Change search
CiteExportLink to record
Permanent link

Direct link
Cite
Citation style
  • apa
  • ieee
  • modern-language-association-8th-edition
  • vancouver
  • Other style
More styles
Language
  • de-DE
  • en-GB
  • en-US
  • fi-FI
  • nn-NO
  • nn-NB
  • sv-SE
  • Other locale
More languages
Output format
  • html
  • text
  • asciidoc
  • rtf
Protein structure prediction: Zinc-binding sites, one-dimensional structure and remote homology
Stockholm University, Faculty of Science, Department of Materials and Environmental Chemistry (MMK). (Sven Hovmöller)
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 [en]
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: urn:nbn:se:su:diva-34094ISBN: 978-91-7155-984-5 (print)OAI: oai:DiVA.org:su-34094DiVA: diva2:284202
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
List of papers
1. Prediction of zinc-binding sites in proteins from sequence
Open this publication in new window or tab >>Prediction of zinc-binding sites in proteins from sequence
2008 (English)In: Bioinformatics, ISSN 1367-4803, E-ISSN 1367-4811, Vol. 24, no 6, 775-782 p.Article in journal (Refereed) Published
Abstract [en]

MOTIVATION: Motivated by the abundance, importance and unique functionality of zinc, both biologically and physiologically, we have developed an improved method for the prediction of zinc-binding sites in proteins from their amino acid sequences. RESULTS: By combining support vector machine (SVM) and homology-based predictions, our method predicts zinc-binding Cys, His, Asp and Glu with 75% precision (86% for Cys and His only) at 50% recall according to a 5-fold cross-validation on a non-redundant set of protein chains from the Protein Data Bank (PDB) (2727 chains, 235 of which bind zinc). Consequently, our method predicts zinc-binding Cys and His with 10% higher precision at different recall levels compared to a recently published method when tested on the same dataset. AVAILABILITY: The program is available for download at www.fos.su.se/~nanjiang/zincpred/download/

Keyword
zinc-binding, Support Vector Machine, Profile
National Category
Bioinformatics and Systems Biology Mathematics
Research subject
Biochemistry; Molecular Biology
Identifiers
urn:nbn:se:su:diva-32774 (URN)10.1093/bioinformatics/btm618 (DOI)000254010400006 ()
Projects
protein structure prediction
Available from: 2009-12-16 Created: 2009-12-16 Last updated: 2017-12-12Bibliographically approved
2. A Novel Method for Accurate One-dimensional Protein Structure Prediction Based on Fragment Matching
Open this publication in new window or tab >>A Novel Method for Accurate One-dimensional Protein Structure Prediction Based on Fragment Matching
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.

Keyword
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:nbn:se:su:diva-32781 (URN)10.1093/bioinformatics/btp679 (DOI)
Projects
protein structure prediction
Available from: 2009-12-16 Created: 2009-12-16 Last updated: 2017-12-12Bibliographically approved
3. Protein homology detection by profile based fragment matching
Open this publication in new window or tab >>Protein homology detection by profile based fragment matching
(English)Manuscript (preprint) (Other academic)
Keyword
fragment matching; remote homology; profile-profile score; shape strings
National Category
Bioinformatics and Systems Biology
Research subject
Molecular Biology
Identifiers
urn:nbn:se:su:diva-34097 (URN)
Projects
protein structure prediction
Available from: 2010-01-05 Created: 2010-01-05 Last updated: 2011-01-04Bibliographically approved
4. Describing and Comparing Protein Structures Using Shape Strings
Open this publication in new window or tab >>Describing and Comparing Protein Structures Using Shape Strings
2008 (English)In: Current protein and peptide science, ISSN 1389-2037, E-ISSN 1875-5550, Vol. 9, no 4, 310-324 p.Article in journal (Refereed) Published
Abstract [en]

Different methods for describing and comparing the structures of the tens of thousands of proteins that have been determined by X-ray crystallography are reviewed. Such comparisons are important for understanding the structures and functions of proteins and facilitating structure prediction, as well as assessing structure prediction methods. We summarize methods in this field emphasizing ways of representing protein structures as one-dimensional geometrical strings. Such strings are based on the shape symbols of clustered regions of φ/Ψ dihedral angle pairs of the polypeptide backbones as described by the Ramachandran plot. These one-dimensional expressions are as compact as secondary structure description but contain more information in loop regions. They can be used for fast searching for similar structures in databases and for comparing similarities between proteins and between the predicted and native structures.

Place, publisher, year, edition, pages
Bentham Science Publishers, 2008
Keyword
Protein structure, secondary structure, structure comparison, Ramachandran plot, dihedral angle, shape strings
National Category
Structural Biology
Research subject
Molecular Biology
Identifiers
urn:nbn:se:su:diva-32779 (URN)000259487200001 ()
Projects
protein structure prediction
Available from: 2009-12-16 Created: 2009-12-16 Last updated: 2017-12-12Bibliographically approved

Open Access in DiVA

fulltext(1101 kB)758 downloads
File information
File name FULLTEXT01.pdfFile size 1101 kBChecksum SHA-512
d5a39f6e7348be38ff1b35666eb587bc66041069efa3163032e94823fae8a3139f2456f666646f30a86d17de87a09e5ec01f00ea6182bddace567a70f4811922
Type fulltextMimetype application/pdf

Search in DiVA

By author/editor
Shu, Nanjiang
By organisation
Department of Materials and Environmental Chemistry (MMK)
Bioinformatics and Systems BiologyBiochemistry and Molecular BiologyBiochemistry and Molecular Biology

Search outside of DiVA

GoogleGoogle Scholar
Total: 758 downloads
The number of downloads is the sum of all downloads of full texts. It may include eg previous versions that are now no longer available

isbn
urn-nbn

Altmetric score

isbn
urn-nbn
Total: 457 hits
CiteExportLink to record
Permanent link

Direct link
Cite
Citation style
  • apa
  • ieee
  • modern-language-association-8th-edition
  • vancouver
  • Other style
More styles
Language
  • de-DE
  • en-GB
  • en-US
  • fi-FI
  • nn-NO
  • nn-NB
  • sv-SE
  • Other locale
More languages
Output format
  • html
  • text
  • asciidoc
  • rtf