ADMET and in silico profiling: predicting lead compounds targeting Mycobacterium abscessus isocitrate lyase
Keywords:
ADMET, artificial intelligence, isocitrate lyase, molecular docking, Mycobacterium abscessus, natural productsAbstract
Mycobacterium abscessus infections exacerbate lung conditions in individuals with cystic fibrosis, bronchiectasis, or low immunity. The existing treatment options are unreliable, owing to the high antibiotic resistance of the bacterium. Isocitrate lyase is an important enzyme in mycobacteria linked with the glyoxylate cycle, facilitating persistent infections within the host. Isocitrate lyase is considered a promising drug target because its inhibition diminishes mycobacterial growth and persistence. The dearth of effective treatment highlighted the need for in silico screening for antimicrobials with improved efficacy and safety. Through literature review, ADMET profiling, and molecular docking, eight natural products were shortlisted based on drug-like properties and Lipinski’s rule. AlphaFold generated high-quality structures of isocitrate lyase. Subsequently, the docking of the eight compounds with the 3D structures of isocitrate lyase was carried out. Binding energies ranged from –5.2 to –8.8 kcal/mol, with bonianic acid A and demethoxycurcumin showing the strongest affinities (–8.8 and –8 kcal/mol) against M. abscessus subsp. bolletii BD and M. abscessus subsp. abscessus ATCC 19977. Bisdemethoxycurcumin exhibited a –7.9 kcal/mol binding energy with M. abscessus subsp. abscessus ATCC 19977. Normal mode analysis confirmed their robustness. These findings support further exploration of bonianic acid A, demethoxycurcumin, and bisdemethoxycurcumin targeting isocitrate lyase, paving the way for future in vitro and in vivo studies.


