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Prediction of protein structural class from the amino acid sequence
Author(s) -
Klein Petr,
Delisi Charles
Publication year - 1986
Publication title -
biopolymers
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.556
H-Index - 125
eISSN - 1097-0282
pISSN - 0006-3525
DOI - 10.1002/bip.360250909
Subject(s) - chemistry , sequence (biology) , reliability (semiconductor) , class (philosophy) , protein data bank , linear discriminant analysis , computational biology , amino acid , pattern recognition (psychology) , statistics , protein structure , artificial intelligence , computer science , biochemistry , mathematics , biology , physics , power (physics) , quantum mechanics
Abstract The multidimensional statistical technique of discriminant analysis is used to allocate amino acid sequences to one of four secondary structural classes: high α content, high β content, mixed α and β, low content of ordered structure. Discrimination is based on four attributes: estimates of percentages of α and β structures, and regular variations in the hydrophobic values of residues along the sequence, occurring with periods of 2 and 3.6 residues. The reliability of the method, estimated by classifying 138 sequences from the Brookhaven Protein Data Bank, is 80%, with no misallocations between α‐rich and β‐rich classes. The reliability can be increased to 84% by making no allocation for proteins classified with odds close to 1. Classification using previously developed secondary structural prediction methods is considerably less reliable, the best result being 64% obtained using predictions based on the Delphi method.