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  • 1. Almagro Armenteros, Jose Juan
    et al.
    Salvatore, Marco
    Stockholm University, Faculty of Science, Department of Biochemistry and Biophysics.
    Emanuelsson, Olof
    Winther, Ole
    von Heijne, Gunnar
    Stockholm University, Faculty of Science, Department of Biochemistry and Biophysics.
    Elofsson, Arne
    Stockholm University, Faculty of Science, Department of Biochemistry and Biophysics.
    Nielsen, Henrik
    Detecting Novel Sequence Signals in Targeting Peptides Using Deep LearningManuscript (preprint) (Other academic)
  • 2. Armenteros, Jose Juan Almagro
    et al.
    Salvatore, Marco
    Stockholm University, Faculty of Science, Department of Biochemistry and Biophysics. Stockholm University, Science for Life Laboratory (SciLifeLab).
    Emanuelsson, Olof
    Winther, Ole
    von Heijne, Gunnar
    Stockholm University, Faculty of Science, Department of Biochemistry and Biophysics. Stockholm University, Science for Life Laboratory (SciLifeLab).
    Elofsson, Arne
    Stockholm University, Faculty of Science, Department of Biochemistry and Biophysics. Stockholm University, Science for Life Laboratory (SciLifeLab).
    Nielsen, Henrik
    Detecting sequence signals in targeting peptides using deep learning2019In: Life science alliance, E-ISSN 2575-1077, Vol. 2, no 5, article id UNSP e201900429Article in journal (Refereed)
    Abstract [en]

    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.

  • 3.
    Basile, Walter
    et al.
    Stockholm University, Faculty of Science, Department of Biochemistry and Biophysics. Stockholm University, Science for Life Laboratory (SciLifeLab).
    Salvatore, Marco
    Stockholm University, Faculty of Science, Department of Biochemistry and Biophysics. Stockholm University, Science for Life Laboratory (SciLifeLab).
    Bassot, Claudio
    Stockholm University, Faculty of Science, Department of Biochemistry and Biophysics. Stockholm University, Science for Life Laboratory (SciLifeLab).
    Elofsson, Arne
    Stockholm University, Faculty of Science, Department of Biochemistry and Biophysics. Stockholm University, Science for Life Laboratory (SciLifeLab). Swedish e-Science Research Center (SeRC), Sweden.
    Why do eukaryotic proteins contain more intrinsically disordered regions?2019In: PloS Computational Biology, ISSN 1553-734X, E-ISSN 1553-7358, Vol. 15, no 7, article id e1007186Article in journal (Refereed)
    Abstract [en]

    Intrinsic disorder is more abundant in eukaryotic than prokaryotic proteins. Methods predicting intrinsic disorder are based on the amino acid sequence of a protein. Therefore, there must exist an underlying difference in the sequences between eukaryotic and prokaryotic proteins causing the (predicted) difference in intrinsic disorder. By comparing proteins, from complete eukaryotic and prokaryotic proteomes, we show that the difference in intrinsic disorder emerges from the linker regions connecting Pfam domains. Eukaryotic proteins have more extended linker regions, and in addition, the eukaryotic linkers are significantly more disordered, 38% vs. 12-16% disordered residues. Next, we examined the underlying reason for the increase in disorder in eukaryotic linkers, and we found that the changes in abundance of only three amino acids cause the increase. Eukaryotic proteins contain 8.6% serine; while prokaryotic proteins have 6.5%, eukaryotic proteins also contain 5.4% proline and 5.3% isoleucine compared with 4.0% proline and ≈ 7.5% isoleucine in the prokaryotes. All these three differences contribute to the increased disorder in eukaryotic proteins. It is tempting to speculate that the increase in serine frequencies in eukaryotes is related to regulation by kinases, but direct evidence for this is lacking. The differences are observed in all phyla, protein families, structural regions and type of protein but are most pronounced in disordered and linker regions. The observation that differences in the abundance of three amino acids cause the difference in disorder between eukaryotic and prokaryotic proteins raises the question: Are amino acid frequencies different in eukaryotic linkers because the linkers are more disordered or do the differences cause the increased disorder?

  • 4.
    Basile, Walter
    et al.
    Stockholm University, Faculty of Science, Department of Biochemistry and Biophysics. Stockholm University, Science for Life Laboratory (SciLifeLab).
    Salvatore, Marco
    Stockholm University, Faculty of Science, Department of Biochemistry and Biophysics. Stockholm University, Science for Life Laboratory (SciLifeLab).
    Elofsson, Arne
    Stockholm University, Faculty of Science, Department of Biochemistry and Biophysics. Stockholm University, Science for Life Laboratory (SciLifeLab). Swedish e-Science Research Center (SeRC), Sweden.
    The classification of orphans is improved by combining searches in both proteomes and genomes2017Manuscript (preprint) (Other academic)
    Abstract [en]

    The identification of de novo created genes is important as it provides a glimpse on the evolutionary processes of gene creation. Potential de novo created genes are identified by selecting genes that have no homologs outside a particular species, but for an accurate detection this identification needs to be correct.

    Genes without any homologs are often referred to as orphans; in addition to de novo created ones, fast evolving genes or genes lost in all related genomes might also be classified as orphans. The identification of orphans is dependent on: (i) a method to detect homologs and (ii) a database including genes from related genomes.

    Here, we set out to investigate how the detection of orphans is influenced by these two factors. Using Saccharomyces cerevisiae we identify that best strategy is to use a combination of searching annotated proteins and a six-frame translation of all ORFs from closely related genomes. Using this strategy we obtain a set of 54 orphans in Drosophila melanogaster and 38 in Drosophila pseudoobscura, significantly less than what is reported in some earlier studies.

