5.3 Protein sequence databases and their utilization
3 min read•july 25, 2024
Protein sequence databases are essential tools in proteomics research. They compile vast amounts of information on protein sequences, structures, and functions from various organisms, enabling researchers to identify and characterize proteins in their samples.
Navigating these databases requires specific search strategies and tools. Researchers must consider database quality, coverage, and relevance to their study organism when selecting databases for protein identification, especially when working with non-model organisms or novel proteins.
Protein Sequence Databases
Major protein sequence databases
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- (Universal Protein Resource) comprehensively compiles high-quality protein data combining manually curated Swiss-Prot and automatically annotated TrEMBL databases providing extensive information on protein function, structure, and interactions (enzyme catalysis, protein-protein binding)
- maintained by National Center for Biotechnology Information integrates data from various sources offering wide range of protein sequences from diverse organisms (humans, bacteria, plants)
- repositories 3D structural data of proteins and nucleic acids containing experimentally determined structures from multiple methods (X-ray crystallography, , cryo-electron microscopy)
- (Reference Sequence Database) curates non-redundant collection of sequences providing stable reference for genome annotation, gene identification, and characterization across species (human genome, model organisms)
Navigation of protein databases
- Utilize database-specific search tools UniProt offers advanced search options filtering by protein name, gene name, and organism NCBI Protein employs Entrez search system with Boolean operators and field-specific queries
- Employ sequence similarity search tools finds similar sequences across databases detects distant evolutionary relationships between proteins
- Access and interpret protein entries extracting amino acid sequences, functional annotations, (phosphorylation, glycosylation), and literature references
- Download data in various formats for raw sequence information / for detailed annotations for structured data exchange between systems
Database Selection and Protein Identification
Database selection for protein identification
- Database quality impacts identification accuracy as curated databases reduce false positives and improve confidence in results while comprehensive databases increase chances of identifying novel or rare proteins
- Consider database selection factors Organism-specific vs general databases based on research focus Include contaminants and decoy sequences for estimation Database size affects search time and statistical power of analysis
- Regularly update databases ensuring access to most current protein information and incorporating newly discovered or characterized proteins
- Create custom databases by combining multiple sources for comprehensive coverage and including predicted protein sequences from genomic data
Protein identification in non-model organisms
- Limited annotated protein sequences necessitate using closely related species' databases and employing techniques for novel peptide identification
- Cross-species protein identification utilizes BLAST searches against multiple organism databases and considers homology-based approaches for protein annotation
- Integrate genomic and transcriptomic data generating theoretical protein databases from sequences and employing approaches to refine gene models and identify novel proteins
- Machine learning and artificial intelligence techniques develop prediction models for protein function and structure in non-model organisms and utilize deep learning algorithms for improved protein identification and characterization
- Collaborative efforts and data sharing contribute to community-driven annotation projects and utilize specialized databases for specific taxonomic groups or ecosystems (marine microbes, extremophiles)
Key Terms to Review (22)
BLAST: BLAST, or Basic Local Alignment Search Tool, is a widely used algorithm for comparing an input biological sequence against a database of sequences to find regions of similarity. This tool helps researchers identify potential functions of proteins, discover homologous sequences, and assess evolutionary relationships among proteins. By efficiently aligning sequences, BLAST plays a crucial role in proteomics and the study of protein sequence databases.
De novo sequencing: De novo sequencing is a method used to determine the amino acid sequence of a protein without prior knowledge of its sequence. This approach is particularly useful for identifying novel proteins or variants and relies heavily on techniques like mass spectrometry and bioinformatics to assemble the sequence from fragment data.
False Discovery Rate: The false discovery rate (FDR) is a statistical method used to estimate the proportion of false positives among the rejected hypotheses in multiple hypothesis testing. It helps researchers control for Type I errors when identifying significant results, particularly in high-dimensional data, where many comparisons are made simultaneously. FDR is crucial for ensuring reliable interpretations in various analytical processes, especially when analyzing proteomics data.
Fasta: FASTA is a text-based format used for representing nucleotide or protein sequences, consisting of a single-line header followed by lines of sequence data. It allows researchers to easily store and share sequences within protein sequence databases, making it an essential tool for bioinformatics and computational biology.
Functional annotation: Functional annotation refers to the process of assigning biological information and functions to proteins based on their sequences. This process is crucial for understanding the roles of proteins in biological systems, as it links sequence data from protein databases to known biological functions, pathways, and interactions. By integrating various types of data, functional annotation enhances our knowledge of protein behavior and significance in cellular contexts.
GenBank: GenBank is a comprehensive public database that stores nucleotide sequences and their associated annotations, which is crucial for molecular biology research. It acts as a central repository for DNA sequences from various organisms and is extensively used for bioinformatics analyses, sequence comparison, and gene identification. As a key component of protein sequence databases, GenBank provides essential data that aids in understanding protein functions and evolutionary relationships.
