2.2 Protein sequence databases
7 min read•august 21, 2024
Protein sequence databases are essential repositories for storing and organizing protein information. They facilitate research in various areas of bioinformatics, supporting tasks like functional analysis, evolutionary studies, and drug discovery.
These databases come in different types, including primary sequence databases, secondary databases, and specialized databases. Each type offers unique features and tools for researchers to explore and analyze protein data, from basic sequence information to complex structural and functional annotations.
Overview of protein databases
- Protein databases serve as central repositories for storing, organizing, and retrieving protein sequence and structure information
- These databases play a crucial role in bioinformatics by facilitating the analysis of protein function, evolution, and interactions
- Protein databases support various research areas including drug discovery, protein engineering, and comparative genomics
Types of protein databases
Primary sequence databases
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- Store raw protein sequence data derived from experimental methods or computational predictions
- Include databases like UniProtKB/Swiss-Prot and 's protein database
- Provide basic information such as amino acid sequences, organism source, and accession numbers
- Often serve as the foundation for other specialized databases and analysis tools
Secondary databases
- Derived from primary databases through computational analysis and annotation
- Offer additional layers of information such as protein families, domains, and functional predictions
- Examples include Pfam (protein families) and PROSITE (protein domains and functional sites)
- Enhance the understanding of protein function and evolution by grouping related sequences
Specialized databases
- Focus on specific aspects of protein biology or particular protein families
- Include databases like enzymes (BRENDA), protein-protein interactions (STRING), and post-translational modifications (PhosphoSitePlus)
- Provide in-depth information for targeted research in specific areas of protein science
- Often integrate data from multiple sources to offer comprehensive views of protein characteristics
Major protein databases
UniProt vs RefSeq
- (Universal Protein Resource) combines Swiss-Prot, TrEMBL, and PIR-PSD databases
- Offers manually curated (Swiss-Prot) and automatically annotated (TrEMBL) protein sequences
- Provides extensive cross-references to other databases and literature
- RefSeq (Reference Sequence Database) maintained by NCBI
- Focuses on providing non-redundant, well-annotated sequences for major organisms
- Includes both proteins and nucleotide sequences
- Key differences include curation approaches, coverage, and integration with other resources
InterPro
- Integrates information from multiple protein signature databases
- Provides a unified view of protein domains, families, and functional sites
- Utilizes various computational methods to predict protein features
- Offers tools for functional analysis and classification of protein sequences
- Regularly updated to incorporate new data and improve annotations
Pfam
- Specializes in protein domain families and their multiple sequence alignments
- Uses hidden Markov models (HMMs) to represent protein families
- Provides both manually curated (Pfam-A) and automatically generated (Pfam-B) families
- Offers tools for domain prediction and visualization of protein architectures
- Widely used for functional annotation and evolutionary studies of proteins
Database curation
Manual vs automatic curation
- Manual curation involves expert biologists reviewing and annotating protein entries
- Provides high-quality, reliable information but is time-consuming and resource-intensive
- Often includes literature-based annotations and experimental evidence
- Automatic curation uses computational methods to annotate proteins
- Allows for rapid processing of large datasets but may introduce errors or inconsistencies
- Relies on algorithms, machine learning, and existing knowledge bases
- Many databases use a combination of both approaches to balance quality and quantity
Quality control measures
- Implement data validation checks to ensure accuracy and consistency of entries
- Use controlled vocabularies and ontologies to standardize annotations
- Employ version control systems to track changes and allow for error correction
- Conduct regular audits and updates to maintain database integrity
- Encourage community feedback and contributions to improve data quality
Protein sequence submission
Submission process
- Typically involves online submission forms or specialized software tools
- Requires providing essential information such as sequence data, organism source, and relevant metadata
- May involve choosing appropriate database based on sequence type and research goals
- Often includes automated checks for sequence quality and format compliance
- Submitters may need to create accounts and agree to data sharing policies
Sequence annotation guidelines
- Provide instructions for including relevant biological information with submitted sequences
- Specify required and optional fields for different types of annotations
- Encourage use of standardized terminology and controlled vocabularies
- Outline best practices for describing experimental methods and evidence
- May include guidelines for handling confidential or proprietary information
Database searching techniques
BLAST for proteins
- Basic Local Alignment Search Tool adapted for protein sequences (BLASTP)
- Allows rapid searching of protein databases to find similar sequences
- Uses a heuristic approach to identify local alignments between query and database sequences
- Provides statistical measures () to assess the significance of matches
- Offers various flavors (PSI-, PHI-BLAST) for more sensitive or specific searches
Position-specific scoring matrices
- Represent the amino acid preferences at each position in a protein family
- Generated from multiple sequence alignments of related proteins
- Used in tools like PSI-BLAST to improve sensitivity in detecting distant homologs
- Allow for more nuanced comparisons by accounting for position-specific conservation patterns
