Biological Membrane-Penetrating Peptides: Computational Prediction and Applications

Biological Membrane-Penetrating Peptides: Computational Prediction and Applications - PubMed

Review

Biological Membrane-Penetrating Peptides: Computational Prediction and Applications

Ewerton Cristhian Lima de Oliveira et al. Front Cell Infect Microbiol. 2022.

Abstract

Peptides comprise a versatile class of biomolecules that present a unique chemical space with diverse physicochemical and structural properties. Some classes of peptides are able to naturally cross the biological membranes, such as cell membrane and blood-brain barrier (BBB). Cell-penetrating peptides (CPPs) and blood-brain barrier-penetrating peptides (B3PPs) have been explored by the biotechnological and pharmaceutical industries to develop new therapeutic molecules and carrier systems. The computational prediction of peptides' penetration into biological membranes has been emerged as an interesting strategy due to their high throughput and low-cost screening of large chemical libraries. Structure- and sequence-based information of peptides, as well as atomistic biophysical models, have been explored in computer-assisted discovery strategies to classify and identify new structures with pharmacokinetic properties related to the translocation through biomembranes. Computational strategies to predict the permeability into biomembranes include cheminformatic filters, molecular dynamics simulations, artificial intelligence algorithms, and statistical models, and the choice of the most adequate method depends on the purposes of the computational investigation. Here, we exhibit and discuss some principles and applications of these computational methods widely used to predict the permeability of peptides into biomembranes, exhibiting some of their pharmaceutical and biotechnological applications.

Keywords: blood-brain barrier; cell membrane; cell-penetrating peptides; drug system carriers; machine learning; peptides; pharmacokinetics; structure activity.

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Conflict of interest statement

The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

Figures

**Graphical Abstract**
Overview of the computational techniques applied to predict the permeability of peptides into biomembranes.

**Figure 1**
Schematic representation of cell membrane showing its main chemical lipidic and protein components.

**Figure 2**
Schematic representation of the blood-brain barrier, showing its main cell components (pericytes, astrocytes, and endothelial cells) and localization in the brain capillary wall.

**Figure 3**
Acceptable tPSA values for the cell membrane permeability. Chameleonic molecules are able to change their conformation to expose polar groups in an aqueous phase, however hide them when translocating through the cell membranes.

**Figure 4**
Representation of main categories on machine learning state-of-art, divided between supervised learning, such as classification and regression problems; and unsupervised learning that includes clustering and dimensionality reduction problems.

**Figure 5**
Structure- and sequence-based molecular descriptors that are applied in the prediction of biomembrane penetrating peptides.

**Figure 6**
Mechanisms of passive penetration of CPPs into the cell membrane (energy-independent mechanisms). **(A)** Passive diffusion (spontaneous translocation), **(B)** Peptide aggregation with pore formation, **(C)** endocytosis. The panels **(D)** and **(E)** represent some subsequent molecular events in the cell: **(D)** endosomal membrane lysis and **(E)** translocation through the cell membrane.

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