INVESTIGADORES
MARANI mariela Mirta
congresos y reuniones científicas
Título:
Integrando la bioprospeccion en anfibios y la inteligencia artificial para descubrir nuevos peptidos antimicrobianos
Autor/es:
MARANI, MARIELA M.
Lugar:
Brasilia
Reunión:
Encuentro; IV Encontro Internacional de Inovacao em Saúde. II Feria de Inovacao tecnológica do Distrito Federal; 2023
Institución organizadora:
People & Science. Univeraidade do Brasilia. NuPMIA. NMT
Resumen:
South America´s biodiversity is characterized by exceptional richness and diversity, particularly in its amphibian population known for unique adaptations, including multifunctional skin producing numerous bioactive compounds, including antimicrobial peptides (AMPs). Despite over 2,500 amphibian species, only 76 have been systematically examined for their secretion, with a predominant focus on the Hylidae and Leptodactylidae.Utilizing skin secretion analysis and transcriptome studies with molecular biology techniques, over 255 distinct peptides have been identified in South American frogs. Notably, various AMP families were discovered, each characterized by sequence variations. Noteworthy, the ocellatin and hylin families stood out, comprising 28 and 35 cataloged peptides in 11 and 13 species of Leptodactylus and Boana genera, respectively. These peptides exhibit a cationic nature with conserved motifs.Nature´s diverse and evolving array of molecules has been a pivotal source for drug development. However, converting native peptides into pharmacologically active agents demands considerations of selectivity, efficacy, predictable metabolism, and safety profiles. The emergence of Artificial Intelligence (AI) provides a cost-effective and time-efficient avenue for predicting and generating peptides with desired properties.Predictive and generative models, tailored for the discovery and design of safe and effective antibacterial peptides, are being developed to optimize both time and cost-effectiveness1. Central to this endeavor are databases, constructed with meticulous attention to mitigate biases that could distort the neural network´s ability to generalize and recognize patterns in novel data.AI promises to revolutionize our understanding of molecular behavior, enabling the design of novel compounds and providing invaluable insights for developing new or improved drug candidates. This paradigm shift holds great promise for advancing drug discovery and design in the field of organic chemistry.