C7: Protein Homology Modeling

Welcome to the pre-lab reading for C7: Protein Homology Modeling. This session will introduce you to one of the most widely used computational methods for predicting the 3D structure of a protein when its amino acid sequence is known.

1. Introduction to Homology Modeling

What is Homology Modeling?

Protein homology modeling, also known as comparative modeling, is a computational technique used to predict the three-dimensional (3D) structure of a “target” protein based on its amino acid sequence and its experimentally determined homologous protein structures, called “templates.”

The fundamental principle behind homology modeling is that proteins with similar sequences (homologs) tend to adopt similar 3D structures. Evolutionarily related proteins often share a common ancestor, and their structures are generally more conserved than their sequences.

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What is the core assumption of homology modeling?

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Proteins with similar amino acid sequences (homologs) will adopt similar three-dimensional structures because structure is generally more conserved than sequence during evolution.

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Why is it Important?

Experimental determination of protein structures (e.g., X-ray crystallography, NMR spectroscopy) can be time-consuming, expensive, and not always feasible for every protein. With the rapid growth of sequence databases from genome sequencing projects, there’s a large gap between the number of known protein sequences and experimentally determined structures. Homology modeling helps bridge this “structure gap” by providing theoretical models that can be used for:

• Understanding protein function and mechanisms.
• Guiding site-directed mutagenesis experiments.
• Identifying active sites and binding pockets.
• Drug design and discovery (e.g., virtual screening, ligand docking).
• Studying protein-protein interactions.
• Analyzing the impact of mutations (e.g., in disease).

2. Basic Principles

Homology modeling relies on the following key observations:

1. Structural Conservation: The 3D structure of a protein is more conserved during evolution than its amino acid sequence. Functionally important regions, like active sites, are often highly conserved structurally.
2. Sequence Similarity Implies Structural Similarity: If a target protein shares significant sequence similarity with one or more proteins of known structure (templates), it is very likely that they share a similar fold.

When is Homology Modeling Applicable?

The accuracy of a homology model heavily depends on the degree of sequence identity between the target and template(s).

• > 50% sequence identity: Models are generally of high accuracy, comparable to low-resolution experimental structures. The core regions are usually well-modeled, with errors mainly in loop regions.
• 30-50% sequence identity (Twilight Zone): Models are often reasonably accurate, correctly capturing the fold. However, significant errors in loop regions and some misalignments can occur. Careful template selection and alignment are crucial.
• < 30% sequence identity (Midnight Zone / Difficult Target): Homology modeling becomes very challenging. While the overall fold might sometimes be predicted correctly if a remote homolog is found, the accuracy is generally low. Methods like protein threading or ab initio modeling might be more appropriate, although homology modeling can still be attempted if reliable templates are identified through sensitive search methods like PSI-BLAST.

3. The Homology Modeling Workflow

The process of homology modeling can be broken down into several key steps:

Homology Modeling Workflow

Step 1 Template Recognition & Selection

Identify suitable template structures in the Protein Data Bank (PDB) that are homologous to the target sequence.

Step 2 Sequence Alignment

Align the target sequence with the selected template sequence(s) accurately.

Step 3 Model Building

Construct the 3D model of the target protein based on the alignment and template structure(s).

Step 4 Model Refinement (Optional but common)

Optimize the model to correct steric clashes and improve its overall geometry and energy.

Step 5 Model Validation & Assessment

Evaluate the quality of the generated model using various structural and energetic criteria.

Let’s delve into each step:

3.1 Template Recognition and Selection

The first and most critical step is to find suitable template structures.

• How? Use the target protein sequence as a query to search against databases of known protein structures (e.g., the Protein Data Bank - PDB). Tools like BLAST (Basic Local Alignment Search Tool) or PSI-BLAST (Position-Specific Iterated BLAST) are commonly used.
• Criteria for good templates:
  • Sequence Identity & Similarity: Higher is generally better.
  • E-value: A lower E-value indicates a more significant match.
  • Coverage: The template should cover a large portion of the target sequence.
  • Resolution (for X-ray structures): Higher resolution (lower Ångström value, e.g., < 2.5 Å) is preferred.
  • R-factor & R-free (for X-ray structures): Lower values indicate better quality experimental data.
  • Completeness: The template should have few or no missing residues, especially in regions aligned with the target.
  • Functional Relevance: If multiple templates exist, choose one that is functionally related to the target protein, if known.
  • Presence of Ligands/Cofactors: If the target is expected to bind a specific ligand, a template structure with a similar ligand bound can be highly advantageous.

