Virtual screening
From DrugPedia: A Wikipedia for Drug discovery
| (6 intermediate revisions not shown.) | |||
| Line 1: | Line 1: | ||
| - | Virtual Screening is the process of screen large no. of molecule using [[in-silico]] methods. | + | Virtual Screening is the process of screen large no. of molecule using [[in-silico]] methods. It involves the use of high-performance computing to analyze large databases of chemical compounds in order to identify possible drug candidates, and is a technology that complements current advances in high-throughput chemical synthesis and biological assay. It brings a more focused approach to HTS by using computational analysis to select a subset of compounds considered to be appropriate for a given protein target. Clearly, this strategy implies that some information is available regarding either the nature of the ligand binding site or the type of ligand that is expected to bind productively, or both. Virtual screening encompasses a variety of computational screens, from the simplistic to the sophisticated, and, hence, can usefully exploit different types of information describing the receptor. |
==Methods== | ==Methods== | ||
There are two methods for virtual screening. | There are two methods for virtual screening. | ||
| Line 5: | Line 5: | ||
#Ligand Based Screening | #Ligand Based Screening | ||
==Structure Based Screening== | ==Structure Based Screening== | ||
| + | Structure based screening involves [[docking]] of each compound (either as a rigid or conformationally flexible model) into a model of the receptor/target molecule. Generally the extents of the expected binding site are defined to limit the search. This virtual screening strategy requires a 3-D database of ligands, a 3-D structure of the target receptor (either derived experimentally or from a model built by homology to related protein structures), and a docking code comprising an efficient searching algorithm and an accurate scoring function. | ||
| + | The attraction of explicit receptor–ligand docking is that it represents the most detailed and relevant computational model for identifying a receptor-focused subset. In addition, it is also one of the least-biased approaches. Application of pharmacophore queries may significantly inhibit the diversity of the compound subset because they are biased by the properties of known ligands. In contrast, the molecular docking program can process an entire chemical database with minimal prefiltering (e.g., to eliminate unstable or toxic moieties) so that the final selection is based on the quality of the docked models rather than a subjective opinion of what properties are expected in a ligand. This route is a very promising one to finding structurally novel ligands, which may make receptor interactions similar to known ligands or may achieve different interactions within other binding sites. | ||
| + | |||
| + | The output of a docking-based screen is a set of 3-D models of the predicted binding mode of each compound against the receptor, together with a ranking that is a measure of the quality of fit, if not a prediction of the binding affinity itself. The 3-D models represent the most detailed basis available for determining which molecules are capable of fitting within the very strict structural constraints of the receptor binding site—such a degree of discrimination is not possible using a pharmacophore model, because available searching software cannot handle queries with the number of points needed to adequately represent the complexities of the binding site. The 3-D models are an important basis for scoring the hits, such that a quantitative ranking is achievable using various scoring methods. These methods are typically empirically derived functions for estimation of free energy of binding, usually calibrated against a data set of heterogeneous receptor–ligand complexes, as determined by X-ray crystallography. | ||
==Ligand Based Screening== | ==Ligand Based Screening== | ||
| - | == | + | In this case first we build the 3-D structure of receptor molecule and calculate their [[pharmacophore]] properties like molecular weight, hydrophobicity etc. These properties is then used to search the database of chemical compound to filter such compounds that have comparable properties. |
| + | |||
| + | A pharmacophore is a simplified 3-D description of the key structural features of a set of known ligands or of the target receptor. The structural features are usually described in terms of discrete hydrogen bond donors or acceptors, lipophilic centers, ring centroids, and so on, separated in terms of distances (more usually, distance ranges). Typically these sites are derived from a set of ligands and, hence, represent those features, common to the ligands, that are deemed to be relevant to activity. A pharmacophore is readily used to search a database of chemical structures. These structures need to contain 3-D models, and preferably a conformationally flexible search is necessary so that compounds are not rejected on the trivial basis that an inappropriate conformer is stored in the database. | ||
| + | |||
| + | ==References== | ||
| + | |||
| + | 1) Waszkowycz B, Perkins TDJ, Sykes RA, Li J (2001). "Large-scale virtual screening for discovering leads in the postgenomic era". IBM Systems Journal 40 (2): 360-376. | ||
Current revision
Virtual Screening is the process of screen large no. of molecule using in-silico methods. It involves the use of high-performance computing to analyze large databases of chemical compounds in order to identify possible drug candidates, and is a technology that complements current advances in high-throughput chemical synthesis and biological assay. It brings a more focused approach to HTS by using computational analysis to select a subset of compounds considered to be appropriate for a given protein target. Clearly, this strategy implies that some information is available regarding either the nature of the ligand binding site or the type of ligand that is expected to bind productively, or both. Virtual screening encompasses a variety of computational screens, from the simplistic to the sophisticated, and, hence, can usefully exploit different types of information describing the receptor.
