PARROT: Prediction of enzyme abundances using protein-constrained metabolic models.
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 abundance and how they are allocated to different cellular functions, respect...
| Main Authors: | , , |
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| Format: | Article |
| Language: | English |
| Published: |
Public Library of Science (PLoS)
2023-10-01
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| Series: | PLoS Computational Biology |
| Online Access: | https://journals.plos.org/ploscompbiol/article/file?id=10.1371/journal.pcbi.1011549&type=printable |
| _version_ | 1797637530376470528 |
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| author | Mauricio Alexander de Moura Ferreira Wendel Batista da Silveira Zoran Nikoloski |
| author_facet | Mauricio Alexander de Moura Ferreira Wendel Batista da Silveira Zoran Nikoloski |
| author_sort | Mauricio Alexander de Moura Ferreira |
| collection | DOAJ |
| description | 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 abundance and how they are allocated to different cellular functions, respectively. Yet, despite advances in protein quantification, it remains challenging to predict condition-specific allocation of enzymes in metabolic networks. Here, using protein-constrained metabolic models, we propose a family of constrained-based approaches, termed PARROT, to predict how much of each enzyme is used based on the principle of minimizing the difference between a reference and an alternative growth condition. 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 between the enzyme allocation of a reference and an alternative condition 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 protein allocation rather than flux redistribution is a governing principle determining steady-state pathway activity for microorganism grown in alternative growth conditions. |
| first_indexed | 2024-03-11T12:50:47Z |
| format | Article |
| id | doaj.art-8daaf27698be406294069d4acf300991 |
| institution | Directory Open Access Journal |
| issn | 1553-734X 1553-7358 |
| language | English |
| last_indexed | 2024-03-11T12:50:47Z |
| publishDate | 2023-10-01 |
| publisher | Public Library of Science (PLoS) |
| record_format | Article |
| series | PLoS Computational Biology |
| spelling | doaj.art-8daaf27698be406294069d4acf3009912023-11-04T05:31:52ZengPublic Library of Science (PLoS)PLoS Computational Biology1553-734X1553-73582023-10-011910e101154910.1371/journal.pcbi.1011549PARROT: Prediction of enzyme abundances using protein-constrained metabolic models.Mauricio Alexander de Moura FerreiraWendel Batista da SilveiraZoran NikoloskiProtein 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 abundance and how they are allocated to different cellular functions, respectively. Yet, despite advances in protein quantification, it remains challenging to predict condition-specific allocation of enzymes in metabolic networks. Here, using protein-constrained metabolic models, we propose a family of constrained-based approaches, termed PARROT, to predict how much of each enzyme is used based on the principle of minimizing the difference between a reference and an alternative growth condition. 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 between the enzyme allocation of a reference and an alternative condition 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 protein allocation rather than flux redistribution is a governing principle determining steady-state pathway activity for microorganism grown in alternative growth conditions.https://journals.plos.org/ploscompbiol/article/file?id=10.1371/journal.pcbi.1011549&type=printable |
| spellingShingle | Mauricio Alexander de Moura Ferreira Wendel Batista da Silveira Zoran Nikoloski PARROT: Prediction of enzyme abundances using protein-constrained metabolic models. PLoS Computational Biology |
| title | PARROT: Prediction of enzyme abundances using protein-constrained metabolic models. |
| title_full | PARROT: Prediction of enzyme abundances using protein-constrained metabolic models. |
| title_fullStr | PARROT: Prediction of enzyme abundances using protein-constrained metabolic models. |
| title_full_unstemmed | PARROT: Prediction of enzyme abundances using protein-constrained metabolic models. |
| title_short | PARROT: Prediction of enzyme abundances using protein-constrained metabolic models. |
| title_sort | parrot prediction of enzyme abundances using protein constrained metabolic models |
| url | https://journals.plos.org/ploscompbiol/article/file?id=10.1371/journal.pcbi.1011549&type=printable |
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