13.3 Tertiary Structure Prediction and Homology Modeling
5 min read•july 30, 2024
Predicting a protein's 3D structure from its amino acid sequence is a crucial challenge in molecular biology. Tertiary structure prediction methods range from physics-based approaches to machine learning models like , which have revolutionized the field with near-experimental accuracy.
leverages known structures to predict the shape of similar proteins. This technique is widely used in drug discovery and protein engineering, but its accuracy depends on the similarity between the target and template sequences. Various tools help assess and refine these predicted structures.
Tertiary Structure Prediction
Fundamentals and Methods
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- Tertiary structure prediction determines the three-dimensional arrangement of a protein's amino acid chain based on its primary sequence
- Ab initio methods predict protein structure from first principles using physics-based energy functions and conformational sampling algorithms
- Template-based methods utilize known protein structures as templates to model the structure of a target protein with similar sequence
- Involves identifying structural homologs and aligning target sequence to template
- Accuracy depends on the degree of similarity between target and template
- Machine learning approaches, particularly deep learning models, have emerged as powerful tools for protein structure prediction
- AlphaFold revolutionized the field by achieving near-experimental accuracy for many proteins
- Other notable examples include and
- and molecular dynamics simulations refine and validate predicted structures
- Help optimize atomic positions and resolve steric clashes
- Can reveal dynamic properties and conformational flexibility of the predicted structure
Factors Affecting Prediction Accuracy
- Sequence length influences prediction difficulty longer proteins generally pose greater challenges
- Short proteins (< 100 amino acids) often yield more accurate predictions
- Large, multi-domain proteins require sophisticated modeling approaches
- Availability of homologous structures impacts prediction quality
- Proteins with many known structural homologs tend to have more accurate predictions
- Orphan proteins with no close homologs rely more heavily on ab initio methods
- Complexity of protein fold affects prediction accuracy
- Simple, globular folds (alpha-helical bundles) are often easier to predict
- Complex folds with intricate topologies (beta-barrels, knotted proteins) present significant challenges
- Presence of post-translational modifications can complicate predictions
- Glycosylation, phosphorylation, and other modifications alter protein structure
- Most prediction methods do not account for these modifications by default
- Intrinsically disordered regions pose unique challenges for structure prediction
- These regions lack a fixed three-dimensional structure
- Specialized methods are required to model their conformational ensembles
Homology Modeling Principles
Fundamental Concepts
- Homology modeling bases on the principle that proteins with similar sequences often have similar three-dimensional structures
- Process involves identifying a suitable template structure, aligning the target sequence to the template, building the model, and refining it
- Sequence identity between the target and template crucially impacts model accuracy
- High sequence identity (> 50%) generally leads to reliable models
- Moderate identity (30-50%) can still produce useful models but with less accuracy in some regions
- Low identity (< 30%) enters the "twilight zone" where modeling becomes challenging
- Threading or fold recognition techniques apply when sequence similarity is low but structural similarity is suspected
- Useful for identifying distant homologs or analogous folds
- Combines sequence alignment with structural information to detect similarities
Applications and Limitations
- Homology modeling widely applies in drug discovery, protein engineering, and functional annotation of novel proteins
- Aids in structure-based drug design by providing 3D models of drug targets
- Guides protein engineering efforts by predicting effects of mutations
- Helps infer protein function based on structural similarities to known proteins
- Quality of homology models improves by using multiple templates and incorporating experimental data
- Multiple templates can provide complementary structural information
- Experimental data (cross-linking, cryo-EM maps) constrains the model and improves accuracy
- Limitations of homology modeling include difficulties in accurately predicting loop regions and modeling proteins with no suitable templates
- Loop regions often differ between homologous proteins and require specialized prediction methods
- Proteins with novel folds or no detectable homologs cannot be modeled using standard homology techniques
Tertiary Structure Quality
Validation Tools and Metrics
- Structure validation tools analyze various aspects of protein geometry to assess model quality
- Bond lengths, angles, and torsions evaluated against expected values from high-resolution structures
- Tools like PROCHECK and MolProbity identify geometric outliers and steric clashes
- Ramachandran plots evaluate the distribution of phi and psi angles in the protein backbone
- Reveals conformational preferences of amino acids
- Helps identify regions with unusual or strained geometry
- Statistical potentials assess the likelihood of a given amino acid arrangement based on known structures
- compares model to statistical expectations
- calculates z-scores to evaluate overall model quality
- Global quality metrics provide overall assessments of model quality
- QMEAN combines multiple scoring functions to evaluate both local and global quality
- measures structural similarity to a reference structure
Assessing Model Reliability
- Local quality estimates identify regions of high and low confidence within a predicted structure
- Residue-level scores help pinpoint well-modeled regions versus those needing refinement
- Tools like ModFOLD provide per-residue quality assessments
- Comparison with experimental data, when available, validates predicted structures
- or NMR data serve as gold standards for structure validation
- Cryo-EM maps can validate overall shape and domain arrangements
- Model ensembles provide insights into the uncertainty and flexibility of predicted structures
- Multiple models generated from slightly different starting conditions or templates
- Ensemble analysis reveals regions of structural variability and potential flexibility
