Retropath: Automated Pipeline for Embedded Metabolic Circuits

Carbonell, Pablo; Parutto, Pierre; Baudier, Claire; Junot, Christophe; Faulon, Jean-Loup

doi:10.1021/sb4001273

Cited by 85 publications

(60 citation statements)

References 46 publications

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“…Several general kinds of argument imply that this may indeed be the case. The first is that we can detect many more small molecules as mass spectral signals in biological samples than we can presently identify [129], possibly as a result of unknown enzyme promiscuity [130–132]. Similarly, from the point of view of metabolic network reconstructions, the latest version of Recon2, Recon2.2 [33], contains 2652 unique chemical species, some 60% more than in Recon1 [31, 133], implying that we are far from discovering them all, and some are known still to be absent [9].…”

Section: Discussionmentioning

confidence: 99%

Analysis of drug–endogenous human metabolite similarities in terms of their maximum common substructures

O’Hagan

Kell

2017

J Cheminform

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In previous work, we have assessed the structural similarities between marketed drugs (‘drugs’) and endogenous natural human metabolites (‘metabolites’ or ‘endogenites’), using ‘fingerprint’ methods in common use, and the Tanimoto and Tversky similarity metrics, finding that the fingerprint encoding used had a dramatic effect on the apparent similarities observed. By contrast, the maximal common substructure (MCS), when the means of determining it is fixed, is a means of determining similarities that is largely independent of the fingerprints, and also has a clear chemical meaning. We here explored the utility of the MCS and metrics derived therefrom. In many cases, a shared scaffold helps cluster drugs and endogenites, and gives insight into enzymes (in particular transporters) that they both share. Tanimoto and Tversky similarities based on the MCS tend to be smaller than those based on the MACCS fingerprint-type encoding, though the converse is also true for a significant fraction of the comparisons. While no single molecular descriptor can account for these differences, a machine learning-based analysis of the nature of the differences (MACCS_Tanimoto vs MCS_Tversky) shows that they are indeed deterministic, although the features that are used in the model to account for this vary greatly with each individual drug. The extent of its utility and interpretability vary with the drug of interest, implying that while MCS is neither ‘better’ nor ‘worse’ for every drug–endogenite comparison, it is sufficiently different to be of value. The overall conclusion is thus that the use of the MCS provides an additional and valuable strategy for understanding the structural basis for similarities between synthetic, marketed drugs and natural intermediary metabolites. Electronic supplementary materialThe online version of this article (doi:10.1186/s13321-017-0198-y) contains supplementary material, which is available to authorized users.

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Section: Discussionmentioning

confidence: 99%

Analysis of drug–endogenous human metabolite similarities in terms of their maximum common substructures

O’Hagan

Kell

2017

J Cheminform

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“…The development of computational design tools such as BNICE [113, 114], PathPred [115], UM‐PPS [116], Retropath [117], Metabolic Tinker [118], Act [119], and others [111] has led to an explosion of proposed novel metabolic pathways and circuits. CFME will be crucial tool in the experimental validation of these pathways.…”

Section: Challenges and Opportunities In Cfmementioning

confidence: 99%

Cell‐free metabolic engineering: Biomanufacturing beyond the cell

Dudley

Karim

Jewett

2014

Biotechnology Journal

292

258

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Industrial biotechnology and microbial metabolic engineering are poised to help meet the growing demand for sustainable, low-cost commodity chemicals and natural products, yet the fraction of biochemicals amenable to commercial production remains limited. Common problems afflicting the current state-of-the-art include low volumetric productivities, build-up of toxic intermediates or products, and byproduct losses via competing pathways. To overcome these limitations, cell-free metabolic engineering (CFME) is expanding the scope of the traditional bioengineering model by using in vitro ensembles of catalytic proteins prepared from purified enzymes or crude lysates of cells for the production of target products. In recent years, the unprecedented level of control and freedom of design, relative to in vivo systems, has inspired the development of engineering foundations for cell-free systems. These efforts have led to activation of long enzymatic pathways (>8 enzymes), near theoretical conversion yields, productivities greater than 100 mg L−1 hr−1, reaction scales of >100L, and new directions in protein purification, spatial organization and enzyme stability. In the coming years, CFME will offer exciting opportunities to (i) debug and optimize biosynthetic pathways, (ii) carry out design-build-test iterations without re-engineering organisms, and (iii) perform molecular transformations when bioconversion yields, productivities, or cellular toxicity limit commercial feasibility.

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“…Reconstructions have become quite advanced by now, with strategies including 'jamborees' [47][48][49], helped by the availability of digital tools (e.g. [50]), including for the reconstruction of non-natural pathways for the biosynthesis of 'novel' products [51,52]. In very few cases are all the kinetic rate equations known with any precision (see e.g.…”

Section: Systems Biology Of Metabolic Networkmentioning

confidence: 99%

Control of metabolite efflux in microbial cell factories: current advances and future prospects

Kell¹

2018

Preprint

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The yield and ease of purification of biotechnological products are typically greatly enhanced if they can be secreted into the external medium. Although not universally appreciated, however, small molecule biotechnological products do not ‘float across’ the phospholipid bilayer portion of biological membranes, and thus they need transporters to assist their passage into the extramembrane and extracellular spaces. Some of these transporters may be reversible, equilibrative transporters that might more normally be used for uptake, while others may have an efflux directionality imposed on them via suitable energy coupling mechanisms. Despite the energetic costs of small molecule synthesis, natural evolution does in fact provide a number of mechanisms by which the secretion of such products can actually enhance fitness. Where available these provide useful starting points, and assaying for such activities is crucial. In particular, in a systems biology approach, we first need to identify such/suitable activities before we can seek to increase them. They may then be improved through promoter engineering, via manipulation of control elements, or by directed evolution of the transporter proteins themselves. Modern methods of synthetic biology provide enormous opportunities for all kinds of efflux transporter engineering; they are just beginning to be realised.

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Retropath: Automated Pipeline for Embedded Metabolic Circuits

Cited by 85 publications

References 46 publications

Analysis of drug–endogenous human metabolite similarities in terms of their maximum common substructures

Analysis of drug–endogenous human metabolite similarities in terms of their maximum common substructures

Cell‐free metabolic engineering: Biomanufacturing beyond the cell

Control of metabolite efflux in microbial cell factories: current advances and future prospects

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