Protein structure prediction
Do short and long floppy protein regions need separate predictors?
Open access · cc by · source: Europe PMC
Training one model for short disordered protein regions and another for long ones, then letting a third model blend them, predicted both kinds well where a single model could not.
Study at a glance
- Design
- Computational / modelling — Two specialised linear-SVM disorder predictors (short and long regions) integrated by a meta-predictor; 10-fold cross-validation plus a blind test on unrelated recent PDB chains, compared with a global predictor and six published predictors
- N
- N=1327 · 1,327 non-redundant training protein sequences; separate blind-test set of 1,304 recent PDB chains
- Population
- Protein sequences with experimentally annotated ordered and intrinsically disordered regions
- Outcome
- Per-chain sensitivity, specificity, their average (ACC) and ROC AUC for predicting disordered residues
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Key findings
Short and long disordered regions had different amino-acid make-ups, and the short-region model needed much smaller windows than the long-region model. The combined VSL2 predictor reached balanced sensitivity above 81% on both short and long regions with overall accuracy of 81.6%, beating a single global model trained on all disorder and several published predictors in per-chain comparisons. Nonlinear models such as neural networks and RBF SVMs gave no significant gain over the linear SVM, and a cheap sequence-only variant lost about 3% accuracy.
Methodology
The authors assembled 1,327 non-redundant protein sequences with labelled ordered and disordered regions and split disordered regions into short (30 residues or fewer) and long (more than 30). They trained a linear SVM for each type, using sliding-window features from amino-acid composition, evolutionary profiles and predicted secondary structure, then trained a meta-predictor to weight the two specialists residue by residue. They evaluated with 10-fold cross-validation and a blind test on 1,304 newer, unrelated structures, against a single global predictor and six earlier tools.
Limitations
Disorder labels mostly come from missing electron density in X-ray structures, which can also arise from crystal packing or rigid but mobile domains, so some 'errors' may be label noise; the authors show examples of this. The 30-residue cut-off is arbitrary, and accuracy dipped for regions of 16 to 30 residues, suggesting length is really a continuum. The composite model also traded away specificity, producing more false positives on ordered regions than its predecessor.
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