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ACCURATE PREDICTION OF PROTEIN SECONDARY STRUCTURAL CLASS WITH FUZZY STRUCTURAL VECTORS

BOBERG, J; SALAKOSKI, T and Vihinen, Mauno LU (1995) In Protein Engineering 8(6). p.505-512
Abstract
The prerequisites for accurate prediction of protein secondary structural class (all-alpha, all-beta, alpha+beta, alpha/beta or multidomain) were studied, and a new similarity-based method is presented for the prediction of the secondary structural class of a protein from its sequence. The new method uses representatives of nuclear families as a learning set. For the sequence to be predicted, the method produces a vector of certainty factors called a fuzzy structural vector, Validation with independent test sets shows that the prediction accuracy of the proposed method has clear dependency on the representativity of the learning set. The representatives obtained from the nuclear families of the Brookhaven Protein Data Bank (PDB) were shown... (More)
The prerequisites for accurate prediction of protein secondary structural class (all-alpha, all-beta, alpha+beta, alpha/beta or multidomain) were studied, and a new similarity-based method is presented for the prediction of the secondary structural class of a protein from its sequence. The new method uses representatives of nuclear families as a learning set. For the sequence to be predicted, the method produces a vector of certainty factors called a fuzzy structural vector, Validation with independent test sets shows that the prediction accuracy of the proposed method has clear dependency on the representativity of the learning set. The representatives obtained from the nuclear families of the Brookhaven Protein Data Bank (PDB) were shown to give accurate predictions for PDB proteins, whilst the amino acid composition-based methods used previously achieve their maximum predictability with relatively limited learning sets, and they remain inaccurate even with highly representative learning sets. The usability of the new method is increased further by the fuzzy structural vectors, which substantially reduce the risk of misclassification and realistically describe vague secondary structural tendencies. (Less)
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author
publishing date
type
Contribution to journal
publication status
published
subject
keywords
AMINO ACID COMPOSITION, FOLDING PATTERNS, FUZZY CLASSIFICATION, LEARNING, SETS, SECONDARY STRUCTURAL CLASS PREDICTION
in
Protein Engineering
volume
8
issue
6
pages
505 - 512
publisher
Oxford University Press
external identifiers
  • wos:A1995RU27200001
  • scopus:0029084068
ISSN
1460-213X
DOI
10.1093/protein/8.6.505
language
English
LU publication?
no
id
89ef8a4e-241d-4c5f-8996-55ae1c4a3591 (old id 3853196)
date added to LUP
2013-06-28 14:37:46
date last changed
2017-08-06 03:32:06
@article{89ef8a4e-241d-4c5f-8996-55ae1c4a3591,
  abstract     = {The prerequisites for accurate prediction of protein secondary structural class (all-alpha, all-beta, alpha+beta, alpha/beta or multidomain) were studied, and a new similarity-based method is presented for the prediction of the secondary structural class of a protein from its sequence. The new method uses representatives of nuclear families as a learning set. For the sequence to be predicted, the method produces a vector of certainty factors called a fuzzy structural vector, Validation with independent test sets shows that the prediction accuracy of the proposed method has clear dependency on the representativity of the learning set. The representatives obtained from the nuclear families of the Brookhaven Protein Data Bank (PDB) were shown to give accurate predictions for PDB proteins, whilst the amino acid composition-based methods used previously achieve their maximum predictability with relatively limited learning sets, and they remain inaccurate even with highly representative learning sets. The usability of the new method is increased further by the fuzzy structural vectors, which substantially reduce the risk of misclassification and realistically describe vague secondary structural tendencies.},
  author       = {BOBERG, J and SALAKOSKI, T and Vihinen, Mauno},
  issn         = {1460-213X},
  keyword      = {AMINO ACID COMPOSITION,FOLDING PATTERNS,FUZZY CLASSIFICATION,LEARNING,SETS,SECONDARY STRUCTURAL CLASS PREDICTION},
  language     = {eng},
  number       = {6},
  pages        = {505--512},
  publisher    = {Oxford University Press},
  series       = {Protein Engineering},
  title        = {ACCURATE PREDICTION OF PROTEIN SECONDARY STRUCTURAL CLASS WITH FUZZY STRUCTURAL VECTORS},
  url          = {http://dx.doi.org/10.1093/protein/8.6.505},
  volume       = {8},
  year         = {1995},
}