Collaborative Research: EAGER:Studying lignocellulosic fine structure and its dynamics in enzymatic hydrolysis of biomass using molecule-recognizing AFM and computational modeling

Michigan Technological University, Houghton MI

Investigators

Abstract

ABSTRACT Lignocellulosic biomass is a composite structure with crystalline cellulose, hydrated hemicellulose, and lignin as major components. It has long been recognized as a potential lowcost and sustainable source of mixed sugars for production of biofuels and other value-added chemicals. Plants have evolved superb mechanisms for resisting assault on their cell wall structural sugars from the microbial and animal kingdoms, collectively known as biomass recalcitrance. These mechanisms are comprised of factors that are believed to contribute to the inefficiency of enzymatic hydrolysis of biomass. The lignocellulosic fine structure, i.e. the way cellulose, hemicelluloses, and lignin are bonding with each other, and how the lignocellulosic fine structure evolves during hydrolysis due to the molecular interactions between biomass and enzymes, is thus crucial for logistic and specific design of enzymes and processes to overcome the above factors that slow down the hydrolytic reactions. However, such urgently needed information is pretty much missing because direct detection of lignocellulose component conformation and distribution is NOT possible so far. In this EAGER project, Investigators Bingqian Xu from University of Georgia and Wen Zhou from Michigan Technological University will employ a unique approach which is to combine the newly developed CBM functionalized AFM (atomic force microscope) technology with computational modeling to directly detect lignocellulose component conformation and distribution, thereby overcoming the long-standing technical difficulties in realizing the dynamics of lignocellulosic components (conformation and distribution) during the enzymatic hydrolysis. An EAGER grant would support this collaborated research to explore this proposed high-risk, high-reward project by getting the much needed data. The aim is a tool and methodology for selection and design of better enzymes and processes to overcome the biomass recalcitrance efficiently. The significance of the proposed research lies in the ability (1) to study the lignocellulosic fine structure in nanometer scale with molecular recognition, (2) to construct the 3D structural image of biomass particle, and (3) to monitor the lignocellulosic fine structure dynamics in hydrolysis. The combination of experimental and computational modeling methods will potentially provide a new approach and evidence to tackle the unsolved lignocelluloses component conformation and distribution, offering molecular scale understanding of the lignocellulose hydrolysis process which could be critical in overcoming biomass recalcitrance. In addition, development of the technology will also add unique capabilities for single molecule studies in other biosystems to probe the biomolecules and their interactions.