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- Abildgaard, Amanda B., et al.
(author)
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HSP70-binding motifs function as protein quality control degrons
- 2023
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In: Cellular and Molecular Life Sciences (CMLS). - : Springer Science and Business Media LLC. - 1420-682X .- 1420-9071. ; 80:1
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Journal article (peer-reviewed)abstract
- Protein quality control (PQC) degrons are short protein segments that target misfolded proteins for proteasomal degradation, and thus protect cells against the accumulation of potentially toxic non-native proteins. Studies have shown that PQC degrons are hydrophobic and rarely contain negatively charged residues, features which are shared with chaperone-binding regions. Here we explore the notion that chaperone-binding regions may function as PQC degrons. When directly tested, we found that a canonical Hsp70-binding motif (the APPY peptide) functioned as a dose-dependent PQC degron both in yeast and in human cells. In yeast, Hsp70, Hsp110, Fes1, and the E3 Ubr1 target the APPY degron. Screening revealed that the sequence space within the chaperone-binding region of APPY that is compatible with degron function is vast. We find that the number of exposed Hsp70-binding sites in the yeast proteome correlates with a reduced protein abundance and half-life. Our results suggest that when protein folding fails, chaperone-binding sites may operate as PQC degrons, and that the sequence properties leading to PQC-linked degradation therefore overlap with those of chaperone binding.
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- Zhang, J., et al.
(author)
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Combining mechanistic and machine learning models for predictive engineering and optimization of tryptophan metabolism
- 2020
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In: Nature Communications. - : Springer Science and Business Media LLC. - 2041-1723 .- 2041-1723. ; 11:1
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Journal article (peer-reviewed)abstract
- Through advanced mechanistic modeling and the generation of large high-quality datasets, machine learning is becoming an integral part of understanding and engineering living systems. Here we show that mechanistic and machine learning models can be combined to enable accurate genotype-to-phenotype predictions. We use a genome-scale model to pinpoint engineering targets, efficient library construction of metabolic pathway designs, and high-throughput biosensor-enabled screening for training diverse machine learning algorithms. From a single data-generation cycle, this enables successful forward engineering of complex aromatic amino acid metabolism in yeast, with the best machine learning-guided design recommendations improving tryptophan titer and productivity by up to 74 and 43%, respectively, compared to the best designs used for algorithm training. Thus, this study highlights the power of combining mechanistic and machine learning models to effectively direct metabolic engineering efforts.
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- Almagro Armenteros, José Juan, et al.
(author)
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SignalP 5.0 improves signal peptide predictions using deep neural networks
- 2019
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In: Nature Biotechnology. - : Springer Science and Business Media LLC. - 1087-0156 .- 1546-1696. ; 37:4, s. 420-423
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Journal article (peer-reviewed)abstract
- Signal peptides (SPs) are short amino acid sequences in the amino terminus of many newly synthesized proteins that target proteins into, or across, membranes. Bioinformatic tools can predict SPs from amino acid sequences, but most cannot distinguish between various types of signal peptides. We present a deep neural network-based approach that improves SP prediction across all domains of life and distinguishes between three types of prokaryotic SPs.
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- Andersson, Tilde, et al.
(author)
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Biogeographical variation in antimicrobial resistance in rivers is influenced by agriculture and is spread through bacteriophages
- 2022
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In: Environmental Microbiology. - : Wiley. - 1462-2912 .- 1462-2920. ; 24:10, s. 4869-4884
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Journal article (peer-reviewed)abstract
- Antibiotic resistance is currently an extensive medical challenge worldwide, with global numbers increasing steadily. Recent data have highlighted wastewater treatment plants as a reservoir of resistance genes. The impact of these findings for human health can best be summarized using a One Health concept. However, the molecular mechanisms impacting resistance spread have not been carefully evaluated. Bacterial viruses, that is bacteriophages, have recently been shown to be important mediators of bacterial resistance genes in environmental milieus and are transferrable to human pathogens. Herein, we investigated the biogeographical impact on resistance spread through river-borne bacteriophages using amplicon deep sequencing of the microbiota, absolute quantification of resistance genes using ddPCR, and phage induction capacity within wastewater. Microbial biodiversity of the rivers is significantly affected by river site, surrounding milieu and time of sampling. Furthermore, areas of land associated with agriculture had a significantly higher ability to induce bacteriophages carrying antibiotic resistance genes, indicating their impact on resistance spread. It is imperative that we continue to analyse global antibiotic resistance problem from a One Health perspective to gain novel insights into mechanisms of resistance spread.
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