De novo protein prediction using artificial intelligence for NF-ΚB inhibition in gastric cancer
Keywords:
De novo proteins, artificial intelligence, gastric cancer, NF-κB factorAbstract
We address the limitations of current gastric cancer treatments and explore the potential of Artificial Intelligence (AI) in developing personalized strategies by predicting de novo proteins designed to inhibit the Nuclear Factor kappa light chain of active B cells (NF-κB), associated with this type of cancer. Thirty proteins with similar characteristics to those stored in standard and experimental databases were predicted. These proteins were evaluated in terms of stability and folding capacity by molecular dynamics simulations, analyzing energies of intra- and intermolecular interactions. Molecular docking was performed between the genes and transcription factors regulated by NF-κB and the predicted proteins. Thermodynamic variables such as Gibbs Free Energy, Dissociation Constant, Enthalpy, Heat Capacity and Entropy were calculated, comparing them with the complexes of the Alpha inhibitor of kappa B and the p65/p50 subunits of NF-κB, in order to verify the protein-protein interaction affinity and its structural conformation. The results showed high affinity and selectivity in the evaluated interactions, concluding that the de novo proteins predicted by AI represent a promising alternative for the design of new drugs and treatments directed against gastric cancer.
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Copyright (c) 2025 Diego Laverde-Lomas, Adahir Sarabia-Galarza, Cinthia Galarza-Galarza, Cristian Galarza-Galarza
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