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  • 1. Almagro Armenteros, José Juan
    et al.
    Tsirigos, Konstantinos D.
    Stockholms universitet, Naturvetenskapliga fakulteten, Institutionen för biokemi och biofysik. Stockholms universitet, Science for Life Laboratory (SciLifeLab). Technical University of Denmark, Denmark; Max Planck Institute for Molecular Genetics, Germany.
    Kaae Sonderby, Casper
    Nordahl Petersen, Thomas
    Winther, Ole
    Brunak, Søren
    von Heijne, Gunnar
    Stockholms universitet, Naturvetenskapliga fakulteten, Institutionen för biokemi och biofysik. Stockholms universitet, Science for Life Laboratory (SciLifeLab).
    Nielsen, Henrik
    SignalP 5.0 improves signal peptide predictions using deep neural networks2019Ingår i: Nature Biotechnology, ISSN 1087-0156, E-ISSN 1546-1696, Vol. 37, nr 4, s. 420-423Artikel i tidskrift (Refereegranskat)
    Abstract [en]

    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.

  • 2. Babbitt, Patricia C.
    et al.
    Bagos, Pantelis G.
    Bairoch, Amos
    Bateman, Alex
    Chatonnet, Arnaud
    Chen, Mark Jinan
    Craik, David J.
    Finn, Robert D.
    Gloriam, David
    Haft, Daniel H.
    Henrissat, Bernard
    Holliday, Gemma L.
    Isberg, Vignir
    Kaas, Quentin
    Landsman, David
    Lenfant, Nicolas
    Manning, Gerard
    Nagano, Nozomi
    Srinivasan, Narayanaswamy
    O'Donovan, Claire
    Pruitt, Kim D.
    Sowdhamini, Ramanathan
    Rawlings, Neil D.
    Saier, Milton H.
    Sharman, Joanna L.
    Spedding, Michael
    Tsirigos, Konstantinos D.
    Stockholms universitet, Naturvetenskapliga fakulteten, Institutionen för biokemi och biofysik. Stockholms universitet, Science for Life Laboratory (SciLifeLab).
    Vastermark, Ake
    Vriend, Gerrit
    Creating a specialist protein resource network: a meeting report for the protein bioinformatics and community resources retreat2015Ingår i: Database: The Journal of Biological Databases and Curation, ISSN 1758-0463, E-ISSN 1758-0463, artikel-id bav063Artikel i tidskrift (Refereegranskat)
    Abstract [en]

    During 11-12 August 2014, a Protein Bioinformatics and Community Resources Retreat was held at the Wellcome Trust Genome Campus in Hinxton, UK. This meeting brought together the principal investigators of several specialized protein resources (such as CAZy, TCDB and MEROPS) as well as those from protein databases from the large Bioinformatics centres (including UniProt and RefSeq). The retreat was divided into five sessions: (1) key challenges, (2) the databases represented, (3) best practices for maintenance and curation, (4) information flow to and from large data centers and (5) communication and funding. An important outcome of this meeting was the creation of a Specialist Protein Resource Network that we believe will improve coordination of the activities of its member resources. We invite further protein database resources to join the network and continue the dialogue.

  • 3. Dimou, Niki L.
    et al.
    Tsirigos, Konstantinos D.
    Stockholms universitet, Naturvetenskapliga fakulteten, Institutionen för biokemi och biofysik. Stockholms universitet, Science for Life Laboratory (SciLifeLab). Swedish e-Science Research Center, Sweden.
    Elofsson, Arne
    Stockholms universitet, Naturvetenskapliga fakulteten, Institutionen för biokemi och biofysik. Stockholms universitet, Science for Life Laboratory (SciLifeLab). Swedish e-Science Research Center, Sweden.
    Bagos, Pantelis G.
    GWAR: robust analysis and meta-analysis of genome-wide association studies2017Ingår i: Bioinformatics, ISSN 1367-4803, E-ISSN 1367-4811, Vol. 33, nr 10, s. 1521-1527Artikel i tidskrift (Refereegranskat)
    Abstract [en]