Genpept: GenPept is a comprehensive database that provides curated protein sequences and their corresponding annotations derived from various sources, including GenBank and other genomic databases. It serves as an essential tool for researchers in proteomics and molecular biology, offering insights into protein structure, function, and relationships across different organisms.
Global alignment: Global alignment is a computational method used in bioinformatics to compare and align two protein or nucleotide sequences from start to finish, ensuring that the entire length of both sequences is taken into account. This approach seeks to maximize the overall similarity between the two sequences, which can help identify conserved regions and functional similarities across different proteins or genes. By considering every part of the sequences, global alignment provides insights into evolutionary relationships and functional roles.
Local alignment: Local alignment is a method in bioinformatics used to identify regions of similarity within sequences, focusing on finding the best matching subsequences between two protein or nucleotide sequences. This approach is essential for comparing proteins that may share functional similarities despite having low overall sequence identity, allowing researchers to focus on the most relevant parts of the sequences.
Mass spectrometry: Mass spectrometry is an analytical technique used to measure the mass-to-charge ratio of ions. It plays a critical role in proteomics, allowing researchers to identify and quantify proteins and their modifications by analyzing peptide fragments generated from proteins.
Ncbi protein: The NCBI protein database is a comprehensive collection of protein sequences and related information maintained by the National Center for Biotechnology Information. It serves as a crucial resource for researchers by providing access to a vast array of protein data, including functional annotations, structure predictions, and links to related literature and genomic information. This database enables users to analyze protein sequences, explore their functions, and understand their evolutionary relationships.
NMR Spectroscopy: NMR spectroscopy, or Nuclear Magnetic Resonance spectroscopy, is an analytical technique used to determine the structure and dynamics of molecules by observing the magnetic properties of atomic nuclei. This technique is particularly useful in studying proteins and their interactions, as it provides insight into molecular conformation and dynamics, which are crucial for understanding protein functions.
Paralogs: Paralogs are genes that have evolved by duplication within a genome and subsequently diverged in function. They are important in the context of protein sequence databases as they provide insights into evolutionary relationships and functional specialization among proteins, allowing researchers to understand the complexity of biological systems.
Post-translational modifications: Post-translational modifications (PTMs) are chemical changes that occur to proteins after their synthesis, impacting their function, activity, stability, and localization. These modifications are crucial for the proper functioning of proteins and play a significant role in various biological processes, influencing how proteins interact within cellular environments and are involved in the regulation of protein-protein interactions.
Protein Data Bank (PDB): The Protein Data Bank (PDB) is a comprehensive online repository that stores 3D structures of proteins, nucleic acids, and complex biomolecular assemblies. It plays a crucial role in bioinformatics and structural biology by providing researchers access to experimentally determined structural data, facilitating the study of protein function, interactions, and dynamics.
Proteogenomics: Proteogenomics is the integrated study of proteomics and genomics that combines genomic data with proteomic analysis to better understand the relationship between genes, transcripts, and the proteins they encode. This approach enhances the identification of protein variants and post-translational modifications by utilizing genomic information, providing insights into biological processes and disease mechanisms.
Proteomic Profiling: Proteomic profiling is the comprehensive analysis of the entire set of proteins expressed by a cell, tissue, or organism at a specific time under defined conditions. This method is crucial for understanding biological processes, as it reveals how proteins function, interact, and change in response to various stimuli or disease states. By utilizing advanced techniques, such as mass spectrometry and bioinformatics, proteomic profiling contributes significantly to fields like drug development, personalized medicine, and toxicity assessment.
Psi-blast: psi-blast, or Position-Specific Iterated BLAST, is an advanced version of the standard BLAST algorithm designed to perform sensitive sequence searches against protein databases. It improves the ability to detect distant homologs by using a position-specific scoring matrix (PSSM) to score sequences, which allows it to better account for the variability in amino acid composition at each position in the query sequence.
Quantitative proteomics: Quantitative proteomics is the study of proteins in a given sample with an emphasis on measuring the abundance and changes in protein expression levels. This field integrates various techniques to analyze protein quantities, allowing for the comparison between different biological states or conditions and providing insights into cellular functions and disease mechanisms.
RefSeq: RefSeq, or the Reference Sequence Database, is a comprehensive resource developed by the National Center for Biotechnology Information (NCBI) that provides curated, accurate, and up-to-date information about reference sequences of genomes, transcripts, and proteins. This database serves as a central repository for genetic information and plays a crucial role in bioinformatics, allowing researchers to access standardized data for comparative studies and functional analysis.
UniProt: UniProt is a comprehensive protein sequence and functional information database that provides high-quality data on protein sequences and their functional annotations. It plays a crucial role in proteomics research by offering tools for sequence alignment, functional analysis, and protein identification, making it an essential resource for researchers studying proteins.
XML: XML, or Extensible Markup Language, is a versatile markup language designed to store and transport data while being both human-readable and machine-readable. It plays a crucial role in the realm of protein sequence databases by facilitating the structured representation of protein data, making it easier to share, manage, and analyze biological information across various platforms and applications.