Incorporation of local structural preference potential improves fold recognition

Fold recognition, or threading, is a popular protein structure modeling approach that uses known structure templates to build structures for those of unknown. The key to the success of fold recognition methods lies in the proper integration of sequence, physiochemical and structural information. Her...

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Bibliographic Details
Published in:PloS one 2011-02, Vol.6 (2), p.e17215-e17215
Main Authors: Hu, Yun, Dong, Xiaoxi, Wu, Aiping, Cao, Yang, Tian, Liqing, Jiang, Taijiao
Format: Article
Language:English
Subjects:
Accuracy
Algorithms
Amino Acid Sequence - physiology
Bioinformatics
Biology
Biophysics
Computational Biology - methods
Computer Science
Databases, Protein
Datasets
Hydrophobic and Hydrophilic Interactions
Hydrophobicity
Incorporation
Laboratories
Mathematics
Methods
Models, Biological
Models, Molecular
Pattern Recognition, Automated - methods
Peptide Fragments - analysis
Peptide Fragments - chemistry
Physics
Physiochemistry
Prediction models
Preferences
Protein Conformation
Protein Engineering - methods
Protein Folding
Protein structure
Protein Structure, Secondary
Proteins
Recognition
Secondary structure
Sequence Analysis, Protein - methods
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Summary:Fold recognition, or threading, is a popular protein structure modeling approach that uses known structure templates to build structures for those of unknown. The key to the success of fold recognition methods lies in the proper integration of sequence, physiochemical and structural information. Here we introduce another type of information, local structural preference potentials of 3-residue and 9-residue fragments, for fold recognition. By combining the two local structural preference potentials with the widely used sequence profile, secondary structure information and hydrophobic score, we have developed a new threading method called FR-t5 (fold recognition by use of 5 terms). In benchmark testings, we have found the consideration of local structural preference potentials in FR-t5 not only greatly enhances the alignment accuracy and recognition sensitivity, but also significantly improves the quality of prediction models.
ISSN:1932-6203
1932-6203
DOI:10.1371/journal.pone.0017215