    Motivation: In the context of genome-wide association studies (GWAS), there is a variety of statistical techniques in order to conduct the analysis, but, in most cases, the underlying genetic model is usually unknown. Under these circumstances, the classical Cochran-Armitage trend test (CATT) is suboptimal. Robust procedures that maximize the power and preserve the nominal type I error rate are preferable. Moreover, performing a meta-analysis using robust procedures is of great interest and has never been addressed in the past. The primary goal of this work is to implement several robust methods for analysis and meta-analysis in the statistical package Stata and subsequently to make the software available to the scientific community. Results: The CATT under a recessive, additive and dominant model of inheritance as well as robust methods based on the Maximum Efficiency Robust Test statistic, the MAX statistic and the MIN2 were implemented in Stata. Concerning MAX and MIN2, we calculated their asymptotic null distributions relying on numerical integration resulting in a great gain in computational time without losing accuracy. All the aforementioned approaches were employed in a fixed or a random effects meta-analysis setting using summary data with weights equal to the reciprocal of the combined cases and controls. Overall, this is the first complete effort to implement procedures for analysis and meta-analysis in GWAS using Stata.

  • 4. Hayat, Sikander
    et al.
    Peters, Christoph
    Stockholms universitet, Naturvetenskapliga fakulteten, Institutionen för biokemi och biofysik. Stockholms universitet, Science for Life Laboratory (SciLifeLab).
    Shu, Nanjiang
    Stockholms universitet, Naturvetenskapliga fakulteten, Institutionen för biokemi och biofysik. Stockholms universitet, Science for Life Laboratory (SciLifeLab).
    Tsirigos, Konstantinos D.
    Stockholms universitet, Naturvetenskapliga fakulteten, Institutionen för biokemi och biofysik. Stockholms universitet, Science for Life Laboratory (SciLifeLab).
    Elofsson, Arne
    Stockholms universitet, Naturvetenskapliga fakulteten, Institutionen för biokemi och biofysik. Stockholms universitet, Science for Life Laboratory (SciLifeLab).
    Inclusion of dyad-repeat pattern improves topology prediction of transmembrane beta-barrel proteins2016Ingår i: Bioinformatics, ISSN 1367-4803, E-ISSN 1367-4811, Vol. 32, nr 10, s. 1571-1573Artikel i tidskrift (Refereegranskat)
    Abstract [en]

    Accurate topology prediction of transmembrane beta-barrels is still an open question. Here, we present BOCTOPUS2, an improved topology prediction method for transmembrane beta-barrels that can also identify the barrel domain, predict the topology and identify the orientation of residues in transmembrane beta-strands. The major novelty of BOCTOPUS2 is the use of the dyad-repeat pattern of lipid and pore facing residues observed in transmembrane beta-barrels. In a cross-validation test on a benchmark set of 42 proteins, BOCTOPUS2 predicts the correct topology in 69% of the proteins, an improvement of more than 10% over the best earlier method (BOCTOPUS) and in addition, it produces significantly fewer erroneous predictions on non-transmembrane beta-barrel proteins.

  • 5.
    Pascarelli, Stefano
    et al.
    Stockholms universitet, Naturvetenskapliga fakulteten, Institutionen för biokemi och biofysik.
    Tsirigos, Konstantinos
    Stockholms universitet, Naturvetenskapliga fakulteten, Institutionen för biokemi och biofysik.
    Shu, Nanjiang
    Stockholms universitet, Naturvetenskapliga fakulteten, Institutionen för biokemi och biofysik.
    Peters, Christoph
    Stockholms universitet, Naturvetenskapliga fakulteten, Institutionen för biokemi och biofysik.
    Elofsson, Arne
    Stockholms universitet, Naturvetenskapliga fakulteten, Institutionen för biokemi och biofysik.
    PRODRES: Fast protein searches using a protein domain-reduced databaseManuskript (preprint) (Övrigt vetenskapligt)
    Abstract [en]

    Motivation: Detection of homologous sequences is a the basis formany bioinformatics applications. Position-Specific Scoring Matrices(PSSMs) or Hidden Markov Models (HMMs) are often created fromthe detected homologous sequences. These are then widely usedin many bioinformatics software in order to incorporate evolutionaryinformation in the prediction process. However, due to the increasein the size of reference databases, there is a continuous decrease inspeed of homology detection even with faster computers.Results: By using PRODRES, we save on average X percent ofthe search time. This pipeline has been exploited in our widely usedtopology prediction software, TOPCONS. In total, more than 5 millionPSSMs have been generated, with an average running time of about1 minute. This corresponds to an approximate 10 times speed-up ofthe whole process.Availability and implementation: A standalone version ofPRODRES can be found in the Github repository https://github.com/-ElofssonLab/PRODRES, while a web-server implementing themethod is available for academic users at http://PRODRES.bioinfo.se/

