Back

The disordered JM motif in RTKs promotes classical DFGout conformation formation via dynamic effect

Chen, X.; Wang, H.; Li, W.; Zhang, M.; Sun, B.

2025-10-28 biophysics
10.1101/2025.10.28.684995 bioRxiv
Show abstract

Receptor tyrosine kinases (RTKs) are validated anti-cancer targets, and targeting their DFGout conformations represents a mainstream strategy for developing highly selective type II inhibitors. RTKs can adopt various DFGout conformations, but only the classical ones with a fully formed back pocket are structurally validated to accommodate type II inhibitors. However, such classical DFGout conformations are scarce, presenting a significant obstacle for selective RTK inhibitor design. Recently, a conserved disordered motif N-terminal to the kinase domain of RTKs, called the juxtamembrane (JM) motif, has been reported to regulate inhibitor binding to the DFGout conformation of VEGFR2, an RTK involved in angiogenesis. In this study, we performed extensive MD simulations to explore the impact of the disordered JM motif on the conformational space of the DFG motif in RTKs and its relationship to inhibitor binding at the active site of VEGFR2. We revealed that in VEGFR2, the disordered JM is highly dynamic and forms transient contacts with the kinase domain, and consequently fine-tunes the DFGout sub-conformational space to shift populations from non-classical to classical DFGout conformations. This dynamic model provides the structural basis underpinning the reported regulatory effects of JM on inhibitor binding toward VEGFR2. Addtionally, we demonstrated that in other RTKs beyond VEGFR2, the disordered JM similarly promotes classical DFGout conformations. Such role of JM is particularly favorable for creating druggable DFGout conformations that can be exploited for designing high-selectivity type II inhibitors.

Matching journals

The top 6 journals account for 50% of the predicted probability mass.

50% of probability mass above

"Similar papers" are the closest papers from that journal in the model's embedding space. They show what the match is built on, but the ranking comes mostly from a classifier over the whole training set, not from these examples alone.