Structure-guided selection of specificity determining positions in the human Kinome

Area of Science:

• Biochemistry
• Structural Biology
• Bioinformatics

Background:

• The human kinome comprises numerous critical drug targets, with protein kinase inhibitors exhibiting diverse selectivity profiles.
• Understanding the complex relationship between protein structural variations and binding specificity is crucial for drug discovery.
• Advancements in protein 3D structure availability offer insights into variations within protein families.

Purpose of the Study:

• To develop a structural bioinformatics approach for analyzing key determinants of binding selectivity.
• To enhance the rational design of drugs with specific selectivity profiles.
• To provide experimentalists with insights into inhibitor selectivity mechanisms and aid lead optimization.

Main Methods:

• A greedy algorithm was developed to identify residue positions in multiple sequence alignments.
• The algorithm analyzes structural and chemical variations at these positions to explain known binding affinities.
• The approach was validated using an extensive dataset from the human kinome.

Main Results:

• The algorithm accurately explains the binding affinity of 38 different kinase inhibitors.
• High precision and accuracy were achieved by analyzing variations in a small subset of residue positions (at most six).
• Functionally important residues influencing inhibitor binding were identified for several inhibitors.

Conclusions:

• The developed bioinformatics approach effectively identifies key determinants of drug binding selectivity.
• This tool aids in the rational design of targeted kinase inhibitors.
• The method can predict binding affinities for unknown protein-inhibitor interactions.