Homology modeling predicts 3D protein structures using evolutionarily related as templates. This technique is crucial when experimental structures are unavailable, enabling structure-based studies in computational molecular biology.
The process involves , model building, refinement, and validation. It relies on the principle that similar protein sequences often have similar structures, leveraging evolutionary relationships to predict unknown structures.
Principles of homology modeling
Homology modeling predicts three-dimensional protein structures based on evolutionarily related proteins
Crucial technique in computational molecular biology enables structure-based studies when experimental structures are unavailable
Relies on the principle that proteins with similar sequences often have similar structures
Concept of protein homology
Top images from around the web for Concept of protein homology
Determining Evolutionary Relationships | OpenStax Biology 2e View original
Is this image relevant?
Protein Structure | Chemistry [Master] View original
Is this image relevant?
Protein homology modelling and its use in South Africa View original
Is this image relevant?
Determining Evolutionary Relationships | OpenStax Biology 2e View original
Is this image relevant?
Protein Structure | Chemistry [Master] View original
Is this image relevant?
1 of 3
Top images from around the web for Concept of protein homology
Determining Evolutionary Relationships | OpenStax Biology 2e View original
Is this image relevant?
Protein Structure | Chemistry [Master] View original
Is this image relevant?
Protein homology modelling and its use in South Africa View original
Is this image relevant?
Determining Evolutionary Relationships | OpenStax Biology 2e View original
Is this image relevant?
Protein Structure | Chemistry [Master] View original
Is this image relevant?
1 of 3
Refers to proteins sharing a common evolutionary ancestor
Homologous proteins often maintain similar structures and functions
Sequence similarity serves as a primary indicator of homology
Distinguishes between orthologs (same function in different species) and paralogs (different functions within same species)
Evolutionary basis for homology
Stems from gene duplication and speciation events
Conserved protein domains reflect functional importance
Mutation rates vary across protein regions (active sites vs surface loops)
Molecular clock hypothesis links sequence divergence to evolutionary time
Applications in structural biology
Enables prediction of protein-protein interaction interfaces
Aids in designing site-directed mutagenesis experiments
Facilitates understanding of protein function and mechanism
Supports interpretation of experimental data (X-ray crystallography, NMR)
Template selection process
Critical step in homology modeling determines overall model quality
Involves searching protein structure databases for suitable templates
Requires balancing sequence similarity with structural quality
Utilizes both sequence-based and structure-based alignment methods
Sequence alignment methods
(Basic Local Alignment Search Tool) identifies potential templates
tools (, ) refine alignments
capture evolutionary information
detect remote homologs
Structural alignment techniques
Superimpose known structures to identify conserved regions
(Distance matrix ALIgnment) algorithm compares protein folds
uses template modeling score for structural similarity
Flexible alignment methods account for domain movements
Template quality assessment
Resolution of X-ray structures impacts template reliability
R-factor and free R-factor indicate experimental structure quality
B-factors reveal regions of structural flexibility
(Qualitative Model Energy ANalysis) evaluates overall template quality
Model building steps
Iterative process combines template information with target sequence
Aims to generate physically realistic protein structures
Involves constructing backbone, modeling loops, and placing side chains
Requires careful consideration of conserved structural features
Backbone generation
Transfers conserved core regions from template to target
Utilizes Cα trace or full backbone atom coordinates
Applies restraints based on secondary structure predictions
Handles insertions and deletions through gap modeling
Loop modeling strategies
Addresses regions of low sequence similarity or structural variability
Ab initio methods generate conformations based on physics principles
Database-driven approaches use fragments from known structures
Combines energy minimization with geometric constraints
Side chain placement
Predicts optimal rotamer configurations for amino acid side chains
Utilizes rotamer libraries derived from high-resolution structures
Considers steric clashes and favorable interactions (hydrogen bonds, salt bridges)
Applies dead-end elimination algorithm to reduce computational complexity
Model refinement techniques
Aims to improve initial homology models through optimization
Addresses local geometric errors and unfavorable interactions
Combines physics-based and knowledge-based approaches
Iterative process often coupled with model quality assessment
Energy minimization approaches
Reduces overall potential energy of the protein structure
Applies force fields (, ) to model atomic interactions
Utilizes gradient descent or conjugate gradient algorithms
