| 1. |
- Uhlén, Mathias, et al.
(författare)
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Tissue-based map of the human proteome
- 2015
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Ingår i: Science. - : American Association for the Advancement of Science (AAAS). - 0036-8075 .- 1095-9203. ; 347:6220, s. 1260419-
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Tidskriftsartikel (refereegranskat)abstract
- Resolving the molecular details of proteome variation in the different tissues and organs of the human body will greatly increase our knowledge of human biology and disease. Here, we present a map of the human tissue proteome based on an integrated omics approach that involves quantitative transcriptomics at the tissue and organ level, combined with tissue microarray-based immunohistochemistry, to achieve spatial localization of proteins down to the single-cell level. Our tissue-based analysis detected more than 90% of the putative protein-coding genes. We used this approach to explore the human secretome, the membrane proteome, the druggable proteome, the cancer proteome, and the metabolic functions in 32 different tissues and organs. All the data are integrated in an interactive Web-based database that allows exploration of individual proteins, as well as navigation of global expression patterns, in all major tissues and organs in the human body.
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| 2. |
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| 3. |
- Almagro Armenteros, José Juan, et al.
(författare)
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SignalP 5.0 improves signal peptide predictions using deep neural networks
- 2019
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Ingår i: Nature Biotechnology. - : Springer Science and Business Media LLC. - 1087-0156 .- 1546-1696. ; 37:4, s. 420-423
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Tidskriftsartikel (refereegranskat)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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| 4. |
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| 5. |
- Andersson, Annika, et al.
(författare)
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Membrane integration and topology of RIFIN and STEVOR proteins of the Plasmodium falciparum parasite
- 2020
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Ingår i: The FEBS Journal. - : Wiley. - 1742-464X .- 1742-4658. ; 287:13, s. 2744-2762
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Tidskriftsartikel (refereegranskat)abstract
- The malarial parasite Plasmodium exports its own proteins to the cell surfaces of red blood cells (RBCs) during infection. Examples of exported proteins include members of the repetitive interspersed family (RIFIN) and subtelomeric variable open reading frame (STEVOR) family of proteins from Plasmodium falciparum. The presence of these parasite-derived proteins on surfaces of infected RBCs triggers the adhesion of infected cells to uninfected cells (rosetting) and to the vascular endothelium potentially obstructing blood flow. While there is a fair amount of information on the localization of these proteins on the cell surfaces of RBCs, less is known about how they can be exported to the membrane and the topologies they can adopt during the process. The first step of export is plausibly the cotranslational insertion of proteins into the endoplasmic reticulum (ER) of the parasite, and here, we investigate the insertion of three RIFIN and two STEVOR proteins into the ER membrane. We employ a well-established experimental system that uses N-linked glycosylation of sites within the protein as a measure to assess the extent of membrane insertion and the topology it assumes when inserted into the ER membrane. Our results indicate that for all the proteins tested, transmembranes (TMs) 1 and 3 integrate into the membrane, so that the protein assumes an overall topology of Ncyt-Ccyt. We also show that the segment predicted to be TM2 for each of the proteins likely does not reside in the membrane, but is translocated to the lumen.
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| 6. |
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| 7. |
- Armenteros, Jose Juan Almagro, et al.
(författare)
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Detecting sequence signals in targeting peptides using deep learning
- 2019
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Ingår i: Life Science Alliance. - : LIFE SCIENCE ALLIANCE LLC. - 2575-1077. ; 2:5
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Tidskriftsartikel (refereegranskat)abstract
- In bioinformatics, machine learning methods have been used to predict features embedded in the sequences. In contrast to what is generally assumed, machine learning approaches can also provide new insights into the underlying biology. Here, we demonstrate this by presenting TargetP 2.0, a novel state-of-the-art method to identify N-terminal sorting signals, which direct proteins to the secretory pathway, mitochondria, and chloroplasts or other plastids. By examining the strongest signals from the attention layer in the network, we find that the second residue in the protein, that is, the one following the initial methionine, has a strong influence on the classification. We observe that two-thirds of chloroplast and thylakoid transit peptides have an alanine in position 2, compared with 20% in other plant proteins. We also note that in fungi and single-celled eukaryotes, less than 30% of the targeting peptides have an amino acid that allows the removal of the N-terminal methionine compared with 60% for the proteins without targeting peptide. The importance of this feature for predictions has not been highlighted before.
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| 8. |
- Baeza-Delgado, Carlos, et al.
(författare)
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Biological insertion of computationally designed short transmembrane segments
- 2016
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Ingår i: Scientific Reports. - : Springer Science and Business Media LLC. - 2045-2322. ; 6
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Tidskriftsartikel (refereegranskat)abstract
- The great majority of helical membrane proteins are inserted co-translationally into the ER membrane through a continuous ribosome-translocon channel. The efficiency of membrane insertion depends on transmembrane (TM) helix amino acid composition, the helix length and the position of the amino acids within the helix. In this work, we conducted a computational analysis of the composition and location of amino acids in transmembrane helices found in membrane proteins of known structure to obtain an extensive set of designed polypeptide segments with naturally occurring amino acid distributions. Then, using an in vitro translation system in the presence of biological membranes, we experimentally validated our predictions by analyzing its membrane integration capacity. Coupled with known strategies to control membrane protein topology, these findings may pave the way to de novo membrane protein design.
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| 9. |
- Bañó-Polo, Manuel, et al.
(författare)
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Transmembrane but not soluble helices fold inside the ribosome tunnel
- 2018
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Ingår i: Nature Communications. - : Springer Science and Business Media LLC. - 2041-1723. ; 9
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Tidskriftsartikel (refereegranskat)abstract
- Integral membrane proteins are assembled into the ER membrane via a continuous ribosome-translocon channel. The hydrophobicity and thickness of the core of the membrane bilayer leads to the expectation that transmembrane (TM) segments minimize the cost of harbouring polar polypeptide backbones by adopting a regular pattern of hydrogen bonds to form a-helices before integration. Co-translational folding of nascent chains into an a-helical conformation in the ribosomal tunnel has been demonstrated previously, but the features governing this folding are not well understood. In particular, little is known about what features influence the propensity to acquire a-helical structure in the ribosome. Using in vitro translation of truncated nascent chains trapped within the ribosome tunnel and molecular dynamics simulations, we show that folding in the ribosome is attained for TM helices but not for soluble helices, presumably facilitating SRP (signal recognition particle) recognition and/or a favourable conformation for membrane integration upon translocon entry.
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| 10. |
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