264832 Adsorption of Model Peptides and the Carbohydrate Binding Module: An Enhanced Sampling Molecular Dynamics Study

Wednesday, October 31, 2012: 4:57 PM
415 (Convention Center )
Michael Deighan, Peter Englund and Jim Pfaendtner, Chemical Engineering, University of Washington, Seattle, WA

The enzymatic breakdown of cellulose and utilization of its component sugars is a popular strategy for generating fuels and chemicals from renewable sources. To optimize this process, it is imperative to understand the structure and function of the molecular machinery involved. Of these machines, cellulases play a critical role by catalyzing the hydrolysis of cellulose. Cellulases ideal for industrial application should be able to hydrolyze insoluble cellulose to minimize the number of feedstock pretreatment steps. Fortunately, several cellulases exist that have evolved to perform this function. These enzymes comprise two domains: the first is known generally as the carbohydrate-binding module (CBM), which recognizes and binds to crystalline cellulose; the second is a catalytic domain that performs hydrolysis. It is the CBM that initially binds to the surface of a cellulosic fragment. This event restricts enzymatic motion normal to the surface but permits 2D motion across the surface plane where hydrolysis can occur [1]. However, experimental observation of adsorption is limited to the resolution of the equipment involved, which is unable to provide atomic-scale insight into the adsorption mechanism. To provide molecular level insight we have used Parallel Tempering Metadynamics in the Well-Tempered Ensemble (PTMetaD-WTE) molecular dynamics (MD) simulations to resolve the roles of residues that contribute to adsorption and surface mobility as well as gain insights into the conformational stability of the CBM in the adsorbed state.

We report: (I) Several PTMetaD-WTE simulations on the adsorption of small host-guest peptides [2, 3]. These simulations are treated as baseline calibrations for the PTMetaD-WTE method used in adsorption applications; and (II) A PTMetaD-WTE study on the adsorption of the CBM and its component residues on crystalline cellulose. PTMetaD-WTE is a recently developed enhanced sampling algorithm. The method itself consists of a combination of the following: 1) Well-Tempered Metadynamics (WTM) which biases a simulation against user-defined collective variables representing intrinsically slow degrees of freedom [4]; 2) Parallel Tempering (PT) which improves the efficiency of WTM by allowing a system to overcome all relevant energy barriers through temperature swapping [5, 6]; and 3) Well-Tempered Ensemble (WTE) which amplifies the potential energy fluctuations of a system, increasing the exchange probability between replicas, and making it possible to run a simulation with much improved convergence over PTMetaD [7].

I. The host-guest peptide framework is represented by the sequence TGTG-X-GTGT, where X is a variable residue. We simulate the adsorption of several of these peptides (where X = {G, T, L, K}) on methyl and carboxyl terminated self-assembled monolayer (SAM) surfaces. The center of mass (COM) of each peptide is biased in these simulations in order to produce many adsorption/desorption events. The results we obtain are compared to existing surface plasmon resonance (SPR) data of identical systems.

II. In these PTMetaD-WTE simulations, the direction normal to the surface was biased with respect to the COM of the adsorbing molecule (CBM or peptide of interest). This, as in (I), permitted the occurrence of multiple adsorption and desorption events within a single simulation. From these preliminary data, we estimate free energy profiles for the CBM and its residues, calculate their most probable adsorption states, and infer coordinated locomotion of the CMB across the cellulosic surface.

References

  1. Demain, Newcomb, and Wu. Microbiol. Mol. Biol. R. 69, 124-154 (2005).
  2. Wei and Latour. Langmuir. 24, 6721-6729 (2008).
  3. Wei and Latour. Langmuir. 25, 5637-5646 (2009).
  4. Barducci, Bussi, and Parrinello. Phys. Rev. Lett. 100, 020603 (2008).
  5. Hansmann. Chem. Phys. Lett. 281, 140-150 (1997).
  6. Bussi, Gervasio, Laio, and Parrinello. J. Am. Chem. Soc. 128, 13435-13441 (2006).
  7. Bonomi and Parrinello. Phys. Rev. Lett. 104, 190610 (2010).

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