Journal
BIOINFORMATICS
Volume 31, Issue 21, Pages 3499-3505Publisher
OXFORD UNIV PRESS
DOI: 10.1093/bioinformatics/btv390
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Funding
- European Research Council [RNA+P=123D]
- European Social Fund through Subcarpathian Doctoral Stipend Fund
- Polish National Science Center [2013/09/B/NZ2/00121]
- Foundation for Polish Science
- EU structural funds [POIG.02.03.00-00-003/09]
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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 angstrom in terms of RMSD.
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