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Machine Learning Discovers Sequences to Boost Drug Delivery


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A peptide that can be attached to phosphorodiamidate morpholino oligomers to aid drug delivery.

Researchers at the Massachusetts Institute of Technology combined experimental chemistry with artificial intelligence to discover non-toxic, highly active peptides that can be attached to phosphorodiamidate morpholino oligomers to aid drug delivery.

Credit: Carly Schissel et al

Researchers at the Massachusetts Institute of Technology (MIT) have developed an approach that uses experimental chemistry and artificial intelligence to facilitate delivery of a drug to treat Duchenne muscular dystrophy (DMD).

The approach aims to determine which cell-penetrating peptides can be attached to antisense phosphorodiamidate morpholino oligomers (PMO) to help permeate the cell nucleus to modify a mutated dystrophin gene and enable production of a key protein typically missing in patients with DMD.

The researchers built a library of 600 miniproteins, each attached to PMO, by mixing and matching 57 peptides, and quantified each miniprotein's ability to deliver drugs across the cell.

MIT's Rafael Gomez-Bombarelli said, "The key innovation is using machine learning to connect the sequence of a peptide, particularly a peptide that includes non-natural amino acids, to experimentally-measured biological activity."

The sequences proposed by the model were found to be more effective than previously known variants.

From MIT News
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Abstracts Copyright © 2021 SmithBucklin, Washington, DC, USA


 

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