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Protein Interactions from the Molecular to the Domain Level
Stockholm University, Faculty of Science, Department of Biochemistry and Biophysics.
2014 (English)Doctoral thesis, comprehensive summary (Other academic)
Abstract [en]

The basic unit of life is the cell, from single-cell bacteria to the largest creatures on the planet. All cells have DNA, which contains the blueprint for proteins. This information is transported in the form of messenger RNA from the genome to ribosomes where proteins are produced. Proteins are the main functional constituents of the cell, they usually have one or several functions and are the main actors in almost all essential biological processes. Proteins are what make the cell alive. Proteins are found as solitary units or as part of large complexes. Proteins can be found in all parts of the cell, the most common place being the cytoplasm, a central space in all cells. They are also commonly found integrated into or attached to various membranes.

Membranes define the cell architecture. Proteins integrated into the membrane have a wide number of responsibilities: they are the gatekeepers of the cell, they secrete cellular waste products, and many of them are receptors and enzymes.

The main focus of this thesis is the study of protein interactions, from the molecular level up to the protein domain level.

In paper I use reoccurring local protein structures to try and predict what sections of a protein interacts with another part using only sequence information. In papers II and III we use a randomization approach on a membrane protein motif that we know interacts with a sphingomyelin lipid to find other candidate proteins that interact with sphingolipids. These are then experimentally verified as sphingolipid-binding. In the last paper, paper IV, we look at how protein domain interaction networks overlap and can be evaluated.

Place, publisher, year, edition, pages
Stockholm: Department of Biochemistry and Biophysics, Stockholm University , 2014. , 52 p.
Keyword [en]
Protein interactions, protein domains, membrane proteins
National Category
Bioinformatics (Computational Biology)
Research subject
Biochemistry towards Bioinformatics
Identifiers
URN: urn:nbn:se:su:diva-101795ISBN: 978-91-7447-854-9 (print)OAI: oai:DiVA.org:su-101795DiVA: diva2:705563
Public defence
2014-04-25, Magnélisalen, Kemiska övningslaboratoriet, Svante Arrhenius väg 16 B, Stockholm, 13:00 (English)
Opponent
Supervisors
Note

At the time of the doctoral defense, the following paper was unpublished and had a status as follows: Paper 3: Manuscript.

Available from: 2014-04-03 Created: 2014-03-17 Last updated: 2014-07-07Bibliographically approved
List of papers
1. Using multi-data hidden Markov models trained on local neighborhoods of protein structure to predict residue-residue contacts
Open this publication in new window or tab >>Using multi-data hidden Markov models trained on local neighborhoods of protein structure to predict residue-residue contacts
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2009 (English)In: Bioinformatics, ISSN 1367-4803, E-ISSN 1367-4811, Vol. 25, no 10, 1264-1270 p.Article in journal (Refereed) Published
Abstract [en]

Motivation: Correct prediction of residue-residue contacts in proteins that lack good templates with known structure would take ab initio protein structure prediction a large step forward. The lack of correct contacts, and in particular long-range contacts, is considered the main reason why these methods often fail. Results: We propose a novel hidden Markov model (HMM)based method for predicting residue-residue contacts from protein sequences using as training data homologous sequences, predicted secondary structure and a library of local neighborhoods (local descriptors of protein structure). The library consists of recurring structural entities incorporating short-, medium- and long-range interactions and is general enough to reassemble the cores of nearly all proteins in the PDB. The method is tested on an external test set of 606 domains with no significant sequence similarity to the training set as well as 151 domains with SCOP folds not present in the training set. Considering the top 0.2 . L predictions (L = sequence length), our HMMs obtained an accuracy of 22.8% for long-range interactions in new fold targets, and an average accuracy of 28.6% for long-, medium- and short- range contacts. This is a significant performance increase over currently available methods when comparing against results published in the literature.

