A critical evaluation of automatic atom mapping algorithms and tools

The identification of the atoms which change their position in chemical reactions is an important knowledge within the field of Metabolic Engineering. This can lead to new advances at different levels from the reconstruction of metabolic networks to the classification of chemical reactions, through the identification of the atomic changes inside a reaction. The Atom Mapping approach was initially developed in the 1960s, but recently suffered important advances, being used in diverse biological and biotechnological studies. The main methodologies used for atom mapping are the Maximum Common Substructure and the Linear Optimization methods, which both require computational know-how and powerful resources to run the underlying tools. In this work, we assessed a number of previously implemented atom mapping frameworks, and built a framework able of managing the different data inputs and outputs, as well as the mapping process provided by each of these third-party tools. We evaluated the admissibility of the calculated atom maps from different algorithms, also assessing if with different approaches we were capable of returning equivalent atom maps for the same chemical reaction.ERDF -European Regional Development Fund(UID/BIO/04469/2013)info:eu-repo/semantics/publishedVersio

FindPrimaryPairs: An efficient algorithm for predicting element-transferring reactant/product pairs in metabolic networks.

The metabolism of individual organisms and biological communities can be viewed as a network of metabolites connected to each other through chemical reactions. In metabolic networks, chemical reactions transform reactants into products, thereby transferring elements between these metabolites. Knowledge of how elements are transferred through reactant/product pairs allows for the identification of primary compound connections through a metabolic network. However, such information is not readily available and is often challenging to obtain for large reaction databases or genome-scale metabolic models. In this study, a new algorithm was developed for automatically predicting the element-transferring reactant/product pairs using the limited information available in the standard representation of metabolic networks. The algorithm demonstrated high efficiency in analyzing large datasets and provided accurate predictions when benchmarked with manually curated data. Applying the algorithm to the visualization of metabolic networks highlighted pathways of primary reactant/product connections and provided an organized view of element-transferring biochemical transformations. The algorithm was implemented as a new function in the open source software package PSAMM in the release v0.30 (https://zhanglab.github.io/psamm/)