EC-BLAST: a tool to automatically search and compare enzyme reactions.

We present EC-BLAST (http://www.ebi.ac.uk/thornton-srv/software/rbl/), an algorithm and Web tool for quantitative similarity searches between enzyme reactions at three levels: bond change, reaction center and reaction structure similarity. It uses bond changes and reaction patterns for all known biochemical reactions derived from atom-atom mapping across each reaction. EC-BLAST has the potential to improve enzyme classification, identify previously uncharacterized or new biochemical transformations, improve the assignment of enzyme function to sequences, and assist in enzyme engineering

Is EC class predictable from reaction mechanism?

We thank the Scottish Universities Life Sciences Alliance (SULSA) and the Scottish Overseas Research Student Awards Scheme of the Scottish Funding Council (SFC) for financial support.Background: We investigate the relationships between the EC (Enzyme Commission) class, the associated chemical reaction, and the reaction mechanism by building predictive models using Support Vector Machine (SVM), Random Forest (RF) and k-Nearest Neighbours (kNN). We consider two ways of encoding the reaction mechanism in descriptors, and also three approaches that encode only the overall chemical reaction. Both cross-validation and also an external test set are used. Results: The three descriptor sets encoding overall chemical transformation perform better than the two descriptions of mechanism. SVM and RF models perform comparably well; kNN is less successful. Oxidoreductases and hydrolases are relatively well predicted by all types of descriptor; isomerases are well predicted by overall reaction descriptors but not by mechanistic ones. Conclusions: Our results suggest that pairs of similar enzyme reactions tend to proceed by different mechanisms. Oxidoreductases, hydrolases, and to some extent isomerases and ligases, have clear chemical signatures, making them easier to predict than transferases and lyases. We find evidence that isomerases as a class are notably mechanistically diverse and that their one shared property, of substrate and product being isomers, can arise in various unrelated ways. The performance of the different machine learning algorithms is in line with many cheminformatics applications, with SVM and RF being roughly equally effective. kNN is less successful, given the role that non-local information plays in successful classification. We note also that, despite a lack of clarity in the literature, EC number prediction is not a single problem; the challenge of predicting protein function from available sequence data is quite different from assigning an EC classification from a cheminformatics representation of a reaction.Publisher PDFPeer reviewe

A retrosynthetic biology approach to metabolic pathway design for therapeutic production

<p>Abstract</p> <p>Background</p> <p>Synthetic biology is used to develop cell factories for production of chemicals by constructively importing heterologous pathways into industrial microorganisms. In this work we present a retrosynthetic approach to the production of therapeutics with the goal of developing an <it>in situ </it>drug delivery device in host cells. Retrosynthesis, a concept originally proposed for synthetic chemistry, iteratively applies reversed chemical transformations (reversed enzyme-catalyzed reactions in the metabolic space) starting from a target product to reach precursors that are endogenous to the chassis. So far, a wider adoption of retrosynthesis into the manufacturing pipeline has been hindered by the complexity of enumerating all feasible biosynthetic pathways for a given compound.</p> <p>Results</p> <p>In our method, we efficiently address the complexity problem by coding substrates, products and reactions into molecular signatures. Metabolic maps are represented using hypergraphs and the complexity is controlled by varying the specificity of the molecular signature. Furthermore, our method enables candidate pathways to be ranked to determine which ones are best to engineer. The proposed ranking function can integrate data from different sources such as host compatibility for inserted genes, the estimation of steady-state fluxes from the genome-wide reconstruction of the organism's metabolism, or the estimation of metabolite toxicity from experimental assays. We use several machine-learning tools in order to estimate enzyme activity and reaction efficiency at each step of the identified pathways. Examples of production in bacteria and yeast for two antibiotics and for one antitumor agent, as well as for several essential metabolites are outlined.</p> <p>Conclusions</p> <p>We present here a unified framework that integrates diverse techniques involved in the design of heterologous biosynthetic pathways through a retrosynthetic approach in the reaction signature space. Our engineering methodology enables the flexible design of industrial microorganisms for the efficient on-demand production of chemical compounds with therapeutic applications.</p

Automatic assignment of reaction operators to enzymatic reactions

Background: Enzymes are classified in a numerical classification scheme introduced by the Nomenclature Committee of the IUBMB based on the overall reaction chemistry. Due to the manifold of enzymatic reactions the system has become highly complex. Assignment of enzymes to the enzyme classes requires a detailed knowledge of the system and manual analysis. Frequently rearrangements and deletions of enzymes and sub-subclasses are necessary. Results: We use the Dugundji–Ugi model for coding of biochemical reactions which is based on electron shift patterns occurring during reactions. Changes of the bonds or of non-bonded valence electrons are expressed by reaction matrices. Our program calculates reaction matrices automatically on the sole basis of substrate and product chemical structures based on a new strategy for maximal common substructure determination, which allows an accurate atom mapping of the substrate and product atoms. The system has been tested for a large set of enzymatic reactions including all sub-subclasses of the EC classification system. Altogether 147 different representative reaction operators were found in the classified enzymes, 121 of which are unique with respect to an EC sub-subclass. The other 26 comprise groups of enzymes with very similar reactions, being identical with respect to the bonds formed and broken. Conclusion: The analysis and comparison of enzymatic reactions according to their electron shift patterns is defining enzyme groups characterised by unique reaction cores. Our results demonstrate the applicability of the Dugundji–Ugi model as a reasonable preclassification system allowing an objective and rational view on biochemical reactions. Availability: The program to generate reaction matrix descriptors is available upon request. Contact: [email protected]