数据库EMBL中的蛋白质编码序列翻译所得,并由计算机根据一定规则进行注释。UniParc归档库将存放于不同数据库中的同一个蛋白质归并到一个记录中以避免冗余,并赋予序列唯一性特定标识符。UniRef参考序列集按相似性程度将UniProtKB和UniParc中的序列分为UniRef100、UniRef90和UniRef50三个数据集。UniProt网站为用户提供了高效实用的高级检索系统和大量帮助文档。UniProt数据库每4周发布新版的同时也发布统计报表,用户可通过统计报表了解该数据库的数据量及更新情况、数据类别和物种分布等基本信息,查看常规注释信息、序列特征注释信息和数据库交叉链接等统计数据。UniProt是目前国际上序列数据最完整、注释信息最丰富的非冗余蛋白质序列数据库,自本世纪初创建以来,为生命科学领域提供了宝贵资源。     

EcoGene: a genome sequence database for Escherichia coli K-12

The EcoGene database provides a set of gene and protein sequences derived from the genome sequence of Escherichia coli K-12. EcoGene is a source of re-annotated sequences for the SWISS-PROT and Colibri databases. EcoGene is used for genetic and physical map compilations in collaboration with the Coli Genetic Stock Center. The EcoGene12 release includes 4293 genes. EcoGene12 differs from the GenBank annotation of the complete genome sequence in several ways, including (i) the revision of 706 predicted or confirmed gene start sites, (ii) the correction or hypothetical reconstruction of 61 frame-shifts caused by either sequence error or mutation, (iii) the reconstruction of 14 protein sequences interrupted by the insertion of IS elements, and (iv) pre-dictions that 92 genes are partially deleted gene fragments. A literature survey identified 717 proteins whose N-terminal amino acids have been verified by sequencing. 12 446 cross-references to 6835 literature citations and s are provided. EcoGene is accessible at a new website: http://bmb.med.miami.edu/EcoGene/EcoWeb. Users can search and retrieve individual EcoGene GenePages or they can download large datasets for incorporation into database management systems, facilitating various genome-scale computational and functional analyses.     

BioMolQuest: integrated database-based retrieval of protein structural and functional information

MOTIVATION: Information about a particular protein or protein family is usually distributed among multiple databases and often in more than one entry in each database. Retrieval and organization of this information can be a laborious task. This task is complicated even further by the existence of alternative terms for the same concept. RESULTS: The PDB, SWISS-PROT, ENZYME, and CATH databases have been imported into a combined relational database, BIOMOLQUEST: A powerful search engine has been built using this database as a back end. The search engine achieves significant improvements in query performance by automatically utilizing cross-references between the legacy databases. The results of the queries are presented in an organized, hierarchical way.     

UniRef: comprehensive and non-redundant UniProt reference clusters

MOTIVATION: Redundant protein sequences in biological databases hinder sequence similarity searches and make interpretation of search results difficult. Clustering of protein sequence space based on sequence similarity helps organize all sequences into manageable datasets and reduces sampling bias and overrepresentation of sequences. RESULTS: The UniRef (UniProt Reference Clusters) provide clustered sets of sequences from the UniProt Knowledgebase (UniProtKB) and selected UniProt Archive records to obtain complete coverage of sequence space at several resolutions while hiding redundant sequences. Currently covering >4 million source sequences, the UniRef100 database combines identical sequences and subfragments from any source organism into a single UniRef entry. UniRef90 and UniRef50 are built by clustering UniRef100 sequences at the 90 or 50% sequence identity levels. UniRef100, UniRef90 and UniRef50 yield a database size reduction of approximately 10, 40 and 70%, respectively, from the source sequence set. The reduced redundancy increases the speed of similarity searches and improves detection of distant relationships. UniRef entries contain summary cluster and membership information, including the sequence of a representative protein, member count and common taxonomy of the cluster, the accession numbers of all the merged entries and links to rich functional annotation in UniProtKB to facilitate biological discovery. UniRef has already been applied to broad research areas ranging from genome annotation to proteomics data analysis. AVAILABILITY: UniRef is updated biweekly and is available for online search and retrieval at http://www.uniprot.org, as well as for download at ftp://ftp.uniprot.org/pub/databases/uniprot/uniref. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.     

