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Cited by 19Antimicrobial Peptides and ActivitiesComputational Drug Discovery MethodsProtein Structure and Dynamics

The DOI matches its Crossref record; 138 of the 138 references it cites were found in a registry or an index.

Computational analysis of antimicrobial peptides targeting key receptors in infection-related cardiovascular diseases: molecular docking and dynamics insights

Open access
Dermawan, Doni, Alotaiq, Nasser·2025·Scientific Reports
DOI 10.1038/s41598-025-93683-1PMC PMC11909139

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Abstract

Infection-related cardiovascular diseases (CVDs) pose a significant health challenge, driving the need for novel therapeutic strategies to target key receptors involved in inflammation and infection. Antimicrobial peptides (AMPs) show the potential to disrupt pathogenic processes and offer a promising approach to CVD treatment. This study investigates the binding potential of selected AMPs with critical receptors implicated in CVDs, aiming to explore their therapeutic potential. A comprehensive computational approach was employed to assess AMP interactions with CVD-related receptors, including ACE2, CRP, MMP9, NLRP3, and TLR4. Molecular docking studies identified AMPs with high binding affinities to these targets, notably Tachystatin, Pleurocidin, and Subtilisin A, which showed strong interactions with ACE2, CRP, and MMP9. Following docking, 100 ns molecular dynamics (MD) simulations confirmed the stability of AMP-receptor complexes, and MM/PBSA calculations provided quantitative insights into binding energies, underscoring the potential of these AMPs to modulate receptor activity in infection and inflammation contexts. The study highlights the therapeutic potential of Tachystatin, Pleurocidin, and Subtilisin A in targeting infection-related pathways in CVDs. These AMPs demonstrate promising receptor binding properties and stability in computational models. Future research should focus on in vitro and in vivo studies to confirm their efficacy and safety, paving the way for potential clinical applications in managing infection-related cardiovascular conditions.

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Crossref · 10.1038/s41598-025-93683-1

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Figures

Fig. 1 Molecular docking simulations illustrate the optimal binding orientations for interactions between AMPs and ACE2. (a) ACE2:DX600 peptide (standard antagonist) complex. (b) ACE2:Hepcidin complex. (c) ACE2:Pleurocidin complex. (d) ACE2:Tachystatin complex. (e) ACE2:Thermolysin complex. (f) ACE2:PvHCt complex. The AMPs depicted were chosen based on their consistently high binding affinities from a broader set of 30 AMPs analyzed in this study.
Fig. 1 Molecular docking simulations illustrate the optimal binding orientations for interactions between AMPs and ACE2. (a) ACE2:DX600 peptide (standard antagonist) complex. (b) ACE2:Hepcidin complex. (c) ACE2:Pleurocidin complex. (d) ACE2:Tachystatin complex. (e) ACE2:Thermolysin complex. (f) ACE2:PvHCt complex. The AMPs depicted were chosen based on their consistently high binding affinities from a broader set of 30 AMPs analyzed in this study.CC BY-NC-ND · PubMed Central
Fig. 2 Molecular docking results of optimal target receptor-AMP complexes, highlighting the lowest binding energy values indicative of a superior affinity. (A) ACE2 complexes. (B) CRP complexes. (C) MMP9 complexes. (D) NLRP3 complexes. (E) TLR4 complexes.
Fig. 2 Molecular docking results of optimal target receptor-AMP complexes, highlighting the lowest binding energy values indicative of a superior affinity. (A) ACE2 complexes. (B) CRP complexes. (C) MMP9 complexes. (D) NLRP3 complexes. (E) TLR4 complexes.CC BY-NC-ND · PubMed Central
Fig. 3 Correlation matrices illustrate the relationships between binding energy (kcal/mol) and individual energy components for each target receptor-AMP complex. These include (A) ACE2 complexes, (B) CRP complexes, (C) MMP9 complexes, (D) NLRP3 complexes, and (E) TLR4 complexes. The matrices measure the degree of association between binding energy, van der Waals energy, electrostatic energy, and desolvation energy. The correlation values range from − 1 to 1, where 1 denotes a perfect positive correlation, −1 indicates a perfect negative correlation, and 0 signifies no correlation.
Fig. 3 Correlation matrices illustrate the relationships between binding energy (kcal/mol) and individual energy components for each target receptor-AMP complex. These include (A) ACE2 complexes, (B) CRP complexes, (C) MMP9 complexes, (D) NLRP3 complexes, and (E) TLR4 complexes. The matrices measure the degree of association between binding energy, van der Waals energy, electrostatic energy, and desolvation energy. The correlation values range from − 1 to 1, where 1 denotes a perfect positive correlation, −1 indicates a perfect negative correlation, and 0 signifies no correlation.CC BY-NC-ND · PubMed Central
Fig. 4 Molecular dynamics (MD) simulation results showing the Root Mean Square Deviation (RMSD) profiles for different complexes: (A) ACE2 complexes and (B) NLRP3 complexes, highlighting their structural stability and dynamic behavior over time.
Fig. 4 Molecular dynamics (MD) simulation results showing the Root Mean Square Deviation (RMSD) profiles for different complexes: (A) ACE2 complexes and (B) NLRP3 complexes, highlighting their structural stability and dynamic behavior over time.CC BY-NC-ND · PubMed Central
Fig. 5 Molecular dynamics (MD) simulation results illustrating the Root Mean Square Fluctuation (RMSF) profiles, highlighting the flexibility of residues within different complexes: (A) ACE2 complexes, (B) CRP complexes, (C) MMP9 complexes, (D) NLRP3 complexes, and (E) TLR4 complexes.
Fig. 5 Molecular dynamics (MD) simulation results illustrating the Root Mean Square Fluctuation (RMSF) profiles, highlighting the flexibility of residues within different complexes: (A) ACE2 complexes, (B) CRP complexes, (C) MMP9 complexes, (D) NLRP3 complexes, and (E) TLR4 complexes.CC BY-NC-ND · PubMed Central

