Greek researchers have developed a new bioinformatics tool to identify potential therapies for chronic inflammatory diseases. Using this approach, they identified and confirmed the therapeutic potential of two small molecules to target a protein called TNF (Tumor Necrosis Factor) that is active in multiple sclerosis, rheumatoid arthritis and other diseases.
Their study, “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),” appeared in PLoS Computational Biology.
TNF is a key protein in normal inflammatory processes, but it can also have adverse effects in chronic inflammatory diseases. Drug companies have long sought to develop anti-TNF drugs to target the protein or its receptor, blocking TNF function. Yet available therapies can be toxic and cause adverse side effects, and not all patients respond to approved anti-TNF therapies.
In the current study, researchers developed a bioinformatics platform to virtually screen about 15,000 small molecules with unknown activities. Focusing on the compound and the protein chemical structures, this new approach identified all small molecules that could disrupt TNF and its receptor interaction.
Because TNF shares structural features with another protein also known to be involved in inflammatory processes called RANKL, the authors used their virtual screen tool to identify compounds that could target both pro-inflammatory proteins.
“This virtual experiment identified nine promising molecules out of thousands of candidates,” Antreas Afantitis, co-senior author of the study, said in a press release.
After validating these compounds, researchers identified two small molecules — T23 and T8 — that could interact with TNF and RANKL, thereby blocking their activity. Neither compound caused toxic side effects.
“[T23 and T8 could be] further optimized to develop improved treatments for a range of inflammatory, autoimmune and bone loss diseases,” said George Kollias, the study’s co-senior author.
In addition, this new virtual drug screening platform may help identify other promising TNF inhibitors as well as find potential treatments for other diseases. “Our proposed methodology and tools can also be expanded and applied to other biological targets that are now gaining attention,” researchers wrote.
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