National Category
Biochemistry and Molecular Biology
Identifiers
urn:nbn:se:su:diva-60075 (URN)10.1093/bioinformatics/btp149 (DOI)000265950600009 ()
Note
authorCount :6Available from: 2011-08-08 Created: 2011-08-08 Last updated: 2017-12-08Bibliographically approved
2. Molecular recognition of a single sphingolipid species by a protein’s transmembrane domain
Open this publication in new window or tab >>Molecular recognition of a single sphingolipid species by a protein’s transmembrane domain
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2012 (English)In: Nature, ISSN 0028-0836, E-ISSN 1476-4687, Vol. 481, no 7382, 525-529 p.Article in journal (Refereed) Published
Abstract [en]

Functioning and processing of membrane proteins critically depend on the way their transmembrane segments are embedded in the membrane. Sphingolipids are structural components of membranes and can also act as intracellular second messengers. Not much is known of sphingolipids binding to transmembrane domains (TMDs) of proteins within the hydrophobic bilayer, and how this could affect protein function. Here we show a direct and highly specific interaction of exclusively one sphingomyelin species, SM 18, with the TMD of the COPI machinery protein p24 (ref. 2). Strikingly, the interaction depends on both the headgroup and the backbone of the sphingolipid, and on a signature sequence (VXXTLXXIY) within the TMD. Molecular dynamics simulations show a close interaction of SM 18 with the TMD. We suggest a role of SM 18 in regulating the equilibrium between an inactive monomeric and an active oligomeric state of the p24 protein, which in turn regulates COPI-dependent transport. Bioinformatic analyses predict that the signature sequence represents a conserved sphingolipid-binding cavity in a variety of mammalian membrane proteins. Thus, in addition to a function as second messengers, sphingolipids can act as cofactors to regulate the function of transmembrane proteins. Our discovery of an unprecedented specificity of interaction of a TMD with an individual sphingolipid species adds to our understanding of why biological membranes are assembled from such a large variety of different lipids.

National Category
Biochemistry and Molecular Biology
Research subject
Biochemistry
Identifiers
urn:nbn:se:su:diva-72360 (URN)10.1038/nature10742 (DOI)000299471800044 ()
Funder
EU, European Research Council, 232648Swedish Research Council, A0525002
Available from: 2012-02-10 Created: 2012-02-08 Last updated: 2017-12-08Bibliographically approved
3. Identification of novel sphingolipid-binding motifs in mammalian membrane proteins
Open this publication in new window or tab >>Identification of novel sphingolipid-binding motifs in mammalian membrane proteins
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(English)Manuscript (preprint) (Other academic)
Abstract [en]

Specific interactions between transmembrane proteins and sphingolipids is a poorly understood phenomenon, and only a couple of instances have been identified. The best characterized example is the sphingolipid-binding motif VXXTLXXIY found in the transmembrane helix of the vesicular transport protein p24. Here, we have used a simple motif- probability algorithm (MOPRO) to identify proteins that contain putative sphingolipid-binding motifs in a dataset comprising full proteomes from mammalian organisms. Four selected candidate proteins all tested positive for sphingolipid binding in a photoaffinity assay. The putative sphingolipid-binding motifs are noticeably enriched in the 7TM family of G-protein coupled receptors, predominantly in transmembrane helix 6. 

Keyword
sphingolipid;, transmembrane helix
National Category
Bioinformatics (Computational Biology)
Research subject
Biochemistry towards Bioinformatics
Identifiers
urn:nbn:se:su:diva-101791 (URN)
Available from: 2014-03-17 Created: 2014-03-17 Last updated: 2014-03-24
4. Comparative analysis and unification of domain-domain interaction networks
Open this publication in new window or tab >>Comparative analysis and unification of domain-domain interaction networks
2009 (English)In: Bioinformatics (Oxford, England), ISSN 1367-4811, Vol. 25, no 22, 3020-5 p.Article in journal (Refereed) Published
Abstract [en]

MOTIVATION: Certain protein domains are known to preferentially interact with other domains. Several approaches have been proposed to predict domain-domain interactions, and over nine datasets are available. Our aim is to analyse the coverage and quality of the existing resources, as well as the extent of their overlap. With this knowledge, we have the opportunity to merge individual domain interaction networks to construct a comprehensive and reliable database. RESULTS: In this article we introduce a new approach towards comparing domain-domain interaction networks. This approach is used to compare nine predicted domain and protein interaction networks. The networks were used to generate a database of unified domain interactions, UniDomInt. Each interaction in the dataset is scored according to the benchmarked reliability of the sources. The performance of UniDomInt is an improvement compared to the underlying source networks and to another composite resource, Domine. AVAILABILITY: http://sonnhammer.sbc.su.se/download/UniDomInt/

Keyword
domain-domain interactions, unified domain interactions
National Category
Natural Sciences
Research subject
Biophysics; Biochemistry
Identifiers
urn:nbn:se:su:diva-35315 (URN)10.1093/bioinformatics/btp522 (DOI)000271564300018 ()19720675 (PubMedID)
Available from: 2010-01-15 Created: 2010-01-15 Last updated: 2014-03-17Bibliographically approved

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