ProClass protein family database

ProClass is a protein family database that organizes non-redundant sequence entries into families defined collectively by PIR superfamilies and PROSITE patterns. By combining global similarities and functional motifs into a single classification scheme, ProClass helps to reveal domain and family relationships and classify multi-domain proteins. The database currently consists of >155 000 sequence entries retrieved from both PIR-International and SWISS-PROT databases. Approximately 92 000 or 60% of the ProClass entries are classified into approximately 6000 families, including a large number of new members detected by our GeneFIND family identification system. The ProClass motif collection contains approximately 72 000 motif sequences and >1300 multiple alignments for all PROSITE patterns, including >21 000 matches not listed in PROSITE and mostly detected from unique PIR sequences. To maximize family information retrieval, the database provides links to various protein family, domain, alignment and structural class databases. With its high classification rate and comprehensive family relationships, ProClass can be used to support full-scale genomic annotation. The database, now being implemented in an object-relational database management system, is available for online sequence search and record retrieval from our WWW server at http://pir.georgetown.edu/gfserver/proclass.html     

The Integr8 project--a resource for genomic and proteomic data

Integr8 (http://www.ebi.ac.uk/integr8/) is providing an integration layer for the exploitation of genomic and proteomic data by drawing on databases maintained at major bioinformatics centres in Europe. Main aims are to store the relationships of biological entities to each other and to entries in other databases, to provide a framework that allows for new kinds of data to be integrated, and to offer an entity-centric view of complete genomes and proteomes. Basic tools for data integration comprise the Proteome Analysis database, the International Protein Index (IPI), the Universal Protein sequence archive (UniParc) and the Genome Reviews. Entry points for the Integr8 portal depend on the users entity of interest: from browsing the taxonomy or with a predetermined species of interest, the species page can be used, and a simple search page leads to different applications when looking for certain protein sequences or genes. Customisable statistics data are available from the BioMart application, and pre-prepared data can be downloaded from the FTP site.     

Construction of validated, non-redundant composite protein sequence databases

A strategy has been developed for the construction of a validated, comprehensive composite protein sequence database. Entries are amalgamated from primary source data bases by a largely automated set of processes in which redundant and trivially different entries are eliminated. A modular approach has been adopted to allow scientific judgement to be used at each stage of database processing and amalgamation. Source databases are assigned a priority depending on the quality of sequence validation and commenting. Rejection of entries from the lower priority database, in each pairwise comparison of databases, is carried out according to optionally defined redundancy criteria based on sequence segment mismatches. Efficient algorithms for this methodology are embodied in the COMPO software system. COMPO has been applied for over 2 years in construction and regular updating of the OWL composite protein sequence database from the source databases NBRF-PIR, SWISS-PROT, a GenBank translation retrieved from the feature tables, NBRF-NEW, NEWAT86, PSD-KYOTO and the sequences contained in the Brookhaven protein structure databank. OWL is part of the ISIS integrated data resource of protein sequence and structure [Akrigg et al. (1988) Nature, 335, 745-746]. The modular nature of the integration process greatly facilitates the frequent updating of OWL following releases of the source databases. The extent of redundancy in these sources is revealed by the comparison process. The advantages of a robust composite database for sequence similarity searching and information retrieval are discussed.     

VIDA: a virus database system for the organization of animal virus genome open reading frames

VIDA is a new virus database that organizes open reading frames (ORFs) from partial and complete genomic sequences from animal viruses. Currently VIDA includes all sequences from GenBank for Herpesviridae, Coronaviridae and Arteriviridae. The ORFs are organized into homologous protein families, which are identified on the basis of sequence similarity relationships. Conserved sequence regions of potential functional importance are identified and can be retrieved as sequence alignments. We use a controlled taxonomical and functional classification for all the proteins and protein families in the database. When available, protein structures that are related to the families have also been included. The database is available for online search and sequence information retrieval at http://www.biochem.ucl.ac.uk/bsm/virus_database/ VIDA.html.     

UNITE: a database providing web-based methods for the molecular identification of ectomycorrhizal fungi

Identification of ectomycorrhizal (ECM) fungi is often achieved through comparisons of ribosomal DNA internal transcribed spacer (ITS) sequences with accessioned sequences deposited in public databases. A major problem encountered is that annotation of the sequences in these databases is not always complete or trustworthy. In order to overcome this deficiency, we report on UNITE, an open-access database. UNITE comprises well annotated fungal ITS sequences from well defined herbarium specimens that include full herbarium reference identification data, collector/source and ecological data. At present UNITE contains 758 ITS sequences from 455 species and 67 genera of ECM fungi. UNITE can be searched by taxon name, via sequence similarity using blastn, and via phylogenetic sequence identification using galaxie. Following implementation, galaxie performs a phylogenetic analysis of the query sequence after alignment either to pre-existing generic alignments, or to matches retrieved from a blast search on the UNITE data. It should be noted that the current version of UNITE is dedicated to the reliable identification of ECM fungi. The UNITE database is accessible through the URL http://unite.zbi.ee     

A comprehensive dictionary of protein accession codes for complete protein accession identifier alias resolving

In mass spectrometry-based proteomics, protein identification results usually consist of peptide sequences and database-dependent accession identifiers of the matching proteins. Often certain annotations are only available in particular databases that in turn must be queried by a certain identifier. In order to simplify and unify the tracing of identified proteins back to their original annotation information, a system capable of set-oriented mapping the different accession identifiers of proteins derived from multiple sequence database sources has been developed. This allows unification of the access to protein information and tracing to other online resources providing additional information as well as resolving cross-references of protein identifications. The interface of seqDB is available via http://www.protein-ms.de following the link to seqDB.     

