RESUMO
It is well known that bacterial communities are an essential component to maintain the balance of terrestrial ecosystems due to the functions and services performed by microorganisms in the environment. The research seeking on alternative energy sources has shown that bacterial communities can bioconvert the chemical energy of an organic substrate into electrical energy, within devices known as microbial fuel cells. For this reason, this class project allows students of Biotechnology, Environmental Science, and Microbiology to apply the appropriate methodology to develop a class project throughout an environmental bacterial community capable of generating electrical energy.
RESUMO
The desirable pharmacological properties and a broad number of therapeutic activities have made peptides promising drugs over small organic molecules and antibody drugs. Nevertheless, toxic effects, such as hemolysis, have hampered the development of such promising drugs. Hence, a reliable computational tool to predict peptide hemolytic toxicity is enormously useful before synthesis and experimental evaluation. Currently, four web servers that predict hemolytic activity using machine learning (ML) algorithms are available; however, they exhibit some limitations, such as the need for a reliable negative set and limited application domain. Hence, we developed a robust model based on a novel theoretical approach that combines network science and a multiquery similarity searching (MQSS) method. A total of 1152 initial models were constructed from 144 scaffolds generated in a previous report. These were evaluated on external data sets, and the best models were fused and improved. Our best MQSS model I1 outperformed all state-of-the-art ML-based models and was used to characterize the prevalence of hemolytic toxicity on therapeutic peptides. Based on our model's estimation, the number of hemolytic peptides might be 3.9-fold higher than the reported.