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

Chemical and Structural Strategies to Selectively Target mTOR Kinase

Dysregulation of the mechanistic target of rapamycin (mTOR) pathway is implicated in cancer and neurological disorder, which identifies mTOR inhibition as promising strategy for the treatment of a variety of human disorders. First-generation mTOR inhibitors include rapamycin and its analogues (rapalogs) which act as allosteric inhibitors of TORC1. Structurally unrelated, ATP-competitive inhibitors that directly target the mTOR catalytic site inhibit both TORC1 and TORC2. Here, we review investigations of chemical scaffolds explored for the development of highly selective ATP-competitive mTOR kinase inhibitors (TORKi). Extensive medicinal chemistry campaigns allowed to overcome challenges related to structural similarity between mTOR and the phosphoinositide 3-kinase (PI3K) family. A broad region of chemical space is covered by TORKi. Here, the investigation of chemical substitutions and physicochemical properties has shed light on the compounds' ability to cross the blood brain barrier (BBB). This work provides insights supporting the optimization of TORKi for the treatment of cancer and central nervous system disorders.

Predicting the Specificity- Determining Positions of Receptor Tyrosine Kinase Axl

Owing to its clinical significance, modulation of functionally relevant amino acids in protein-protein complexes has attracted a great deal of attention. To this end, many approaches have been proposed to predict the partner-selecting amino acid positions in evolutionarily close complexes. These approaches can be grouped into sequence-based machine learning and structure-based energy-driven methods. In this work, we assessed these methods’ ability to map the specificity-determining positions of Axl, a receptor tyrosine kinase involved in cancer progression and immune system diseases. For sequence-based predictions, we used SDPpred, Multi-RELIEF, and Sequence Harmony. For structure-based predictions, we utilized HADDOCK refinement and molecular dynamics simulations. As a result, we observed that (i) sequence-based methods overpredict partner-selecting residues of Axl and that (ii) combining Multi-RELIEF with HADDOCK-based predictions provides the key Axl residues, covered by the extensive molecular dynamics simulations. Expanding on these results, we propose that a sequence-structure-based approach is necessary to determine specificity-determining positions of Axl, which can guide the development of therapeutic molecules to combat Axl misregulation.