A database of unique protein sequence identifiers for proteome studies

In proteome studies, identification of proteins requires searching protein sequence databases. The public protein sequence databases (e.g., NCBInr, UniProt) each contain millions of entries, and private databases add thousands more. Although much of the sequence information in these databases is redundant, each database uses distinct identifiers for the identical protein sequence and often contains unique annotation information. Users of one database obtain a database-specific sequence identifier that is often difficult to reconcile with the identifiers from a different database. When multiple databases are used for searches or the databases being searched are updated frequently, interpreting the protein identifications and associated annotations can be problematic. We have developed a database of unique protein sequence identifiers called Sequence Globally Unique Identifiers (SEGUID) derived from primary protein sequences. These identifiers serve as a common link between multiple sequence databases and are resilient to annotation changes in either public or private databases throughout the lifetime of a given protein sequence. The SEGUID Database can be downloaded (http://bioinformatics.anl.gov/SEGUID/) or easily generated at any site with access to primary protein sequence databases. Since SEGUIDs are stable, predictions based on the primary sequence information (e.g., pI, Mr) can be calculated just once; we have generated approximately 500 different calculations for more than 2.5 million sequences. SEGUIDs are used to integrate MS and 2-DE data with bioinformatics information and provide the opportunity to search multiple protein sequence databases, thereby providing a higher probability of finding the most valid protein identifications.     

O-GLYCBASE version 4.0: a revised database of O-glycosylated proteins.

O-GLYCBASE is a database of glycoproteins with O-linked glycosylation sites. Entries with at least one experimentally verified O-glycosylation site have been compiled from protein sequence databases and literature. Each entry contains information about the glycan involved, the species, sequence, a literature reference and http-linked cross-references to other databases. Version 4.0 contains 179 protein entries, an approximate 15% increase over the last version. Sequence logos representing the acceptor specificity patterns for GalNAc, GlcNAc, mannosyl and xylosyl transferases are shown. The O-GLYCBASE database is available through the WWW at http://www.cbs.dtu.dk/databases/OGLYCBASE/     

iProClass: an integrated, comprehensive and annotated protein classification database

The iProClass database is an integrated resource that provides comprehensive family relationships and structural and functional features of proteins, with rich links to various databases. It is extended from ProClass, a protein family database that integrates PIR superfamilies and PROSITE motifs. The iProClass currently consists of more than 200,000 non-redundant PIR and SWISS-PROT proteins organized with more than 28,000 superfamilies, 2600 domains, 1300 motifs, 280 post-translational modification sites and links to more than 30 databases of protein families, structures, functions, genes, genomes, literature and taxonomy. Protein and family summary reports provide rich annotations, including membership information with length, taxonomy and keyword statistics, full family relationships, comprehensive enzyme and PDB cross-references and graphical feature display. The database facilitates classification-driven annotation for protein sequence databases and complete genomes, and supports structural and functional genomic research. The iProClass is implemented in Oracle 8i object-relational system and available for sequence search and report retrieval at http://pir.georgetown.edu/iproclass/.     

GenBank.

The GenBank (Registered Trademark symbol) sequence database incorporates DNA sequences from all available public sources, primarily through the direct submission of sequence data from individual laboratories and from large-scale sequencing projects. Most submitters use the BankIt (Web) or Sequin programs to format and send sequence data. Data exchange with the EMBL Data Library and the DNA Data Bank of Japan helps ensure comprehensive worldwide coverage. GenBank data is accessible through NCBI's integrated retrieval system, Entrez, which integrates data from the major DNA and protein sequence databases along with taxonomy, genome and protein structure information. MEDLINE (Registered Trademark symbol) s from published articles describing the sequences are included as an additional source of biological annotation through the PubMed search system. Sequence similarity searching is offered through the BLAST series of database search programs. In addition to FTP, Email, and server/client versions of Entrez and BLAST, NCBI offers a wide range of World Wide Web retrieval and analysis services based on GenBank data. The GenBank database and related resources are freely accessible via the URL: http://www.ncbi.nlm.nih.gov     

