2.3 Structure databases
8 min read•august 21, 2024
Structure databases are essential tools in bioinformatics, storing 3D structural information of biological molecules. These databases enable researchers to analyze molecular interactions, design drugs, and predict protein functions by providing a wealth of structural data.
The main types of structure databases include protein, nucleic acid, and small molecule databases. Each type serves specific research purposes, from understanding protein folding to supporting drug discovery efforts. Major databases like , , and offer comprehensive structural information for various biomolecules.
Overview of structure databases
- Structure databases in bioinformatics store and organize three-dimensional structural information of biological molecules
- These databases play a crucial role in understanding molecular interactions, drug design, and protein function prediction
- Bioinformaticians use structure databases to analyze and compare molecular structures, enabling insights into evolutionary relationships and disease mechanisms
Types of structure databases
Protein structure databases
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- Store experimentally determined 3D structures of proteins
- Include information on protein folding, domains, and functional sites
- Contain structures determined by , , and cryo-electron microscopy
- Facilitate protein structure prediction and comparative modeling studies
Nucleic acid structure databases
- House 3D structures of DNA and RNA molecules
- Provide information on nucleic acid conformations and base-pairing interactions
- Support research in gene regulation, RNA folding, and nucleic acid-protein interactions
- Aid in the design of nucleic acid-based therapeutics and gene editing tools
Small molecule databases
- Contain structural information of small organic and inorganic compounds
- Include data on ligands, drug-like molecules, and natural products
- Support drug discovery efforts and chemical informatics research
- Enable virtual screening and structure-activity relationship studies
Major structure databases
Protein Data Bank (PDB)
- Primary repository for experimentally determined 3D structures of proteins and nucleic acids
- Contains over 180,000 structures as of 2023
- Provides free access to structural data for researchers worldwide
- Supports multiple file formats (PDB, , ) for data representation
- Integrates with other databases and tools for comprehensive structural analysis
Nucleic Acid Database (NaDB)
- Specialized database for 3D structures of nucleic acids
- Focuses on DNA and RNA structures, including complex structures like ribozymes
- Offers advanced search capabilities based on sequence and structural features
- Provides tools for analyzing base-pairing patterns and backbone conformations
- Complements the PDB by offering nucleic acid-specific annotations and analysis tools
Cambridge Structural Database (CSD)
- World's largest repository of small molecule crystal structures
- Contains over 1 million structures of organic and metal-organic compounds
- Supports drug discovery by providing information on molecular interactions and packing
- Offers tools for crystal structure prediction and polymorph analysis
- Enables structure-based design of new materials and pharmaceuticals
Data representation formats
PDB file format
- Traditional format for representing macromolecular structures
- Uses a fixed-width column format with specific record types (ATOM, HETATM, CONECT)
- Provides information on atomic coordinates, connectivity, and experimental details
- Limitations include restricted atom count and lack of support for large structures
mmCIF format
- Macromolecular Crystallographic Information File format
- Addresses limitations of the with a more flexible and extensible structure
- Uses key-value pairs to represent structural and experimental data
- Supports larger structures and more detailed annotations
- Becoming the preferred format for structure deposition and exchange
XML-based formats
- Include formats like PDBML (XML version of PDB) and CML (Chemical Markup Language)
- Provide machine-readable representation of structural data
- Enable easy parsing and integration with web-based tools and services
- Support validation and data exchange between different software systems
- Allow for more detailed metadata and annotations compared to traditional formats
Database search methods
Sequence-based searches
- Allow users to find structures based on protein or nucleic acid sequences
- Utilize algorithms like or for sequence similarity searches
- Enable identification of structurally characterized homologs
- Support evolutionary studies and structure prediction efforts
- Integrate with sequence databases (UniProt, GenBank) for comprehensive analysis
Structure-based searches
- Enable users to find similar structures based on 3D coordinates or structural motifs
- Utilize algorithms like DALI, VAST, or CE for structural alignment and comparison
- Support identification of structurally conserved domains and functional sites
- Aid in understanding protein folding and evolutionary relationships
- Facilitate structure-based drug design and protein engineering efforts
Chemical similarity searches