Balances bond lengths, angles, and non-bonded interactions
Molecular dynamics simulations
Simulates protein motion over time to explore conformational space
Applies Newton's equations of motion to atoms in the system
Requires careful selection of simulation parameters (temperature, pressure)
Analyzes trajectory data to identify stable conformations
Knowledge-based scoring functions
Derives statistical potentials from known protein structures
Evaluates model quality based on observed residue-residue interactions
Incorporates solvation effects and hydrogen bonding patterns
Combines with physics-based terms for comprehensive assessment
Model validation and assessment
Critical step ensures reliability of homology models
Employs multiple complementary evaluation methods
Identifies potential errors and areas for improvement
Guides iterative refinement of model structures
Stereochemical quality checks
Analyzes bond lengths, angles, and dihedral angles
assesses backbone conformations
PROCHECK evaluates overall geometric quality
Identifies steric clashes and unfavorable interactions
Statistical potential analysis
(Discrete Optimized Protein Energy) score assesses atomic distances
Closer integration with experimental methods accelerates structural biology
Machine learning approaches
Deep learning models (AlphaFold) achieve near-experimental accuracy
Graph neural networks capture long-range interactions in protein structures
Generative models produce diverse conformational ensembles
Transfer learning leverages information across protein families
Integration with experimental methods
density maps guide modeling of large protein complexes
NMR data provides dynamic information for flexible region modeling
Cross-linking mass spectrometry constrains protein-protein docking
Integrative modeling combines diverse experimental and computational data
Improvements in ab initio modeling
Advances in force fields improve physics-based structure prediction
Enhanced sampling methods explore conformational space more efficiently
Coarse-grained models enable modeling of larger systems
Hybrid approaches combine template-based and ab initio methods for challenging targets
Key Terms to Review (33)
Alphafold: AlphaFold is an advanced artificial intelligence system developed by DeepMind that predicts protein structures with remarkable accuracy. It uses deep learning techniques to analyze the amino acid sequences of proteins and predict their 3D conformations, making it a significant breakthrough in the field of structural biology. The ability of AlphaFold to predict tertiary structures and facilitate homology modeling has transformed how scientists understand protein folding and function.
Amber: Amber is a term often associated with a specific type of stop codon in genetics, particularly in the context of molecular biology and protein synthesis. It plays a crucial role in signaling the termination of protein translation, which connects to various computational methods for modeling proteins, evaluating energy states, and understanding molecular mechanics.
BLAST: BLAST, or Basic Local Alignment Search Tool, is a bioinformatics algorithm used for comparing an input sequence against a database of sequences to identify regions of similarity. It helps researchers find homologous sequences quickly, playing a crucial role in dynamic programming methods, pairwise alignments, and both local and global alignments to analyze biological data.
CHARMM: CHARMM (Chemistry at HARvard Macromolecular Mechanics) is a widely-used molecular modeling software package that focuses on the simulation of biomolecules like proteins, nucleic acids, and lipids. It provides tools for energy minimization, molecular dynamics simulations, and analysis of molecular structures, making it essential for understanding molecular interactions and dynamics. CHARMM utilizes various force fields to accurately model the physical properties of molecules and plays a significant role in homology modeling and molecular mechanics.
Clustal Omega: Clustal Omega is a widely used multiple sequence alignment tool designed to align multiple protein or nucleotide sequences simultaneously, taking advantage of a progressive alignment strategy. It employs dynamic programming to optimize the alignment process, ensuring high accuracy and efficiency, making it particularly useful in primary structure analysis and homology modeling contexts.
Cryo-em: Cryo-electron microscopy (cryo-EM) is a cutting-edge imaging technique that allows for the visualization of biological samples at cryogenic temperatures. By rapidly freezing samples and using electron beams to obtain high-resolution images, cryo-EM enables researchers to observe the structures of proteins and other macromolecules in their native states, making it a vital tool in structural biology and homology modeling.
DALI: DALI stands for 'Distance All Ligand Interaction,' which is a computational method used in molecular biology for comparing the spatial arrangement of proteins and their ligands. This approach helps in understanding how different structural conformations affect binding affinities and interactions, making it a crucial tool in homology modeling. By utilizing DALI, researchers can align and evaluate the similarity between protein structures, guiding them in predicting how similar proteins will behave in relation to ligands.
