2.2 Protein sequence databases

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

• Store raw protein sequence data derived from experimental methods or computational predictions
• Include databases like UniProtKB/Swiss-Prot and GenBank'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

• UniProt (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 (E-value) to assess the significance of matches
• Offers various flavors (PSI-BLAST, 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 ClustalW, MUSCLE, and T-Coffee
• Provides insights into functional domains, 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 (PDB) 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 Gene Ontology (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 sequence alignment 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