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Generación de péptidos antimicrobianos mediante redes neuronales recurrentes
Synthetic antimicrobial peptides generation using recurrent neural networks
DOI:
https://doi.org/10.15446/dyna.v88n216.88799Palabras clave:
resistencia antimicrobiana;, péptidos sintéticos;, virtual screening;, aprendizaje profundo (es)antimicrobial resistance;, synthetic peptides;, virtual screening;, deep learning (en)
Los péptidos antimicrobianos (AMP) han tomado importancia en el desarrollo de nuevos antibióticos debido a su papel como inhibidores, no solo de bacterias sino también de virus, hongos y parásitos, entre otros. Desde el descubrimiento de los AMP, se han reportado miles, sin embargo, muchos de ellos no son adecuados para aplicaciones terapéuticas debido a sus largas secuencias de aminoácidos, baja potencia antimicrobiana y altos costos de producción. En este trabajo, proponemos utilizar redes neuronales recurrentes (RNN) con células LSTM para generar péptidos más potentes y económicos. Realizamos diferentes experimentos generando AMP sintéticos entre 12 y 20 aminoácidos. Los resultados muestran que podemos usar RNN y mejorar el proceso de generación en comparación con el método de plantillas manuales.
The antimicrobial peptides (AMPs) have taken importance in the development of new antibiotics because of their role as an inhibitor, not only of bacteria but also of viruses, fungi and parasites, among others. Since the discovery of AMPs, thousands have been reported, however, many of them are not suitable for therapeutic applications due to their long amino acid sequences, low antimicrobial potency and high production costs. In this work, we propose to use recurrent neural networks (RNN) with LSTM cells in order to generate more potent and economical peptides. We perform different experiments generating synthetic AMPs between 12 and 20 amino acids. The results show that we can use RNN and improve the generation process compared with the template method.
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