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Prediction of specificity-determining residues for small-molecule kinase inhibitors.

Caffrey DR, Lunney EA, Moshinsky DJ - BMC Bioinformatics (2008)

Bottom Line: S-Filter correctly predicts specificity determinants that were described by independent groups.S-Filter also predicts a number of novel specificity determinants that can often be justified by further structural comparison.The method identifies potential specificity determinants that are not readily apparent, and provokes further investigation at the structural level.

View Article: PubMed Central - HTML - PubMed

Affiliation: Pfizer Research Technology Center, 620 Memorial Drive, Cambridge, MA 02139, USA. daniel.caffrey@gmail.com

ABSTRACT

Background: Designing small-molecule kinase inhibitors with desirable selectivity profiles is a major challenge in drug discovery. A high-throughput screen for inhibitors of a given kinase will typically yield many compounds that inhibit more than one kinase. A series of chemical modifications are usually required before a compound exhibits an acceptable selectivity profile. Rationalizing the selectivity profile for a small-molecule inhibitor in terms of the specificity-determining kinase residues for that molecule can be an important step toward the goal of developing selective kinase inhibitors.

Results: Here we describe S-Filter, a method that combines sequence and structural information to predict specificity-determining residues for a small molecule and its kinase selectivity profile. Analysis was performed on seven selective kinase inhibitors where a structural basis for selectivity is known. S-Filter correctly predicts specificity determinants that were described by independent groups. S-Filter also predicts a number of novel specificity determinants that can often be justified by further structural comparison.

Conclusion: S-Filter is a valuable tool for analyzing kinase selectivity profiles. The method identifies potential specificity determinants that are not readily apparent, and provokes further investigation at the structural level.

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Heatmap for the kinase selectivity profile. Mean percent inhibition data for all compounds (1 μM) run in duplicate, are displayed as false colors according to the legend.
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Figure 2: Heatmap for the kinase selectivity profile. Mean percent inhibition data for all compounds (1 μM) run in duplicate, are displayed as false colors according to the legend.

Mentions: Seven kinase inhibitors (Figure 1) were tested in our kinase panel (Figure 2). The raw data for the kinase assays are provided in Additional file 1 and are also available at . The seven compounds were chosen because they have been determined to be selective in other kinase panels [12,16-18] and their three-dimensional structures have been solved in complex with a relevant kinase [11,19-24]. To ensure each compound belongs to a distinct chemical series, we require all compounds to have pair-wise Daylight® fingerprint [25] Tanimoto scores less than 0.5. For the purpose of validating the method, it is desirable to select compounds where the selectivity determinants are described by independent research groups. Selectivity determinants were previously described for four of the seven compounds [11,20,22,24,26]. The PDB codes are listed in Table 1.


Prediction of specificity-determining residues for small-molecule kinase inhibitors.

Caffrey DR, Lunney EA, Moshinsky DJ - BMC Bioinformatics (2008)

Heatmap for the kinase selectivity profile. Mean percent inhibition data for all compounds (1 μM) run in duplicate, are displayed as false colors according to the legend.
© Copyright Policy - open-access
Related In: Results  -  Collection

License
Show All Figures
getmorefigures.php?uid=PMC2655090&req=5

Figure 2: Heatmap for the kinase selectivity profile. Mean percent inhibition data for all compounds (1 μM) run in duplicate, are displayed as false colors according to the legend.
Mentions: Seven kinase inhibitors (Figure 1) were tested in our kinase panel (Figure 2). The raw data for the kinase assays are provided in Additional file 1 and are also available at . The seven compounds were chosen because they have been determined to be selective in other kinase panels [12,16-18] and their three-dimensional structures have been solved in complex with a relevant kinase [11,19-24]. To ensure each compound belongs to a distinct chemical series, we require all compounds to have pair-wise Daylight® fingerprint [25] Tanimoto scores less than 0.5. For the purpose of validating the method, it is desirable to select compounds where the selectivity determinants are described by independent research groups. Selectivity determinants were previously described for four of the seven compounds [11,20,22,24,26]. The PDB codes are listed in Table 1.

Bottom Line: S-Filter correctly predicts specificity determinants that were described by independent groups.S-Filter also predicts a number of novel specificity determinants that can often be justified by further structural comparison.The method identifies potential specificity determinants that are not readily apparent, and provokes further investigation at the structural level.

View Article: PubMed Central - HTML - PubMed

Affiliation: Pfizer Research Technology Center, 620 Memorial Drive, Cambridge, MA 02139, USA. daniel.caffrey@gmail.com

ABSTRACT

Background: Designing small-molecule kinase inhibitors with desirable selectivity profiles is a major challenge in drug discovery. A high-throughput screen for inhibitors of a given kinase will typically yield many compounds that inhibit more than one kinase. A series of chemical modifications are usually required before a compound exhibits an acceptable selectivity profile. Rationalizing the selectivity profile for a small-molecule inhibitor in terms of the specificity-determining kinase residues for that molecule can be an important step toward the goal of developing selective kinase inhibitors.

Results: Here we describe S-Filter, a method that combines sequence and structural information to predict specificity-determining residues for a small molecule and its kinase selectivity profile. Analysis was performed on seven selective kinase inhibitors where a structural basis for selectivity is known. S-Filter correctly predicts specificity determinants that were described by independent groups. S-Filter also predicts a number of novel specificity determinants that can often be justified by further structural comparison.

Conclusion: S-Filter is a valuable tool for analyzing kinase selectivity profiles. The method identifies potential specificity determinants that are not readily apparent, and provokes further investigation at the structural level.

Show MeSH