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Using molecular dynamics simulations to prioritize and understand AI-generated cell penetrating peptides

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 spotlig...

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Autores principales: Tran, Duy Phuoc, Tada, Seiichi, Yumoto, Akiko, Kitao, Akio, Ito, Yoshihiro, Uzawa, Takanori, Tsuda, Koji
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Nature Publishing Group UK 2021
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8137933/
https://www.ncbi.nlm.nih.gov/pubmed/34017051
http://dx.doi.org/10.1038/s41598-021-90245-z
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author Tran, Duy Phuoc
Tada, Seiichi
Yumoto, Akiko
Kitao, Akio
Ito, Yoshihiro
Uzawa, Takanori
Tsuda, Koji
author_facet Tran, Duy Phuoc
Tada, Seiichi
Yumoto, Akiko
Kitao, Akio
Ito, Yoshihiro
Uzawa, Takanori
Tsuda, Koji
author_sort Tran, Duy Phuoc
collection PubMed
description 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.
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spelling pubmed-81379332021-05-25 Using molecular dynamics simulations to prioritize and understand AI-generated cell penetrating peptides Tran, Duy Phuoc Tada, Seiichi Yumoto, Akiko Kitao, Akio Ito, Yoshihiro Uzawa, Takanori Tsuda, Koji Sci Rep Article 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. Nature Publishing Group UK 2021-05-20 /pmc/articles/PMC8137933/ /pubmed/34017051 http://dx.doi.org/10.1038/s41598-021-90245-z Text en © The Author(s) 2021 https://creativecommons.org/licenses/by/4.0/Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/ (https://creativecommons.org/licenses/by/4.0/) .
spellingShingle Article
Tran, Duy Phuoc
Tada, Seiichi
Yumoto, Akiko
Kitao, Akio
Ito, Yoshihiro
Uzawa, Takanori
Tsuda, Koji
Using molecular dynamics simulations to prioritize and understand AI-generated cell penetrating peptides
title Using molecular dynamics simulations to prioritize and understand AI-generated cell penetrating peptides
title_full Using molecular dynamics simulations to prioritize and understand AI-generated cell penetrating peptides
title_fullStr Using molecular dynamics simulations to prioritize and understand AI-generated cell penetrating peptides
title_full_unstemmed Using molecular dynamics simulations to prioritize and understand AI-generated cell penetrating peptides
title_short Using molecular dynamics simulations to prioritize and understand AI-generated cell penetrating peptides
title_sort using molecular dynamics simulations to prioritize and understand ai-generated cell penetrating peptides
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8137933/
https://www.ncbi.nlm.nih.gov/pubmed/34017051
http://dx.doi.org/10.1038/s41598-021-90245-z
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