Abstract
Alcohol oxidase (AOx) enzymes have gained significant attention for their potential in industrial applications due to their unique ability to catalyze irreversible oxidation of diverse alcohol substrates without external co-factors. This study revisits and enhances in-silico work on Aspergillus terreus MTCC6324 recombinant AOx (rAOx) enzyme, combining artificial intelligence (AlphaFold), molecular docking (AutoDock Vina) techniques and Molecular dynamics (MD) simulations (Desmond). Comprehensive sequence analysis revealed a high degree of conservation among 23 AOx amino acid sequences from various Aspergillus species highlights conserved regions, affirming its GXGXXG Rossmann fold motif. AlphaFold-predicted 3D structure of rAOx demonstrated improved stereo-chemical stability compared to I-TASSER predicted structure with 87.6% amino acid residues in most favourable region of Ramachandran plot compared to 79.5%, respectively. Molecular docking revealed the binding affinities of co-factors FAD and diverse alcohol substrates, with cinnamyl alcohol exhibiting robust interaction with rAOx holoenzyme. MD simulations further elucidate the stability and dynamics of rAOx-FAD-cinnamyl alcohol complex over 100 nanoseconds. The simulations showcase FAD’s stable binding within the protein core and highlights transient substrate interactions, dissociating within the active site after 75 ns suggesting a substrate sequestration mechanism. The study unveils substrate sequestration mechanism wherein cinnamyl alcohol exhibits temporary binding, leading to quick detachment from active site, mimicking reported exponential kinetics. This study not only validates previous findings but also offers a comprehensive understanding of intricate dynamics governing rAOx enzymatic activity. The improved sequence-to-structure prediction and detailed molecular insights into substrate sequestration provide a valuable foundation for future experimental investigations and rational design of bio-catalytic processes.
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