ИСТИНА

Trypanosoma cruzi (T. cruzi) is a parasite that causes Chagas disease, also known as American trypanosomiasis. It is transmitted by contact with infected blood-sucking insects that live in rural areas of Latin America. Chagas disease can be life-threatening if left untreated. There are two drugs that have proven efficacy against T. cruzi infection: nifurtimox and benznidazole. However, these drugs have limitations such as severe side effects, questionable effectiveness in chronic cases, and possible resistance. Therefore, there is a need for new drugs that are safer, more effective, and more accessible. TcGAL, or L-galactonolactone dehydrogenase from T. cruzi (EC 1.3.2.3), is an enzyme that catalyzes the final step of vitamin C biosynthesis in the parasite and is essential for its survival. Humans get vitamin C from food, but T. cruzi can only use the vitamin C it produces. This makes TcGAL a candidate target for new drugs. TcGAL is a membranotropic enzyme that is unstable in vitro. However, in our group this challenge was overcome by stabilizing TcGAL in micelles that mimic its natural membrane environment. This allows its function and kinetic properties to be studied with different combinations of substrates and inhibitors. The instability of TcGAL makes it impossible to use conventional methods such as X-ray crystallography or NMR spectroscopy to determine its structure. Instead, we use computational techniques that do not require structural data. The aim of this work is to construct and validate a 3D structure of this protein, which will enable structure-based drug design. We predicted the structure of TcGAL using a neural network approach, AlphaFold 2, and then used homology modeling to include the structure of the enzyme cofactor. Molecular dynamics simulations were then performed to explore the conformational space and flexibility of TcGAL in bulk. Ensemble docking was then used to detect binding sites of known substrates and inhibitors for different conformations of the predicted structure. Finally, we modeled the bulk environment using numerical methods to account for pH changes during the reaction. Our computational results were validated by comparison with experimental data and showed reasonable agreement, supporting that the structure obtained by this technique is credible enough. The existing model will be improved to account for membrane effects and to better elucidate the catalytic mechanism and inhibition mode in order to estimate the binding of new drug molecules.