Applications of computational science for understanding enzymatic deconstruction of cellulose

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Understanding the molecular-level mechanisms that enzymes employ to deconstruct plant cell walls is a fundamental scientific challenge with significant ramifications for renewable fuel production from biomass. In nature, bacteria and fungi use enzyme cocktails that include processive and non-processive cellulases and hemicellulases to convert cellulose and hemicellulose to soluble sugars. Catalyzed by an accelerated biofuels R&D portfolio, there is now a wealth of new structural and experimental insights related to cellulases and the structure of plant cell walls. From this background, computational approaches commonly used in other fields are now poised to offer insights complementary to experiments designed to probe mechanisms of plant cell wall deconstruction. Here we outline the current status of computational approaches for a collection of critical problems in cellulose deconstruction. We discuss path sampling methods to measure rates of elementary steps of enzyme action, coarse-grained modeling for understanding macromolecular, cellulosomal complexes, methods to screen for enzyme improvements, and studies of cellulose at the molecular level. Overall, simulation is a complementary tool to understand carbohydrate-active enzymes and plant cell walls, which will enable industrial processes for the production of advanced, renewable fuels.

Introduction

Enzymatic deconstruction of plant cell walls to fermentable sugars is a primary, near-term option currently being pursued for the production of advanced biofuels. Driven by significant, international R&D efforts for biofuels, there exists a new wealth of experimental data about the chemistry and structure of plant cell walls and the mechanisms that cellulase and hemicellulase enzymes use to deconstruct cell wall polysaccharides [1]. It is noteworthy, however, that we still lack sufficient data to explain cell wall deconstruction, even though elucidation of these steps is crucial to develop enhanced conversion processes. Here, we discuss the challenges, opportunities, and early successes of theory and simulation to aid our understanding of mechanisms of plant cell wall deconstruction and in catalyst design. We also present, where appropriate, examples computational methods that can be applied to a given problem into the complex problem of cell wall deconstruction with the advent of improved simulation codes and computational power (Figure 1). We limit the scope to research of cellulose and cellulases from the last three years, but many of the discussions are extendable to hemicellulases, chitinases, and other carbohydrate-active enzymes [2•, 3•]. We hope that this opinion illustrates to computational researchers from other fields that new, exciting opportunities exist in biomass conversion, and to biomass researchers that their work can be greatly enhanced by using computational science.

Section snippets

Plant cell wall polymer models

The study of cellulose requires: (i) reliable structural models and (ii) accurate potentials to describe cellulose. Structural models of plant cellulose have been proposed with 36 cellulose chains per elementary fibril (microfibril), although this has not been directly verified experimentally [4]. Further experimental characterization of plant microfibrils will aid in the construction of more accurate representations of microfibrils for simulations. Two popular atomistic potentials for

Free cellulases

Free cellulases usually consist of one or more carbohydrate-binding modules (CBMs), one or more linkers, and catalytic domain (CD), as shown in Figure 2. The CDs of processive cellulases have tunnels for threading cellulose chains, whereas CDs of non-processive cellulases instead have clefts for binding to accessible chains [15]. Here we discuss the Trichoderma reesei Family 7 processive cellobiohydrolase (Cel7A), as it has been thoroughly characterized experimentally. Also, cellulases often

Cellulosomal enzymes

Complexed enzymes are found in some bacteria and a few fungi where multiple carbohydrate-active enzymes are bound to protein scaffolds via cohesin–dockerin interactions to form a complex termed ‘cellulosome’ [49]. There are many open questions at multiple resolutions regarding the cellulosome structure and function for which simulations can offer valuable insight. Here, we review several open questions across multiple length and time scales, which are summarized in Figure 3.

At low resolution,

Computational screening of cellulases for improved activity

The primary method used for improving cellulase activity to date is increasing protein thermal stability, for which computation offers significant benefits [58]. Heinzelman et al. computationally recombined 8 structural blocks from three wild-type Family 6 cellulases (Cel6A) to produce a library of Cel6A cellulases with improved thermal stability and activity [58]. High-throughput, computational screening tools like that used by Heinzelman et al. or with Rosetta [59] will aid the design of more

Conclusion

Knowledge of the elementary steps in cellulase action is essential for building enhanced models of cellulose deconstruction, which will in turn guide development of enhanced cellulase systems. Driven by the wealth of new experimental data on cellulases and cellulose, computer simulations are beginning to play an increasingly significant role in understanding the structure–function relationships of enzymatic cellulose deconstruction. By using a versatile portfolio of computational methods,

References and recommended reading

Papers of particular interest, published within the period of review, have been highlighted as:

  • • of special interest

  • •• of outstanding interest

Acknowledgements

We thank the DOE Office of the Biomass Program (Cel7A modeling), the DOE BER BioEnergy Science Center (cellulosome modeling), and the DOE ASCR SciDAC program for funding (cellulose modeling). EAB holds the Maynard I. and Elaine Wishner Chair of Bio-organic Chemistry at the Weizmann Institute of Science.

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