Combining Chemoinformatics with Bioinformatics: In Silico Prediction of Bacterial Flavor-Forming Pathways by a Chemical Systems Biology Approach “Reverse Pathway Engineering”

Abstract

The incompleteness of genome-scale metabolic models is a major bottleneck for systems biology approaches, which are based on large numbers of metabolites as identified and quantified by metabolomics. Many of the revealed secondary metabolites and/or their derivatives, such as flavor compounds, are non-essential in metabolism, and many of their synthesis pathways are unknown. In this study, we describe a novel approach, Reverse Pathway Engineering (RPE), which combines chemoinformatics and bioinformatics analyses, to predict the “missing links” between compounds of interest and their possible metabolic precursors by providing plausible chemical and/or enzymatic reactions. We demonstrate the added-value of the approach by using flavor-forming pathways in lactic acid bacteria (LAB) as an example. Established metabolic routes leading to the formation of flavor compounds from leucine were successfully replicated. Novel reactions involved in flavor formation, i.e. the conversion of alpha-hydroxy-isocaproate to 3-methylbutanoic acid and the synthesis of dimethyl sulfide, as well as the involved enzymes were successfully predicted. These new insights into the flavor-formation mechanisms in LAB can have a significant impact on improving the control of aroma formation in fermented food products. Since the input reaction databases and compounds are highly flexible, the RPE approach can be easily extended to a broad spectrum of applications, amongst others health/disease biomarker discovery as well as synthetic biology.

Role of lactic acid bacteria on the yogurt flavour: A review

“…Such issue will benefit from improvements of functional annotation of the key enzymes in the formation of flavour compounds. Furthermore, the technological progress made in high-throughput analysis methods, such as genomics, proteomics, and metabolomics, [59,86,87] and the use of genome-scale metabolic models [88] has been driving the development of new approaches to understand, control and steer aroma formation in dairy fermentation processes.…”

“…[20] The ultimate aim is to obtain more control of the flavour-forming process executed by the fermenting LAB. To achieve this target, some strategies are suggested: i) screening new bacteria for the detection of the presence and activity of key enzymes involved in aroma production using omics-based approaches; [86] ii) changing the ratio among different strains in mixed starter cultures to achieve the relative abundance of specific aroma-forming strains at different steps in the fermentation process; and [19] iii) varying physicochemical parameters to influence the microbial physiology leading to modulation of aroma production. [60] The limited knowledge of the complex network of flavour-forming pathways of LAB also hampers the progress of genetically modified LAB as starter cultures for industrial production of flavour compounds.…”

Symposium review: Genomic investigations of flavor formation by dairy microbiota

“…In the latter method, final products of metabolism are taken as a starting point, and the enzymatic machinery of bacteria is analyzed to connect these metabolites with potential precursors. This method successfully predicted novel metabolic routes in LAB, such as the generation of the leucine metabolite 3-methylbutanoic acid, which has rancid, cheesy, putrid aroma (Liu et al, 2014).…”

Reinforcement Learning for Bio-Retrosynthesis

“…Secondly, a number of parameters are often included within the pathway search, decided by the software designer and with limited capacity for a user to incorporate his own knowledge, solving for both retrosynthesis and parameters optimization. Some examples include enzyme performance , predicted yield (Campodonico et al 2014;Carbonell et al 2014;Liu et al 2014;Cho et al 2010;Tokic et al 2018), thermodynamics or cofactor usage (Kumar et al 2018). Moreover, those tools do not include the latest advances in combinatorial search space exploration, pioneered in the field of Artificial Intelligence.…”

“…Such tools include pathway design software, to assist the metabolic engineer in finding new pathways for production of a target of interest. While some tools restrict themselves to reactions already present in databases (Rodrigo et al 2008;Moriya et al 2010), others allow the generation of de novo reactions, using retrosynthesis algorithms (Henry, Broadbelt, and Hatzimanikatis 2010;Carbonell et al 2011;Yim et al 2011;Carbonell et al 2014;Liu et al 2014;Campodonico et al 2014;Hadadi et al 2016 ;Kumar et al 2018;Delépine et al 2018). At its core, a retrosynthesis algorithm is simple: break down a target molecule into simpler molecules that can be combined chemically or enzymatically to produce it, and iterate recursively until all required compounds are either commercially available or present in the microbial strain of choice.…”