Macromolecular interaction mechanism of the bacteriocin EntDD14 with the receptor binding domain (RBD) for the inhibition of SARS-CoV-2 and the JN.1 variant: Biomedical study based on elastic networks, stochastic Markov models, and macromolecular volumetric analysis

Luis Moncayo Molina, María Erlinda Aguaiza Pichazaca, José Isidro Yamasqui Padilla, María Eufemia Pinos Calle, Karla Maribel Yamasqui Pinos, Arlene Cardozo Urdaneta, Carla Lossada, Yovani Marrero-Ponce, Felix Martinez-Rios, Ysaías J. Alvarado, Aleivi Pérez, Lenin González-Paz

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Abstract

Bacteriocins, a class of molecules produced by bacteria, exhibit potent antimicrobial properties, including antiviral activities. The urgent need for treatments against SARS-CoV-2 has proposed bacteriocins such as enterocin DD14 (EntDD14) as potential therapeutic agents. However, the mechanism of macromolecular interaction of EntDD14 for the inhibition of SARS-CoV-2 is not yet fully understood, and its efficacy against variants like JN.1 has not been completely established. To address these knowledge gaps, biocomputational analyses were employed using a diverse set of tools, including Markov state models and volumetric analyses. This analysis revealed a favorable interaction between EntDD14 and the receptor-binding domain (RBD) of SARS-CoV-2. Furthermore, it was found that EntDD14 induces changes in the flexibility of the RBD and alters the distribution and size of its internal cavities, particularly in the JN.1 variant. These findings align with experimental observations and support the inhibitory mechanism of EntDD14 against SARS-CoV-2. Additionally, they suggest that EntDD14 may possess a broader spectrum of action, encompassing the JN.1 variant. This study paves the way for future investigations and therapeutic applications, encouraging further exploration of the antiviral activity of bacteriocins like EntDD14 against variants of concern like JN.1. However, additional experimental demonstrations are warranted to substantiate these findings.

Original languageEnglish
Article number107388
JournalBiophysical Chemistry
Volume318
DOIs
StatePublished - Mar 2025

Keywords

  • AMPs
  • COVID-19
  • Elastic network models

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