Disulfide connectivity prediction using secondary structure information and diresidue frequencies

Motivation: We describe a stand-alone algorithm to predict disulfide bond partners in a protein given only the amino acid sequence, using a novel neural network architecture (the diresidue neural network), and given input of symmetric flanking regions of N-terminus and C-terminus half-cystines augme...

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Bibliographic Details
Published in:Bioinformatics 2005-05, Vol.21 (10), p.2336-2346
Main Authors: Ferrè, F., Clote, P.
Format: Article
Language:English
Subjects:
Algorithms
Artificial Intelligence
Binding Sites
Biological and medical sciences
Computer Simulation
Cysteine - chemistry
Disulfides - analysis
Disulfides - chemistry
Fundamental and applied biological sciences. Psychology
General aspects
Mathematics in biology. Statistical analysis. Models. Metrology. Data processing in biology (general aspects)
Models, Chemical
Protein Binding
Protein Conformation
Protein Interaction Mapping - methods
Protein Structure, Secondary
Proteins - analysis
Proteins - chemistry
Sequence Alignment - methods
Sequence Analysis, Protein - methods
Software
Structure-Activity Relationship
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Summary:Motivation: We describe a stand-alone algorithm to predict disulfide bond partners in a protein given only the amino acid sequence, using a novel neural network architecture (the diresidue neural network), and given input of symmetric flanking regions of N-terminus and C-terminus half-cystines augmented with residue secondary structure (helix, coil, sheet) as well as evolutionary information. The approach is motivated by the observation of a bias in the secondary structure preferences of free cysteines and half-cystines, and by promising preliminary results we obtained using diresidue position-specific scoring matrices. Results: As calibrated by receiver operating characteristic curves from 4-fold cross-validation, our conditioning on secondary structure allows our novel diresidue neural network to perform as well as, and in some cases better than, the current state-of-the-art method. A slight drop in performance is seen when secondary structure is predicted rather than being derived from three-dimensional protein structures. Availability: http://clavius.bc.edu/~clotelab/DiANNA Contact: clote@bc.edu Supplementary information: Supplementary tables and figures, and the complete list of PDB codes of monomers used, can be found at http://clavius.bc.edu/~clotelab/
ISSN:1367-4803
1460-2059
1367-4811
DOI:10.1093/bioinformatics/bti328