Homology modeling, also known as comparative modeling, is a computational technique used to predict the three-dimensional structure of a protein based on known structures of homologues (templates). It is typically used when an experimental structure is not available, but close homologues are. More recently, deep learning protein prediction methods that utilize similar concepts are used. Homology modeling involves the following steps:
- Template Identification: Identifying one or more known homologous structures (templates) with significant sequence similarity to the desired target.
- Sequence Alignment: Aligning the target sequence with the template sequences.
- Model Building: Constructing a 3D model of the target based on the alignment and the structures of the templates.
- Model Refinement: Refining the model using energy minimization and/or molecular dynamics simulations.
- Model Validation: Assessing the quality of the model by e.g. retrieval rates of known ligands.
Importance in Computational Drug Discovery
- Structure Prediction: Homology modeling provides structural information for targets whose structures are not experimentally determined, aiding in understanding their function and interactions.
- Target Identification: Structural models of targets can be used to identify potential binding sites for drug molecules.
- Ligand Docking: Homology models can be used for molecular docking studies that predict if and how small molecules interact with the target.
- Virtual Screening: Structural models enable structure-based virtual screening of large libraries of compounds to identify potential drug candidates (through docking).
- Rational Drug Design: Binding site structural information from homology models can facilitate the rational design of molecules with improved binding affinity and specificity.
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