  • 5.
    Salvatore, Marco
    Stockholm University, Faculty of Science, Department of Biochemistry and Biophysics. Science for Life Laboratory.
    Predicting the route: from protein sequence to sorting in eukaryotic cell2019Doctoral thesis, comprehensive summary (Other academic)
    Abstract [en]

    Proteins need to be localised in the correct compartment of a eukaryotic cell to function correctly. Therefore, a protein needs to be transported to the right location. Specific signals present in the protein sequence direct proteins to different subcellular localisations. The correct transport is essential for the life of the cell, while, possible errors during the transport can cause irreversible damage and interfere with the activities of surrounding proteins. For more than 30 years, the development of methods to identify the localisation of proteins using both experimental and computational approaches has been an important research area. The objective of this thesis is to develop better computational methods for the classification of the subcellular localisation of eukaryotic proteins. I first describe the development of a consensus method, SubCons, which improves the subcellular prediction of human proteins. Next, I present the SubCons web-server as well as an additional benchmark using protein annotation from novel mass-spectrometry studies in two eukaryotic organisms Mus musculus and Drosophila melanogaster. Then, I present the new version of TargetP and how deep learning can improve the identification of N-terminal sorting signals by focusing on relevant biological signatures. Finally, I describe the development of a novel method for sub-nuclear localisation prediction. Here, I show that the performance of a deep convolutional neural network is improved when using an augmented dataset of homologous proteins.

  • 6.
    Salvatore, Marco
    et al.
    Stockholm University, Faculty of Science, Department of Biochemistry and Biophysics.
    Almagro Armenteros, Jose Juan
    Lamb, John
    Stockholm University, Faculty of Science, Department of Biochemistry and Biophysics.
    Mileti, Enrichetta
    Winther, Ole
    Nielsen, Henrik
    Elofsson, Arne
    Stockholm University, Faculty of Science, Department of Biochemistry and Biophysics.
    Improved sub-nuclear prediction by Deep Learning using an augmented datasetManuscript (preprint) (Other academic)
  • 7.
    Salvatore, Marco
    et al.
    Stockholm University, Faculty of Science, Department of Biochemistry and Biophysics. Stockholm University, Science for Life Laboratory (SciLifeLab).
    Shu, Nanjiang
    Stockholm University, Faculty of Science, Department of Biochemistry and Biophysics. Stockholm University, Science for Life Laboratory (SciLifeLab).
    Elofsson, Arne
    Stockholm University, Faculty of Science, Department of Biochemistry and Biophysics. Stockholm University, Science for Life Laboratory (SciLifeLab).
    The SubCons webserver: A user friendly web interface for state-of-the-art subcellular localization prediction2018In: Protein Science, ISSN 0961-8368, E-ISSN 1469-896X, Vol. 27, no 1, p. 195-201Article in journal (Refereed)
    Abstract [en]

    SubCons is a recently developed method that predicts the subcellular localization of a protein. It combines predictions from four predictors using a Random Forest classifier. Here, we present the user-friendly web-interface implementation of SubCons. Starting from a protein sequence, the server rapidly predicts the subcellular localizations of an individual protein. In addition, the server accepts the submission of sets of proteins either by uploading the files or programmatically by using command line WSDL API scripts. This makes SubCons ideal for proteome wide analyses allowing the user to scan a whole proteome in few days. From the web page, it is also possible to download precalculated predictions for several eukaryotic organisms. To evaluate the performance of SubCons we present a benchmark of LocTree3 and SubCons using two recent mass-spectrometry based datasets of mouse and drosophila proteins. The server is available at http://subcons.bioinfo.se/

  • 8.
    Salvatore, Marco
    et al.
    Stockholm University, Faculty of Science, Department of Biochemistry and Biophysics. Stockholm University, Science for Life Laboratory (SciLifeLab).
    Warholm, Per
    Stockholm University, Faculty of Science, Department of Biochemistry and Biophysics. Stockholm University, Science for Life Laboratory (SciLifeLab).
    Shu, Nanjiang
    Stockholm University, Faculty of Science, Department of Biochemistry and Biophysics. Stockholm University, Science for Life Laboratory (SciLifeLab).
    Basile, Walter
    Stockholm University, Faculty of Science, Department of Biochemistry and Biophysics. Stockholm University, Science for Life Laboratory (SciLifeLab).
    Elofsson, Arne
    Stockholm University, Faculty of Science, Department of Biochemistry and Biophysics. Stockholm University, Science for Life Laboratory (SciLifeLab).
    SubCons: a new ensemble method for improved human subcellular localization predictions2017In: Bioinformatics, ISSN 1367-4803, E-ISSN 1367-4811, Vol. 33, no 16, p. 2464-2470Article in journal (Refereed)
    Abstract [en]

    Motivation: Knowledge of the correct protein subcellular localization is necessary for understanding the function of a protein. Unfortunately large-scale experimental studies are limited in their accuracy. Therefore, the development of prediction methods has been limited by the amount of accurate experimental data. However, recently large-scale experimental studies have provided new data that can be used to evaluate the accuracy of subcellular predictions in human cells. Using this data we examined the performance of state of the art methods and developed SubCons, an ensemble method that combines four predictors using a Random Forest classifier. Results: SubCons outperforms earlier methods in a dataset of proteins where two independent methods confirm the subcellular localization. Given nine subcellular localizations, SubCons achieves an F1-Score of 0.79 compared to 0.70 of the second bestmethod. Furthermore, at a FPR of 1% the true positive rate (TPR) is over 58% for SubCons compared to less than 50% for the best individual predictor.

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