Predicting Protein Disorder for N-, C- and Internal Regions

抄録

Logistic regression (LR), discriminant analysis (DA), and neural networks (NN) were used to predict ordered and disordered regions in proteins. Training data were from a set of non-redundant X-ray crystal structures, with the data being partitioned into N-terminal, C-terminal and internal (I) regions. The DA and LR methods gave almost identical 5-cross validation accuracies that averaged to the following values: 75.9±3.1%(N-regions), 70.7±1.5%(I-regions), and 74.6±4.4%(C-regions). NN predictions gave slightly higher scores: 78.8±1.2%(N-regions), 72.5±1.2%(I-regions), and 75.3±3.3%(C-regions). Predictions improved with length of the disordered regions. Averaged over the three methods, values ranged from 52% to 78% for length=9-14 to≥21, respectively, for I-regions, from 72% to 81% for length=5 to 12-15, respectively, for N-regions, and from 70% to 80% for length=5 to 12-15, respectively, for C-regions. These data support the hypothesis that disorder is encoded by the amino acid sequence.