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 API and a VEP Plugin for PON-P2 is now available.

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Other prediction tools developed in our group

PON-BTK: Prediction of amino acid substitutions in the kinase domain of Bruton tyrosine kinase (BTK)
PON-Diso: Prediction method to predict the effect of disorder regions in proteins due to amino acid substitutions
PON-MMR2: Prediction method for amino acid substitutions in mismatch repair (MMR) proteins
PON-mt-tRNA: Prediction method for pathogenicity of mitochondrial tRNA variations
PON-Sol: Prediction method for effects of amino acid substitutions on protein solubility
PPSC: Prediction of protein stability changes due to amino acid substitutions
PON-PS: Prediction of severity due to amino acid substitutions

Other tolerance prediction methods


Benchmark variation datasets

VariBench: Benchmark variation datasets
VariSNP: Benchmark neutral variation datasets