1: Introduction
    An increasing array of target molecules with a wide range of applications could be synthesized within microorganisms through various strategies in the field of metabolic engineering as well as synthetic biology[1].One of the main challenges in biosynthesis or metabolic engineering is selecting an appropriate chassis host for producing a specified target chemical[2-4]. To this end, CF-Targeter was developed to assist biologists in selecting high-yield production strains for given target molecules. This web server contains nearly 50,000,000 biosynthetic pathways calculated for more than one month by a supercomputer and supports host organism selection from 70 host organisms, which covers bacteria, eukaryotes and archaea, for the production of more than 6,000 compounds. With the retrieved organisms, several heterologous biosynthetic pathways can be shown in length order, and maximum theoretical yields can be calculated under defined organism physiological states and growth conditions. To the best of our knowledge, this work is a first user-friendly web server to assist in selecting organism hosts for specified target production.

2.1: Query Submitted -- Name Search
    *Choose Name module, and then input a target name. 2.2: Result-- Name Search

    2.2.1: Name-Search Result - 1st step : Candidate chassises enumeration based on heterologous pathway length.


    2.2.2: Name-Search Result - 2nd step : Heterologous pathways enumeration for target compound production within specified chassis-host.

    2.2.3: Name-Search Result - 3rd step : Theoretical yields calculation / Thermodynamic feasibility analysis
            (1): Main Carbon Resource/Condition: Main Carbon Resource and Condition allow users to specify organism growth condition (main carbon source/oxygen state).
            (2): Biomass: It represents a percentage that minimum growth rate of mutant type accounts for maximum theoretical growth rate of WT type. Users could enter a decimal between 0 and 1.
            (3): Thermodynamic feasibility analysis: Thermodynamic feasibility of heterologous reaction is represented by ΔG'm, of which negative value means that the reaction is thermodynamically favorable, and vice versa. ΔG'm could be calculated in real time by using equilibrator-api [5] under customized ph, ionic strength, and temperature. Moreover, we used the 1 mM concentration for all reactants.


3.1: Query Submitted -- Fragment Search
    *Choose Fragment module, and then input a target SMILES.[The retrieval process of the other two modeuls(Maximum Common Structure(MCS)/Similarity) is similar to Fragment-searching module,so here we only take Fragment as an example.] 3.2: Result -- Fragment Search

    3.2.1: Fragment-Search Result - 1st step : Enumeration of compounds that contain the input molecule in structure,and then select one compound as target for next step.

    3.2.2: Fragment-Search Result - 2nd step: Candidate chassises enumeration based on heterologous pathway length. 

    3.2.3: Fragment-Search Result - 3rd step:  Heterologous pathways enumeration in specified organism host for producing the compound that contains target molucule in structure. 

    3.2.4: Fragment-Search Result - 4th step : Theoretical yields calculation
            (1): Main Carbon Resource/Condition: Main Carbon Resource and Condition allow users to specify organism growth condition (main carbon source/oxygen state).
            (2): Biomass: It represents a percentage that minimum growth rate of mutant type accounts for maximum theoretical growth rate of WT type. Users could enter a decimal between 0 and 1.
            (3): Thermodynamic feasibility analysis: Thermodynamic feasibility of heterologous reaction is represented by ΔG'm, which negative value means that the reaction is favorable in thermodynamics, and vice versa. ΔG'm could be calculated real-timely by using equilibrator-api [5] under customized ph, ionic strength, and temperature. Besides, we used the 1 mM concentration for all reactants.
4: References:
        [1]: Computational methods in metabolic engineering for strain design. Long, M. R., Ong, W. K., and Reed, J. L. Current. Opintion. Biotechnology.2015
        [2]: Bioinformatics for the synthetic biology of natural products: integrating across the Design–Build– Test cycle. Pablo Carbonell,Andrew Currin, Adrian J. Jervis. Natural Product Reports. 2012
        [3]: Microbial Chassis Assisting Retrosynthesis. Milsee Mol, Vineetha Mandlik, and Shailza SinghSystems Biology Application in Synthetic Biology.2016
        [4]: Retrosynthetic Design of Heterologous Pathways. Pablo Carbonell, Anne-Gae¨lle Planson, and Jean-Loup FaulonMethods in Molecular Biology.2013
        [5]: Consistent estimation of Gibbs energy using component contributions. Noor E, Haraldsdóttir HS, Milo R, Fleming RMT.PLoS Comput Biol.2013
5: Contact us
        Send your comments or error report to RxnFinder Team.
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