Computational Tools for Modeling
Software for Structure Prediction
- Popular homology modeling software includes , , and Phyre2
- MODELLER allows for custom scripting and advanced modeling protocols
- SWISS-MODEL provides a user-friendly web interface for automated modeling
- Phyre2 excels at detecting and modeling distant homologs
- Ab initio prediction tools like Rosetta and I-TASSER employ fragment assembly and threading approaches
- Rosetta uses a Monte Carlo approach to sample conformational space
- I-TASSER combines threading with for template-free regions
- AlphaFold and RoseTTAFold represent state-of-the-art deep learning methods for protein structure prediction
- AlphaFold 2 achieves unprecedented accuracy across a wide range of proteins
- RoseTTAFold offers a balance between speed and accuracy for large-scale predictions
Analysis and Refinement Tools
- Molecular dynamics software such as GROMACS and NAMD refine and analyze structure stability
- Simulate protein behavior in explicit solvent environments
- Reveal dynamic properties and conformational changes over time
- Visualization tools like PyMOL and Chimera enable detailed inspection and analysis of predicted structures
- Allow for creation of publication-quality images and animations
- Provide built-in analysis tools for measuring distances, angles, and surface properties
- Web servers like SAVES (Structure Analysis and Verification Server) provide integrated structure validation services
- Combines multiple validation tools (PROCHECK, VERIFY3D, ERRAT) in one platform
- Generates comprehensive reports on model quality
- Proper use of these tools requires understanding their underlying algorithms, strengths, and limitations
- Each tool has specific assumptions and biases that affect interpretation
- Combining multiple tools and metrics provides a more robust assessment of model quality
Key Terms to Review (25)
Ab initio modeling: Ab initio modeling is a computational approach used to predict the three-dimensional structure of a molecule, primarily proteins, based solely on its amino acid sequence without relying on homologous templates. This method utilizes principles from physics and chemistry to explore the potential energy landscape of the molecule, enabling the prediction of its most stable configuration. Ab initio modeling is particularly valuable when no homologous structures are available for comparison, making it essential for novel protein structures.
Active site: The active site is a specific region on an enzyme where substrate molecules bind and undergo a chemical reaction. This site is essential for the enzyme's catalytic activity and is typically composed of amino acids that create a unique three-dimensional shape, allowing for specific interactions with substrates. The nature of the active site plays a crucial role in determining the enzyme's function and specificity.
AlphaFold: AlphaFold is an artificial intelligence program developed by DeepMind that predicts protein structures with remarkable accuracy. It leverages deep learning techniques to analyze amino acid sequences and model their three-dimensional configurations, addressing a long-standing challenge in biology related to the determination of protein structures. This technology has significant implications for understanding biological processes, drug discovery, and advancing our knowledge in molecular biology.
Binding affinity: Binding affinity refers to the strength of the interaction between a protein and its ligand, typically quantified by how tightly the ligand binds to the protein. High binding affinity means that a ligand will bind effectively to its target, often leading to a stable complex that is crucial for biological functions. Understanding binding affinity is key in predicting molecular interactions and modeling protein structures, as well as determining how well ligands can compete for binding sites.
Chaperone: A chaperone is a type of protein that assists in the proper folding of other proteins, preventing misfolding and aggregation. These helper proteins play a crucial role in ensuring that newly synthesized polypeptides acquire their correct three-dimensional structure, which is vital for their function. Chaperones also assist in refolding denatured proteins and can help transport proteins to specific locations within the cell.
Dope (discrete optimized protein energy) score: The dope score is a numerical value used to assess the quality of a predicted protein structure based on its energy state. It is calculated using a statistical potential derived from known protein structures, comparing the predicted model against the observed features of the target. A lower dope score typically indicates a more favorable and stable protein conformation, making it an essential tool in tertiary structure prediction and homology modeling.
Energy minimization: Energy minimization is a computational technique used to find the most stable molecular conformation by reducing the system's potential energy. This process is critical in predicting how molecules fold and interact, as lower energy states typically correspond to more stable and favorable structures, particularly in the context of modeling protein tertiary structures and homology.
Gdt-ts (global distance test total score): gdt-ts is a scoring metric used to evaluate the accuracy of predicted protein structures by comparing them to a reference structure. It calculates the total number of residues that are within a certain distance threshold from the corresponding residues in the reference structure, helping researchers assess the quality of homology models and tertiary structure predictions.
Homology modeling: Homology modeling is a computational technique used to predict the three-dimensional structure of a protein based on its sequence alignment with one or more known structures of related proteins. This method leverages the principle that evolutionary related proteins share similar structures, allowing researchers to build accurate models of proteins whose structures have not been experimentally determined. It is closely tied to various aspects of molecular biology, including structural prediction, interaction studies, and the representation of protein structures.
Model refinement: Model refinement is the process of improving a computational model's accuracy and reliability by iteratively adjusting its parameters and structures based on new data or insights. This step is crucial in ensuring that the predictions made by the model closely align with experimental or observed data, particularly in fields like molecular biology where understanding protein structure and function is essential.
