Abstract Enzyme turnover numbers ( k cat ) are key to understanding cellular metabolism, proteome allocation and physiological diversity, but experimentally measured k cat data are sparse and noisy. Here we provide a deep learning approach (DLKcat) for high-throughput k cat prediction for metabolic enzymes from any organism merely from substrate structures and protein sequences. DLKcat can capture k cat changes for mutated enzymes and identify amino acid residues with a strong impact on k cat values. We applied this approach to predict genome-scale k cat values for more than 300 yeast species. Additionally, we designed a Bayesian pipeline to parameterize enzyme-constrained genome-scale metabolic models from predicted k cat values. The resulting models outperformed the corresponding original enzyme-constrained genome-scale metabolic models from previous pipelines in predicting phenotypes and proteomes, and enabled us to explain phenotypic differences. DLKcat and the enzyme-constrained genome-scale metabolic model construction pipeline are valuable tools to uncover global trends of enzyme kinetics and physiological diversity, and to further elucidate cellular metabolism on a large scale. 

/pdf/deep-learning-based-kcat-prediction-enables-improved-enzyme-pkqkgv3j.pdf

Deep learning-based kcat prediction enables improved enzyme-constrained model reconstruction

Eukaryotic cells are used as cell factories to produce and secrete multitudes of recombinant pharmaceutical proteins, including several of the current top-selling drugs. Due to the essential role and complexity of the secretory pathway, improvement for recombinant protein production through metabolic engineering has traditionally been relatively ad-hoc; and a more systematic approach is required to generate novel design principles. Here, we present the proteome-constrained genome-scale protein secretory model of yeast Saccharomyces cerevisiae (pcSecYeast), which enables us to simulate and explain phenotypes caused by limited secretory capacity. We further apply the pcSecYeast model to predict overexpression targets for the production of several recombinant proteins. We experimentally validate many of the predicted targets for α-amylase production to demonstrate pcSecYeast application as a computational tool in guiding yeast engineering and improving recombinant protein production. 

/pdf/improving-recombinant-protein-production-by-yeast-through-2ext44rl.pdf

Improving recombinant protein production by yeast through genome-scale modeling using proteome constraints

Abstract Yeasts have been widely used for production of bread, beer and wine, as well as for production of bioethanol, but they have also been designed as cell factories to produce various chemicals, advanced biofuels and recombinant proteins. To systematically understand and rationally engineer yeast metabolism, genome-scale metabolic models (GEMs) have been reconstructed for the model yeast Saccharomyces cerevisiae and nonconventional yeasts. Here, we review the historical development of yeast GEMs together with their recent applications, including metabolic flux prediction, cell factory design, culture condition optimization and multi-yeast comparative analysis. Furthermore, we present an emerging effort, namely the integration of proteome constraints into yeast GEMs, resulting in models with improved performance. At last, we discuss challenges and perspectives on the development of yeast GEMs and the integration of proteome constraints.

/pdf/genome-scale-modeling-of-yeast-metabolism-retrospectives-and-2so629nj.pdf

Genome-scale modeling of yeast metabolism: retrospectives and perspectives

Abstract Photosystem I (PSI) enables photo-electron transfer and regulates photosynthesis in the bioenergetic membranes of cyanobacteria and chloroplasts. Being a multi-subunit complex, its macromolecular organization affects the dynamics of photosynthetic membranes. Here we reveal a chloroplast PSI from the green alga Chlamydomonas reinhardtii that is organized as a homodimer, comprising 40 protein subunits with 118 transmembrane helices that provide scaffold for 568 pigments. Cryogenic electron microscopy identified that the absence of PsaH and Lhca2 gives rise to a head-to-head relative orientation of the PSI–light-harvesting complex I monomers in a way that is essentially different from the oligomer formation in cyanobacteria. The light-harvesting protein Lhca9 is the key element for mediating this dimerization. The interface between the monomers is lacking PsaH and thus partially overlaps with the surface area that would bind one of the light-harvesting complex II complexes in state transitions. We also define the most accurate available PSI–light-harvesting complex I model at 2.3 Å resolution, including a flexibly bound electron donor plastocyanin, and assign correct identities and orientations to all the pigments, as well as 621 water molecules that affect energy transfer pathways. 