  • 6.
    Peters, Christoph
    et al.
    Stockholms universitet, Naturvetenskapliga fakulteten, Institutionen för biokemi och biofysik. Stockholms universitet, Science for Life Laboratory (SciLifeLab).
    Tsirigos, Kostantionos D.
    Stockholms universitet, Naturvetenskapliga fakulteten, Institutionen för biokemi och biofysik. Stockholms universitet, Science for Life Laboratory (SciLifeLab).
    Shu, Nanjiang
    Stockholms universitet, Naturvetenskapliga fakulteten, Institutionen för biokemi och biofysik. Stockholms universitet, Science for Life Laboratory (SciLifeLab).
    Elofsson, Arne
    Stockholms universitet, Naturvetenskapliga fakulteten, Institutionen för biokemi och biofysik. Stockholms universitet, Science for Life Laboratory (SciLifeLab).
    Improved topology prediction using the terminal hydrophobic helices rule2016Ingår i: Bioinformatics, ISSN 1367-4803, E-ISSN 1367-4811, Vol. 32, nr 8, s. 1158-1162Artikel i tidskrift (Refereegranskat)
    Abstract [en]

    Motivation: The translocon recognizes sufficiently hydrophobic regions of a protein and inserts them into the membrane. Computational methods try to determine what hydrophobic regions are recognized by the translocon. Although these predictions are quite accurate, many methods still fail to distinguish marginally hydrophobic transmembrane (TM) helices and equally hydrophobic regions in soluble protein domains. In vivo, this problem is most likely avoided by targeting of the TM-proteins, so that non-TM proteins never see the translocon. Proteins are targeted to the translocon by an N-terminal signal peptide. The targeting is also aided by the fact that the N-terminal helix is more hydrophobic than other TM-helices. In addition, we also recently found that the C-terminal helix is more hydrophobic than central helices. This information has not been used in earlier topology predictors.

    Results: Here, we use the fact that the N- and C-terminal helices are more hydrophobic to develop a new version of the first-principle-based topology predictor, SCAMPI. The new predictor has two main advantages; first, it can be used to efficiently separate membrane and non-membrane proteins directly without the use of an extra prefilter, and second it shows improved performance for predicting the topology of membrane proteins that contain large non-membrane domains.

    Availability and implementation: The predictor, a web server and all datasets are available at http://scampi.bioinfo.se/.

  • 7. Piovesan, Damiano
    et al.
    Tabaro, Francesco
    Micetic, Ivan
    Necci, Marco
    Quaglia, Federica
    Oldfield, Christopher J.
    Aspromonte, Maria Cristina
    Davey, Norman E.
    Davidovic, Radoslav
    Dosztanyi, Zsuzsanna
    Elofsson, Arne
    Stockholms universitet, Naturvetenskapliga fakulteten, Institutionen för biokemi och biofysik. Stockholms universitet, Science for Life Laboratory (SciLifeLab).
    Gasparini, Alessandra
    Hatos, Andras
    Kajava, Andrey V.
    Kalmar, Lajos
    Leonardi, Emanuela
    Lazar, Tamas
    Macedo-Ribeiro, Sandra
    Macossay-Castillo, Mauricio
    Meszaros, Attila
    Minervini, Giovanni
    Murvai, Nikoletta
    Pujols, Jordi
    Roche, Daniel B.
    Salladini, Edoardo
    Schad, Eva
    Schramm, Antoine
    Szabo, Beata
    Tantos, Agnes
    Tonello, Fiorella
    Tsirigos, Konstantinos D.
    Stockholms universitet, Naturvetenskapliga fakulteten, Institutionen för biokemi och biofysik. Stockholms universitet, Science for Life Laboratory (SciLifeLab).
    Veljkovic, Nevena
    Ventura, Salvador
    Vranken, Wim
    Warholm, Per
    Stockholms universitet, Naturvetenskapliga fakulteten, Institutionen för biokemi och biofysik. Stockholms universitet, Science for Life Laboratory (SciLifeLab).
    Uversky, Vladimir N.
    Dunker, A. Keith
    Longhi, Sonia
    Tompa, Peter
    Tosatto, Silvio C. E.
    DisProt 7.0: a major update of the database of disordered proteins2017Ingår i: Nucleic Acids Research, ISSN 0305-1048, E-ISSN 1362-4962, Vol. 45, nr D1, s. d219-D227Artikel i tidskrift (Refereegranskat)
    Abstract [en]

