Biofuel production from lignocellulosic materials is considered to be a promising option to substantially reduce the dependence on petroleum [1
]. The conversion of lignocellulosic biomass (agronomic residues, paper wastes, energy crops) into ethanol consists of the extraction and pretreatment of cellulose from the biomass, hydrolysis (the enzymatic breakdown of crystalline cellulose fibers into monomer glucose) and finally the fermentation of glucose to ethanol. Current approaches mainly differ from one another in the method of pretreatment. Cost-competitive production of ethanol is currently prevented by the low efficiency of converting cellulose into glucose [4
]. Greater efficiency may be achievable through improvements in hydrolysis.
Enzymatic hydrolysis of cellulose is a complex reaction. In the classical model, the heterogeneous catalytic cleavage of the glycosidic bond takes place on a crystalline cellulose surface and requires the cooperative action of three classes of aqueous enzymes, collectively known as cellulases. These are (i) endoglucanases, (ii) exoglucanases or cellobiohydrolases and (iii) β-glucosidases. Recently, it has been proposed that oxidative enzymes (monoxygenases) may also play a role in cleaving glycosidic bonds, although this new mechanism may be restricted to certain types of microbes [5
]. It is widely accepted that endoglucanases cleave β-1,4-D-glycosidic bonds at random sites within both amorphous and crystalline polysaccharide chains, creating new chain ends on the cellulose surface [6
]. Exoglucanases prefer to hydrolyze crystalline cellulose chains by acting on the free chain ends and releasing cellobiose units in a processive manner. Soluble cellobiose units are then converted into glucose by β-glucosidases. Consequently, these enzymes display strong synergy [12
The classical chemical kinetics assumption of uniformly mixed systems does not hold in the case of enzymatic hydrolysis of cellulose fiber, as it is heterogeneous in nature. Such reactions are rather characterized by time-dependent rate constants and non-uniform concentration variation of reacting species. Although kinetic models [11
] have been used to explain various features of the enzymatic hydrolysis of cellulose, they fail to account for spatial details of the cellulose substrate as well as the specificity of binding sites. Recently, Zhou and colleagues [23
] proposed a “morphology-plus-kinetics rate equation approach” that explicitly captures the hydrolytic evolution of cellulose substrate. In addition, a kinetic model was developed based on population-balance equations in which a distribution of chains with different chain-lengths was explored [26
]. Yet, these models give little insight regarding the action of cellulases at the molecular level.
It is imperative to develop spatial models of cellulose degradation because spatial effects such as enzyme crowding on the cellulose surface have been shown to lead to a reduction in hydrolysis rates. In order to account for the spatial heterogeneity of the system during cellulose hydrolysis, a cellular automata model [28
] was developed to study the effect of different parameters such as enzyme binding and hydrolysis on the overall kinetics of cellulose by the cellulases. Alternatively, all-atom molecular dynamics (MD) simulations can provide details of molecular level events at high precision. Recent MD simulation studies [29
] have proven effective for understanding enzyme-substrate binding, processivity and activity. However, because of length and time scale limitations, it is not currently possible to simulate the entire crystalline cellulose degradation process using all-atom MD simulations.
We have developed a coarse-grained stochastic model that captures the interaction of endo- and exo-cellulases with crystalline cellulose at a mesoscopic level. This model was specifically designed to improve our understanding of the molecular-level details of the enzymatic hydrolysis of crystalline cellulose. This paper introduces the basic framework and demonstrates how this model can be an effective and easily modifiable testing platform for new hypotheses based on experimental data on various cellulase components and substrate characteristics. By capturing the reactive nature of the cellulose substrate and the activities of non-complexed cellulases at the molecular level, this method forms a bridge between all-atom MD studies and deterministic reaction-rate approaches. To the best of our knowledge, it is the first model that is able to relate the synergetic action of multiple enzymes to molecular level details such as the hydrogen bond network of a cellulose substrate.