| 1. |
- Chen, Xin, 1980, et al.
(author)
-
Dataset for suppressors of amyloid-beta toxicity and their functions in recombinant protein production in yeast
- 2022
-
In: Data in Brief. - : Elsevier BV. - 2352-3409. ; 42
-
Journal article (peer-reviewed)abstract
- The production of recombinant proteins at high levels often induces stress-related phenotypes by protein misfolding or aggregation. These are similar to those of the yeast Alzheimer's disease (AD) model in which amyloid-beta peptides (A beta 42) were accumulated [1,2] . We have previously identified suppressors of A beta 42 cytotoxicity via the genome-wide synthetic genetic array (SGA) [3] and here we use them as metabolic engineering targets to evaluate their potentiality on recombinant protein production in yeast Saccharomyces cerevisiae. In order to investigate the mechanisms linking the genetic modifications to the improved recombinant protein production, we perform systems biology approaches (transcriptomics and proteomics) on the resulting strain and intermediate strains. The RNAseq data are preprocessed by the nf-core/RNAseq pipeline and analyzed using the Platform for Integrative Analysis of Omics (PIANO) package [4] . The quantitative proteome is analyzed on an Orbitrap Fusion Lumos mass spectrometer interfaced with an Easy-nLC1200 liquid chromatography (LC) system. LC-MS data files are processed by Proteome Discoverer version 2.4 with Mascot 2.5.1 as a database search engine. The original data presented in this work can be found in the research paper titled "Suppressors of Amyloid-beta Toxicity Improve Recombinant Protein Produc-tion in yeast by Reducing Oxidative Stress and Tuning Cellu-lar Metabolism", by Chen et al. [5] . (C) 2022 The Author(s). Published by Elsevier Inc.
|
|
| 2. |
- Chen, Xin, 1980, et al.
(author)
-
Suppressors of amyloid-β toxicity improve recombinant protein production in yeast by reducing oxidative stress and tuning cellular metabolism
- 2022
-
In: Metabolic Engineering. - : Elsevier BV. - 1096-7176 .- 1096-7184. ; 72, s. 311-324
-
Journal article (peer-reviewed)abstract
- High-level production of recombinant proteins in industrial microorganisms is often limited by the formation of misfolded proteins or protein aggregates, which consequently induce cellular stress responses. We hypothesized that in a yeast Alzheimer's disease (AD) model overexpression of amyloid-β peptides (Aβ42), one of the main peptides relevant for AD pathologies, induces similar phenotypes of cellular stress. Using this humanized AD model, we previously identified suppressors of Aβ42 cytotoxicity. Here we hypothesize that these suppressors could be used as metabolic engineering targets to alleviate cellular stress and improve recombinant protein production in the yeast Saccharomyces cerevisiae. Forty-six candidate genes were individually deleted and twenty were individually overexpressed. The positive targets that increased recombinant α-amylase production were further combined leading to an 18.7-fold increased recombinant protein production. These target genes are involved in multiple cellular networks including RNA processing, transcription, ER-mitochondrial complex, and protein unfolding. By using transcriptomics and proteomics analyses, combined with reverse metabolic engineering, we showed that reduced oxidative stress, increased membrane lipid biosynthesis and repressed arginine and sulfur amino acid biosynthesis are significant pathways for increased recombinant protein production. Our findings provide new insights towards developing synthetic yeast cell factories for biosynthesis of valuable proteins.
|
|
| 3. |
- Kroll, Alexander, et al.
(author)
-
A general model to predict small molecule substrates of enzymes based on machine and deep learning
- 2023
-
In: Nature Communications. - 2041-1723 .- 2041-1723. ; 14:1
-
Journal article (peer-reviewed)abstract
- For most proteins annotated as enzymes, it is unknown which primary and/or secondary reactions they catalyze. Experimental characterizations of potential substrates are time-consuming and costly. Machine learning predictions could provide an efficient alternative, but are hampered by a lack of information regarding enzyme non-substrates, as available training data comprises mainly positive examples. Here, we present ESP, a general machine-learning model for the prediction of enzyme-substrate pairs with an accuracy of over 91% on independent and diverse test data. ESP can be applied successfully across widely different enzymes and a broad range of metabolites included in the training data, outperforming models designed for individual, well-studied enzyme families. ESP represents enzymes through a modified transformer model, and is trained on data augmented with randomly sampled small molecules assigned as non-substrates. By facilitating easy in silico testing of potential substrates, the ESP web server may support both basic and applied science.
|
|
| 4. |
- Kroll, Alexander, et al.
