Difference between revisions of "Template:Groningen/Model"

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     <p>Our integrated styrene production pathway in Saccharomyces cerevisiae relies on several key properties to make the process the most efficient in ideal conditions. Our enzyme scaffold which supposedly enhances enzyme activity by bringing the enzymes in closer proximity to each other, and closer to cellulose by a cellulose binding domain. Furthermore our yeast strain needs several metabolic optimizations to maximize styrene production levels. </p>
 
     <p>Our integrated styrene production pathway in Saccharomyces cerevisiae relies on several key properties to make the process the most efficient in ideal conditions. Our enzyme scaffold which supposedly enhances enzyme activity by bringing the enzymes in closer proximity to each other, and closer to cellulose by a cellulose binding domain. Furthermore our yeast strain needs several metabolic optimizations to maximize styrene production levels. </p>
  
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     <p>To verify whether an enzyme scaffold indeed enhances enzyme activity, a <a href="https://2018.igem.org/Team:Groningen/Model/Stochastic_Modeling">mathematical stochastic model</a> was created to describe catalyzed degradation of cellulose in the presence of our enzymes. The model, based on work by Levine et al. [1], includes an exoglucanase and an endoglucanase, both in bound together and separately in solution.  
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     <p>To verify whether an enzyme scaffold indeed enhances enzyme activity, a <a href="https://2018.igem.org/Team:Groningen/Model/Stochastic_Modeling">mathematical model</a> was created to describe catalyzed degradation of cellulose in the presence of our enzymes. The model, based on work by Levine et al. [1], includes an exoglucanase and an endoglucanase, both in bound together and separately in solution.  
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    The model predicts a 14 fold increase in enzyme activity for the bound enzymes, relative to free enzymes. It is likely that the ꞵ-glucanase benefits even more from proximity to the exo- and endoglucanases. This result provides a strong justification for binding the enzymes together in a protein scaffold.</p>
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  The model provides a justification for the use of scaffolding and insight into how to choose enzymes for a scaffold</p>
  
 
     <p>As it is likely that substrate availability is higher when the scaffold is strongly bound to cellulose, we explored the binding characteristics of the cellulose binding domain that is attached to our minicellulosome scaffold using <a href="https://2018.igem.org/Team:Groningen/Model/Molecular_Dynamics">coarse grained molecular dynamics</a>. The cellulose binding domain (CBD) of Cip3A from Clostridium thermocellum (PDB 1NBC by Tormo et al. [2]) was modeled in the presence of a single cellulose fiber using coarse grained molecular dynamics simulations [3]. The simulations affirm the hypothesized binding mechanism of CBD to cellulose, and show that CBD from C. thermocellum has a very high affinity for cellulose fibers. From these simulations, the potential of mean force of the binding interaction could be computed, from which the binding free energy could be derived. The results show that our CBD binds even stronger to cellulose than some other cellulose binding domains known from literature. These results justify including the CBD in the enzyme scaffold as it will likely increase the activity of the complex by substrate proximity.</p>
 
     <p>As it is likely that substrate availability is higher when the scaffold is strongly bound to cellulose, we explored the binding characteristics of the cellulose binding domain that is attached to our minicellulosome scaffold using <a href="https://2018.igem.org/Team:Groningen/Model/Molecular_Dynamics">coarse grained molecular dynamics</a>. The cellulose binding domain (CBD) of Cip3A from Clostridium thermocellum (PDB 1NBC by Tormo et al. [2]) was modeled in the presence of a single cellulose fiber using coarse grained molecular dynamics simulations [3]. The simulations affirm the hypothesized binding mechanism of CBD to cellulose, and show that CBD from C. thermocellum has a very high affinity for cellulose fibers. From these simulations, the potential of mean force of the binding interaction could be computed, from which the binding free energy could be derived. The results show that our CBD binds even stronger to cellulose than some other cellulose binding domains known from literature. These results justify including the CBD in the enzyme scaffold as it will likely increase the activity of the complex by substrate proximity.</p>

Revision as of 13:51, 17 October 2018

Modeling Overview

Our integrated styrene production pathway in Saccharomyces cerevisiae relies on several key properties to make the process the most efficient in ideal conditions. Our enzyme scaffold which supposedly enhances enzyme activity by bringing the enzymes in closer proximity to each other, and closer to cellulose by a cellulose binding domain. Furthermore our yeast strain needs several metabolic optimizations to maximize styrene production levels.