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Citations

Cites

Research works printed but not linked: 3

  1. Dermawan, D., Bahtiar, R. & Sofian, F. F. Implementation of green supply chain management (GSCM) in the pharmaceutical industry in Indonesia: feasibility analysis and case studies. J. Ilm Farm. 15, 23–29 (2019).
  2. Zhang, Q. Y. et al. Antimicrobial peptides: mechanism of action, activity and clinical potential. Mil Med. Res. 8, 48. https://doi.org/10.1186/s40779-021-00343-2 (2021). · DOI
  3. Gagat, P., Ostrówka, M., Duda-Madej, A. & Mackiewicz, P. Enhancing antimicrobial peptide activity through modifications of charge, hydrophobicity, and structure. Int. J. Mol. Sci. 25 https://doi.org/10.3390/ijms251910821 (2024). · DOI

Metadata

Authors
Dermawan, D., & Alotaiq, N.
Affiliations
  • Warsaw University of Technology (PL)
  • Imam Mohammad ibn Saud Islamic University (SA)
Corresponding author
Alotaiq, Nasser
ORCID
Venue
Scientific Reports
Publisher
Springer Science and Business Media LLC
Published
2025-03-14
Volume
15
Issue
1
Pages
8896-8896
ISSN
2045-2322 (ISSN-L)
DOI
10.1038/s41598-025-93683-1
PMC
PMC11909139
OpenAlex
W4408473383
Cited by
19
References
138
Citations by year
2026: 11 · 2025: 8
FWCI
10.41
Type
journal article
Language
en
Open access
Licence
Locations
  • Scientific Reports (publishedVersion, cc-by-nc-nd)
  • Preprints.org (acceptedVersion, cc-by)
  • Research Square (acceptedVersion, cc-by)
  • Europe PMC (PubMed Central) (publishedVersion, cc-by-nc-nd)
  • PubMed Central (publishedVersion, cc-by-nc-nd)
  • PubMed (publishedVersion)
  • DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
Funders
Deanship of Scientific Research at Imam Mohammad Ibn Saud Islamic University (IMSIU-DDRSP2501)
Topics
  • Life Sciences › Immunology and Microbiology › Microbiology › Antimicrobial Peptides and Activities
  • Life Sciences › Pharmacology, Toxicology and Pharmaceutics › Drug Discovery › Computational Drug Discovery Methods
  • Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Protein Structure and Dynamics
Keywords
antimicrobial peptides, ACE2, C-reactive protein, MMP-9, NLRP3 inflammasome, TLR4, pleurocidin, subtilisin A, peptide-receptor binding, binding affinity, binding energy, inflammation
MeSH terms
Angiotensin-Converting Enzyme 2, Antimicrobial Peptides, Cardiovascular Diseases, Humans, Protein Binding, Molecular Dynamics Simulation, Molecular Docking Simulation

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Bibliographic data: Semantic Scholar, OpenAlex, Crossref.

Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).

Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.