AccScience Publishing / AIH / Volume 1 / Issue 2 / DOI: 10.36922/aih.2284
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ORIGINAL RESEARCH ARTICLE

Discovery of new antibiotics using AI-guided spectroscopy and 3D drug-protein computer simulation technologies to combat MDR bacteria-associated mortality

Asit Kumar Chakraborty1* Meghna Maity1 Sumana Sahoo1
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1 Department of Biochemistry and Biotechnology, Faculty of Oriental Institute of Science and Technology, Vidyasagar University, Midnapore, West Bengal, India
AIH 2024, 1(2), 76–95; https://doi.org/10.36922/aih.2284
Submitted: 21 November 2023 | Accepted: 17 January 2024 | Published: 23 April 2024
© 2024 by the Author (s). This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution 4.0 International License ( https://creativecommons.org/licenses/by/4.0/ )
Abstract

Multidrug-resistant (MDR), extensively drug-resistant (XDR), and totally drug-resistant bacteria can cause sepsis and death in patients due to their ability to inactivate most antibiotics, including ampicillin, tetracycline, streptomycin, chloramphenicol, erythromycin, and ciprofloxacin. This paper aims to review recent advancements in synthetic antibiotics, lantibiotics, and phytoantibiotics and to present our research on phytoantibiotics, specifically focusing on CU1 and NU2. While third- and fifth-generation synthetic antibiotics such as meropenem, moxifloxacin, amikacin, and tigecycline are currently relied upon for treating MDR infections, research is underway to develop peptide antibiotics known as lantibiotics (e.g., nisins, bacteriocins, and salivaricins). Lantibiotics such as nisin-A and salivaricin-B have demonstrated efficacy in curing numerous MDR infections, while phytochemicals such as artemisinin and quinine have shown effectiveness against chloroquine-resistant Plasmodium falciparum infections (malaria). In our study, we utilized techniques such as mass spectroscopy, nuclear magnetic resonance, and Fourier transform infrared spectroscopy in conjunction with artificial intelligence (AI) and computer simulation technologies to determine the structure of phytochemicals. Our results revealed that CU1, derived from Cassia fistula bark ethanol extract, exhibits potent antibiotic activity against XDR bacteria by targeting the RNA polymerases of Escherichia coli and Mycobacterium tuberculosis. Consequently, our MDR-Cure extract containing CU1 represents a promising antibacterial Ayurvedic medicine specifically tailored for skin and nail infections. Similarly, NU2 poly-fluorophosphate-glycosides from Suregada multiflora roots ethanol extract exhibited strong inhibitory effects on XDR bacteria by targeting DNA topoisomerase I. Recently, many cyclic peptide antibiotics have been synthesized in vitro using computer-guided AI technologies to predict 3D drug-enzyme interactions and are currently undergoing clinical trials. Our ultimate goal is to combat XDR bacteria-associated deaths, which are predicted to escalate as we approach 2050.

Keywords
Lantibiotics
Phytoantibiotics
Meropenem
Moxifloxacin
Salivaricin
Extensively drug-resistant tuberculosis
Funding
None.
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Conflict of interest
The authors declare that they have no competing interests.
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Artificial Intelligence in Health, Electronic ISSN: 3029-2387 Published by AccScience Publishing