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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 OSI-774 selectivity. The 3D structure of CHK1 (PDB 2BRH, wire rendering with grey carbons) was superposed onto the structure of EGFR (hidden) in complex with OSI-774 (PDB 1M17, ball and stick rendering with green carbons). OSI-774 appears to clash (orange lines) with Leu 84 (ball and stick rendering with grey carbons) of CHK1, whereas Thr 766 (stick rendering with green carbons) of EGFR accommodates the compound. Based on these putative steric clashes, we propose that S-Filter correctly predicted Thr 766 as a specificity determinant.
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Figure 8: Structural basis for OSI-774 selectivity. The 3D structure of CHK1 (PDB 2BRH, wire rendering with grey carbons) was superposed onto the structure of EGFR (hidden) in complex with OSI-774 (PDB 1M17, ball and stick rendering with green carbons). OSI-774 appears to clash (orange lines) with Leu 84 (ball and stick rendering with grey carbons) of CHK1, whereas Thr 766 (stick rendering with green carbons) of EGFR accommodates the compound. Based on these putative steric clashes, we propose that S-Filter correctly predicted Thr 766 as a specificity determinant.

Mentions: Although OSI-774 does not access the so called specificity pocket that lies beyond Thr 766, the presence of a bulkier residue may prevent inhibition. For example, when CHK1 is superposed onto EGFR, OSI-774 appears to clash with Leu 84 of CHK1 (Figure 8). This putative steric clash suggests that a bulkier residue in place of Thr 766 will not accommodate OSI-774. Based on this putative steric clash, we propose that S-Filter correctly predicted Thr 766 and we self-designate the prediction as a true positive in Table 1. As Cys 773 is at the opening of the binding site, it is not clear how it could be specificity-determining, and it is designated as a false positive. This false positive highlights the shortcomings of S-Filter and the need to carefully assess all predictions before proceeding. In summary, the above observations suggest that one of the two predictions is correct.


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

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

Structural basis for OSI-774 selectivity. The 3D structure of CHK1 (PDB 2BRH, wire rendering with grey carbons) was superposed onto the structure of EGFR (hidden) in complex with OSI-774 (PDB 1M17, ball and stick rendering with green carbons). OSI-774 appears to clash (orange lines) with Leu 84 (ball and stick rendering with grey carbons) of CHK1, whereas Thr 766 (stick rendering with green carbons) of EGFR accommodates the compound. Based on these putative steric clashes, we propose that S-Filter correctly predicted Thr 766 as a specificity determinant.
© Copyright Policy - open-access
Related In: Results  -  Collection

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

Figure 8: Structural basis for OSI-774 selectivity. The 3D structure of CHK1 (PDB 2BRH, wire rendering with grey carbons) was superposed onto the structure of EGFR (hidden) in complex with OSI-774 (PDB 1M17, ball and stick rendering with green carbons). OSI-774 appears to clash (orange lines) with Leu 84 (ball and stick rendering with grey carbons) of CHK1, whereas Thr 766 (stick rendering with green carbons) of EGFR accommodates the compound. Based on these putative steric clashes, we propose that S-Filter correctly predicted Thr 766 as a specificity determinant.
Mentions: Although OSI-774 does not access the so called specificity pocket that lies beyond Thr 766, the presence of a bulkier residue may prevent inhibition. For example, when CHK1 is superposed onto EGFR, OSI-774 appears to clash with Leu 84 of CHK1 (Figure 8). This putative steric clash suggests that a bulkier residue in place of Thr 766 will not accommodate OSI-774. Based on this putative steric clash, we propose that S-Filter correctly predicted Thr 766 and we self-designate the prediction as a true positive in Table 1. As Cys 773 is at the opening of the binding site, it is not clear how it could be specificity-determining, and it is designated as a false positive. This false positive highlights the shortcomings of S-Filter and the need to carefully assess all predictions before proceeding. In summary, the above observations suggest that one of the two predictions is 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