Dope: In computational molecular biology, 'dope' refers to a scoring function used in homology modeling to evaluate the quality of protein structures. It helps to assess how well a model aligns with known structures by measuring differences in energy and providing a statistical basis for structural comparison. The dope score aids researchers in identifying the most accurate models for further analysis and experimentation.
Functional Annotation: Functional annotation is the process of assigning biological functions to gene products, such as proteins, based on various types of data, including sequence similarity, structural information, and experimental results. This process allows researchers to infer the roles of genes in biological pathways and systems, making it essential for understanding organismal biology and disease mechanisms.
Gdt-ts: gdt-ts (Global Distance Test - Total Score) is a scoring metric used to evaluate the quality of protein structure predictions by comparing the predicted structure against a reference structure. It measures the overall structural similarity by calculating the root mean square deviation (RMSD) of corresponding atoms, allowing researchers to assess how closely the predicted model aligns with the actual structure. This score is crucial in the context of homology modeling, where accurate predictions are essential for understanding protein function and interactions.
Hidden Markov Models (HMMs): Hidden Markov Models are statistical models that represent systems that transition between hidden states over time, where the system is assumed to be a Markov process with unobservable states. HMMs are particularly powerful for applications like sequence analysis in molecular biology, allowing researchers to infer biological sequences and structures based on observed data, making them crucial in the context of homology modeling.
Homologous sequences: Homologous sequences are segments of DNA, RNA, or protein that share a common ancestry due to divergence from a common ancestor. These sequences can provide critical insights into evolutionary relationships, as they often retain similar functions and structures, making them essential for tasks like comparing genes or proteins across different species and predicting the structure of proteins based on known homologs.
Model refinement: Model refinement is the process of improving a computational model to better represent the biological structure or function it aims to simulate. This iterative procedure often involves adjusting parameters, optimizing the model's geometry, and incorporating experimental data to enhance accuracy and predictive power. By continually refining models, researchers can achieve results that align more closely with observed biological phenomena.
Modeller: A modeller is a computational tool or software used for creating three-dimensional structures of biomolecules based on known homologous structures. This technique leverages the relationship between sequences and structures to predict the arrangement of atoms in a protein or nucleic acid, which is essential for understanding its function and interactions.
Multiple Sequence Alignment: Multiple sequence alignment is a method used to align three or more biological sequences, such as DNA, RNA, or protein sequences, to identify similarities and differences among them. This technique is crucial for understanding evolutionary relationships, functional elements, and conserved regions across different organisms. It plays a significant role in various analyses, including local and global alignments, profile-based alignments, primary structure analysis, and homology modeling.
Muscle: Muscle refers to a type of tissue in the body that has the ability to contract and produce movement. It plays a vital role in facilitating various bodily functions, including movement, posture maintenance, and heat generation. In computational molecular biology, understanding muscle proteins and their sequences can help in analyzing structure and function relationships, particularly through methods like multiple sequence alignment and homology modeling.
Nucleic acids: Nucleic acids are large biomolecules essential for all forms of life, primarily consisting of long chains of nucleotides. They are fundamental in storing and transmitting genetic information through their two main types: DNA (deoxyribonucleic acid) and RNA (ribonucleic acid). These molecules play a key role in the processes of coding, decoding, regulation, and expression of genes, making them vital for cellular functions and homology modeling.
Position-specific scoring matrices: Position-specific scoring matrices (PSSMs) are mathematical representations that score the likelihood of each possible amino acid or nucleotide at each position in a sequence alignment. They are crucial for analyzing biological sequences, allowing researchers to identify conserved regions and make predictions about function based on the primary structure of proteins or nucleic acids. PSSMs play a key role in both analyzing primary structures and modeling homology, providing insights into evolutionary relationships and functional characteristics.
Proq: ProQ is a computational tool used for the assessment of protein structures, specifically for predicting the quality of models generated through homology modeling. It evaluates the accuracy of the structural models by analyzing various geometric and statistical parameters, helping researchers identify potentially problematic regions within the protein model. ProQ is especially useful in refining and improving homology models before they are used for further analysis or experimental validation.
Prosa: Prosa refers to a specific type of structural representation of proteins that can be utilized to facilitate homology modeling. This term is often used to describe the simplified or abstracted representations of protein structures, which enable researchers to predict and model the three-dimensional conformations of proteins based on known structures of related proteins. Understanding prosa is essential for accurately generating reliable protein models that aid in studying biological functions and interactions.
