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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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Structural basis for Fasudil selectivity. The 3D structure of PKA (PDB 1Q8T, wire rendering with grey carbons) superposed onto the structure of Rock1 (hidden) in complex with Fasudil (PDB 2ESM, ball and stick rendering with green carbons). Ala 215 (Stick rendering with green carbons) of Rock1 is smaller than Thr 183 (ball and stick rendering with grey carbons) of PKA which protrudes the pocket of ROCK (mustard surface). By mutating Thr 183 in PKA to the corresponding position in Rock (Ala 215), Bonn et al demonstrated that Fasudil inhibited the mutant PKA at levels similar to ROCK [26]. The smaller residue was proposed by Bonn et al to allow Fasudil deeper access to the active site. Based on the above observations, we conclude that S-Filter correctly predicted Ala 215 as a specificity determinant.
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Figure 10: Structural basis for Fasudil selectivity. The 3D structure of PKA (PDB 1Q8T, wire rendering with grey carbons) superposed onto the structure of Rock1 (hidden) in complex with Fasudil (PDB 2ESM, ball and stick rendering with green carbons). Ala 215 (Stick rendering with green carbons) of Rock1 is smaller than Thr 183 (ball and stick rendering with grey carbons) of PKA which protrudes the pocket of ROCK (mustard surface). By mutating Thr 183 in PKA to the corresponding position in Rock (Ala 215), Bonn et al demonstrated that Fasudil inhibited the mutant PKA at levels similar to ROCK [26]. The smaller residue was proposed by Bonn et al to allow Fasudil deeper access to the active site. Based on the above observations, we conclude that S-Filter correctly predicted Ala 215 as a specificity determinant.

Mentions: Fasudil selectively inhibits ROCK1 in our kinase panel (Figure 2). Structural and mutational studies implicate Ala 215 along with Ile 82 as selectivity determinants [24,26]. Bonn et al demonstrated that Fasudil is not a potent inhibitor of PKA. By mutating Thr 183 in PKA to the corresponding position in Rock (Ala 215), they demonstrated that Fasudil inhibited the mutant PKA at levels similar to ROCK. Similarly, by mutating Leu 49 in PKA to the corresponding residue in ROCK (Ile 82), they observed inhibition levels similar to wild type ROCK. Figure 10 suggests a slightly bulkier threonine in place of Ala 215 could prevent Fasudil from binding deep in the active site. Based on these experimental observations, we designate Ala 215 as a true positive in Table 1. S-Filter failed to predict Ile 82 and we designate it as a false negative in Table 1. S-Filter also predicted Val 137 and Met 153 as specificity determinants for Fasudil. We designate both of these predictions as false positives. In summary, the above observations suggest that one of the three predictions are correct.


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

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

Structural basis for Fasudil selectivity. The 3D structure of PKA (PDB 1Q8T, wire rendering with grey carbons) superposed onto the structure of Rock1 (hidden) in complex with Fasudil (PDB 2ESM, ball and stick rendering with green carbons). Ala 215 (Stick rendering with green carbons) of Rock1 is smaller than Thr 183 (ball and stick rendering with grey carbons) of PKA which protrudes the pocket of ROCK (mustard surface). By mutating Thr 183 in PKA to the corresponding position in Rock (Ala 215), Bonn et al demonstrated that Fasudil inhibited the mutant PKA at levels similar to ROCK [26]. The smaller residue was proposed by Bonn et al to allow Fasudil deeper access to the active site. Based on the above observations, we conclude that S-Filter correctly predicted Ala 215 as a specificity determinant.
© Copyright Policy - open-access
Related In: Results  -  Collection

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

Figure 10: Structural basis for Fasudil selectivity. The 3D structure of PKA (PDB 1Q8T, wire rendering with grey carbons) superposed onto the structure of Rock1 (hidden) in complex with Fasudil (PDB 2ESM, ball and stick rendering with green carbons). Ala 215 (Stick rendering with green carbons) of Rock1 is smaller than Thr 183 (ball and stick rendering with grey carbons) of PKA which protrudes the pocket of ROCK (mustard surface). By mutating Thr 183 in PKA to the corresponding position in Rock (Ala 215), Bonn et al demonstrated that Fasudil inhibited the mutant PKA at levels similar to ROCK [26]. The smaller residue was proposed by Bonn et al to allow Fasudil deeper access to the active site. Based on the above observations, we conclude that S-Filter correctly predicted Ala 215 as a specificity determinant.
Mentions: Fasudil selectively inhibits ROCK1 in our kinase panel (Figure 2). Structural and mutational studies implicate Ala 215 along with Ile 82 as selectivity determinants [24,26]. Bonn et al demonstrated that Fasudil is not a potent inhibitor of PKA. By mutating Thr 183 in PKA to the corresponding position in Rock (Ala 215), they demonstrated that Fasudil inhibited the mutant PKA at levels similar to ROCK. Similarly, by mutating Leu 49 in PKA to the corresponding residue in ROCK (Ile 82), they observed inhibition levels similar to wild type ROCK. Figure 10 suggests a slightly bulkier threonine in place of Ala 215 could prevent Fasudil from binding deep in the active site. Based on these experimental observations, we designate Ala 215 as a true positive in Table 1. S-Filter failed to predict Ile 82 and we designate it as a false negative in Table 1. S-Filter also predicted Val 137 and Met 153 as specificity determinants for Fasudil. We designate both of these predictions as false positives. In summary, the above observations suggest that one of the three predictions are correct.

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