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Prediction of amphiphilic cell-penetrating peptide building blocks from protein-derived amino acid sequences for engineering of drug delivery nanoassemblies
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SYSNO ASEP 0534088 Document Type J - Journal Article R&D Document Type Journal Article Subsidiary J Článek ve WOS Title Prediction of amphiphilic cell-penetrating peptide building blocks from protein-derived amino acid sequences for engineering of drug delivery nanoassemblies Author(s) Feger, G. (FR)
Angelov, Borislav (FZU-D) ORCID
Angelova, A. (FR)Number of authors 3 Source Title Journal of Physical Chemistry B. - : American Chemical Society - ISSN 1520-6106
Roč. 124, č. 20 (2020), s. 4069-4078Number of pages 10 s. Language eng - English Country US - United States Keywords small-angle scattering ; structural-characterization ; bioactive peptides ; rational design ; active peptides ; helical peptide ; surfactant ; nanotubes Subject RIV BO - Biophysics OECD category Biophysics R&D Projects EF16_019/0000789 GA MŠMT - Ministry of Education, Youth and Sports (MEYS) EF15_003/0000447 GA MŠMT - Ministry of Education, Youth and Sports (MEYS) Method of publishing Limited access Institutional support FZU-D - RVO:68378271 UT WOS 000537425700007 EID SCOPUS 85085265479 DOI 10.1021/acs.jpcb.0c01618 Annotation Amphiphilic molecules, forming self-assembled nanoarchitectures, are typically composed of hydrophobic and hydrophilic domains. Peptide amphiphiles can be designed from two, three, or four building blocks imparting novel structural and functional properties and affinities for interaction with cellular membranes or intracellular organelles. Here we present a combined numerical approach to design amphiphilic peptide scaffolds that are derived from the human nuclear K-i-67 protein. K-i-67 acts, like a biosurfactant, as a steric and electrostatic charge barrier against the collapse of mitotic chromosomes. The proposed predictive design of new K-i-67 protein-derived amphiphilic amino acid sequences exploits the computational outcomes of a set of web-accessible predictors, which are based on machine learning methods.
Workplace Institute of Physics Contact Kristina Potocká, potocka@fzu.cz, Tel.: 220 318 579 Year of Publishing 2021 Electronic address https://doi.org/10.1021/acs.jpcb.0c01618
Number of the records: 1