- Allow users to find structures based on chemical properties or substructures
- Utilize fingerprint-based methods or graph matching algorithms
- Support drug discovery by identifying compounds with similar properties
- Enable scaffold hopping and lead optimization in medicinal chemistry
- Integrate with chemical databases (PubChem, ChEMBL) for comprehensive analysis
Structure visualization tools
PyMOL
- Popular open-source molecular visualization system
- Offers high-quality 3D rendering of molecular structures
- Supports creation of publication-quality images and animations
- Provides scripting capabilities for custom analysis and visualization
- Integrates with other bioinformatics tools for structure analysis and modeling
Chimera
- Extensible molecular modeling system developed by UCSF
- Offers a wide range of tools for structure analysis, including sequence alignment
- Supports visualization of large molecular assemblies and electron microscopy data
- Provides interfaces to external web services and databases
- Enables creation of custom extensions and plugins for specialized analyses
Jmol
- Java-based open-source molecular viewer
- Offers cross-platform compatibility and web browser integration
- Supports a wide range of chemical file formats
- Provides a scripting language for custom visualizations and analyses
- Enables interactive exploration of molecular structures in educational settings
Data quality and validation
R-factor and R-free
- Statistical measures used to assess the quality of crystallographic structure models
- R-factor measures the agreement between the observed and calculated structure factors
- R-free calculated using a subset of data not used in refinement, reducing overfitting
- Lower values indicate better agreement between the model and experimental data
- Typical R-factor values for well-refined structures range from 0.15 to 0.25
Ramachandran plots
- Visualize the distribution of backbone dihedral angles (φ and ψ) in protein structures
- Help identify energetically favorable and unfavorable conformations
- Used to validate protein structures and identify potential errors in model building
- Regions of the plot correspond to secondary structure elements (α-helices, β-sheets)
- Outliers in Ramachandran plots may indicate problematic regions in the structure
B-factors and occupancy
- B-factors (temperature factors) indicate the degree of thermal motion or disorder
- Higher B-factors suggest greater flexibility or uncertainty in atomic positions
- Occupancy represents the fraction of molecules in which an atom occupies a specific position
- Used to model alternate conformations or partial occupancy of ligands
- Help in assessing the reliability and flexibility of different regions in a structure
Structure prediction methods
Homology modeling
- Predicts 3D structure of a protein based on its sequence similarity to known structures
- Relies on the principle that similar sequences adopt similar structures
- Involves template selection, alignment, model building, and refinement steps
- Accuracy depends on the degree of sequence similarity and quality of template structures
- Widely used for protein function prediction and virtual screening in drug discovery
Ab initio prediction
- Predicts protein structure from sequence alone, without relying on known structures
- Uses physics-based force fields and conformational sampling algorithms
- Computationally intensive and typically limited to small proteins (< 100 residues)
- Recent advances in deep learning (AlphaFold) have significantly improved accuracy
- Enables structure prediction for proteins with no known homologs
Protein threading
- Predicts protein structure by aligning the sequence to known structural templates
- Evaluates the compatibility of the sequence with different structural folds
- Combines sequence and structure information to identify the most likely
- Useful for detecting remote homologs and predicting structures of novel protein families
- Often used as an intermediate approach between homology modeling and ab initio methods
Integration with other databases
UniProt integration
- Links structure data to comprehensive protein sequence and functional information
- Enables mapping of structural features to sequence annotations
- Facilitates analysis of structure-function relationships across protein families
- Provides cross-references between PDB entries and UniProt accession numbers
- Supports integration of experimental and predicted structural data
Gene Ontology annotations
- Associates structural data with standardized functional annotations
- Enables analysis of structure-function relationships across different organisms
- Supports functional interpretation of structural features and domains
- Facilitates identification of structurally characterized proteins with specific functions
- Integrates structural data into broader biological knowledge frameworks
Pathway databases
- Links structural data to metabolic and signaling pathway information
- Enables visualization of protein structures in the context of biological pathways
- Supports analysis of protein-protein interactions and complex formation
- Facilitates understanding of how structural changes impact pathway function
- Integrates structural data with systems biology approaches (KEGG, Reactome)
