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PreprintCited by 26Antimicrobial Peptides and ActivitiesAntimicrobial agents and applicationsChemical Synthesis and Analysis

The DOI matches its Crossref record; 1 of the 1 references it cites were found in a registry or an index.

Novel Antimicrobial Peptides Designed Using Recurrent Neural Network Reduce Mortality in Experimental Sepsis

Open access
Bolatchiev, Albert·2021·Antibiotics
DOI 10.21203/rs.3.rs-1128449/v1PMC PMC8944797

Preprint — published as 10.3390/antibiotics11030411 · Paper page

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Abstract

The amino acid sequences of 198 novel peptides were obtained using a generative long short-term memory recurrent neural network (LTSM RNN). To assess their antimicrobial effect, I synthesized 5 out of 198 generated peptides. The PEP-38 and PEP-137 peptides were active in vitro against carbapenem-resistant isolates of Klebsiella aerogenes (n=12) and K. pneumoniae (n=18). PEP-137 was also active against Pseudomonas aeruginosa (n=17). The remaining three peptides (PEP-36, PEP-136 and PEP-174) showed no antibacterial effect. Then I investigated the effect of PEP-38 and PEP-137 (a single intraperitoneal administration of a 100 µg dose 30 min after infection) on animal survival in an experimental murine model of K. pneumoniae-induced sepsis. As a control, I used two groups of mice: one received sterile saline, and the other received inactive in vitro PEP-36 (a single 100 µg dose). The PEP-36 peptide was shown to provide the highest survival rate (66.7%). PEP-137 showed a survival rate of 50%. PEP-38 was found to be ineffective. The data obtained can be used to develop new antibacterial peptide drugs to combat antibiotic resistance.

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Crossref · 10.21203/rs.3.rs-1128449/v1

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Figures

Figure 1 Comparison of peptide characteristics between the training data (Training, orange), the generated sequences (Sampled, blue), the pseudo-random sequences with the same amino acid distribution as in the training set (Ran, purple), and the manually created hypothetical amphipathic helices (Hel, green). The horizontal dashed lines represent the mean (violin plots) and median (box plots) values; the whiskers extend to the outermost non-outlier data points. Graphs from left to right: Eisenberg hydrophobicity, Eisenberg hydrophobic moment, and sequence length. The figure was generated using the modlAMP’s GlobalAnalysis.plot_summary method in Python [16].
Figure 1 Comparison of peptide characteristics between the training data (Training, orange), the generated sequences (Sampled, blue), the pseudo-random sequences with the same amino acid distribution as in the training set (Ran, purple), and the manually created hypothetical amphipathic helices (Hel, green). The horizontal dashed lines represent the mean (violin plots) and median (box plots) values; the whiskers extend to the outermost non-outlier data points. Graphs from left to right: Eisenberg hydrophobicity, Eisenberg hydrophobic moment, and sequence length. The figure was generated using the modlAMP’s GlobalAnalysis.plot_summary method in Python [16].CC BY · PubMed Central
Figure 2 Comparison of probability of survival (%) in each group: control (sterile saline), PEP-36, PEP-38 and PEP-137. The peptides were injected once in a dose of 100 μg 30 min after infection of mice with 6.75 × 108 CFU suspension of a carbapenem-resistant isolate of K. pneumoniae. *—significant differences from the control group using the Kaplan-Meier method and Log-rank (Mantel-Cox) test.
Figure 2 Comparison of probability of survival (%) in each group: control (sterile saline), PEP-36, PEP-38 and PEP-137. The peptides were injected once in a dose of 100 μg 30 min after infection of mice with 6.75 × 108 CFU suspension of a carbapenem-resistant isolate of K. pneumoniae. *—significant differences from the control group using the Kaplan-Meier method and Log-rank (Mantel-Cox) test.CC BY · PubMed Central
Figure 3 Visual comparison of the structures of LL-37 (yellow; PDB ID: 2K6O) and synthesized peptides: PEP-137—blue, PEP-38—red, PEP-36—green (by AlphaFold).
Figure 3 Visual comparison of the structures of LL-37 (yellow; PDB ID: 2K6O) and synthesized peptides: PEP-137—blue, PEP-38—red, PEP-36—green (by AlphaFold).CC BY · PubMed Central
Figure 4 Initial and final snapshot of peptide-membrane system before and after 225 ns of MD simulation. The membrane is shown in lines (cyan) and the peptides are shown in helical structure (colored according to the type of amino acids). Phosphate atoms of the membrane are shown in gray color.
Figure 4 Initial and final snapshot of peptide-membrane system before and after 225 ns of MD simulation. The membrane is shown in lines (cyan) and the peptides are shown in helical structure (colored according to the type of amino acids). Phosphate atoms of the membrane are shown in gray color.CC BY · PubMed Central
Figure 5 Top view of the peptide-membrane system (left) at 225 ns of MD simulation showing the unravelling of the helical structure. Phosphate atoms of the membrane are shown in gray color. Side view (right) showing the penetration of peptide at 225 ns.
Figure 5 Top view of the peptide-membrane system (left) at 225 ns of MD simulation showing the unravelling of the helical structure. Phosphate atoms of the membrane are shown in gray color. Side view (right) showing the penetration of peptide at 225 ns.CC BY · PubMed Central

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Metadata

Authors
Bolatchiev, A.
Affiliations
Stavropol State Medical University (RU)
Corresponding author
Bolatchiev, Albert
ORCID
Bolatchiev, Albert
Venue
Antibiotics
Publisher
Research Square Platform LLC
Published
2021-12-10
Volume
11
Issue
3
Pages
411-411
ISSN
2079-6382 (ISSN-L)
DOI
10.21203/rs.3.rs-1128449/v1
PMC
PMC8944797
OpenAlex
W4220710498
Cited by
26
References
1
Citations by year
2026: 5 · 2025: 5 · 2024: 6 · 2023: 7 · 2022: 3
FWCI
3.28
Type
journal article
Language
en
Open access
Licence
Locations
  • Antibiotics (publishedVersion, cc-by)
  • Research Square (acceptedVersion, cc-by)
  • PubMed Central (publishedVersion, cc-by)
  • PubMed (publishedVersion)
  • DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion)
Funders
Russian Science Foundation (20-75-00004)
Topics
  • Life Sciences › Immunology and Microbiology › Microbiology › Antimicrobial Peptides and Activities
  • Life Sciences › Immunology and Microbiology › Applied Microbiology and Biotechnology › Antimicrobial agents and applications
  • Physical Sciences › Chemistry › Organic Chemistry › Chemical Synthesis and Analysis
Keywords
antimicrobial peptides, de novo peptide design, recurrent neural networks, long short-term memory, antibiotic resistance, carbapenem resistance, Klebsiella pneumoniae, Enterobacter aerogenes, Pseudomonas aeruginosa, sepsis, murine sepsis model, survival rate, antibacterial activity

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