Using molecular dynamics simulations to prioritize and understand AI-generated cell penetrating peptides
| Autor: | Yoshihiro Ito, Koji Tsuda, Akiko Yumoto, Akio Kitao, Seiichi Tada, Duy Phuoc Tran, Takanori Uzawa |
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| Jazyk: | angličtina |
| Rok vydání: | 2021 |
| Předmět: |
0301 basic medicine
Computer science Cell Survival Science Peptide 02 engineering and technology Computational biology Cell-Penetrating Peptides Molecular Dynamics Simulation Article 03 medical and health sciences Molecular dynamics Membrane biophysics Computational biophysics Artificial Intelligence Machine learning Statistical inference Leverage (statistics) Humans Sample variance Amino Acid Sequence chemistry.chemical_classification Multidisciplinary Cell Membrane Reproducibility of Results Experimental validation 021001 nanoscience & nanotechnology Computational biology and bioinformatics 030104 developmental biology chemistry Drug delivery Medicine Permeation and transport 0210 nano-technology Biological physics HeLa Cells |
| Zdroj: | Scientific Reports Scientific Reports, Vol 11, Iss 1, Pp 1-9 (2021) |
| ISSN: | 2045-2322 |
| Popis: | 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. |
| Databáze: | OpenAIRE |
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