It’s often beneficial to use multiple templates if available, especially if different templates cover different regions of the target sequence well.

3.2 Sequence Alignment

Once templates are selected, the target sequence must be accurately aligned with the template sequence(s). This alignment dictates which residues in the target will adopt the coordinates of which residues in the template.

• Importance: Errors in alignment, especially in regions of low sequence similarity or where insertions/deletions (indels) occur, will lead to incorrect model structures.
• Tools: Sequence alignment programs like ClustalW, T-Coffee, or specialized alignment tools within modeling packages are used. Manual inspection and adjustment of alignments, guided by structural information (e.g., secondary structure elements, conserved motifs, domain boundaries), are often necessary for critical regions.

Figure: A simplified view of target-template alignment. Note how gaps in one sequence correspond to insertions in the other.

3.3 Model Building

This step involves constructing the 3D coordinates for the target protein.

1. Backbone Generation:
   • For regions where the target and template sequences align without gaps, the backbone coordinates (N, Cα, C, O atoms) of the aligned template residues are copied to the corresponding target residues.
2. Loop Modeling:
   • Insertions and deletions (indels) often occur in loop regions, which are typically found on the protein surface. If the target sequence has an insertion relative to the template, or if a loop in the template is not suitable, these regions need to be modeled.
   • Knowledge-based methods: Search a database of known loop structures (from PDB) for loops of the correct length and anchor geometry that fit the gap.
   • Ab initio methods: Use conformational search algorithms, often combined with energy functions, to predict the loop conformation. This is more challenging and computationally intensive.
3. Side-Chain Modeling:
   • If a residue in the target is identical to the aligned residue in the template, its side-chain coordinates can be copied.
   • If the residues are different (mutated), the side-chain conformation needs to be predicted. This is typically done using libraries of common side-chain conformations (rotamers) derived from high-resolution crystal structures. The choice of rotamer is guided by steric constraints and energy considerations.

3.4 Model Refinement

The initial model built by copying coordinates and modeling loops/side-chains might contain steric clashes, strained bond lengths or angles, or other unfavorable interactions.

• Purpose: To improve the stereochemical quality and overall conformational energy of the model.
• Methods:
  • Energy Minimization: Uses force fields (like CHARMM, AMBER) to identify and relieve steric clashes and optimize local geometry. This primarily affects side-chain positions and makes minor adjustments to the backbone.
  • Molecular Dynamics (MD) Simulations: Can be used for more extensive sampling of conformational space. Short MD simulations might help relax the structure. However, long MD simulations can sometimes lead the model astray if the initial model is far from the native state or if the force field is not perfectly accurate.

Caution: Over-refinement can sometimes move the model further away from the true native structure, especially if the initial model has significant errors or if the refinement protocol is too aggressive. Refinement generally does not correct large-scale errors originating from incorrect template selection or misalignments.

3.5 Model Validation and Assessment

This is a crucial step to estimate the quality and reliability of the generated homology model. No model is perfect, and understanding its potential inaccuracies is essential.

• Stereochemical Quality:
  • Ramachandran Plot: Checks the energetically allowed regions for backbone dihedral angles (phi, psi) of amino acid residues. Programs like PROCHECK or MolProbity generate these plots. Outliers (residues in disallowed regions) indicate potential problems.
    • Image Idea: A sample Ramachandran plot showing allowed and disallowed regions.
  • Bond lengths, bond angles, planarity of peptide bonds.
• Packing Quality and Fold Compatibility:
  • Verify3D, ProSA-web (Protein Structure Analysis): These tools assess how well a given amino acid sequence fits into a 3D environment. They assign a score to each residue based on its structural environment and compare it to scores from high-resolution experimental structures.
  • Z-score (e.g., from ProSA-web): Indicates the overall quality of the model compared to native proteins of similar size. A Z-score within the range typical for native proteins suggests a reliable fold.
• Energy-based Scores: Calculate the potential energy of the model using molecular mechanics force fields. Lower energy generally implies a more stable (and hopefully more native-like) structure.
• Qualitative Assessment: Visual inspection using molecular graphics programs (e.g., PyMOL, ChimeraX) to check for obvious flaws like buried hydrophobic residues on the surface, exposed hydrophilic residues in the core, or unnatural packing.

