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A Novel Method for Accurate One-dimensional Protein Structure Prediction Based on Fragment Matching
Stockholm University, Faculty of Science, Department of Materials and Environmental Chemistry (MMK).
Stockholm University, Faculty of Science, Department of Materials and Environmental Chemistry (MMK).
Stockholm University, Faculty of Science, Department of Materials and Environmental Chemistry (MMK).
2010 (English)In: Bioinformatics, ISSN 1367-4803, E-ISSN 1367-4811, Vol. 26, no 4, 470-477 p.Article in journal (Refereed) Published
Abstract [en]

Motivation: The precise prediction of one-dimensional (1D) protein structure as represented by the protein secondary structure and 1D string of discrete state of dihedral angles (i.e. Shape Strings) is a prerequisite for the successful prediction of three-dimensional (3D) structure as well as protein-protein interaction. We have developed a novel 1D structure prediction method, called Frag1D, based on a straightforward fragment matching algorithm and demonstrated its success in the prediction of  three sets of 1D structural alphabets, i.e. the classical three-state secondary structure, three-state Shape Strings and eight-state Shape Strings.

Results: By exploiting the vast protein sequence and protein structure data available, we have brought secondary structure prediction closer to the expected theoretical limit. When tested by a leave-one-out cross validation on a non-redundant set of PDB cutting at 30% sequence identity containing 5860 protein chains, the overall per-residue accuracy for secondary structure prediction, i.e. Q3 is 82.9%. The overall per-residue accuracy for three-state and eight-state Shape Strings are 85.1% and 71.5% respectively. We have also benchmarked our program with the latest version of PSIPRED for secondary structure prediction and our program predicted 0.3% better in Q3 when tested on 2241 chains with the same training set. For Shape Strings, we compared our method with a recently published method with the same dataset and definition as used by that method. Our program predicted at 2.2% better in accuracy for three-state Shape Strings. By quantitatively investigating the effect of data base size on 1D structure prediction we show that the accuracy increases by about 1% with every doubling of the database size.

Place, publisher, year, edition, pages
2010. Vol. 26, no 4, 470-477 p.
Keyword [en]
protein secondary structure, shape strings, profile-profile, fragment matching
National Category
Biochemistry and Molecular Biology Structural Biology Bioinformatics (Computational Biology)
Research subject
Biochemistry; Molecular Biology
Identifiers
URN: urn:nbn:se:su:diva-32781DOI: 10.1093/bioinformatics/btp679OAI: oai:DiVA.org:su-32781DiVA: diva2:281530
Projects
protein structure prediction
Available from: 2009-12-16 Created: 2009-12-16 Last updated: 2017-12-12Bibliographically approved
In thesis
1. Protein structure prediction: Zinc-binding sites, one-dimensional structure and remote homology
Open this publication in new window or tab >>Protein structure prediction: Zinc-binding sites, one-dimensional structure and remote homology
2010 (English)Doctoral thesis, comprehensive summary (Other academic)
Abstract [en]

Predicting the three-dimensional (3D) structure of proteins is a central problem in biology. These computationally predicted 3D protein structures have been successfully applied in many fields of biomedicine, e.g. family assignments and drug discovery. The accurate detection of remotely homologous templates is critical for the successful prediction of the 3D structure of proteins. Also, the prediction of one-dimensional (1D) protein structures such as secondary structures and shape strings are useful for predicting the 3D structure of proteins and important for understanding the sequence-structure relationship. In addition, the prediction of the functional sites of proteins, such as metal-binding sites, can not only reveal the important function of proteins (even in the absence of the 3D structure) but also facilitate the prediction of the 3D structure.

Here, three novel methods in the field of protein structure prediction are presented: PREDZINC, a method for predicting zinc-binding sites in proteins; Frag1D, a method for predicting the 1D structure of proteins; and FragMatch, a method for detecting remotely homologous proteins. These methods compete satisfactorily with the best methods previously published and contribute to the task of protein structure prediction.

Place, publisher, year, edition, pages
Stockholm: Department of Materials and Environmental Chemistry (MMK), Stockholm University, 2010. 81 p.
Keyword
protein structure prediction, zinc-binding, profile, homology detection, shape string
National Category
Bioinformatics and Systems Biology Biochemistry and Molecular Biology Biochemistry and Molecular Biology
Research subject
Structural Chemistry
Identifiers
urn:nbn:se:su:diva-34094 (URN)978-91-7155-984-5 (ISBN)
Public defence
2010-02-09, Magnélisalen, Kemiska övningslaboratoriet, Svante Arrhenius väg 16 B, Magnélisalen, 10:00 (English)
Opponent
Supervisors
Projects
Protein structure prediction
Note
At the time of the doctoral defense, the following paper was unpublished and had a status as follows: Paper 3: Manuscript.Available from: 2010-01-18 Created: 2010-01-05 Last updated: 2010-12-29Bibliographically approved

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