(author)
-
Deep learning allows genome-scale prediction of Michaelis constants from structural features
- 2021
-
In: PLoS Biology. - : Public Library of Science (PLoS). - 1544-9173 .- 1545-7885. ; 19:10
-
Journal article (peer-reviewed)abstract
- AU The:Michaelis Pleaseconfirmthatallheadinglevelsarerepresentedcorrectly constant KM describes the affinity of an enzyme : for a specific substrate and is a central parameter in studies of enzyme kinetics and cellular physiology. As measurements of KM are often difficult and time-consuming, experimental estimates exist for only a minority of enzyme–substrate combinations even in model organisms. Here, we build and train an organism-independent model that successfully predicts KM values for natural enzyme–substrate combinations using machine and deep learning methods. Predictions are based on a task-specific molecular fingerprint of the substrate, generated using a graph neural network, and on a deep numerical representation of the enzyme’s amino acid sequence. We provide genome-scale KM predictions for 47 model organisms, which can be used to approximately relate metabolite concentrations to cellular physiology and to aid in the parameterization of kinetic models of cellular metabolism.
|
|
| 5. |
- Li, Gang, 1991, et al.
(author)
-
Performance of Regression Models as a Function of Experiment Noise
- 2021
-
In: Bioinformatics and Biology Insights. - : SAGE Publications. - 1177-9322. ; 15
-
Journal article (peer-reviewed)abstract
- Background: A challenge in developing machine learning regression models is that it is difficult to know whether maximal performance has been reached on the test dataset, or whether further model improvement is possible. In biology, this problem is particularly pronounced as sample labels (response variables) are typically obtained through experiments and therefore have experiment noise associated with them. Such label noise puts a fundamental limit to the metrics of performance attainable by regression models on the test dataset. Results: We address this challenge by deriving an expected upper bound for the coefficient of determination (R2) for regression models when tested on the holdout dataset. This upper bound depends only on the noise associated with the response variable in a dataset as well as its variance. The upper bound estimate was validated via Monte Carlo simulations and then used as a tool to bootstrap performance of regression models trained on biological datasets, including protein sequence data, transcriptomic data, and genomic data. Conclusions: The new method for estimating upper bounds for model performance on test data should aid researchers in developing ML regression models that reach their maximum potential. Although we study biological datasets in this work, the new upper bound estimates will hold true for regression models from any research field or application area where response variables have associated noise.
|
|
| 6. |
- Peng, Martin, et al.
(author)
-
Modeling-Assisted Design of Thermostable Benzaldehyde Lyases from Rhodococcus erythropolis for Continuous Production of α-Hydroxy Ketones
- 2022
-
In: ChemBioChem. - : Wiley. - 1439-7633 .- 1439-4227. ; 23:7
-
Journal article (peer-reviewed)abstract
- Enantiopure α-hydroxy ketones are important building blocks of active pharmaceutical ingredients (APIs), which can be produced by thiamine-diphosphate-dependent lyases, such as benzaldehyde lyase. Here we report the discovery of a novel thermostable benzaldehyde lyase from Rhodococcus erythropolis R138 (ReBAL). While the overall sequence identity to the only experimentally confirmed benzaldehyde lyase from Pseudomonas fluorescens Biovar I (PfBAL) was only 65 %, comparison of a structural model of ReBAL with the crystal structure of PfBAL revealed only four divergent amino acids in the substrate binding cavity. Based on rational design, we generated two ReBAL variants, which were characterized along with the wild-type enzyme in terms of their substrate spectrum, thermostability and biocatalytic performance in the presence of different co-solvents. We found that the new enzyme variants have a significantly higher thermostability (up to 22 °C increase in T50) and a different co-solvent-dependent activity. Using the most stable variant immobilized in packed-bed reactors via the SpyCatcher/SpyTag system, (R)-benzoin was synthesized from benzaldehyde over a period of seven days with a stable space-time-yield of 9.3 mmol ⋅ L-1 ⋅ d−1. Our work expands the important class of benzaldehyde lyases and therefore contributes to the development of continuous biocatalytic processes for the production of α-hydroxy ketones and APIs.
|
|
| 7. |
- Casey, John R., et al.
(author)
-
Basin-scale biogeography of marine phytoplankton reflects cellular-scale optimization of metabolism and physiology
- 2022
-
In: Science advances. - : American Association for the Advancement of Science (AAAS). - 2375-2548. ; 8:3
-
Journal article (peer-reviewed)abstract
- Extensive microdiversity within Prochlorococcus, the most abundant marine cyanobacterium, occurs at scales from a single droplet of seawater to ocean basins. To interpret the structuring role of variations in genetic potential, as well as metabolic and physiological acclimation, we developed a mechanistic constraint-based modeling framework that incorporates the full suite of genes, proteins, metabolic reactions, pigments, and biochemical compositions of 69 sequenced isolates spanning the Prochlorococcus pangenome. Optimizing each strain to the local, observed physical and chemical environment along an Atlantic Ocean transect, we predicted variations in strain-specific patterns of growth rate, metabolic configuration, and physiological state, defining subtle niche subspaces directly attributable to differences in their encoded metabolic potential. Predicted growth rates covaried with observed ecotype abundances, affirming their significance as a measure of fitness and inferring a nonlinear density dependence of mortality. Our study demonstrates the potential to interpret global-scale ecosystem organization in terms of cellular-scale processes.
|
|
| 8. |
- Gajdoš, Matúš, et al.
