Premium
Prediction of the Secondary Structure of Proteins from the Amino Acid Sequence with Artificial Neural Networks
Author(s) -
Schneider Gisbert,
Wrede Paul
Publication year - 1993
Publication title -
angewandte chemie international edition in english
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 5.831
H-Index - 550
eISSN - 1521-3773
pISSN - 0570-0833
DOI - 10.1002/anie.199311411
Subject(s) - artificial neural network , sequence (biology) , artificial intelligence , computer science , protein secondary structure , amino acid , protein structure prediction , peptide sequence , machine learning , computational biology , chemistry , protein structure , biology , biochemistry , gene
Secondary structures of amino acid sequences can be predicted with over 70% accuracy in Heidelberg with the aid of artificial neural networks. This improvement over the accuracy of statistical methods is extremely important in view of the rational design of peptides and proteins and the processing of data in sequence data banks. The potential of neural networks is thus demonstrated once again (see also the review “Neural Networks in Chemistry” by J. Gasteiger and J. Zupan in the April issue of Angewandte Chemie ).