Toward predicting protein topology: an approach to identifying beta hairpins.
Although secondary structure prediction methods have recently improved, progress from secondary to tertiary structure prediction has been limited. A promising but largely unexplored route to this goal is to predict structure motifs from secondary structure knowledge. Here we present a novel method for the recognition of beta hairpins that combines secondary structure predictions and threading methods by using a database search and a neural network approach. The method successfully predicts 48 and 77%, respectively, of all of hairpin and nonhairpin beta-coil-beta motifs in a protein database. We find that the main contributors to motif recognition are predicted accessibility and turn propensities.
de la Cruz
Xavier
X
Institut Català per la Recerca i Estudis Avançats (ICREA), Passeig Lluis Companys, 23, 08018 Barcelona, Spain. xavier@husky.bq.ub.es
Hutchinson
E Gail
EG
Shepherd
Adrian
A
Thornton
Janet M
JM
D016208:Databases, Factual; D016678:Genome; D008958:Models, Molecular; D016571:Neural Networks, Computer; D011237:Predictive Value of Tests; D017433:Protein Structure, Secondary; D017434:Protein Structure, Tertiary; D011506:Proteins / Q000737:chemistry* / Q000235:genetics; D015203:Reproducibility of Results
D011506:Proteins