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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...
Autores principales: | , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Nature Publishing Group UK
2021
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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. |
format | Online Article Text |
id | pubmed-8137933 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2021 |
publisher | Nature Publishing Group UK |
record_format | MEDLINE/PubMed |
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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