SAV-pred is a resource dedicated to predicting the pathogenic effect of single amino acid variants (SAVs) in specific proteins based on structure-activity relationships (SAR) classification models. Currently, resource contains SAR models for 25 core conditions from Recommended Uniform Screening Panel. The models were trained on Uniprot protein sequence data and annotated variants from ClinVar, humsavar and dbSNP databases using Bayesian-like approach and Multilevel Neighborhoods of Atoms (MNA) descriptors. The approach includes transformation the letter text representation of peptides with altered amino acid to MOL V3000 format structural formula.

Predictions are made by special version of PASS (Prediction of Activity Spectra for Substances) technology (http://www.way2drug.com/PASSonline) which was modified for generation up to 15 levels of MNA descriptors. The most accurate SAR models based on the appropriate level of MNA descriptors and length of peptides were selected for each protein (see Training set).

Please cite us: Zadorozhny, A.D.; Rudik, A.V.; Filimonov, D.A.; Lagunin, A.A. SAV-Pred: A Freely Available Web Application for the Prediction of Pathogenic Amino Acid Substitutions for Monogenic Hereditary Diseases Studied in Newborn Screening. Int. J. Mol. Sci. 2023, 24, 2463. https://doi.org/10.3390/ijms24032463