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Title
Japanese:Using molecular dynamics simulations to prioritize and understand AI-generated cell penetrating peptides 
English:Using molecular dynamics simulations to prioritize and understand AI-generated cell penetrating peptides 
Author
Japanese: TranPhuoc Duy, 北尾彰朗.  
English: Duy Phuoc Tran, Akio Kitao.  
Language English 
Journal/Book name
Japanese:Scientific Reports 
English:Scientific Reports 
Volume, Number, Page Vol. 11    No. 1   
Published date May 20, 2021 
Publisher
Japanese: 
English: 
Conference name
Japanese: 
English: 
Conference site
Japanese: 
English: 
Official URL http://dx.doi.org/10.1038/s41598-021-90245-z
 
DOI https://doi.org/10.1038/s41598-021-90245-z
Abstract <jats:title>Abstract</jats:title><jats:p>Cell-penetrating peptides have important therapeutic applications in drug delivery, but the variety of known cell-penetrating peptides is still limited. With a promise to accelerate peptide development, artificial intelligence (AI) techniques including deep generative models are currently in spotlight. Scientists, however, are often overwhelmed by an excessive number of unannotated sequences generated by AI and find it difficult to obtain insights to prioritize them for experimental validation. To avoid this pitfall, we leverage molecular dynamics (MD) simulations to obtain mechanistic information to prioritize and understand AI-generated peptides. A mechanistic score of permeability is computed from five steered MD simulations starting from different initial structures predicted by homology modelling. To compensate for variability of predicted structures, the score is computed with sample variance penalization so that a peptide with consistent behaviour is highly evaluated. Our computational pipeline involving deep learning, homology modelling, MD simulations and synthesizability assessment generated 24 novel peptide sequences. The top-scoring peptide showed a consistent pattern of conformational change in all simulations regardless of initial structures. As a result of wet-lab-experiments, our peptide showed better permeability and weaker toxicity in comparison to a clinically used peptide, TAT. Our result demonstrates how MD simulations can support de novo peptide design by providing mechanistic information supplementing statistical inference.</jats:p>

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