APOGEE 2: multi-layer machine-learning model for the interpretable prediction of mitochondrial missense variants

Salvatore Daniele Bianco, Luca Parca, Francesco Petrizzelli, Tommaso Biagini, Agnese Giovannetti, Niccolò Liorni, Alessandro Napoli, Massimo Carella, Vincent Procaccio, Marie T. Lott, Shiping Zhang, Angelo Luigi Vescovi, Douglas C. Wallace, Viviana Caputo and Tommaso Mazza ()
Additional contact information
Salvatore Daniele Bianco: Fondazione IRCCS Casa Sollievo della Sofferenza
Luca Parca: Fondazione IRCCS Casa Sollievo della Sofferenza
Francesco Petrizzelli: Fondazione IRCCS Casa Sollievo della Sofferenza
Tommaso Biagini: Fondazione IRCCS Casa Sollievo della Sofferenza
Agnese Giovannetti: Fondazione IRCCS Casa Sollievo della Sofferenza
Niccolò Liorni: Fondazione IRCCS Casa Sollievo della Sofferenza
Alessandro Napoli: Fondazione IRCCS Casa Sollievo della Sofferenza
Massimo Carella: Fondazione IRCCS Casa Sollievo della Sofferenza
Vincent Procaccio: University of Angers, Genetics Department CHU Angers, Mitolab UMR CNRS 6015-INSERM U1083
Marie T. Lott: The Children’s Hospital of Philadelphia
Shiping Zhang: The Children’s Hospital of Philadelphia
Angelo Luigi Vescovi: Regenerative Medicine and Innovative Therapies, Fondazione IRCSS Casa Sollievo della Sofferenza
Douglas C. Wallace: The Children’s Hospital of Philadelphia
Viviana Caputo: Sapienza University of Rome
Tommaso Mazza: Fondazione IRCCS Casa Sollievo della Sofferenza

Nature Communications, 2023, vol. 14, issue 1, 1-13

Abstract: Abstract Mitochondrial dysfunction has pleiotropic effects and is frequently caused by mitochondrial DNA mutations. However, factors such as significant variability in clinical manifestations make interpreting the pathogenicity of variants in the mitochondrial genome challenging. Here, we present APOGEE 2, a mitochondrially-centered ensemble method designed to improve the accuracy of pathogenicity predictions for interpreting missense mitochondrial variants. Built on the joint consensus recommendations by the American College of Medical Genetics and Genomics/Association for Molecular Pathology, APOGEE 2 features an improved machine learning method and a curated training set for enhanced performance metrics. It offers region-wise assessments of genome fragility and mechanistic analyses of specific amino acids that cause perceptible long-range effects on protein structure. With clinical and research use in mind, APOGEE 2 scores and pathogenicity probabilities are precompiled and available in MitImpact. APOGEE 2’s ability to address challenges in interpreting mitochondrial missense variants makes it an essential tool in the field of mitochondrial genetics.

Date: 2023
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DOI: 10.1038/s41467-023-40797-7

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