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Computational modeling and druggability assessment of Aggregatibacter actinomycetemcomitans leukotoxin.

Authors :
Hakmi, Mohammed
Bouricha, El Mehdi
El Harti, Jaouad
Amzazi, Said
Belyamani, Lahcen
Khanfri, Jamal Eddine
Ibrahimi, Azeddine
Source :
Computer Methods & Programs in Biomedicine. Jul2022, Vol. 222, pN.PAG-N.PAG. 1p.
Publication Year :
2022

Abstract

• LtxA of Aggregatibacter actinomycetemcomitans plays an important role in immune system evasion • The lack of a tertiary structure of LtxA limits its understanding at the molecular level • The elucidation of the structural model of LtxA could open new avenues for the discovery of novel leads to disrupt its activity The leukotoxin (LtxA) of Aggregatibacter actinomycetemcomitans (A. actinomycetemcomitans) is a protein exotoxin belonging to the repeat-in-toxin family (RTX). Numerous studies have demonstrated that LtxA may play a critical role in the pathogenicity of A. actinomycetemcomitans since hyper-leukotoxic strains have been associated with severe disease. Accordingly, considerable effort has been made to elucidate the mechanisms by which LtxA interacts with host cells and induce their death. However, these attempts have been hampered by the unavailability of a tertiary structure of the toxin, which limits the understanding of its molecular properties and mechanisms. In this paper, we used homology and template free modeling algorithms to build the complete tertiary model of LtxA at atomic level in its calcium-bound Holo-state. The resulting model was refined by energy minimization, validated by Molprobity and ProSA tools, and subsequently subjected to a cumulative 600ns of all-atom classical molecular dynamics simulation to evaluate its structural aspects. The druggability of the proposed model was assessed using Fpocket and FTMap tools, resulting in the identification of four putative cavities and fifteen binding hotspots that could be targeted by rational drug design tools to find new ligands to inhibit LtxA activity. [ABSTRACT FROM AUTHOR]

Details

Language :
English
ISSN :
01692607
Volume :
222
Database :
Academic Search Index
Journal :
Computer Methods & Programs in Biomedicine
Publication Type :
Academic Journal
Accession number :
157711716
Full Text :
https://doi.org/10.1016/j.cmpb.2022.106952