(author)
-
Chiral Alcohols from Alkenes and Water: Directed Evolution of a Styrene Hydratase
- 2023
-
In: Angewandte Chemie - International Edition. - : Wiley. - 1433-7851 .- 1521-3773. ; 62:7
-
Journal article (peer-reviewed)abstract
- Enantioselective synthesis of chiral alcohols through asymmetric addition of water across an unactivated alkene is a highly sought-after transformation and a big challenge in catalysis. Herein we report the identification and directed evolution of a fatty acid hydratase from Marinitoga hydrogenitolerans for the highly enantioselective hydration of styrenes to yield chiral 1-arylethanols. While directed evolution for styrene hydration was performed in the presence of heptanoic acid to mimic fatty acid binding, the engineered enzyme displayed remarkable asymmetric styrene hydration activity in the absence of the small molecule activator. The evolved styrene hydratase provided access to chiral alcohols from simple alkenes and water with high enantioselectivity (>99 : 1 e.r.) and could be applied on a preparative scale.
|
|
| 9. |
- Gast, Veronica, 1992, et al.
(author)
-
Engineering Saccharomyces cerevisiae for the production and secretion of Affibody molecules
- 2022
-
In: Microbial Cell Factories. - : Springer Science and Business Media LLC. - 1475-2859. ; 21:1
-
Journal article (peer-reviewed)abstract
- BACKGROUND: Affibody molecules are synthetic peptides with a variety of therapeutic and diagnostic applications. To date, Affibody molecules have mainly been produced by the bacterial production host Escherichia coli. There is an interest in exploring alternative production hosts to identify potential improvements in terms of yield, ease of production and purification advantages. In this study, we evaluated the feasibility of Saccharomyces cerevisiae as a production chassis for this group of proteins. RESULTS: We examined the production of three different Affibody molecules in S. cerevisiae and found that these Affibody molecules were partially degraded. An albumin-binding domain, which may be attached to the Affibody molecules to increase their half-life, was identified to be a substrate for several S. cerevisiae proteases. We tested the removal of three vacuolar proteases, proteinase A, proteinase B and carboxypeptidase Y. Removal of one of these, proteinase A, resulted in intact secretion of one of the targeted Affibody molecules. Removal of either or both of the two additional proteases, carboxypeptidase Y and proteinase B, resulted in intact secretion of the two remaining Affibody molecules. The produced Affibody molecules were verified to bind their target, human HER3, as potently as the corresponding molecules produced in E. coli in an in vitro surface-plasmon resonance binding assay. Finally, we performed a fed-batch fermentation with one of the engineered protease-deficient S. cerevisiae strains and achieved a protein titer of 530 mg Affibody molecule/L. CONCLUSION: This study shows that engineered S. cerevisiae has a great potential as a production host for recombinant Affibody molecules, reaching a high titer, and for proteins where endotoxin removal could be challenging, the use of S. cerevisiae obviates the need for endotoxin removal from protein produced in E. coli.
|
|
| 10. |
- Gast, Veronica, 1992, et al.
(author)
-
The Yeast eIF2 Kinase Gcn2 Facilitates H 2 O 2 -Mediated Feedback Inhibition of Both Protein Synthesis and Endoplasmic Reticulum Oxidative Folding during Recombinant Protein Production
- 2021
-
In: Applied and Environmental Microbiology. - 1098-5336 .- 0099-2240. ; 87:15, s. e0030121-16
-
Journal article (peer-reviewed)abstract
- Recombinant protein production is a known source of oxidative stress. However, knowledge of which reactive oxygen species are involved or the specific growth phase in which stress occurs remains lacking. Using modern, hypersensitive genetic H2O2-specific probes, microcultivation, and continuous measurements in batch culture, we observed H2O2 accumulation during and following the diauxic shift in engineered Saccharomyces cerevisiae, correlating with peak α-amylase production. In agreement with previous studies supporting a role of the translation initiation factor kinase Gcn2 in the response to H2O2, we find that Gcn2-dependent phosphorylation of eIF2α increases alongside translational attenuation in strains engineered to produce large amounts of α-amylase. Gcn2 removal significantly improved α-amylase production in two previously optimized high-producing strains but not in the wild type. Gcn2 deficiency furthermore reduced intracellular H2O2 levels and the Hac1 splicing ratio, while expression of antioxidants and the endoplasmic reticulum (ER) disulfide isomerase PDI1 increased. These results suggest protein synthesis and ER oxidative folding are coupled and subject to feedback inhibition by H2O2. IMPORTANCE Recombinant protein production is a multibillion dollar industry. Optimizing the productivity of host cells is, therefore, of great interest. In several hosts, oxidants are produced as an unwanted side product of recombinant protein production. The buildup of oxidants can result in intracellular stress responses that could compromise the productivity of the host cell. Here, we document a novel protein synthesis inhibitory mechanism that is activated by the buildup of a specific oxidant (H2O2) in the cytosol of yeast cells upon the production of recombinant proteins. At the center of this inhibitory mechanism lies the protein kinase Gcn2. By removing Gcn2, we observed a doubling of recombinant protein productivity in addition to reduced H2O2 levels in the cytosol. In this study, we want to raise awareness of this inhibitory mechanism in eukaryotic cells to further improve protein production and contribute to the development of novel protein-based therapeutic strategies.
|
|
