Protein characteristics substantially influence the propensity of activity cliffs among kinase inhibitors

Abstract

Activity cliffs (ACs) are pairs of structurally similar molecules with significantly different affinities for a biotarget, posing a challenge in computer-assisted drug discovery. This study focuses on protein kinases, significant therapeutic targets, with some exhibiting ACs while others do not despite numerous inhibitors. The hypothesis that the presence of ACs is dependent on the target protein and its complete structural context is explored. Machine learning models were developed to link protein properties to ACs, revealing specific tripeptide sequences and overall protein properties as critical factors in ACs occurrence. The study highlights the importance of considering the entire protein matrix rather than just the binding site in understanding ACs. This research provides valuable insights for drug discovery and design, paving the way for addressing ACs-related challenges in modern computational approaches.

Figure 1

Counts of protein kinases in different classes as function to the count of reported MMPs in ChEMBL database.

Figure 2

Three-dimensional plot of the top three principal components calculated based on Protr 9920 descriptors for the collected modelled list of protein kinases (red filled circles) compared to 509 known protein kinases (black filled circles).

Figure 3

Crystallographic structures of KDR co-crystallized with AC pair (PDB codes: 3CP9 and 3CPC). (A) Superimposi tion of complexed ligands C19 (purple) and C52 (cyan) within KDR showing binding interactions anchoring the bound ligands, H-bonds are shown as green dotted lines, hydrophobic and π-stacking interactions are shown as pink and light pink dotted lines. (B) Water-accessible surface (Brown, Connelly’s Surface) covering KDR protein complexed with superimposed AC pair. (C, D) Chemical structure of C19 and C52.