GDFuzz3D: a method for protein 3D structure reconstruction from contact maps, based on a non-Euclidean distance function. (30th June 2015)
- Record Type:
- Journal Article
- Title:
- GDFuzz3D: a method for protein 3D structure reconstruction from contact maps, based on a non-Euclidean distance function. (30th June 2015)
- Main Title:
- GDFuzz3D: a method for protein 3D structure reconstruction from contact maps, based on a non-Euclidean distance function
- Authors:
- Pietal, Michal J.
Bujnicki, Janusz M.
Kozlowski, Lukasz P. - Abstract:
- Abstract : Motivation: To date, only a few distinct successful approaches have been introduced to reconstruct a protein 3D structure from a map of contacts between its amino acid residues (a 2D contact map). Current algorithms can infer structures from information-rich contact maps that contain a limited fraction of erroneous predictions. However, it is difficult to reconstruct 3D structures from predicted contact maps that usually contain a high fraction of false contacts. Results: We describe a new, multi-step protocol that predicts protein 3D structures from the predicted contact maps. The method is based on a novel distance function acting on a fuzzy residue proximity graph, which predicts a 2D distance map from a 2D predicted contact map. The application of a Multi-Dimensional Scaling algorithm transforms that predicted 2D distance map into a coarse 3D model, which is further refined by typical modeling programs into an all-atom representation. We tested our approach on contact maps predicted de novo by MULTICOM, the top contact map predictor according to CASP10. We show that our method outperforms FT-COMAR, the state-of-the-art method for 3D structure reconstruction from 2D maps. For all predicted 2D contact maps of relatively low sensitivity (60–84%), GDFuzz3D generates more accurate 3D models, with the average improvement of 4.87 Å in terms of RMSD. Availability and implementation: GDFuzz3D server and standalone version are freely available atAbstract : Motivation: To date, only a few distinct successful approaches have been introduced to reconstruct a protein 3D structure from a map of contacts between its amino acid residues (a 2D contact map). Current algorithms can infer structures from information-rich contact maps that contain a limited fraction of erroneous predictions. However, it is difficult to reconstruct 3D structures from predicted contact maps that usually contain a high fraction of false contacts. Results: We describe a new, multi-step protocol that predicts protein 3D structures from the predicted contact maps. The method is based on a novel distance function acting on a fuzzy residue proximity graph, which predicts a 2D distance map from a 2D predicted contact map. The application of a Multi-Dimensional Scaling algorithm transforms that predicted 2D distance map into a coarse 3D model, which is further refined by typical modeling programs into an all-atom representation. We tested our approach on contact maps predicted de novo by MULTICOM, the top contact map predictor according to CASP10. We show that our method outperforms FT-COMAR, the state-of-the-art method for 3D structure reconstruction from 2D maps. For all predicted 2D contact maps of relatively low sensitivity (60–84%), GDFuzz3D generates more accurate 3D models, with the average improvement of 4.87 Å in terms of RMSD. Availability and implementation: GDFuzz3D server and standalone version are freely available at http://iimcb.genesilico.pl/gdserver/GDFuzz3D/ . Contact: iamb@genesilico.pl Supplementary information: Supplementary data are available at Bioinformatics online. … (more)
- Is Part Of:
- Bioinformatics. Volume 31:Number 21(2015)
- Journal:
- Bioinformatics
- Issue:
- Volume 31:Number 21(2015)
- Issue Display:
- Volume 31, Issue 21 (2015)
- Year:
- 2015
- Volume:
- 31
- Issue:
- 21
- Issue Sort Value:
- 2015-0031-0021-0000
- Page Start:
- 3499
- Page End:
- 3505
- Publication Date:
- 2015-06-30
- Subjects:
- Bioinformatics -- Periodicals
Genomics -- Data processing -- Periodicals
Computational biology -- Periodicals
572.80285 - Journal URLs:
- http://bioinformatics.oxfordjournals.org ↗
http://firstsearch.oclc.org ↗
http://ukcatalogue.oup.com/ ↗ - DOI:
- 10.1093/bioinformatics/btv390 ↗
- Languages:
- English
- ISSNs:
- 1367-4803
- Deposit Type:
- Legaldeposit
- View Content:
- Available online (eLD content is only available in our Reading Rooms) ↗
- Physical Locations:
- British Library DSC - 2072.348000
British Library DSC - BLDSS-3PM
British Library HMNTS - ELD Digital store - Ingest File:
- 12387.xml