RESUMEN
Protein-protein interactions (PPIs) play a pivotal role in the regulation of many physiological processes. The dysfunction of some PPIs interactions led to the alteration of different biological pathways causing various diseases including cancer. In this context, the inhibition of PPIs represents an attractive strategy for the design of new antitumoral agents. In recent years, computational approaches were successfully used to study the interactions between proteins, providing useful hints for the design of small molecules able to modulate PPIs. Targeting PPIs presents several challenges mainly due to the large and flat binding surface that lack the typical binding pockets of traditional drug targets. Despite these hurdles, substantial progress has been made in the last decade resulting in the identification of PPI modulators where some of them even found clinical use. This study focuses on MUC1-CIN85 PPI which is involved in the migration and invasion of cancer cells. Particularly, we investigated the presence of druggable binding sites on the CIN85 surface which provided new insights for the structure-based design of novel MUC1-CIN85 PPI inhibitors as anti-metastatic agents.
Asunto(s)
Proteínas Adaptadoras Transductoras de Señales/genética , Mucina-1/genética , Neoplasias/genética , Mapas de Interacción de Proteínas/genética , Sitios de Unión/genética , Movimiento Celular/genética , Proliferación Celular/genética , Simulación por Computador , Humanos , Invasividad Neoplásica/genética , Invasividad Neoplásica/patología , Neoplasias/tratamiento farmacológico , Neoplasias/patología , Unión Proteica/genética , Dominios Homologos src/genéticaRESUMEN
The modulation of protein-protein interactions (PPIs) by small molecules represents a valuable strategy for pharmacological intervention in several human diseases. In this context, computer-aided drug discovery techniques offer useful resources to predict the network of interactions governing the recognition process between protein partners, thus furnishing relevant information for the design of novel PPI modulators. In this work, we focused our attention on the MUC1-CIN85 complex as a crucial PPI controlling cancer progression and metastasis. MUC1 is a transmembrane glycoprotein whose extracellular domain contains a variable number of tandem repeats (VNTRs) regions that are highly glycosylated in normal cells and under-glycosylated in cancer. The hypo-glycosylation fosters the exposure of the backbone to new interactions with other proteins, such as CIN85, that alter the intracellular signalling in tumour cells. Herein, different computational approaches were combined to investigate the molecular recognition pattern of MUC1-CIN85 PPI thus unveiling new structural information useful for the design of MUC1-CIN85 PPI inhibitors as potential anti-metastatic agents.