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[https://doi.org/10.1038/s41467-023-40797-7](https://doi.org/10.1038/s41467-023-40797-7)  \nAPOGEE 2: multi-layer machine-learning model for the interpretable prediction of mitochondrial missense variants  \nReceived: 23 January 2023  \n\n| Accepted: 10 August 2023 |\n| --- |\n| |\n| Check for updates |\n\nSalvatore Daniele Bianco1,2, Luca Parca 1,3, Francesco Petrizzelli1,  \nTommaso Biagini1, Agnese Giovannetti 4, Niccolò Liorni1,2, Alessandro Napoli1, Massimo Carella5, Vincent Procaccio6,7, Marie T. Lott 7, Shiping Zhang7,8, Angelo Luigi Vescovi9, Douglas C. Wallace7,10, Viviana Caputo2 &  \nTommaso Mazza 1   \nMitochondrial 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 ofvariants 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.  \nMitochondria are responsible for many of the most important functions in eukaryotic cells. They use oxidative phosphorylation (OXPHOS) to produce large amounts of adenosine triphosphate (ATP), store calcium for cell signaling, generate heat, and mediate cell growth and death. Mitochondria, in contrast to the nuclear genome (nDNA), have a smaller repertoire of DNA-repair pathways. They counteract Muller’s ratchet1, the progressive accumulation of deleterious  \nmutations, with a process known asthe mtDNA bottleneck, which, while controversial2, explains the increase in cell-to-cell variability in terms of mutant load during development. A unique mechanism for the degradation of mutated DNA molecules3 eliminates defective cells, allowing the mutant load to be stabilized over generations. Despite this, the mtDNA to nDNA variant ratios range from a few folds in nonvertebrates up to at least 20 folds in vertebrates4. The lack of  \n1Bioinformatics Laboratory, Fondazione IRCCS Casa Sollievo della Sofferenza, S. Giovanni Rotondo (FG), Italy. 2Department of Experimental Medicine, Sapienza University of Rome, Rome, Italy. 3Italian Space Agency, Rome, Italy. 4Clinical Genomics Laboratory, Fondazione IRCCS Casa Sollievo della Sofferenza, S. Giovanni Rotondo (FG), Italy. 5Medical Genetics Laboratory, Fondazione IRCCS Casa Sollievo della Sofferenza, S. Giovanni Rotondo, (FG), Italy. 6University of Angers, Genetics Department CHU Angers, Mitolab UMR CNRS 6015-INSERM U1083, F-49000 Angers, France. 7Center for Mitochondrial and Epigenomic Medicine, The Children’s Hospital of Philadelphia, Philadelphia, PA, USA. 8Department of Biomedical and Health Informatics, The Children’s Hospital of Philadelphia, Philadelphia, PA, USA. 9ISBReMIT Institute for Stem Cell Biology, Regenerative Medicine and Innovative Therapies, Fondazione IRCSS Casa Sollievo della Sofferenza, S. Giovanni Rotondo (FG), Italy. 10Department of Pediatrics, Division of Human Genetics, The Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA. e-mail: [t.mazza@css-mendel.it](t.mazza@css-mendel.it)  \nprotective histones in mitochondria, the proximity of mtDNA to the electron transport chain, which is a primary cell","cbCaivdm3HYLsVEJ","https://ap.wps.com/l/cbCaivdm3HYLsVEJ","pdf",7980376,13,"English","# APOGEE 2 overview\n# Mitochondrial context and variant interpretation challenges\n# Training data, guidelines, and evidence criteria","[{\"question\":\"What problem does APOGEE 2 address in mitochondrial genetics?\",\"answer\":\"It improves interpretation of mitochondrial missense variants by producing more accurate pathogenicity predictions despite variability in clinical manifestations and mechanistic complexity.\"},{\"question\":\"How is APOGEE 2 constructed to enhance prediction performance?\",\"answer\":\"It is an ensemble method that incorporates an improved machine-learning approach and a curated training set, aligned with ACMG/AMP consensus recommendations.\"},{\"question\":\"What outputs does APOGEE 2 provide to support analysis?\",\"answer\":\"It delivers region-wise assessments of genome fragility and mechanistic analyses identifying amino acids that can create perceptible long-range effects on protein structure.\"}]","APOGEE 2 - multi-layer machine-learning model for the interpretable prediction of mitochondrial missense variants | PDF",33]