Proteins: Proteins are large, complex molecules composed of one or more long chains of amino acids, which play critical roles in the structure, function, and regulation of the body's tissues and organs. They are essential for numerous biological processes, including enzyme activity, signaling, immune responses, and transport. Understanding proteins is key to many areas of molecular biology, including techniques used to model and predict their structures and interactions.
Qmean: qmean is a statistical measure used in the evaluation of protein models, particularly in homology modeling. It assesses the quality of a model by providing a quantitative score that reflects how well the predicted structure aligns with known reference structures. A higher qmean score indicates better model quality, making it an essential metric in determining the reliability of homology models.
Ramachandran Plot: A Ramachandran plot is a graphical representation that illustrates the allowed and disallowed dihedral angles (phi and psi) of amino acid residues in a protein structure. This plot is crucial for understanding protein folding, as it helps in predicting the conformation of proteins based on steric hindrance and backbone geometry, making it especially important in homology modeling where the structure of a protein is inferred based on its sequence similarity to known structures.
Root-mean-square deviation (rmsd): Root-mean-square deviation (rmsd) is a measure used to quantify the differences between predicted and observed values, particularly in the context of molecular structures. It calculates the square root of the average squared deviations of atomic positions, providing a single value that reflects how similar or different two structures are. rmsd is crucial for evaluating the accuracy of models generated through techniques like homology modeling and for assessing the quality of molecular docking simulations.
Sequence alignment: Sequence alignment is a method used to arrange the sequences of DNA, RNA, or proteins to identify regions of similarity that may indicate functional, structural, or evolutionary relationships. This technique is crucial for comparing biological sequences and can be applied using algorithms to assess the degree of similarity, as well as to predict structures and functions based on these comparisons.
Structural conservation: Structural conservation refers to the preservation of the three-dimensional arrangement of atoms in a protein or nucleic acid that has remained relatively unchanged throughout evolution. This concept is crucial for understanding how similar structures can perform analogous functions across different organisms, indicating evolutionary relationships and functional similarities among biomolecules.
Structure-based drug design: Structure-based drug design is a method used in drug discovery that relies on the three-dimensional structure of biological molecules to identify and develop new medications. This approach involves analyzing the structure of target proteins to understand how potential drug compounds can interact with them, leading to optimized therapeutic agents. It connects molecular biology with computational techniques, which include homology modeling and drug repurposing strategies.
Swiss-model: The swiss-model is a computational tool used in homology modeling to predict the three-dimensional structures of proteins based on known structures of homologous proteins. It allows researchers to generate accurate models of proteins when experimental methods like X-ray crystallography or NMR spectroscopy are not feasible, facilitating studies in protein function, interactions, and drug design.
Template selection: Template selection is the process of choosing a suitable template structure from a database to model a target protein whose structure is unknown. This choice is crucial because the accuracy of the homology model greatly depends on how closely related the template is to the target in terms of sequence and structural similarity. A good template can lead to a more reliable and functional model, making this step fundamental in homology modeling.
Tm-align: tm-align is a computational tool used to align protein structures based on their three-dimensional conformations. It employs a modified dynamic programming algorithm to maximize the structural similarity between two proteins, making it particularly useful in homology modeling to assess how well a model protein aligns with a template structure. By accurately comparing protein folds, tm-align helps in understanding evolutionary relationships and functional similarities.
Tm-score: The tm-score is a quantitative measure used to assess the similarity between two protein structures. It ranges from 0 to 1, where a score closer to 1 indicates high structural similarity, while a score closer to 0 suggests greater dissimilarity. This scoring system is particularly useful in homology modeling, as it helps to evaluate how closely a modeled protein aligns with a known reference structure.
Verify3d: verify3d is a computational tool used in structural biology to assess the quality of three-dimensional models of macromolecules, particularly proteins. It evaluates how well the geometry of the model aligns with known structural data and identifies potential errors or inconsistencies that could impact further analysis or applications. This tool plays a crucial role in homology modeling by providing a means to validate models generated based on template structures.
Z-score: A z-score is a statistical measurement that describes a value's relationship to the mean of a group of values, expressed in terms of standard deviations from the mean. In the context of homology modeling, z-scores are crucial for assessing the quality and reliability of predicted protein structures by comparing them to known structures, providing insight into how well the model aligns with expected values.