To verify whether an enzyme scaffold indeed enhances enzyme activity, a mathematical model was created to describe catalyzed degradation of cellulose in the presence of our enzymes. The model, based on work by Levine et al. [1], includes an exoglucanase and an endoglucanase, both in bound together and separately in solution. The model provides a justification for the use of scaffolding and insight into how to choose enzymes for a scaffold

As it is likely that substrate availability is higher when the scaffold is strongly bound to cellulose, we explored the binding characteristics of the cellulose binding domain that is attached to our minicellulosome scaffold using coarse grained molecular dynamics. The cellulose binding domain (CBD) of Cip3A from Clostridium thermocellum (PDB 1NBC by Tormo et al. [2]) was modeled in the presence of a single cellulose fiber using coarse grained molecular dynamics simulations [3]. The simulations affirm the hypothesized binding mechanism of CBD to cellulose, and show that CBD from C. thermocellum has a very high affinity for cellulose fibers. From these simulations, the potential of mean force of the binding interaction could be computed, from which the binding free energy could be derived. The results show that our CBD binds even stronger to cellulose than some other cellulose binding domains known from literature. These results justify including the CBD in the enzyme scaffold as it will likely increase the activity of the complex by substrate proximity.

Finally flux balance modeling was used to model the metabolism of S. cerevisiae after introduction of our heterologous pathway enabling styrene production. Yeast GEM 7.6 [4] was used to model the metabolic network of yeast. Furthermore, the OptForce algorithm [5], implemented in the COBRA toolbox [6], was used to find compatible sets of up- or down-regulated genes. When optimizing for biomass, it was shown that 65% of maximum styrene production is still compatible with normal growth of yeast, justifying our choice for yeast as our host organism.

To speed up simulations, our models were run using the Groningen University’s state of the art 4000+ core Peregrine cluster.

References

[1] Levine, S. E., Fox, J. M., Blanch, H. W., & Clark, D. S. (2010). A mechanistic model of the enzymatic hydrolysis of cellulose. Biotechnology and Bioengineering. https://doi.org/10.1002/bit.22789

[2] Tormo, J., Lamed, R., Chirino, A. J., Morag, E., Bayer, E. A., Shoham, Y., & Steitz, T. A. (1996). Crystal structure of a bacterial family-III cellulose-binding domain: a general mechanism for attachment to cellulose. The EMBO Journal, 15(21), 5739–5751. Retrieved from http://www.ncbi.nlm.nih.gov/pubmed/8918451

[3] Marrink, S. J., Risselada, H. J., Yefimov, S., Tieleman, D. P., & Vries, A. H. de. (2007). The MARTINI Force Field: Coarse Grained Model for Biomolecular Simulations. J. Phys. Chem. https://doi.org/10.1021/JP071097F

[4] Sánchez, B. J., Zhang, C., Nilsson, A., Lahtvee, P., Kerkhoven, E. J., & Nielsen, J. (2017). Improving the phenotype predictions of a yeast genome‐scale metabolic model by incorporating enzymatic constraints. Molecular Systems Biology. https://doi.org/10.15252/msb.20167411

[5] Ranganathan, S., Suthers, P. F., & Maranas, C. D. (2010). OptForce: An optimization procedure for identifying all genetic manipulations leading to targeted overproductions. PLoS Computational Biology. https://doi.org/10.1371/journal.pcbi.1000744

[6] Heirendt, L., Arreckx, S., Pfau, T., Mendoza, S. N., Richelle, A., Heinken, A., … Fleming, R. M. T. (2017). Creation and analysis of biochemical constraint-based models: the COBRA Toolbox v3.0. ArXiv. https://doi.org/10.1038/protex.2011.234