(490d) Optimization of Enzyme Mixture for Simultaneous Hydrolysis of Cellulose and Hemicelluloses Based on a Novel Mechanistic Model

Lignocellulosic biomass are mainly comprised of intertwined cellulose and hemicelluloses which are both important resources for the production of biofuels and could contribute to reducing the current dependence on traditional fossil fuels. A novel mechanistic model for enzymatic hydrolysis of cellulose and hemicellulose simultaneously is developed that takes into consideration explicitly the time evolution of morphologies of intertwining cellulose and hemicelluloses within substrate during enzymatic hydrolysis. This morphology evolution is driven by hydrolytic chain fragmentation and solubilization, which is, in return, profoundly affected by the substrate morphology. An advanced and generalized site concentration formalism that considers different polysaccharide chain types and different monomer unit types on chains and a morphology-plus-kinetics approach then couples the time-dependent morphology with chain fragmentation and solubilization resulted from enzymatic reactions between various bonds in cellulose and hemicelluloses and a mixture of cellulases and hemicellulases. Based on the model, the optimal mixtures of cellulases and hemicellulase are predicted for maximizing the conversion level of  different types of hemicellulose-cellulosic substrates. The most important cellulases and hemicellulases necessary to hydrolyze the substrates are tested together during hydrolysis, including endo-acting cellulases and hemicellulases (EG and EX), exo-acting cellulases (CBH I and II) and oligomer-acting cellulases and hemicellulases (βG and βX).  These enzymes are thought to exist a best ratio during hydrolysis due to their different functions in which EG and EX randomly hydrolyze internal bonds on polysaccharide chains of cellulose and hemicelluloses, CBH I and II cleave cellobioses from cellulosic chain ends and βG and βX hydrolyze oligomers into monomers in solution. These results demonstrate not only the utility of the model for enzyme optimization, but also a rational strategy for the development of more efficient enzyme mixtures.