期刊
JOURNAL OF PHYSICAL CHEMISTRY B
卷 124, 期 20, 页码 4069-4078出版社
AMER CHEMICAL SOC
DOI: 10.1021/acs.jpcb.0c01618
关键词
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资金
- project Structural dynamics of biomolecular systems (ELIBIO) from European Regional Development Fund [CZ.02.1.01/0.0/0.0/15_003/0000447]
- Synchrotron SOLEIL (France) [20170933, 20181489]
- project Advanced research using high-intensity laser produced photons and particles from European Regional Development Fund [CZ.02.1.01/0.0/0.0/16_019/0000789]
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. The ensemble of such artificial intelligence algorithms, involving support vector machine (SVM), random forest (RF) classifiers, and neural networks (NN), enables the nanoengineering of a broad range of innovative peptide materials for therapeutic delivery in various applications. Amphiphilic cell-penetrating peptides (CPP), derived from natural protein sequences, may spontaneously form self-assembled nanocarriers characterized by enhanced cellular uptake. Thanks to their inherent low immunogenicity, they may enable the safe delivery of therapeutic molecules across the biological barriers.
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