RetSynth: determining all optimal and sub-optimal synthetic pathways that facilitate synthesis of target compounds in chassis organisms

Abstract

Background The efficient biological production of industrially and economically important compounds is a challenging problem. Brute-force determination of the optimal pathways to efficient production of a target chemical in a chassis organism is computationally intractable. Many current methods provide a single solution to this problem, but fail to provide all optimal pathways, optional sub-optimal solutions or hybrid biological/non-biological solutions. Results Here we present RetSynth, software with a novel algorithm for determining all optimal biological pathways given a starting biological chassis and target chemical. By dynamically selecting constraints, the number of potential pathways scales by the number of fully independent pathways and not by the number of overall reactions or size of the metabolic network. This feature allows all optimal pathways to be determined for a large number of chemicals and for a large corpus of potential chassis organisms. Additionally, this software contains other features including the ability to collect data from metabolic repositories, perform flux balance analysis, and to view optimal pathways identified by our algorithm using a built-in visualization module. This software also identifies sub-optimal pathways and allows incorporation of non-biological chemical reactions, which may be performed after metabolic production of precursor molecules. Conclusions The novel algorithm designed for RetSynth streamlines an arduous and complex process in metabolic engineering. Our stand-alone software allows the identification of candidate optimal and additional sub-optimal pathways, and provides the user with necessary ranking criteria such as target yield to decide which route to select for target production. Furthermore, the ability to incorporate non-biological reactions into the final steps allows determination of pathways to production for targets that cannot be solely produced biologically. With this comprehensive suite of features RetSynth exceeds any open-source software or webservice currently available for identifying optimal pathways for target production.

Integrated pathway mining and selection of an artificial CYP79-mediated bypass to improve benzylisoquinoline alkaloid biosynthesis

“…There are two general approaches for the design of synthetic pathways: metabolic network searches and chemical structure-based reaction prediction. Network searches cover known biosynthetic pathways from starting substrates to target compounds [10][11][12][13]. The advantage of the metabolic network searches is high confidence for known heterologous enzymatic reactions; however, unreported biological reactions are not included.…”

ClusterCAD 2.0: an updated computational platform for chimeric type I polyketide synthase and nonribosomal peptide synthetase design

“…Many tools have been developed to assist with general retrobiosynthesis and metabolic engineering (14)(15)(16)(17)(18), but to our knowledge none have been developed to specifically assist PKS and/or NRPS engineering. ClusterCAD, however, informs PKS and NRPS engineering by providing a paradigm, database, and tools in which PKS and NRPS parts can be identified for combinatorial biosynthesis based on amino acid sequence similarity or target-intermediate substructure similarity tools, maximizing the chance of a functional engineered chimeric PKS or NRPS (19).…”

Finding branched pathways in metabolic network via atom group tracking

“…Many efforts have been devoted to automatically find metabolic pathways, which can be classified into stoichiometric methods and graph-based pathfinding methods. Stoichiometric methods [7][8][9][10][11] typically establish stoichiometry optimization models to find the pathways that convert a source compound to a target compound in metabolic networks [12,13], and are usually applied for modelling specific metabolic systems [14]. On the other hand, a metabolic network can be represented as a graph where the metabolites can be denoted as nodes and the reactions can be denoted as edges [15].…”