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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.


Ligand homology modeling using ProBiS-ligands web server on the example of butyrylcholinesterase enzyme (PDB code: 4tpk). On the right side of the screen is the list of predicted ligands with their corresponding Z-scores, specificities and PDB codes of protein structures from which they were transposed. The selected ligand’s row is highlighted orange. On the left side is the Jsmol viewer that contains the three-dimensional pose of the selected predicted ligand (galantamine, sticks, violet) and the predicted binding amino-acid residues (sticks, CPK colors, black labels)
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Fig2: Ligand homology modeling using ProBiS-ligands web server on the example of butyrylcholinesterase enzyme (PDB code: 4tpk). On the right side of the screen is the list of predicted ligands with their corresponding Z-scores, specificities and PDB codes of protein structures from which they were transposed. The selected ligand’s row is highlighted orange. On the left side is the Jsmol viewer that contains the three-dimensional pose of the selected predicted ligand (galantamine, sticks, violet) and the predicted binding amino-acid residues (sticks, CPK colors, black labels)

Mentions: Also a recently developed, freely-available web server, which enables ligand homology modeling, is ProBiS-ligands (Fig. 2) [45], a method that uses a fast maximum clique algorithm [21] to screen the non-redundant PDB database, to find structurally similar target binding sites to user provided input query protein, independent of their sequence or global structural similarity. Ligands which are bound in the identified similar binding sites are transposed to the query protein by rotation and translation of their atoms’ coordinates generated by the superimposition matrices acquired from the initial superposition of the query and target proteins. ProBiS-ligands is able to predict protein–protein, protein-small molecules, protein-nucleic acid, and protein-ion interactions. The performance of the web server was assessed with a test set [54] containing 500 proteins models and their corresponding experimental structures. The success of ligand prediction was measured by calculating the correspondence between the predicted ligand binding sites, i.e. query residues <4 Å from the first cluster of predicted small molecules or ions and the actual known binding sites for each of the 500 proteins. This binding site prediction results were evaluated with the Matthews correlation coefficient, precision and recall (for definitions of each see, e.g. [54]). Moreover to assess the similarity of predicted ligands with the actual ones, each highest scoring (by Z-score) predicted ligand from the first small molecule or ion cluster was compared with the actual known ligands using an in-house developed 2D molecular graph matching algorithm. Ligand similarities were expressed as the Tanimoto coefficients and were averaged across all comparisons. ProBiS-ligands showed encouraging results, with the average ligand similarity Tanimoto coefficient of 0.61 (0.55) and MCC of 0.54 (0.41) (the values in parenthesis are for the modeled structure). Even when all templates sharing >10 % sequence identity with the target were removed (to simulate the lack of similar templates that frequently occur in real world simulations), reasonable predictions for the experimental protein structures were still possible with the average ligand similarity of 0.40 and MCC of 0.28. In contrary, for protein models, templates of at least 20-30 % sequence identity were required to obtain similar accuracy.Fig. 2


Modeling enzyme-ligand binding in drug discovery.

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

Ligand homology modeling using ProBiS-ligands web server on the example of butyrylcholinesterase enzyme (PDB code: 4tpk). On the right side of the screen is the list of predicted ligands with their corresponding Z-scores, specificities and PDB codes of protein structures from which they were transposed. The selected ligand’s row is highlighted orange. On the left side is the Jsmol viewer that contains the three-dimensional pose of the selected predicted ligand (galantamine, sticks, violet) and the predicted binding amino-acid residues (sticks, CPK colors, black labels)
© Copyright Policy - OpenAccess
Related In: Results  -  Collection

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

Fig2: Ligand homology modeling using ProBiS-ligands web server on the example of butyrylcholinesterase enzyme (PDB code: 4tpk). On the right side of the screen is the list of predicted ligands with their corresponding Z-scores, specificities and PDB codes of protein structures from which they were transposed. The selected ligand’s row is highlighted orange. On the left side is the Jsmol viewer that contains the three-dimensional pose of the selected predicted ligand (galantamine, sticks, violet) and the predicted binding amino-acid residues (sticks, CPK colors, black labels)
Mentions: Also a recently developed, freely-available web server, which enables ligand homology modeling, is ProBiS-ligands (Fig. 2) [45], a method that uses a fast maximum clique algorithm [21] to screen the non-redundant PDB database, to find structurally similar target binding sites to user provided input query protein, independent of their sequence or global structural similarity. Ligands which are bound in the identified similar binding sites are transposed to the query protein by rotation and translation of their atoms’ coordinates generated by the superimposition matrices acquired from the initial superposition of the query and target proteins. ProBiS-ligands is able to predict protein–protein, protein-small molecules, protein-nucleic acid, and protein-ion interactions. The performance of the web server was assessed with a test set [54] containing 500 proteins models and their corresponding experimental structures. The success of ligand prediction was measured by calculating the correspondence between the predicted ligand binding sites, i.e. query residues <4 Å from the first cluster of predicted small molecules or ions and the actual known binding sites for each of the 500 proteins. This binding site prediction results were evaluated with the Matthews correlation coefficient, precision and recall (for definitions of each see, e.g. [54]). Moreover to assess the similarity of predicted ligands with the actual ones, each highest scoring (by Z-score) predicted ligand from the first small molecule or ion cluster was compared with the actual known ligands using an in-house developed 2D molecular graph matching algorithm. Ligand similarities were expressed as the Tanimoto coefficients and were averaged across all comparisons. ProBiS-ligands showed encouraging results, with the average ligand similarity Tanimoto coefficient of 0.61 (0.55) and MCC of 0.54 (0.41) (the values in parenthesis are for the modeled structure). Even when all templates sharing >10 % sequence identity with the target were removed (to simulate the lack of similar templates that frequently occur in real world simulations), reasonable predictions for the experimental protein structures were still possible with the average ligand similarity of 0.40 and MCC of 0.28. In contrary, for protein models, templates of at least 20-30 % sequence identity were required to obtain similar accuracy.Fig. 2

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.