Dataset Information

Mathematical model for empirically optimizing large scale production of soluble protein domains.

ABSTRACT:

Background

Efficient dissection of large proteins into their structural domains is critical for high throughput proteome analysis. So far, no study has focused on mathematically modeling a protein dissection protocol in terms of a production system. Here, we report a mathematical model for empirically optimizing the cost of large-scale domain production in proteomics research.

Results

The model computes the expected number of successfully producing soluble domains, using a conditional probability between domain and boundary identification. Typical values for the model's parameters were estimated using the experimental results for identifying soluble domains from the 2,032 Kazusa HUGE protein sequences. Among the 215 fragments corresponding to the 24 domains that were expressed correctly, 111, corresponding to 18 domains, were soluble. Our model indicates that, under the conditions used in our pilot experiment, the probability of correctly predicting the existence of a domain was 81% (175/215) and that of predicting its boundary was 63% (111/175). Under these conditions, the most cost/effort-effective production of soluble domains was to prepare one to seven fragments per predicted domain.

Conclusions

Our mathematical modeling of protein dissection protocols indicates that the optimum number of fragments tested per domain is actually much smaller than expected a priori. The application range of our model is not limited to protein dissection, and it can be utilized for designing various large-scale mutational analyses or screening libraries.

SUBMITTER: Chikayama E

PROVIDER: S-EPMC2843616 | biostudies-literature | 2010 Mar

REPOSITORIES: biostudies-literature

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Publications

Mathematical model for empirically optimizing large scale production of soluble protein domains.

Chikayama Eisuke E Kurotani Atsushi A Tanaka Takanori T Yabuki Takashi T Miyazaki Satoshi S Yokoyama Shigeyuki S Kuroda Yutaka Y

BMC bioinformatics 20100301

<h4>Background</h4>Efficient dissection of large proteins into their structural domains is critical for high throughput proteome analysis. So far, no study has focused on mathematically modeling a protein dissection protocol in terms of a production system. Here, we report a mathematical model for empirically optimizing the cost of large-scale domain production in proteomics research.<h4>Results</h4>The model computes the expected number of successfully producing soluble domains, using a conditi ...[more]

PMID: 20193068

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Project description:BackgroundModels of metabolism are often used in biotechnology and pharmaceutical research to identify drug targets or increase the direct production of valuable compounds. Due to the complexity of large metabolic systems, a number of conclusions have been drawn using mathematical methods with simplifying assumptions. For example, constraint-based models describe changes of internal concentrations that occur much quicker than alterations in cell physiology. Thus, metabolite concentrations and reaction fluxes are fixed to constant values. This greatly reduces the mathematical complexity, while providing a reasonably good description of the system in steady state. However, without a large number of constraints, many different flux sets can describe the optimal model and we obtain no information on how metabolite levels dynamically change. Thus, to accurately determine what is taking place within the cell, finer quality data and more detailed models need to be constructed.ResultsIn this paper we present a computational framework, DMPy, that uses a network scheme as input to automatically search for kinetic rates and produce a mathematical model that describes temporal changes of metabolite fluxes. The parameter search utilises several online databases to find measured reaction parameters. From this, we take advantage of previous modelling efforts, such as Parameter Balancing, to produce an initial mathematical model of a metabolic pathway. We analyse the effect of parameter uncertainty on model dynamics and test how recent flux-based model reduction techniques alter system properties. To our knowledge this is the first time such analysis has been performed on large models of metabolism. Our results highlight that good estimates of at least 80% of the reaction rates are required to accurately model metabolic systems. Furthermore, reducing the size of the model by grouping reactions together based on fluxes alters the resulting system dynamics.ConclusionThe presented pipeline automates the modelling process for large metabolic networks. From this, users can simulate their pathway of interest and obtain a better understanding of how altering conditions influences cellular dynamics. By testing the effects of different parameterisations we are also able to provide suggestions to help construct more accurate models of complete metabolic systems in the future.

Project description:The Australian tobacco plant Nicotiana benthamiana is becoming increasingly popular as a platform for protein production and metabolic engineering. In this system, gene expression is achieved transiently by infiltrating N. benthamiana plants with suspensions of Agrobacterium tumefaciens carrying vectors with the target genes. To infiltrate larger numbers of plants, vacuum infiltration is the most efficient approach known, which is already used on industrial scale. Current laboratory-scale solutions for vacuum infiltration, however, either require expensive custom-tailored equipment or produce large amounts of biologically contaminated waste. To overcome these problems and lower the burden to establish vacuum infiltration in new laboratories, we present here 3D-printed plant holders for vacuum infiltration. We demonstrate that our plant holders are simple to use and enable a throughput of around 40 plants per hour. In addition, our 3D-printed plant holders are made from autoclavable material, which tolerate at least 12 autoclave cycles, helping to limit the production of contaminated waste and thus contributing to increased sustainability in research. In conclusion, our plant holders provide a simple, robust, safe and transparent platform for laboratory-scale vacuum infiltration that can be readily adopted by new laboratories interested in protein and metabolite production in Nicotiana benthamiana. Practical application Transient expression in Nicotiana benthamiana provides a popular and rapid system for producing proteins in a plant host. To infiltrate larger numbers of plants (typically >20), vacuum infiltration is the method of choice. However, no system has been described so far which is robust to use and can be used without expensive and complex equipment. Our autoclavable 3D-printed plant holders presented here will greatly reduce the efforts required to adopt the vacuum infiltration technique in new laboratories. They are easy to use and can be autoclaved at least 12 times, which contributes to waste reduction and sustainability in research laboratories. We anticipate that the 3D printing design provided here will drastically lower the bar for new groups to employ vacuum infiltration for producing proteins and metabolites in Nicotiana benthamiana.

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Dataset Information

Mathematical model for empirically optimizing large scale production of soluble protein domains.

Background

Results

Conclusions

Publications

Mathematical model for empirically optimizing large scale production of soluble protein domains.

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