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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 GW-572016. The 3D structure of p38 (PDB 1A9U, wire rendering with grey carbons) was superposed onto the structure of EGFR (hidden) in complex with GW-572016 (PDB 1XKK, ball and stick rendering with green carbons). GW-572016 appears to clash (orange lines) with Ile 84 (ball and stick rendering with grey carbons) of p38. In contrast, Cys 775 (stick rendering with green carbons) of EGFR accommodates the compound. Importantly, Ile 84 is buried deep in the protein where there is little room to maneuver and clearly protrudes the pocket (mustard) of EGFR. Based on this putative steric clash, we propose that S-Filter correctly predicted Cys 775 as specificity determinant for GW-572016.
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Figure 9: Structural basis for GW-572016. The 3D structure of p38 (PDB 1A9U, wire rendering with grey carbons) was superposed onto the structure of EGFR (hidden) in complex with GW-572016 (PDB 1XKK, ball and stick rendering with green carbons). GW-572016 appears to clash (orange lines) with Ile 84 (ball and stick rendering with grey carbons) of p38. In contrast, Cys 775 (stick rendering with green carbons) of EGFR accommodates the compound. Importantly, Ile 84 is buried deep in the protein where there is little room to maneuver and clearly protrudes the pocket (mustard) of EGFR. Based on this putative steric clash, we propose that S-Filter correctly predicted Cys 775 as specificity determinant for GW-572016.

Mentions: GW-572016 selectively inhibits EGFR in our kinase panel (Figure 2). S-Filter predicts Cys 775, as a specificity determinant for GW-572016. Wood et al do not explicitly describe specificity determinants for GW-572016[23]. To the best of our knowledge, this is the first time that Cys 775 has been proposed as a selectivity-determining residue. When p38 is superposed onto EGFR, GW-572016 appears to clash with Ile 84 of p38 (Figure 9). This putative steric clash suggests that many of the kinases in our panel with a bulkier residue in place of Cys 775 will not accommodate GW-572016. In contrast, cysteine has a non-branched side chain that presumably allows it to rotate and accommodate GW-572016. Based on this putative steric clash, we propose that S-Filter correctly predicted Cys 775 and we self-designate the prediction as a true positive in Table 1


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

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

Structural basis for GW-572016. The 3D structure of p38 (PDB 1A9U, wire rendering with grey carbons) was superposed onto the structure of EGFR (hidden) in complex with GW-572016 (PDB 1XKK, ball and stick rendering with green carbons). GW-572016 appears to clash (orange lines) with Ile 84 (ball and stick rendering with grey carbons) of p38. In contrast, Cys 775 (stick rendering with green carbons) of EGFR accommodates the compound. Importantly, Ile 84 is buried deep in the protein where there is little room to maneuver and clearly protrudes the pocket (mustard) of EGFR. Based on this putative steric clash, we propose that S-Filter correctly predicted Cys 775 as specificity determinant for GW-572016.
© Copyright Policy - open-access
Related In: Results  -  Collection

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

Figure 9: Structural basis for GW-572016. The 3D structure of p38 (PDB 1A9U, wire rendering with grey carbons) was superposed onto the structure of EGFR (hidden) in complex with GW-572016 (PDB 1XKK, ball and stick rendering with green carbons). GW-572016 appears to clash (orange lines) with Ile 84 (ball and stick rendering with grey carbons) of p38. In contrast, Cys 775 (stick rendering with green carbons) of EGFR accommodates the compound. Importantly, Ile 84 is buried deep in the protein where there is little room to maneuver and clearly protrudes the pocket (mustard) of EGFR. Based on this putative steric clash, we propose that S-Filter correctly predicted Cys 775 as specificity determinant for GW-572016.
Mentions: GW-572016 selectively inhibits EGFR in our kinase panel (Figure 2). S-Filter predicts Cys 775, as a specificity determinant for GW-572016. Wood et al do not explicitly describe specificity determinants for GW-572016[23]. To the best of our knowledge, this is the first time that Cys 775 has been proposed as a selectivity-determining residue. When p38 is superposed onto EGFR, GW-572016 appears to clash with Ile 84 of p38 (Figure 9). This putative steric clash suggests that many of the kinases in our panel with a bulkier residue in place of Cys 775 will not accommodate GW-572016. In contrast, cysteine has a non-branched side chain that presumably allows it to rotate and accommodate GW-572016. Based on this putative steric clash, we propose that S-Filter correctly predicted Cys 775 and we self-designate the prediction as a true positive 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