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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 PP1 selectivity. The 3D structure of CDK2 (PDB 2A4L, wire rendering with grey carbons) was superposed onto the structure of HCK (hidden) in complex with PP1 (PDB 1QCF, ball and stick rendering with green carbons). PP1 makes a number of clashes (orange lines) with Phe 80 (ball and stick rendering with grey carbons) of CDK2 whereas Thr 338 (stick rendering with green carbons) of HCK accommodates the compound. By mutating the threonine to a bulkier residue, Liu et el demonstrated that this position was an important specificity determinant for PP1[32]. We therefore conclude that S-Filter correctly predicted Thr 338 as a specificity determinant.
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Figure 7: Structural basis for PP1 selectivity. The 3D structure of CDK2 (PDB 2A4L, wire rendering with grey carbons) was superposed onto the structure of HCK (hidden) in complex with PP1 (PDB 1QCF, ball and stick rendering with green carbons). PP1 makes a number of clashes (orange lines) with Phe 80 (ball and stick rendering with grey carbons) of CDK2 whereas Thr 338 (stick rendering with green carbons) of HCK accommodates the compound. By mutating the threonine to a bulkier residue, Liu et el demonstrated that this position was an important specificity determinant for PP1[32]. We therefore conclude that S-Filter correctly predicted Thr 338 as a specificity determinant.

Mentions: PP1 is selectively inhibits SRC, LCK, EGFR, and ABL in our kinase panel (Figure 2). The three-dimensional structure of PP1 was solved in complex with HCK. We refer to HCK numbering in the text below. S-Filter predicts Thr 338 and Gly 344 as specificity determinants. Schindler et al describe Thr 338 as a specificity-determining residue for PP1 [20]. Liu et el demonstrated that a single residue difference at position 338 could account for the differences in potency observed between SRC and v-SRC [32]. By mutating Ile 338 to different residue types, including threonine, they demonstrated that the presence of a small residue was necessary for potent inhibition by PP1. Liu et al concluded that kinases with a bulkier residue at position 338 will clash with PP1 (Figure 7). These experimental observations support the prediction of Thr 338 as a specificity determinant and we 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 PP1 selectivity. The 3D structure of CDK2 (PDB 2A4L, wire rendering with grey carbons) was superposed onto the structure of HCK (hidden) in complex with PP1 (PDB 1QCF, ball and stick rendering with green carbons). PP1 makes a number of clashes (orange lines) with Phe 80 (ball and stick rendering with grey carbons) of CDK2 whereas Thr 338 (stick rendering with green carbons) of HCK accommodates the compound. By mutating the threonine to a bulkier residue, Liu et el demonstrated that this position was an important specificity determinant for PP1[32]. We therefore conclude that S-Filter correctly predicted Thr 338 as a specificity determinant.
© Copyright Policy - open-access
Related In: Results  -  Collection

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

Figure 7: Structural basis for PP1 selectivity. The 3D structure of CDK2 (PDB 2A4L, wire rendering with grey carbons) was superposed onto the structure of HCK (hidden) in complex with PP1 (PDB 1QCF, ball and stick rendering with green carbons). PP1 makes a number of clashes (orange lines) with Phe 80 (ball and stick rendering with grey carbons) of CDK2 whereas Thr 338 (stick rendering with green carbons) of HCK accommodates the compound. By mutating the threonine to a bulkier residue, Liu et el demonstrated that this position was an important specificity determinant for PP1[32]. We therefore conclude that S-Filter correctly predicted Thr 338 as a specificity determinant.
Mentions: PP1 is selectively inhibits SRC, LCK, EGFR, and ABL in our kinase panel (Figure 2). The three-dimensional structure of PP1 was solved in complex with HCK. We refer to HCK numbering in the text below. S-Filter predicts Thr 338 and Gly 344 as specificity determinants. Schindler et al describe Thr 338 as a specificity-determining residue for PP1 [20]. Liu et el demonstrated that a single residue difference at position 338 could account for the differences in potency observed between SRC and v-SRC [32]. By mutating Ile 338 to different residue types, including threonine, they demonstrated that the presence of a small residue was necessary for potent inhibition by PP1. Liu et al concluded that kinases with a bulkier residue at position 338 will clash with PP1 (Figure 7). These experimental observations support the prediction of Thr 338 as a specificity determinant and we 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