- Useful for identifying conserved functional or structural motifs in protein sequences
Protein sequence analysis tools
Multiple sequence alignment
- Aligns three or more protein sequences to identify conserved regions and evolutionary relationships
- Tools include , MUSCLE, and
- Provides insights into , active sites, and structurally important residues
- Serves as a foundation for phylogenetic analysis and protein structure prediction
- Can be visualized using color-coding schemes to highlight conservation patterns
Motif identification
- Detects short, conserved patterns in protein sequences that may indicate functional or structural importance
- Utilizes databases of known motifs (PROSITE) or de novo motif discovery algorithms
- Helps in predicting protein function, localization, and post-translational modifications
- Can identify regulatory elements or binding sites in proteins
- Often used in conjunction with other sequence analysis tools for comprehensive protein characterization
Protein structure databases
PDB overview
- Protein Data Bank () serves as the primary repository for experimentally determined 3D structures of proteins and nucleic acids
- Contains structures solved by X-ray crystallography, NMR spectroscopy, and cryo-electron microscopy
- Provides atomic coordinates, experimental details, and associated metadata for each entry
- Offers tools for structure visualization, analysis, and comparison
- Widely used in structural biology, drug design, and protein engineering studies
SCOP and CATH
- Structural Classification of Proteins (SCOP) and Class, Architecture, Topology, Homology (CATH) databases
- Organize protein structures into hierarchical classification schemes based on structural and evolutionary relationships
- SCOP focuses on evolutionary relationships and manual curation
- Classifies structures into classes, folds, superfamilies, and families
- CATH uses a combination of automatic and manual methods
- Organizes structures into classes, architectures, topologies, and homologous superfamilies
- Both databases provide insights into protein structure-function relationships and evolutionary patterns
Integration with other resources
Gene ontology associations
- Links protein entries to standardized (GO) terms
- Describes protein functions, biological processes, and cellular components
- Facilitates functional annotation and comparison across different species
- Enables systematic analysis of protein sets based on shared functional characteristics
- Integrates experimental evidence codes to indicate the reliability of annotations
Pathway databases
- Connect protein entries to biological pathway information
- Examples include KEGG (Kyoto Encyclopedia of Genes and Genomes) and Reactome
- Provide context for understanding protein roles in cellular processes and metabolic networks
- Enable visualization of protein interactions and regulatory relationships
- Support systems biology approaches and interpretation of high-throughput data
Challenges in protein databases
Redundancy issues
- Multiple entries for the same or highly similar proteins can complicate database searches and analyses
- Arise from factors such as different splice variants, sequencing errors, or submissions from multiple sources
- Can lead to biased results in statistical analyses or overrepresentation of certain protein families
- Addressed through clustering algorithms, non-redundant datasets, and careful curation processes
- Requires balancing the need for comprehensive coverage with the desire for streamlined, non-redundant data
Sequence errors
- Incorrect protein sequences can result from experimental errors, computational mistakes, or annotation issues
- May lead to misinterpretation of protein function or structure
- Can propagate through databases if not caught and corrected
- Addressed through quality control measures, community feedback, and integration of multiple data sources
- Highlights the importance of ongoing curation and validation efforts in maintaining database accuracy
Future directions
Machine learning applications
- Developing advanced algorithms for improved protein function prediction and annotation
- Enhancing and structure prediction methods using deep learning approaches
- Automating aspects of database curation and quality control
- Improving search and retrieval systems for more efficient and accurate database queries
- Facilitating the integration and interpretation of diverse protein-related data sources
Integration of multi-omics data
- Incorporating data from proteomics, genomics, transcriptomics, and metabolomics studies
- Providing a more comprehensive view of protein function in biological systems
- Enabling the study of protein regulation at multiple levels (transcriptional, translational, post-translational)
- Supporting systems biology approaches to understand complex cellular processes
- Facilitating the development of personalized medicine approaches based on integrated protein-level data
Key Terms to Review (18)
BLAST: BLAST, which stands for Basic Local Alignment Search Tool, is a bioinformatics algorithm used to compare a nucleotide or protein sequence against a database of sequences. It helps identify regions of similarity between sequences, making it a powerful tool for functional annotation, evolutionary studies, and data retrieval in biological research.
ClustalW: ClustalW is a widely used bioinformatics tool for multiple sequence alignment, allowing researchers to align protein or nucleotide sequences to identify regions of similarity. By analyzing these alignments, ClustalW helps in understanding evolutionary relationships and functional similarities among sequences, which is essential for protein function prediction and phylogenetic studies.
Downloading: Downloading refers to the process of transferring data from a remote server to a local device, enabling users to access and utilize the information stored on the server. In the context of protein sequence databases, downloading is essential for researchers who need to retrieve protein sequences for analysis, comparison, or further research. This process not only allows access to vast amounts of protein data but also supports various bioinformatics applications, such as sequence alignment and structural predictions.