    The Database of Protein Disorder (DisProt, URL: www.disprot.org) has been significantly updated and upgraded since its last major renewal in 2007. The current release holds information on more than 800 entries of IDPs/IDRs, i.e. intrinsically disordered proteins or regions that exist and function without a well-defined three-dimensional structure. We have re-curated previous entries to purge DisProt from conflicting cases, and also upgraded the functional classification scheme to reflect continuous advance in the field in the past 10 years or so. We define IDPs as proteins that are disordered along their entire sequence, i.e. entirely lack structural elements, and IDRs as regions that are at least five consecutive residues without well-defined structure. We base our assessment of disorder strictly on experimental evidence, such as X-ray crystallography and nuclear magnetic resonance ( primary techniques) and a broad range of other experimental approaches (secondary techniques). Confident and ambiguous annotations are highlighted separately. DisProt 7.0 presents classified knowledge regarding the experimental characterization and functional annotations of IDPs/IDRs, and is intended to provide an invaluable resource for the research community for a better understanding structural disorder and for developing better computational tools for studying disordered proteins.

  • 8.
    Tsirigos, Konstantinos
    Stockholms universitet, Naturvetenskapliga fakulteten, Institutionen för biokemi och biofysik.
    Bioinformatics Methods for Topology Prediction of Membrane Proteins2017Doktorsavhandling, sammanläggning (Övrigt vetenskapligt)
    Abstract [en]

    Membrane proteins are key elements of the cell since they are associated with a variety of very important biological functions crucial to its survival. They are implicated in cellular recognition and adhesion, act as molecular receptors, transport substrates through membranes and exhibit specific enzymatic activity.This thesis is focused on integral membrane proteins, most of which contain transmembrane segments that form an alpha helix and are composed of mainly hydrophobic residues, spanning the lipid bilayer. A more specialized and less well-studied case, is the case of integral membrane proteins found in the outer membrane of Gram-negative bacteria and (presumably) in the outer envelope of mitochondria and chloroplasts, proteins whose transmembrane segments are formed by amphipathic beta strands that create a closed barrel (beta-barrels). The importance of transmembrane proteins, as well as the inherent difficulties in crystallizing and obtaining three-dimensional structures of these, dictates the need for developing computational algorithms and tools that will allow for a reliable and fast prediction of their structural and functional features. In order to elucidate their function, we must acquire knowledge about their structure and topology with relation to the membrane. Therefore, a large number of computational methods have been developed in order to predict the transmembrane segments and the overall topology of transmembrane proteins. In this thesis, I initially describe a large-scale benchmark of many topology prediction tools in order to devise a strategy that will allow for better detection of alpha-helical membrane proteins in a proteome. Then, I give a description of construction of improved machine-learning algorithms and computer software for accurate topology prediction of transmembrane proteins and discrimination of such proteins from non-transmembrane proteins. Finally, I introduce a fast way to obtain a position-specific scoring matrix, which is essential for modern topology prediction methods.

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    Bioinformatics Methods for Tropology Prediction of Membrane Proteins
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  • 9.
    Tsirigos, Konstantinos D.
    et al.
    Stockholms universitet, Naturvetenskapliga fakulteten, Institutionen för biokemi och biofysik. Stockholms universitet, Science for Life Laboratory (SciLifeLab). University of Thessaly, Greece .
    Elofsson, Arne
    Stockholms universitet, Naturvetenskapliga fakulteten, Institutionen för biokemi och biofysik. Stockholms universitet, Science for Life Laboratory (SciLifeLab).
    Bagos, Pantelis G.
    PRED-TMBB2: improved topology prediction and detection of beta-barrel outer membrane proteins2016Ingår i: Bioinformatics, ISSN 1367-4803, E-ISSN 1367-4811, Vol. 32, nr 17, s. 665-671Artikel i tidskrift (Refereegranskat)
    Abstract [en]

