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Computational investigations in inhibition of alcohol/aldehyde dehydrogenase in lignocellulosic hydrolysates

Karan KumariD, Azeeza SiddiqaiD, Pragati, Priti ChandaneiD, Saanya YadaviD, Mahima KoriiD, A ShivramiD, Lepakshi BarboraiD, Vijayanand S. MoholkariD

Abstract

Second generation alcoholic biofuels synthesis from lignocellulosic biomass (LB) consists three steps viz., pre-treatment, detoxification, and fermentation. This dilute acid pre-treatment process generates several compounds like acids, aldehydes, ketones, oxides and their phenolic derivatives that are potential inhibitors of some of the crucial enzymes in the metabolic pathway of ABE fermentation. With application of hybrid quantum mechanics/ molecular mechanics (QM/MM) approach, our aim is to discern the molecular mechanism of inhibition of key AADs across solventogenic species. The objectives of present study are: (1) identification and homology modelling of key AADs; (2) validation, quality assessment and physiochemical characterization of the modelled enzymes; (3) identification, construction and optimization of chemical structure of potent microbial inhibitors in LH; and (4) applications of hybrid QM/MM simulations to profile the molecular interactions between microbial inhibitors and key AADs. Our computational investigation has revealed various important facets of inhibition of the AAD enzymes, which could guide structural biologist in designing efficient and robust enzymes. Moreover, our methodology also provides a general framework which could applied for deciphering the molecular mechanism of inhibition behaviour of other enzymes. HighlightsO_LIHomology modelling of 7 alcohol/aldehyde dehydrogenase (AAD) in solventogenic Clostridia C_LIO_LIIdentification and structural optimization of potent microbial inhibitors in lignocellulosic hydrolysates C_LIO_LIQM/MM simulations to profile the molecular interactions between 10 inhibitors and 7 AADs C_LIO_LIDiscernment of the molecular mechanism of inhibition of key alcohol/aldehyde dehydrogenase C_LIO_LIA methodological framework for deciphering the molecular mechanism of enzyme inhibition C_LI

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