Applications in bioinformatics
Drug discovery
- Utilizes structural data for structure-based drug design and virtual screening
- Enables identification of binding sites and prediction of protein-ligand interactions
- Supports lead optimization through analysis of protein-ligand complex structures
- Facilitates design of selective inhibitors targeting specific protein conformations
- Integrates structural data with pharmacophore modeling and QSAR analysis
Protein engineering
- Uses structural data to guide rational design of proteins with enhanced properties
- Enables identification of key residues for mutagenesis experiments
- Supports design of protein-protein interfaces for creating novel biomolecules
- Facilitates engineering of enzyme active sites for improved catalytic efficiency
- Integrates structural data with directed evolution and computational design approaches
Evolutionary studies
- Analyzes structural conservation and divergence across protein families
- Enables reconstruction of ancestral protein structures and functions
- Supports identification of structurally constrained regions in proteins
- Facilitates understanding of how protein structure evolves over time
- Integrates structural data with phylogenetic analysis and molecular evolution studies
Challenges and limitations
Experimental limitations
- limits of experimental techniques affect structural detail and accuracy
- Crystallization difficulties for certain proteins (membrane proteins, intrinsically disordered regions)
- Potential artifacts introduced by crystal packing or experimental conditions
- Challenges in capturing dynamic and flexible regions of proteins
- Limited representation of physiological conditions in structural experiments
Computational challenges
- High computational requirements for large-scale structure prediction and analysis
- Difficulties in accurately modeling protein dynamics and conformational changes
- Challenges in predicting protein-protein and protein-ligand interactions
- Limitations in force fields and scoring functions for structure prediction and docking
- Balancing accuracy and speed in structure-based computational methods
Data standardization issues
- Inconsistencies in data formats and annotations across different databases
- Challenges in integrating structural data with other types of biological data
- Difficulties in representing and analyzing large macromolecular complexes
- Need for improved methods to assess and communicate structure quality and reliability
- Challenges in keeping pace with the rapid growth of structural data and new experimental techniques
Key Terms to Review (20)
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.
Chimera: In biological terms, a chimera refers to an organism or cell that contains genetically distinct tissues, originating from two or more different zygotes. This phenomenon can occur naturally, such as in the case of individuals who develop from the fusion of multiple embryos, or it can be artificially created in laboratories for various research purposes. Chimeras are significant in understanding genetic variation, cell lineage tracing, and developmental biology, especially within the realms of structural and protein databases, as well as protein folding prediction.
CSD: CSD stands for Crystallographic Structural Database, which is a repository of crystal structure data for various materials, including organic and inorganic compounds. This database allows researchers to access and share detailed structural information derived from X-ray diffraction and other methods, facilitating the study of material properties and interactions at the atomic level. By organizing and providing access to crystallographic data, CSD plays a crucial role in fields such as chemistry, materials science, and bioinformatics.
Domain: In the context of bioinformatics, a domain refers to a distinct structural and functional unit within a protein that is often associated with specific biochemical activities. Domains can be thought of as building blocks of proteins, allowing them to perform various functions such as binding to other molecules, catalyzing reactions, or providing structural support. The identification and classification of domains are essential for understanding protein function and evolution.
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.
Fold: In bioinformatics, a fold refers to a specific three-dimensional shape that a protein or RNA molecule adopts, which is crucial for its biological function. Understanding folds helps scientists categorize proteins into families and can provide insights into their evolutionary relationships, stability, and interaction capabilities. The study of folds is essential in the context of structure databases, where these conformations are stored and analyzed to facilitate research and discovery.
MmCIF: The macromolecular Crystallographic Information File (mmCIF) is a data format used to store information about macromolecular structures, including proteins and nucleic acids, derived from X-ray crystallography. This format is designed to accommodate the complexity of large biomolecules and provide a standard way to represent structural data, facilitating better data sharing and interoperability among researchers in structural biology.
