Physicochemical Features and Peculiarities of Interaction of AMP with the Membrane

Abstract

Antimicrobial peptides (AMPs) are anti-infectives that have the potential to be used as a novel and untapped class of biotherapeutics. Modes of action of antimicrobial peptides include interaction with the cell envelope (cell wall, outer- and inner-membrane). A comprehensive understanding of the peculiarities of interaction of antimicrobial peptides with the cell envelope is necessary to perform a rational design of new biotherapeutics, against which working out resistance is hard for microbes. In order to enable de novo design with low cost and high throughput, in silico predictive models have to be invoked. To develop an efficient predictive model, a comprehensive understanding of the sequence-to-function relationship is required. This knowledge will allow us to encode amino acid sequences expressively and to adequately choose the accurate AMP classifier. A shared protective layer of microbial cells is the inner, plasmatic membrane. The interaction of AMP with a biological membrane (native and/or artificial) has been comprehensively studied. We provide a review of mechanisms and results of interactions of AMP with the cell membrane, relying on the survey of physicochemical, aggregative, and structural features of AMPs. The potency and mechanism of AMP action are presented in terms of amino acid compositions and distributions of the polar and apolar residues along the chain, that is, in terms of the physicochemical features of peptides such as hydrophobicity, hydrophilicity, and amphiphilicity. The survey of current data highlights topics that should be taken into account to come up with a comprehensive explanation of the mechanisms of action of AMP and to uncover the physicochemical faces of peptides, essential to perform their function. Many different approaches have been used to classify AMPs, including machine learning. The survey of knowledge on sequences, structures, and modes of actions of AMP allows concluding that only possessing comprehensive information on physicochemical features of AMPs enables us to develop accurate classifiers and create effective methods of prediction. Consequently, this knowledge is necessary for the development of design tools for peptide-based antibiotics.

Keywords: antimicrobial activity; antimicrobial peptides; database; physicochemical features; synergism.

Figure 1

Amino acid composition of ribosomal peptides of DBAASP presenting as the difference from UniProt (https://dbaasp.org/statistics, accessed on 20 March 2021).

Figure 2

Amino acid composition of (a) linear; (b) disulfide-bonded, and (c) cyclic ribosomal peptides of DBAASP, presented as the difference from UniProt (https://dbaasp.org/statistics, accessed on 20 March 2021).

Figure 3

Frequencies of the appearance of pairs of (a) positively charged residues (R, K, H) and (b) hydrophobic residues (V, I, L, F, M) with the i residues (i = 0, 1, 2,…, 10) between them. fi- Observed frequencies; Fi- the average value of the frequencies assessed on the base of random sequences, and σi are their standard deviations. Fi frequencies are estimated for random sequences generated by shuffling of the sequences of the considered set of peptides. The assessments were performed using the tools of the page of “Statistics” (https://dbaasp.org/statistics, accessed on 20 March 2021) of the DBAASP [17].

Figure 4

Frequencies of the appearance of pairs of amino acid with the i residues between them, for: (a) Cys (C) in the disulfide-bonded ribosomal AMPs set; (b) Trp (W) in the full set of ribosomal AMPs; (c) Gly (G) in the full set of ribosomal AMPs; The assessments have been performed using the tools of the page of “Statistics” (https://dbaasp.org/statistics, accessed on 20 March 2021) of the DBAASP [17].

Figure 5

AMP’s lengths distributions for: (a) disulfide-bonded and (b) linear peptides (according to the data of DBAASP (https://dbaasp.org/statistics, accessed on 20 March 2021).

Figure 6

Frequencies of the appearance of pairs of amino acid with the i residues between them, for: (a) Gly-Pro in the set of short (17–30 aa) linear ribosomal AMPs; (b) Pro-Gly in the set of short (17–30 aa) linear ribosomal AMPs; (c) Gly-Pro in the set of very short (10–15 aa) linear ribosomal AMPs; (d) Pro-Gly in the set of very short (10–15 aa) linear ribosomal AMPs. The assessments have been performed using the tools of the page of “Statistics” (https://dbaasp.org/statistics, accessed on 20 March 2021) of the DBAASP [17].

Figure 7

Representative structure constructed on the basis of MD trajectories simulated for the AMP Indolicidin (DBAASP [17] ID = 4807). Side chain heavy atoms are represented in ball-and-stick, and the backbone is represented in cartoon colored by residue index. Triptophane side chains are presented in red and basic amino acids side chains in green.

Figure 8

Representative structures constructed on the basis of MD trajectories simulated for the AMPs: (a) R9 (DBAASP [17] ID = 9015), and (b) Astacidin (DBAASP [17] ID = 2145). Side chain heavy atoms are represented in ball-and-stick, and the backbone is represented in cartoon color-coded by residue index.

Figure 9

Distribution of the normalized hydrophobic moment (µ) of short (8–23 aa), linear, ribosomal AMPs using the tools of the page of “Statistics” (https://dbaasp.org/statistics, accessed on 20 March 2021) of the DBAASP [17]. The fitting has done by the sum of two Gaussian curve (red).

Figure 10

Distribution of the Isoelectric Point of ribosomal AMPs using the tools of the page of “Statistics” (https://dbaasp.org/statistics, accessed on 20 March 2021) of the DBAASP [17]. The fitting has done by the sum of two Gaussian curve (red).