蛋白质组学中新蛋白质鉴定的研究方法和策略

当前,基于生物质谱进行蛋白质鉴定的技术已经成为蛋白质组学研究的支撑技术之一.产生的数据主要使用数据库搜索的方法进行处理,这种方法的一大缺陷是不能鉴定数据库中未包含的蛋白质,因此如何充分利用质谱数据对蛋白质组研究的意义很大,而新蛋白质鉴定更是其中一个重要的内容.新蛋白质鉴定是蛋白质鉴定的一个方面,新蛋白质的定义按照序列和功能的已知程度分为3个层次;以蛋白质鉴定的方法为基础,目前新蛋白质鉴定的方法可分为denovo测序和相似序列搜索结合的方法以及搜索EST、基因组等核酸数据库的方法2大类;两者各有利弊.存在各自的问题和相应处理的策略.不同的研究者可以根据具体目的应用和发展不同的鉴定方法,同时新蛋白质的鉴定也将随着蛋白质组学研究的发展而更加完善.     

Genomic BLAST: custom-defined virtual databases for complete and unfinished genomes

BLAST (Basic Local Alignment Search Tool) searches against DNA and protein sequence databases have become an indispensable tool for biomedical research. The proliferation of the genome sequencing projects is steadily increasing the fraction of genome-derived sequences in the public databases and their importance as a public resource. We report here the availability of Genomic BLAST, a novel graphical tool for simplifying BLAST searches against complete and unfinished genome sequences. This tool allows the user to compare the query sequence against a virtual database of DNA and/or protein sequences from a selected group of organisms with finished or unfinished genomes. The organisms for such a database can be selected using either a graphic taxonomy-based tree or an alphabetical list of organism-specific sequences. The first option is designed to help explore the evolutionary relationships among organisms within a certain taxonomy group when performing BLAST searches. The use of an alphabetical list allows the user to perform a more elaborate set of selections, assembling any given number of organism-specific databases from unfinished or complete genomes. This tool, available at the NCBI web site http://www.ncbi.nlm.nih.gov/cgi-bin/Entrez/genom_table_cgi, currently provides access to over 170 bacterial and archaeal genomes and over 40 eukaryotic genomes.     

Informatics for protein identification by mass spectrometry

High throughput protein analysis (i.e., proteomics) first became possible when sensitive peptide mass mapping techniques were developed, thereby allowing for the possibility of identifying and cataloging most 2D gel electrophoresis spots. Shortly thereafter a few groups pioneered the idea of identifying proteins by using peptide tandem mass spectra to search protein sequence databases. Hence, it became possible to identify proteins from very complex mixtures. One drawback to these latter techniques is that it is not entirely straightforward to make matches using tandem mass spectra of peptides that are modified or have sequences that differ slightly from what is present in the sequence database that is being searched. This has been part of the motivation behind automated de novo sequencing programs that attempt to derive a peptide sequence regardless of its presence in a sequence database. The sequence candidates thus generated are then subjected to homology-based database search programs (e.g., BLAST or FASTA). These homology search programs, however, were not developed with mass spectrometry in mind, and it became necessary to make minor modifications such that mass spectrometric ambiguities can be taken into account when comparing query and database sequences. Finally, this review will discuss the important issue of validating protein identifications. All of the search programs will produce a top ranked answer; however, only the credulous are willing to accept them carte blanche.     

Filling-in Void and Sparse Regions in Protein Sequence Space by Protein-Like Artificial Sequences Enables Remarkable Enhancement in Remote Homology Detection Capability

Protein functional annotation relies on the identification of accurate relationships, sequence divergence being a key factor. This is especially evident when distant protein relationships are demonstrated only with three-dimensional structures. To address this challenge, we describe a computational approach to purposefully bridge gaps between related protein families through directed design of protein-like “linker” sequences. For this, we represented SCOP domain families, integrated with sequence homologues, as multiple profiles and performed HMM-HMM alignments between related domain families. Where convincing alignments were achieved, we applied a roulette wheel-based method to design 3,611,010 protein-like sequences corresponding to 374 SCOP folds. To analyze their ability to link proteins in homology searches, we used 3024 queries to search two databases, one containing only natural sequences and another one additionally containing designed sequences. Our results showed that augmented database searches showed up to 30% improvement in fold coverage for over 74% of the folds, with 52 folds achieving all theoretically possible connections. Although sequences could not be designed between some families, the availability of designed sequences between other families within the fold established the sequence continuum to demonstrate 373 difficult relationships. Ultimately, as a practical and realistic extension, we demonstrate that such protein-like sequences can be “plugged-into” routine and generic sequence database searches to empower not only remote homology detection but also fold recognition. Our richly statistically supported findings show that complementary searches in both databases will increase the effectiveness of sequence-based searches in recognizing all homologues sharing a common fold.