/pdf/algal-photosystem-i-dimer-and-high-resolution-model-of-psi-3nx9jmmc.pdf

Algal photosystem I dimer and high-resolution model of PSI-plastocyanin complex

Mitochondrial ATP generation is more proteome efficient than glycolysis.

When conditions change, unicellular organisms rewire their metabolism to sustain cell maintenance and cellular growth. Such rewiring may be understood as resource re-allocation under cellular constraints. Eukaryal cells contain metabolically active organelles such as mitochondria, competing for cytosolic space and resources, and the nature of the relevant cellular constraints remain to be determined for such cells. Here, we present a comprehensive metabolic model of the yeast cell, based on its full metabolic reaction network extended with protein synthesis and degradation reactions. The model predicts metabolic fluxes and corresponding protein expression by constraining compartment-specific protein pools and maximising growth rate. Comparing model predictions with quantitative experimental data suggests that under glucose limitation, a mitochondrial constraint limits growth at the onset of ethanol formation-known as the Crabtree effect. Under sugar excess, however, a constraint on total cytosolic volume dictates overflow metabolism. Our comprehensive model thus identifies condition-dependent and compartment-specific constraints that can explain metabolic strategies and protein expression profiles from growth rate optimisation, providing a framework to understand metabolic adaptation in eukaryal cells. 

/pdf/whole-cell-modeling-in-yeast-predicts-compartment-specific-18auidpb.pdf

Whole-cell modeling in yeast predicts compartment-specific proteome constraints that drive metabolic strategies

Proteins are molecular machines that provide structure and perform vital transport, signalling and enzymatic roles. Proteins expressed by cells require tight regulation of their concentration, folding, localisation, and modifications; however, this state of protein homeostasis is continuously perturbed by tissue-level stresses. While cells in healthy tissues are able to buffer against these perturbations, for example, by expression of chaperone proteins, protein homeostasis is lost in ageing, and can lead to protein aggregation characteristic of protein folding diseases. Here, we review reports of a progressive disconnect between transcriptomic and proteomic regulation during cellular ageing. We discuss how age-associated changes to cellular responses to specific stressors in the tissue microenvironment are exacerbated by loss of ribosomal proteins, ribosomal pausing, and mistranslation. 

Translation is an emerging constraint on protein homeostasis in ageing.

MD2 pineapple (Ananas comosus) is the second most important tropical crop that preserves crassulacean acid metabolism (CAM), which has high water-use efficiency and is fast becoming the most consumed fresh fruit worldwide. Despite the significance of environmental efficiency and popularity, until very recently, its genome sequence has not been determined and a high-quality annotated proteome has not been available. Here, we have undertaken a pilot proteogenomic study, analyzing the proteome of MD2 pineapple leaves using liquid chromatography-mass spectrometry (LC-MS/MS), which validates 1781 predicted proteins in the annotated F153 (V3) genome. In addition, a further 603 peptide identifications are found that map exclusively to an independent MD2 transcriptome-derived database but are not found in the standard F153 (V3) annotated proteome. Peptide identifications derived from these MD2 transcripts are also cross-referenced to a more recent and complete MD2 genome annotation, resulting in 402 nonoverlapping peptides, which in turn support 30 high-quality gene candidates novel to both pineapple genomes. Many of the validated F153 (V3) genes are also supported by an independent proteomics data set collected for an ornamental pineapple variety. The contigs and peptides have been mapped to the current F153 genome build and are available as bed files to display a custom gene track on the Ensembl Plants region viewer. These analyses add to the knowledge of experimentally validated pineapple genes and demonstrate the utility of transcript-derived proteomics to discover both novel genes and genetic structure in a plant genome, adding value to its annotation.

Proteogenomic Gene Structure Validation in the Pineapple Genome.

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Whole-cell modeling in yeast predicts compartment-specific proteome constraints that drive metabolic strategies

Translation is an emerging constraint on protein homeostasis in ageing.

Proteogenomic Gene Structure Validation in the Pineapple Genome.