Mining biology
Biological mining refers to the process of retrieving biological information from the world by integrating computational biology, genomics, and machine learning. This approach aims to discover and develop new antibiotics and other useful molecules.[1]
In 2019, the De la Fuente Lab team proposed a digital approach to explore existing biological data repositories, such as genomes, metagenomes, and proteomes, as a means to expedite antibiotic discovery.[2] By utilizing advancements in artificial intelligence, this biological mining strategy facilitates the rapid identification of new molecules on a larger scale, moving beyond the slow pace of traditional experimental methods. To implement this, artificial intelligence models have been developed and trained on standardized experimental datasets to predict the antimicrobial properties of specific amino acid sequences, establishing a new paradigm in the field of antibiotic discovery. This research has also led to the identification of a novel class of peptides termed encrypted peptides.
Through the process of biological mining, researchers have identified hidden antimicrobial agents within diverse genomes, encompassing both living and extinct species, such as guavanine-2,[3] mamutusin, and neandertalin.[4]
References
- ↑ Fuente-Nunez, Cesar de la (2024-11-26). "Mining biology for antibiotic discovery". PLOS Biology. 22 (11): e3002946. doi:10.1371/journal.pbio.3002946. ISSN 1545-7885.
- ↑ Fuente-Nunez, Cesar de la (2024-11-26). "Mining biology for antibiotic discovery". PLOS Biology. 22 (11): e3002946. doi:10.1371/journal.pbio.3002946. ISSN 1545-7885.
- ↑ Porto, William F.; Irazazabal, Luz; Alves, Eliane S. F.; Ribeiro, Suzana M.; Matos, Carolina O.; Pires, Állan S.; Fensterseifer, Isabel C. M.; Miranda, Vivian J.; Haney, Evan F.; Humblot, Vincent; Torres, Marcelo D. T.; Hancock, Robert E. W.; Liao, Luciano M.; Ladram, Ali; Lu, Timothy K. (2018-04-16). "In silico optimization of a guava antimicrobial peptide enables combinatorial exploration for peptide design". Nature Communications. 9 (1): 1490. doi:10.1038/s41467-018-03746-3. ISSN 2041-1723.
- ↑ Wan, Fangping; Torres, Marcelo D. T.; Peng, Jacqueline; de la Fuente-Nunez, Cesar (2024-07). "Deep-learning-enabled antibiotic discovery through molecular de-extinction". Nature Biomedical Engineering. 8 (7): 854–871. doi:10.1038/s41551-024-01201-x. ISSN 2157-846X. Check date values in:
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