AUTHOR=Yang Yang , Shao Aibin , Vihinen Mauno 

TITLE=PON-All: Amino Acid Substitution Tolerance Predictor for All Organisms

JOURNAL=Frontiers in Molecular Biosciences

VOLUME=Volume 9 - 2022

YEAR=2022

URL=https://www.frontiersin.org/journals/molecular-biosciences/articles/10.3389/fmolb.2022.867572

DOI=10.3389/fmolb.2022.867572

ISSN=2296-889X

ABSTRACT=Genetic variations are investigated in human and many other organisms. Interpretation of the identified variations can be challenging. Although some dedicated prediction methods have been developed and some tools for human variants can be used also for other organisms, the performance and species range have had limitations. We developed a novel variant pathogenicity/tolerance predictor for amino acid substitutions in any organism. The method, PON-All, is a machine learning tool trained on human, animal and plant variants. Two versions are provided, one with Gene Ontology annotations and another without these details. Gene Ontology annotations are not available or are partial for many organisms of interest. The methods provide predictions for three classes: pathogenic, benign and variants of unknown significance. On blind test, when using GO annotations, accuracy was 0.913 and MCC 0.827. When GO features were not used accuracy was 0.856 and MCC 0.712. The performance is the best for human and plant variants, and somewhat lower for animal variants. This is because the number of known disease-causing variants in animals is rather small. The method was compared to several other tools and was found to have a superior performance. PON-All is freely available at http://structure.bmc.lu.se/PON-All and http://8.133.174.28:8999/.