TNF (specific NF-kB Induction) Prediction
Developing drugs that disrupt protein-protein interactions (PPIs) is a difficult task in pharmaceutical research. The interaction between protein Tumor Necrosis Factor (TNF) and its receptors is implicated in several physiological functions and diseases, such as rheumatoid and psoriatic arthritis, Crohn’s disease, and multiple sclerosis. Despite their potency, current medications that block the interaction between TNF and its receptors are also associated with many adverse functions. Here, we employ comprehensive computational and experimental methods to discover novel small molecules that are direct inhibitors of TNF function. Functionality for RANKL, a second, clinically-relevant member of the TNF protein family, was also examined. Using a combination of an in silico drug discovery pipeline, which includes structure- and ligand-based modeling, and in vitro experiments, we identified compounds T8 and T23 as dual inhibitors of TNF and RANKL. These compounds present low toxicity and may be further optimized in drug design targeting TNF and RANKL to develop improved treatments for a range of inflammatory and autoimmune diseases.
Τo encourage and facilitate the reuse of our predictive model, the consensus model has been made publicly available online via the Enalos Cloud Platform. Our model is hosted under the following url: http://enalos.insilicotox.com/TNFPubChem/ and is easily accessible online from any browser, also supporting mobile devices. The interested user can make their own predictions using the user friendly graphical interface that allows multiple options for submitting a new structure. First, a sketcher is available where a new molecule can be drawn and structurally modified. The structure can be either directly submitted to generate a prediction or can be copied as SMILES. The second option includes the SMILES notation submission in the upper right of the web page, where the user can paste one or more SMILES notations for one or a batch of molecules and then submit the whole list for prediction. Finally, with the third option the user can upload an SDF file with multiple entries and submit the file for prediction.
When the model “TNFPubChem” is selected and the structures are submitted in either way, the prediction is generated as an html page or a CSV file. The prediction outcome includes a classification for each of the given structures and an indication of whether the prediction can be considered reliable or not, based on the domain of applicability of the model (Fig 4). The web service does not require special computational skills and can be easily used by scientists of different disciplines, including chemists, biologists, physicists, and engineers, involved or interested in the biological evaluation of TNF inhibitors.
Melagraki, G., Ntougkos, E., Rinotas, V., Papaneophytou, C., Leonis, G., Mavromoustakos, T., Kontopidis, G., Douni, E., Afantitis, A*., Kollias, G. Cheminformatics-aided discovery of small-molecule Protein-Protein Interaction (PPI) dual inhibitors of Tumor Necrosis Factor (TNF) and Receptor Activator of NF-κB Ligand (RANKL) (2017) PLoS Computational Biology, 13 (4), art. no. e1005372 (link)