    Motivation: The PRED-TMBB method is based on Hidden Markov Models and is capable of predicting the topology of beta-barrel outer membrane proteins and discriminate them from water-soluble ones. Here, we present an updated version of the method, PRED-TMBB2, with several newly developed features that improve its performance. The inclusion of a properly defined end state allows for better modeling of the beta-barrel domain, while different emission probabilities for the adjacent residues in strands are used to incorporate knowledge concerning the asymmetric amino acid distribution occurring there. Furthermore, the training was performed using newly developed algorithms in order to optimize the labels of the training sequences. Moreover, the method is retrained on a larger, non-redundant dataset which includes recently solved structures, and a newly developed decoding method was added to the already available options. Finally, the method now allows the incorporation of evolutionary information in the form of multiple sequence alignments. Results: The results of a strict cross-validation procedure show that PRED-TMBB2 with homology information performs significantly better compared to other available prediction methods. It yields 76% in correct topology predictions and outperforms the best available predictor by 7%, with an overall SOV of 0.9. Regarding detection of beta-barrel proteins, PRED-TMBB2, using just the query sequence as input, achieves an MCC value of 0.92, outperforming even predictors designed for this task and are much slower.

  • 10.
    Tsirigos, Konstantinos D.
    et al.
    Stockholms universitet, Naturvetenskapliga fakulteten, Institutionen för biokemi och biofysik. Stockholms universitet, Science for Life Laboratory (SciLifeLab).
    Hennerdal, Aron
    Stockholms universitet, Naturvetenskapliga fakulteten, Institutionen för biokemi och biofysik. Stockholms universitet, Science for Life Laboratory (SciLifeLab).
    Käll, Lukas
    Stockholms universitet, Naturvetenskapliga fakulteten, Institutionen för biokemi och biofysik. Stockholms universitet, Science for Life Laboratory (SciLifeLab).
    Elofsson, Arne
    Stockholms universitet, Naturvetenskapliga fakulteten, Institutionen för biokemi och biofysik. Stockholms universitet, Science for Life Laboratory (SciLifeLab).
    A guideline to proteome-wide alpha-helical membrane protein topology predictions2013Ingår i: Proteomics, ISSN 1615-9853, E-ISSN 1615-9861, Vol. 12, nr 14, s. 2282-2294Artikel i tidskrift (Refereegranskat)
    Abstract [en]

    For current state-of-the-art methods, the prediction of correct topology of membrane proteins has been reported to be above 80%. However, this performance has only been observed in small and possibly biased data sets obtained from protein structures or biochemical assays. Here, we test a number of topology predictors on an unseen set of proteins of known structure and also on four genome-scale data sets, including one recent large set of experimentally validated human membrane proteins with glycosylated sites. The set of glycosylated proteins is also used to examine the ability of prediction methods to separate membrane from nonmembrane proteins. The results show that methods utilizing multiple sequence alignments are overall superior to methods that do not. The best performance is obtained by TOPCONS, a consensus method that combines several of the other prediction methods. The best methods to distinguish membrane from nonmembrane proteins belong to the Phobius group of predictors. We further observe that the reported high accuracies in the smaller benchmark sets are not quite maintained in larger scale benchmarks. Instead, we estimate the performance of the best prediction methods for eukaryotic membrane proteins to be between 60% and 70%. The low agreement between predictions from different methods questions earlier estimates about the global properties of the membrane proteome. Finally, we suggest a pipeline to estimate these properties using a combination of the best predictors that could be applied in large-scale proteomics studies of membrane proteins.

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  • 11.
    Tsirigos, Konstantinos D.
    et al.
    Stockholms universitet, Naturvetenskapliga fakulteten, Institutionen för biokemi och biofysik. Stockholms universitet, Science for Life Laboratory (SciLifeLab).
    Peters, Christoph
    Stockholms universitet, Naturvetenskapliga fakulteten, Institutionen för biokemi och biofysik. Stockholms universitet, Science for Life Laboratory (SciLifeLab).
    Shu, Nanjiang
    Stockholms universitet, Naturvetenskapliga fakulteten, Institutionen för biokemi och biofysik. Stockholms universitet, Science for Life Laboratory (SciLifeLab).
    Käll, Lukas
    Stockholms universitet, Naturvetenskapliga fakulteten, Institutionen för biokemi och biofysik. Stockholms universitet, Science for Life Laboratory (SciLifeLab).
    Elofsson, Arne
    Stockholms universitet, Naturvetenskapliga fakulteten, Institutionen för biokemi och biofysik. Stockholms universitet, Science for Life Laboratory (SciLifeLab).
    The TOPCONS web server for consensus prediction of membrane protein topology and signal peptides2015Ingår i: Nucleic Acids Research, ISSN 0305-1048, E-ISSN 1362-4962, Vol. 43, nr W1, s. W401-W407Artikel i tidskrift (Refereegranskat)
    Abstract [en]