E-value: The e-value, or expect value, is a statistical measure used in bioinformatics to indicate the number of times one might expect to see a match between sequences purely by chance. It helps assess the significance of alignments in various applications such as sequence databases, pairwise alignment, local alignment, and scoring matrices. A lower e-value indicates a more significant match, which is crucial for identifying biologically relevant similarities between sequences.
Fasta: FASTA is a text-based format for representing nucleotide or protein sequences, where each sequence is preceded by a header line that starts with a '>' character. This format is widely used in bioinformatics for storing and sharing sequence data, allowing for easy identification and retrieval of biological sequences.
Functional Domains: Functional domains are specific regions within a protein that are associated with distinct biological activities. These domains often have unique structures that enable the protein to perform specific tasks, such as binding to other molecules or catalyzing chemical reactions. Understanding functional domains is crucial for analyzing how proteins operate within living organisms and for classifying them in databases.
GenBank: GenBank is a comprehensive public database of nucleotide sequences and their associated information, serving as a vital resource for researchers in molecular biology and bioinformatics. It allows users to access an extensive collection of genetic information, which is crucial for tasks like genome annotation, sequence analysis, and understanding molecular evolution.
Gene Ontology: Gene Ontology (GO) is a framework for the representation of gene and gene product attributes across all species, providing a structured vocabulary that describes gene functions in terms of biological processes, cellular components, and molecular functions. This system facilitates consistent annotations of genes and their products, making it easier to analyze and compare functional data across different organisms.
Identity percentage: Identity percentage is a metric used to quantify the similarity between two sequences, indicating the proportion of identical residues or nucleotides in a given alignment. It helps researchers assess how closely related two proteins or genomes are, which is crucial for understanding evolutionary relationships, functional similarities, and potential biological roles. This percentage plays a significant role in the analysis of sequence data from databases, the evaluation of pairwise alignments, and the comparison of whole genomes.
Motif identification: Motif identification is the process of detecting recurring patterns or sequences within biological sequences, such as proteins or nucleic acids, that are often associated with specific functions or structural features. This process plays a crucial role in understanding the biological significance of these sequences by revealing functional elements that may be conserved across different organisms.
PDB: PDB stands for the Protein Data Bank, which is a comprehensive repository for three-dimensional structural data of biological macromolecules, primarily proteins and nucleic acids. It serves as a critical resource for researchers in various fields, providing access to a wealth of structural information that helps in understanding protein functions, interactions, and mechanisms. The PDB facilitates the integration of structural data with sequence databases and supports tools for data retrieval and submission, making it an essential hub in bioinformatics and structural biology.
Primary Structure: Primary structure refers to the specific sequence of amino acids in a protein, which is determined by the genetic code. This linear arrangement is crucial as it dictates how the protein will fold into its higher-level structures and ultimately influence its function. The order of these amino acids can significantly affect the protein's stability, activity, and interactions with other molecules.
Querying: Querying refers to the process of requesting information from a database by specifying certain criteria. In the context of protein sequence databases, querying allows researchers to extract specific protein sequences, annotations, or related data from large repositories, making it easier to find relevant information for their studies. This process is crucial for bioinformatics as it enables the analysis of protein functions, structures, and interactions based on the available data.
Secondary structure: Secondary structure refers to the local folding patterns of a protein that are stabilized by hydrogen bonds between the backbone atoms. Common types of secondary structures include alpha helices and beta sheets, which play crucial roles in determining the overall shape and function of proteins, impacting their interactions and biological activities.
Sequence Alignment: Sequence alignment is a method used to arrange sequences of DNA, RNA, or protein to identify regions of similarity that may indicate functional, structural, or evolutionary relationships. This technique is fundamental in various applications, such as comparing genomic sequences to study evolution, identifying genes, or predicting protein structures.
Smith-Waterman Algorithm: The Smith-Waterman algorithm is a dynamic programming method used for local sequence alignment, which identifies the optimal alignment between two sequences. It is particularly effective for finding regions of similarity in nucleotide or protein sequences, allowing researchers to highlight conserved sequences even when there are gaps or mutations.
T-Coffee: t-Coffee (Tree-Based Consistency Objective Function for Alignment Evaluation) is a progressive multiple sequence alignment method that combines various sequence alignment algorithms to generate a more accurate and consistent alignment of protein sequences. This method emphasizes the importance of using information from all available sequences and previously calculated alignments, thus allowing for better handling of complex alignments where traditional methods may struggle.
UniProt: UniProt is a comprehensive protein sequence and functional information database that provides a rich source of data for the scientific community. It aims to support the understanding of protein function, structure, and interactions by providing well-annotated protein sequences along with associated biological information. UniProt serves as a critical resource for studying protein sequences, predicting their functions, and understanding their folding mechanisms.