NADB: NADB, or Nucleic Acid Database, is a specialized database that stores and provides access to structural information about nucleic acids, such as DNA and RNA. This database is essential for bioinformaticians and researchers who study the structure and function of nucleic acids, offering tools for data retrieval, visualization, and analysis of nucleic acid structures, including their sequences and conformations.
NMR Spectroscopy: NMR spectroscopy, or nuclear magnetic resonance spectroscopy, is a powerful analytical technique used to determine the structure and dynamics of molecules, particularly proteins and nucleic acids. It exploits the magnetic properties of certain atomic nuclei, providing detailed information about the molecular environment and interactions at an atomic level, making it essential for understanding protein structure and function, analyzing interactions with ligands, and aiding in drug design.
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.
Pdb format: PDB format is a file format used to store three-dimensional structural data of biological macromolecules, primarily proteins and nucleic acids. It allows for the detailed representation of atomic coordinates, connectivity, and various annotations associated with a molecular structure, making it essential for structural biology and bioinformatics. This format enables researchers to share and analyze structural data efficiently, fostering advancements in understanding protein functions and interactions.
Pdbml: pdbml is an XML-based format used for representing structural biology data, specifically protein and nucleic acid structures derived from the Protein Data Bank (PDB). This format allows for easy integration of structural data with other bioinformatics resources, enhancing data exchange and interoperability among different computational tools and databases. By providing a standardized way to encode three-dimensional structures, pdbml facilitates advanced analyses and visualization in the field of structural biology.
PyMOL: PyMOL is an open-source molecular visualization system that is widely used in bioinformatics and structural biology for visualizing and analyzing molecular structures, particularly proteins and nucleic acids. Its powerful graphical capabilities allow users to manipulate 3D representations of biomolecules, making it an essential tool for studying interactions, structural databases, and protein folding predictions.
Quaternary Structure: Quaternary structure refers to the complex arrangement of multiple polypeptide chains or subunits that come together to form a functional protein. This level of protein structure is crucial because it determines how proteins interact and function in biological processes, impacting their overall stability and activity. Understanding quaternary structure is vital for studying protein interactions, their functions, and for predicting how changes in this structure can lead to various diseases.
R-value: The r-value is a statistical measure that quantifies the strength and direction of a linear relationship between two variables. In the context of structure databases, it plays a crucial role in assessing the reliability of the data obtained from structural studies, particularly in crystallography and protein structures. A high r-value indicates a strong correlation, while a low r-value suggests a weaker or non-linear relationship, which is essential for evaluating the accuracy of models and predictions in structural biology.
Resolution: Resolution refers to the smallest distance between two points that can be distinguished as separate in a structural representation. In the context of structural databases, it is critical for determining the clarity and quality of molecular structures, such as proteins and nucleic acids. Higher resolution indicates finer detail and more accurate representation of the molecular geometry, which is essential for understanding biological functions and interactions.
Root mean square deviation (rmsd): Root mean square deviation (rmsd) is a statistical measure used to quantify the difference between values predicted by a model or an experimental data set and the values actually observed. In structural biology, rmsd is commonly applied to assess the similarity between two protein structures, enabling researchers to evaluate how closely predicted models resemble known structures. This term plays a critical role in structure databases and ab initio protein structure prediction, as it helps gauge the accuracy and reliability of various computational models.
Structural Superposition: Structural superposition is a computational technique used to align and compare the three-dimensional structures of biological macromolecules, such as proteins and nucleic acids, to assess their similarities and differences. This method is crucial for understanding structural relationships between molecules, which can reveal functional similarities, evolutionary relationships, and aid in drug design and protein engineering.
Tertiary structure: Tertiary structure refers to the overall three-dimensional shape of a protein that is formed by the folding of its secondary structures, such as alpha helices and beta sheets, into a compact, functional form. This structure is crucial because it determines how the protein interacts with other molecules and performs its biological functions, linking it to aspects like protein function prediction and structure databases.
X-ray crystallography: X-ray crystallography is a powerful analytical technique used to determine the atomic and molecular structure of a crystal by diffracting X-ray beams through it. This method allows scientists to visualize the arrangement of atoms in proteins and other biological macromolecules, making it essential for understanding their structure and function.