PON-P2 predicts the pathogenicity (harmfulness) of amino acid substitutions. It is a machine learning-based approach and utilizes amino acid features, Gene Ontology (GO) annotations, evolutionary conservation, and if available, annotations of functional sites. Note that, PON-P2 is NOT a meta-predictor. PON-P2 estimates the reliability of predictions and groups the variants into pathogenic, neutral and unknown classes. Read more
Performance of PON-P2 has been extensively tested. For details, see here. Performance of PON-P2 on additional datasets such as predictSNPSelected and SwissVarSelected datasets are also available here. PON-P2 has been shown to work also on cancer variants. PON-P2 predictions for amino acid substitutions in COSMIC (v68) and data published in Harmful somatic amino acid substitutions affect key pathways in cancers is publicly available here.
PON-P2 was the best performing method in a recent comparison and outperformed protein-specific predictors in 85% of the proteins (Riera et. al. 2016).NEWS: PON-P2 prediction for total Human Proteome is available here.
