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Modeling enzyme-ligand binding in drug discovery.

Konc J, Lešnik S, Janežič D - J Cheminform (2015)

Bottom Line: Enzymes are one of the most important groups of drug targets, and identifying possible ligand-enzyme interactions is of major importance in many drug discovery processes.Novel computational methods have been developed that can apply the information from the increasing number of resolved and available ligand-enzyme complexes to model new unknown interactions and therefore contribute to answer open questions in the field of drug discovery like the identification of unknown protein functions, off-target binding, ligand 3D homology modeling and induced-fit simulations.

View Article: PubMed Central - PubMed

Affiliation: National Institute of Chemistry, Hajdrihova 19, SI-1000 Ljubljana, Slovenia ; Faculty of Mathematics, Natural Sciences and Information Technologies, University of Primorska, Glagoljaška 8, SI-6000 Koper, Slovenia.

ABSTRACT

Enzymes are one of the most important groups of drug targets, and identifying possible ligand-enzyme interactions is of major importance in many drug discovery processes. Novel computational methods have been developed that can apply the information from the increasing number of resolved and available ligand-enzyme complexes to model new unknown interactions and therefore contribute to answer open questions in the field of drug discovery like the identification of unknown protein functions, off-target binding, ligand 3D homology modeling and induced-fit simulations.

No MeSH data available.


Related in: MedlinePlus

Flowchart of binding site comparison with subsequent ligand transposition
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Fig1: Flowchart of binding site comparison with subsequent ligand transposition

Mentions: Predicting ligands that bind with sufficient strength to a corresponding protein is a challenging task in biochemistry and has significant implication in the discovery of new drug candidates. Many approaches have been developed for this task; the most commonly used being molecular docking [1]. However one of the main drawbacks of classical template-free docking is that every molecule is docked ab initio, and no information from existing similar protein–ligand complexes is taken into consideration. Therefore alternative approaches that use information from existing protein–ligand complexes, which can be obtained from freely-available databases, such as the Protein Data Bank [2] are becoming increasingly important. The main assumption of such approaches is that similar protein binding sites bind similar ligands, and thus a known ligand from one protein can be transposed to a similar binding site in another protein that was previously not known to bind this ligand. Transposition of ligands is based on accurate alignments of three-dimensional amino-acid patterns or of their corresponding functional groups in the proteins’ binding sites; due to their local nature in the binding sites, such alignments may not be possible with standard sequence or structure alignment approaches. Ligand transposition shown in Fig. 1 can thus be a powerful approach, which can be used in pharmaceutical applications such as drug repositioning [3–6], ligand-homology modeling [7–9], induced-fit simulation [10] and binding site prediction [11–13]. Information about the software described in the following sections is available in Table 1.Fig. 1


Modeling enzyme-ligand binding in drug discovery.

Konc J, Lešnik S, Janežič D - J Cheminform (2015)

Flowchart of binding site comparison with subsequent ligand transposition
© Copyright Policy - OpenAccess
Related In: Results  -  Collection

License 1 - License 2
Show All Figures
getmorefigures.php?uid=PMC4594084&req=5

Fig1: Flowchart of binding site comparison with subsequent ligand transposition
Mentions: Predicting ligands that bind with sufficient strength to a corresponding protein is a challenging task in biochemistry and has significant implication in the discovery of new drug candidates. Many approaches have been developed for this task; the most commonly used being molecular docking [1]. However one of the main drawbacks of classical template-free docking is that every molecule is docked ab initio, and no information from existing similar protein–ligand complexes is taken into consideration. Therefore alternative approaches that use information from existing protein–ligand complexes, which can be obtained from freely-available databases, such as the Protein Data Bank [2] are becoming increasingly important. The main assumption of such approaches is that similar protein binding sites bind similar ligands, and thus a known ligand from one protein can be transposed to a similar binding site in another protein that was previously not known to bind this ligand. Transposition of ligands is based on accurate alignments of three-dimensional amino-acid patterns or of their corresponding functional groups in the proteins’ binding sites; due to their local nature in the binding sites, such alignments may not be possible with standard sequence or structure alignment approaches. Ligand transposition shown in Fig. 1 can thus be a powerful approach, which can be used in pharmaceutical applications such as drug repositioning [3–6], ligand-homology modeling [7–9], induced-fit simulation [10] and binding site prediction [11–13]. Information about the software described in the following sections is available in Table 1.Fig. 1

Bottom Line: Enzymes are one of the most important groups of drug targets, and identifying possible ligand-enzyme interactions is of major importance in many drug discovery processes.Novel computational methods have been developed that can apply the information from the increasing number of resolved and available ligand-enzyme complexes to model new unknown interactions and therefore contribute to answer open questions in the field of drug discovery like the identification of unknown protein functions, off-target binding, ligand 3D homology modeling and induced-fit simulations.

View Article: PubMed Central - PubMed

Affiliation: National Institute of Chemistry, Hajdrihova 19, SI-1000 Ljubljana, Slovenia ; Faculty of Mathematics, Natural Sciences and Information Technologies, University of Primorska, Glagoljaška 8, SI-6000 Koper, Slovenia.

ABSTRACT

Enzymes are one of the most important groups of drug targets, and identifying possible ligand-enzyme interactions is of major importance in many drug discovery processes. Novel computational methods have been developed that can apply the information from the increasing number of resolved and available ligand-enzyme complexes to model new unknown interactions and therefore contribute to answer open questions in the field of drug discovery like the identification of unknown protein functions, off-target binding, ligand 3D homology modeling and induced-fit simulations.

No MeSH data available.


Related in: MedlinePlus