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In this work, we describe how the Erbin PDZ domain interacts with Smad3, a transductor of the Transforming Growth Factor-beta (TGFbeta) pathway, via its MH2 domain. This interaction was described as important for TGFbeta signaling as it could potentially repress the transcriptional activity of the growth factor. In order to clarify our preliminary experimental observations pointing this interaction, we built a 3D model of the Erbin PDZ/Smad3 MH2 complex and checked its stability using molecular dynamics simulations. This model pointed out charged residues in Smad3 and Erbin which could be important for the interaction. By introducing point mutations of these residues within the proposed binding domains, we experimentally confirmed that arginine 279, glutamic acid 246 in Smad3 and glutamic acid 1321 in Erbin are important for the binding. These data suggest a possible novel interface of binding in the Erbin PDZ domain and reveal an unconventional mode of interaction for a PDZ domain and its ligand.

The receptor tyrosine kinase Met is crucial for the genetic program causing cancer progression and metastasis. Its nodal function during aggressiveness and resistance acquisition poses Met inhibition as an obligatory step in anti-cancer targeted therapy. Here, we applied a "Met-focussed" forward chemical biological screen to discover new agents antagonizing Met-triggered biological functions. The identified new scaffold, JLK1360, has a dual mechanism of action towards Met: it impairs Met signalling and also prevents its restoration after degradation. Docking and molecular dynamics provide evidences on the interacting mode of JLK1360 within the Met ATP-binding pocket. Moreover, computational and biochemical studies also highlighted that JLK1360 has a good degree of selectivity towards Met than other RTKs tested. Altogether, these findings demonstrate that the approach we have applied is a powerful strategy to identify compounds with combined properties towards a chosen target. Our studies show how integration of chemistry, biology and computational analysis can provide robust strategies to identify new inhibitory scaffolds suitable for further development of anti-cancer targeted therapies.