In Silico Identification of Potential Druggable Binding Sites on CIN85 SH3 Domain

Discovery of a septin-4 covalent binder with antimetastatic activity in a mouse model of melanoma

Cancer spreading through metastatic processes is one of the major causes of tumour-related mortality. Metastasis is a complex phenomenon which involves multiple pathways ranging from cell metabolic alterations to changes in the biophysical phenotype of cells and tissues. In the search for new effective anti-metastatic agents, we modulated the chemical structure of the lead compound AA6, in order to find the structural determinants of activity, and to identify the cellular target responsible of the downstream anti-metastatic effects observed. New compounds synthesized were able to inhibit in vitro B16-F10 melanoma cell invasiveness, and one selected compound, CM365, showed in vivo anti-metastatic effects in a lung metastasis mouse model of melanoma. Septin-4 was identified as the most likely molecular target responsible for these effects. This study showed that CM365 is a promising molecule for metastasis prevention, remarkably effective alone or co-administered with drugs normally used in cancer therapy, such as paclitaxel.

An Evolutionary Conservation and Druggability Analysis of Enzymes Belonging to the Bacterial Shikimate Pathway

Enzymes belonging to the shikimate pathway have long been considered promising targets for antibacterial drugs because they have no counterpart in mammals and are essential for bacterial growth and virulence. However, despite decades of research, there are currently no clinically relevant antibacterial drugs targeting any of these enzymes, and there are legitimate concerns about whether they are sufficiently druggable, i.e., whether they can be adequately modulated by small and potent drug-like molecules. In the present work, in silico analyses combining evolutionary conservation and druggability are performed to determine whether these enzymes are candidates for broad-spectrum antibacterial therapy. The results presented here indicate that the substrate-binding sites of most enzymes in this pathway are suitable drug targets because of their reasonable conservation and druggability scores. An exception was the substrate-binding site of 3-deoxy-D-arabino-heptulosonate-7-phosphate synthase, which was found to be undruggable because of its high content of charged residues and extremely high overall polarity. Although the presented study was designed from the perspective of broad-spectrum antibacterial drug development, this workflow can be readily applied to any antimicrobial target analysis, whether narrow- or broad-spectrum. Moreover, this research also contributes to a deeper understanding of these enzymes and provides valuable insights into their properties.

Interference of Polydatin/Resveratrol in the ACE2:Spike Recognition during COVID-19 Infection. A Focus on Their Potential Mechanism of Action through Computational and Biochemical Assays

In the search for new therapeutic strategies to contrast SARS-CoV-2, we here studied the interaction of polydatin (PD) and resveratrol (RESV)-two natural stilbene polyphenols with manifold, well known biological activities-with Spike, the viral protein essential for virus entry into host cells, and ACE2, the angiotensin-converting enzyme present on the surface of multiple cell types (including respiratory epithelial cells) which is the main host receptor for Spike binding. Molecular Docking simulations evidenced that both compounds can bind Spike, ACE2 and the ACE2:Spike complex with good affinity, although the interaction of PD appears stronger than that of RESV on all the investigated targets. Preliminary biochemical assays revealed a significant inhibitory activity of the ACE2:Spike recognition with a dose-response effect only in the case of PD.

Exploring Molecular Contacts of MUC1 at CIN85 Binding Interface to Address Future Drug Design Efforts

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.