Which of the following plots is commonly used to assess the stereochemical quality of a protein model's backbone?

• Hydropathy Plot
• Ramachandran Plot
• Contact Map
• Distance Matrix

4. Tools and Servers for Homology Modeling

Numerous software packages and web servers are available for homology modeling. Some popular ones include:

• Automated Web Servers:
  • SWISS-MODEL: A fully automated protein structure homology-modeling server. User-friendly and widely used.
  • Phyre2 (Protein Homology/analogY Recognition Engine V 2.0): Uses advanced remote homology detection methods to build 3D models, predict secondary structure, and functional annotations.
  • I-TASSER (Iterative Threading ASSEmbly Refinement): A hierarchical approach that first identifies structural templates from the PDB by LOMETS (a meta-threading approach), then assembles full-length atomic models by iterative template-based fragment assembly simulations. It also provides functional annotations. While powerful, it combines threading and ab initio modeling with homology modeling principles.
• Software Packages (requiring local installation and more user control):
  • MODELLER: A popular program for homology or comparative modeling of protein 3D structures. It implements comparative protein structure modeling by satisfaction of spatial restraints. (You will learn more about MODELLER in subsequent lab sessions C8 and C9).
  • Discovery Studio (BIOVIA), MOE (Chemical Computing Group): Comprehensive commercial modeling suites that include homology modeling capabilities.

Watch this video for an overview of SWISS-MODEL:

(Video: SWISS-MODEL: an automated protein homology modelling server by The SIB Swiss Institute of Bioinformatics)

5. Applications of Homology Modeling

Homology models, despite being theoretical, have numerous practical applications:

• Predicting Protein Function: A model can reveal conserved structural motifs or active site geometries, suggesting function.
• Rational Drug Design: Models are used for docking studies to predict how small molecules (potential drugs) bind to a target protein.
• Understanding Protein-Protein Interactions: Models can help identify potential interaction interfaces.
• Analyzing Effects of Mutations: A model can show how a mutation might affect protein stability, folding, or interaction with other molecules (e.g., in genetic diseases or drug resistance).
• Guiding Experimental Design: Models can help design constructs for protein expression or plan site-directed mutagenesis experiments to test functional hypotheses.

6. Limitations of Homology Modeling

While powerful, homology modeling has limitations:

• Dependency on Template Quality: The accuracy of the model is fundamentally limited by the quality of the template structure(s) and the target-template sequence identity. “Garbage in, garbage out.”
• Low Sequence Identity: Modeling is very difficult if sequence identity to the closest known structure is low (less than 30%). In such cases, the alignment becomes unreliable, and the resulting model may have an incorrect fold.
• Modeling Loops and Insertions/Deletions: These regions often have high variability and are the most challenging parts to model accurately.
• Side-Chain Packing: Predicting the precise conformations of side chains, especially on the surface, can be inaccurate.
• Dynamic Features: Homology modeling typically produces a static model. It does not inherently capture protein dynamics, conformational changes, or the effects of solvent.
• Cannot Predict Novel Folds: By definition, homology modeling relies on known folds. It cannot predict entirely new protein architectures.
• Errors in Template Structure: Any errors or inaccuracies in the experimental template structure will likely be propagated to the model.

Homology modeling can accurately predict completely novel protein folds that have never been seen before.

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7. Conclusion

Protein homology modeling is a valuable and widely used technique in bioinformatics for predicting protein structures. Understanding its principles, workflow, strengths, and limitations is crucial for generating meaningful models and interpreting them correctly. The accuracy of the model is highly dependent on the sequence identity to the template and the quality of the alignment. Careful template selection and rigorous model validation are essential steps in the process.

In the upcoming lab sessions, you will get hands-on experience with homology modeling tools, particularly MODELLER.

Complete the sentence about homology modeling accuracy:

The accuracy of a homology model is highly dependent on the between the target sequence and the template structure, as well as the quality of the .

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Prepare any questions you have for the lab session!