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Using multi-data hidden Markov models trained on local neighborhoods of protein structure to predict residue-residue contacts
Stockholm University, Faculty of Science, Department of Biochemistry and Biophysics.
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2009 (English)In: Bioinformatics, ISSN 1367-4803, E-ISSN 1460-2059, 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.

Place, publisher, year, edition, pages
2009. Vol. 25, no 10, 1264-1270 p.
National Category
Biochemistry and Molecular Biology
URN: urn:nbn:se:su:diva-60075DOI: 10.1093/bioinformatics/btp149ISI: 000265950600009OAI: diva2:433005
authorCount :6Available from: 2011-08-08 Created: 2011-08-08 Last updated: 2014-03-17Bibliographically approved
In thesis
1. Protein Interactions from the Molecular to the Domain Level
Open this publication in new window or tab >>Protein Interactions from the Molecular to the Domain Level
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.
Protein interactions, protein domains, membrane proteins
National Category
Bioinformatics (Computational Biology)
Research subject
Biochemistry towards Bioinformatics
urn:nbn:se:su:diva-101795 (URN)978-91-7447-854-9 (ISBN)
Public defence
2014-04-25, Magnélisalen, Kemiska övningslaboratoriet, Svante Arrhenius väg 16 B, Stockholm, 13:00 (English)

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

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