Modeller: A modeller is a computational tool or software used in structural biology to predict the three-dimensional (3D) structures of biomolecules, particularly proteins, based on known structures of homologous proteins. This process is crucial for understanding molecular functions and interactions, as it allows researchers to infer the likely conformation of a protein from closely related sequences.
Molecular Dynamics (MD): Molecular dynamics (MD) is a computational simulation method used to analyze the physical movements of atoms and molecules over time. By solving Newton's equations of motion, MD allows researchers to observe the behavior of molecular systems, which is critical for understanding interactions, stability, and dynamics in biological structures, especially in relation to tertiary structure prediction and homology modeling.
Monte Carlo Simulations: Monte Carlo simulations are a computational technique that uses random sampling to estimate mathematical functions and model the probability of different outcomes in a process that cannot easily be predicted due to the intervention of random variables. This method is widely used for assessing risks and uncertainties in various fields, including molecular biology, where it helps in understanding complex biological systems and processes by simulating numerous scenarios based on probabilistic distributions.
NMR Spectroscopy: NMR spectroscopy is a powerful analytical technique used to determine the structure and dynamics of molecules by observing the magnetic properties of atomic nuclei. It provides detailed information about the molecular environment and interactions, making it particularly useful for understanding protein folding, interactions, and conformational states in various biological systems.
Prosa-web: Prosa-web is a computational tool used for predicting protein tertiary structures based on sequence data. It combines various methodologies to generate models that help in understanding protein folding, interactions, and functions, making it particularly useful in the field of molecular biology for homology modeling.
Protein Data Bank (PDB): The Protein Data Bank (PDB) is a comprehensive repository for 3D structural data of biological macromolecules, primarily proteins and nucleic acids. It serves as a crucial resource for researchers in structural biology, enabling them to access, share, and analyze the intricate details of molecular structures, which are essential for understanding protein functions and interactions. This database plays a significant role in the prediction of tertiary structures and homology modeling by providing experimental data that can be used to infer and build models of similar proteins.
Qmean score: The qmean score is a statistical measure used to evaluate the quality of predicted protein structures by comparing them to known experimental structures. It integrates information from various structural features, such as bond lengths, angles, and dihedral angles, providing a single score that reflects the overall accuracy of the model. This score is particularly useful in assessing homology models and tertiary structure predictions, helping researchers determine how closely a computationally generated model resembles actual biological structures.
Root-mean-square deviation (rmsd): Root-mean-square deviation (rmsd) is a measure of the average distance between the atoms of superimposed proteins. It quantifies how closely two structures resemble each other by calculating the square root of the average of the squared differences between corresponding atom positions. This metric is essential in assessing the accuracy of tertiary structure predictions and homology modeling, helping to evaluate the quality of modeled structures against known references.
RosettaFold: RosettaFold is a computational method for predicting protein tertiary structures using deep learning techniques. By leveraging advanced machine learning algorithms, it builds on the foundation of the Rosetta software suite, which has been a staple in protein modeling. This approach combines experimental data with the power of artificial intelligence to improve accuracy and efficiency in predicting how proteins fold and interact.
Secondary structure elements: Secondary structure elements refer to specific arrangements of amino acids within a protein that are stabilized by hydrogen bonds, primarily forming patterns such as alpha helices and beta sheets. These structures play a critical role in the overall conformation and function of proteins, serving as building blocks for tertiary structures and influencing protein stability, flexibility, and interactions.
Swiss-model: Swiss-Model is a web-based tool used for predicting the three-dimensional structure of proteins based on their amino acid sequences through homology modeling. It allows researchers to generate high-quality protein models by aligning the target sequence with known structures in databases, enabling insights into protein function and interactions.
Template selection: Template selection is the process of choosing an appropriate structural template from a database to guide the modeling of a target protein's tertiary structure. This is crucial because the accuracy of predicted structures heavily depends on how well the selected template resembles the target sequence, influencing the final modeled structure's reliability and biological relevance.
TrRosetta: trRosetta is a computational method for predicting the tertiary structure of proteins using deep learning techniques. By analyzing patterns in known protein structures and their sequences, trRosetta can infer the spatial arrangement of amino acids and provide detailed models of protein folding. This tool is particularly useful for understanding protein interactions and functions, bridging gaps in experimental data and providing insights into molecular biology.
UniProt: UniProt is a comprehensive protein sequence and functional information database that provides detailed annotations for proteins from various organisms. It plays a crucial role in bioinformatics by offering a centralized resource for protein sequences, their functions, structures, and interactions, facilitating various computational analyses in molecular biology.
X-ray crystallography: X-ray crystallography is a powerful analytical technique used to determine the atomic and molecular structure of a crystal by measuring the diffraction patterns produced when X-rays are scattered by the crystal's atoms. This method allows researchers to visualize the three-dimensional arrangement of atoms within a molecule, providing crucial insights into the structure and function of biological macromolecules, such as proteins and nucleic acids.