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PARROT: Prediction of enzyme abundances using protein-constrained metabolic models
  • Maurício Alexander de Moura Ferreira,
  • Wendel Batista da Silveira,
  • Zoran Nikoloski
Maurício Alexander de Moura Ferreira
Department of Microbiology, Federal University of Viçosa
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Wendel Batista da Silveira
Department of Microbiology, Federal University of Viçosa
Zoran Nikoloski
Systems Biology and Mathematical Modelling, Max Planck Institute of Molecular Plant Physiology, Bioinformatics, Institute of Biochemistry and Biology, University of Potsdam, Potsdam, Germany

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Protein allocation determines the activity of cellular pathways and affects growth across all organisms. Therefore, different experimental and machine learning approaches have been developed to quantify and predict protein abundances, respectively. Yet, despite advances in protein quantification, it remains challenging to predict condition-specific allocation of enzymes in metabolic networks. Here we propose a family of constrained-based approaches, termed PARROT, to predict enzyme allocations based on the principle of minimizing the enzyme allocation adjustment using protein-constrained metabolic models. To this end, PARROT variants model the minimization of enzyme reallocation using four different (combinations of) distance functions. We demonstrate that the PARROT variant that minimizes the Manhattan distance of enzyme allocations outperforms existing approaches based on the parsimonious distribution of fluxes or enzymes for both Escherichia coli and Saccharomyces cerevisiae. Further, we show that the combined minimization of flux and enzyme allocation adjustment leads to inconsistent predictions. Together, our findings indicate that minimization of resource, rather than flux, redistribution is a governing principle determining steady-state pathway activity for microorganism grown in suboptimal conditions.
Availability and implementation: The implementation of PARROT can be found in the GitHub repository: https://github.com/mauricioamf/PARROT