Network-based prediction of metabolic enzymes' subcellular localization

Abstract

Motivation: Revealing the subcellular localization of proteins within membrane-bound compartments is of a major importance for inferring protein function. Though current high-throughput localization experiments provide valuable data, they are costly and time-consuming, and due to technical difficulties not readily applicable for many Eukaryotes. Physical characteristics of proteins, such as sequence targeting signals and amino acid composition are commonly used to predict subcellular localizations using computational approaches. Recently it was shown that protein–protein interaction (PPI) networks can be used to significantly improve the prediction accuracy of protein subcellular localization. However, as high-throughput PPI data depend on costly high-throughput experiments and are currently available for only a few organisms, the scope of such methods is yet limited.Results: This study presents a novel constraint-based method for predicting subcellular localization of enzymes based on their embedding metabolic network, relying on a parsimony principle of a minimal number of cross-membrane metabolite transporters. In a cross-validation test of predicting known subcellular localization of yeast enzymes, the method is shown to be markedly robust, providing accurate localization predictions even when only 20% of the known enzyme localizations are given as input. It is shown to outperform pathway enrichment-based methods both in terms of prediction accuracy and in its ability to predict the subcellular localization of entire metabolic pathways when no a-priori pathway-specific localization data is available (and hence enrichment methods are bound to fail). With the number of available metabolic networks already reaching more than 600 and growing fast, the new method may significantly contribute to the identification of enzyme localizations in many different organisms.Contact: shira.mintz@weizmann.ac.il; tomersh@cs.technion.ac.il

Reconstruction of Arabidopsis metabolic network models accounting for subcellular compartmentalization and tissue-specificity

“…To extend the genome-scale Arabidopsis model reconstructed in the above step to account for subcellular compartmentalization of metabolic processes, we used a variant of our previously developed pertaining method (17). Given the known localization of a subset of the enzymes in the network, the method predicts the most likely localization of the remaining enzymes based on a parsimony principle of minimizing the number of metabolite transmembrane transport required to activate the enzymes with a known localization in the corresponding compartments.…”

“…(ii) Several assumptions are used in the reconstruction process, such as the assumption of minimal addition of reactions and directionality relaxation to resolve network gaps, and the assumption of minimal number of metabolite transmembrane transport used in the localization assignment. These assumptions, although commonly used for similar purposes (12,17), may in some cases be oversimplified and lead to false identification of gap-filling solutions. (iii) The resulting models do not explicitly account for transcriptional and metabolic regulation, data that is still unavailable for most Arabidopsis metabolic functions, and thus cannot accurately predict some of the organism's functions.…”

Model-based Confirmation of Alternative Substrates of Mitochondrial Electron Transport Chain

“…The promise of mathematical modeling lies in its potential to quantitatively encompass the multitude of scenarios under which a given biological process may operate and, as a result, elucidate which components contribute to the emerging behavior (1)(2)(3)(4)(5)(6)(7)(8). For this purpose, the spatial separation of metabolites and biochemical reactions into their cellular compartments is an important feature to provide an accurate assessment of energetic limitations that may be lost in decompartmentalized models (9,10). Nevertheless, the predictive power of decompartmentalized models can be increased by providing more realistic constraints and objectives (10).…”

The SuBliMinaL Toolbox: automating steps in the reconstruction of metabolic networks

“…Such inferred reactions would be added as "modeling" reactions, and associated with an appropriate evidence code, indicating that they should be subject to subsequent manual curation. The inference of enzyme and reaction localisation, based upon the network topology of partially compartmentalised metabolic models, has been reported [46], and the approach followed by the toolbox -to generate a partially compartmentalised model for subsequent refinement -supports this inference method.…”