    TOPCONS (http://topcons.net/) is a widely used web server for consensus prediction of membrane protein topology. We hereby present a major update to the server, with some substantial improvements, including the following: (i) TOPCONS can now efficiently separate signal peptides from transmembrane regions. (ii) The server can now differentiate more successfully between globular and membrane proteins. (iii) The server now is even slightly faster, although a much larger database is used to generate the multiple sequence alignments. For most proteins, the final prediction is produced in a matter of seconds. (iv) The user-friendly interface is retained, with the additional feature of submitting batch files and accessing the server programmatically using standard interfaces, making it thus ideal for proteome-wide analyses. Indicatively, the user can now scan the entire human proteome in a few days. (v) For proteins with homology to a known 3D structure, the homology-inferred topology is also displayed. (vi) Finally, the combination of methods currently implemented achieves an overall increase in performance by 4% as compared to the currently available best-scoring methods and TOPCONS is the only method that can identify signal peptides and still maintain a state-of-the-art performance in topology predictions.

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  • 12.
    Virkki, Minttu
    et al.
    Stockholms universitet, Naturvetenskapliga fakulteten, Institutionen för biokemi och biofysik. Stockholms universitet, Science for Life Laboratory (SciLifeLab).
    Boekel, Carolina
    Stockholms universitet, Naturvetenskapliga fakulteten, Institutionen för biokemi och biofysik.
    Illergård, Kristoffer
    Stockholms universitet, Naturvetenskapliga fakulteten, Institutionen för biokemi och biofysik. Stockholms universitet, Science for Life Laboratory (SciLifeLab).
    Peters, Christoph
    Stockholms universitet, Naturvetenskapliga fakulteten, Institutionen för biokemi och biofysik. Stockholms universitet, Science for Life Laboratory (SciLifeLab).
    Shu, Nanjiang
    Stockholms universitet, Naturvetenskapliga fakulteten, Institutionen för biokemi och biofysik. Stockholms universitet, Science for Life Laboratory (SciLifeLab).
    Tsirigos, Konstantinos D.
    Stockholms universitet, Naturvetenskapliga fakulteten, Institutionen för biokemi och biofysik. Stockholms universitet, Science for Life Laboratory (SciLifeLab).
    Elofsson, Arne
    Stockholms universitet, Naturvetenskapliga fakulteten, Institutionen för biokemi och biofysik. Stockholms universitet, Science for Life Laboratory (SciLifeLab).
    von Heijne, Gunnar
    Stockholms universitet, Naturvetenskapliga fakulteten, Institutionen för biokemi och biofysik. Stockholms universitet, Science for Life Laboratory (SciLifeLab).
    Nilsson, IngMarie
    Stockholms universitet, Naturvetenskapliga fakulteten, Institutionen för biokemi och biofysik.
    Large Tilts in Transmembrane Helices Can Be Induced during Tertiary Structure Formation2014Ingår i: Journal of Molecular Biology, ISSN 0022-2836, E-ISSN 1089-8638, Vol. 426, nr 13, s. 2529-2538Artikel i tidskrift (Refereegranskat)
    Abstract [en]

    While early structural models of helix-bundle integral membrane proteins posited that the transmembrane a-helices [transmembrane helices (TMHs)] were orientated more or less perpendicular to the membrane plane, there is now ample evidence from high-resolution structures that many TMHs have significant tilt angles relative to the membrane. Here, we address the question whether the tilt is an intrinsic property of the TMH in question or if it is imparted on the TMH during folding of the protein. Using a glycosylation mapping technique, we show that four highly tilted helices found in multi-spanning membrane proteins all have much shorter membrane-embedded segments when inserted by themselves into the membrane than seen in the high-resolution structures. This suggests that tilting can be induced by tertiary packing interactions within the protein, subsequent to the initial membrane-insertion step.

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