Accurate protein function prediction is an important subject in bioinformatics, especially where sequentially and structurally similar proteins have different functions. Malate dehydrogenase and L-Lactate dehydrogenase are two evolutionary related enzymes, which exist in a wide variety of organisms. These enzymes are sequentially and structurally similar and share common active site residues, spatial patterns and molecular mechanisms. Here, we study various features of the active site cavity of 229 PDB chain entries and try to classify them automatically by various classifiers including the support vector machine, k nearest neighbour and random forest methods. The results show that the support vector machine yields the highest predictive performance among mentioned classifiers. Despite very close and conserved patterns among Malate dehydrogenases and L-Lactate dehydrogenases, the SVM predicts the function efficiently and achieves 0.973 Matthew's correlation coefficient and 0.987 F-score. The same approach can be used in other enzyme families for automatic discrimination between homologous enzymes with common active site elements, however, acting on different substrates.

The aim of present study was to study bioinformatic interaction of silver nanoparticles (Ag NPs) and L- Lactate dehydrogenase enzymein Toxoplasma gondii to determine harmful effect of silver nanoparticle on the enzyme. In this study we used Metal Detector Predicts v2.0 software, DiANNA 1.1 web server, Molecular docking web server and 3D Ligand Site server, to analyze enzyme structure and Ag NPs inhibitory effect on its function. The results obtained from docking showed that free energy of binding from docking score for L- Lactate dehydrogenase was -3.83 kcal/mol and inhibition constant (Ki) equal to 1.56 mM. Ag NPs may facilitate the interaction with Cysteine in 10 positions of enzyme. Ag NPs naturally interact with the membrane of pathogens and disrupt the membrane integrity, and silver ions bind to sulfur, oxygen, and nitrogen of essential biological molecules and inhibit pathogens growth.

Silver nanoparticles because of their antibacterial properties have become one of the most widely used combinations of nanomaterial and due to the development of nanoparticles, there are the risks of releasing of such materials to the environment from products containing them. In this study, six types of a filter containing silver nanoparticles containing 0. 25, 0. 5 and 1 percentage nanosilver with and without Aminopropyl triethoxysilane is used to control the Saprolegnia fungus in Persian sturgeon egg incubators. Two of six filters contain one percentage of silver nanoparticles could prevent Saprolegnia fungus completely as the optimal filter. The effects of these two types of optimized filters on embryos of Persian sturgeon kept in the incubator examined by studying the changes of some oxidative enzymes including glutathione peroxidase and Lactate dehydrogenase and also by studying Lactate changes by HNMR, in the second and fourth days of the incubation period. The results showed that the use of filters containing silver nanoparticles due to the release of silver from the filter into the water reduced glutathione peroxidase in both sampling days and reduced Lactate and Lactate dehydrogenase in second incubation day and increased them in the fourth day. So measuring these enzymes is a good biomarker for early assessment of those intracellular changes in fish that are affected by such filters.