Contents |
[edit] Methods
There are two methods for virtual screening.
- Structure Based Screening
- Ligand Based Screening
[edit] Structure Based Screening
Structure based screening involves docking of each compound (either as a rigid or conformationally flexible model) into a model of the receptor/target molecule. Generally the extents of the expected binding site are defined to limit the search. This virtual screening strategy requires a 3-D database of ligands, a 3-D structure of the target receptor (either derived experimentally or from a model built by homology to related protein structures), and a docking code comprising an efficient searching algorithm and an accurate scoring function.
The attraction of explicit receptor–ligand docking is that it represents the most detailed and relevant computational model for identifying a receptor-focused subset. In addition, it is also one of the least-biased approaches. Application of pharmacophore queries may significantly inhibit the diversity of the compound subset because they are biased by the properties of known ligands. In contrast, the molecular docking program can process an entire chemical database with minimal prefiltering (e.g., to eliminate unstable or toxic moieties) so that the final selection is based on the quality of the docked models rather than a subjective opinion of what properties are expected in a ligand. This route is a very promising one to finding structurally novel ligands, which may make receptor interactions similar to known ligands or may achieve different interactions within other binding sites.
The output of a docking-based screen is a set of 3-D models of the predicted binding mode of each compound against the receptor, together with a ranking that is a measure of the quality of fit, if not a prediction of the binding affinity itself. The 3-D models represent the most detailed basis available for determining which molecules are capable of fitting within the very strict structural constraints of the receptor binding site—such a degree of discrimination is not possible using a pharmacophore model, because available searching software cannot handle queries with the number of points needed to adequately represent the complexities of the binding site. The 3-D models are an important basis for scoring the hits, such that a quantitative ranking is achievable using various scoring methods. These methods are typically empirically derived functions for estimation of free energy of binding, usually calibrated against a data set of heterogeneous receptor–ligand complexes, as determined by X-ray crystallography.
[edit] Ligand Based Screening
In this case first we build the 3-D structure of receptor molecule and calculate their pharmacophore properties like molecular weight, hydrophobicity etc. These properties is then used to search the database of chemical compound to filter such compounds that have comparable properties.
A pharmacophore is a simplified 3-D description of the key structural features of a set of known ligands or of the target receptor. The structural features are usually described in terms of discrete hydrogen bond donors or acceptors, lipophilic centers, ring centroids, and so on, separated in terms of distances (more usually, distance ranges). Typically these sites are derived from a set of ligands and, hence, represent those features, common to the ligands, that are deemed to be relevant to activity. A pharmacophore is readily used to search a database of chemical structures. These structures need to contain 3-D models, and preferably a conformationally flexible search is necessary so that compounds are not rejected on the trivial basis that an inappropriate conformer is stored in the database.
[edit] References
1) Waszkowycz B, Perkins TDJ, Sykes RA, Li J (2001). "Large-scale virtual screening for discovering leads in the postgenomic era". IBM Systems Journal 40 (2): 360-376.
