Basic framework of AlphaMissense model. AlphaMissense utilizes a missense variant as input to predict its likelihood of being pathogenic. The algorithm is refined using population frequency data of variants observed in Homo sapiens and other primate taxa, calibrating the confidence intervals of known disease-causing mutations. As a result, AlphaMissense categorizes the missense variant as likely benign, likely pathogenic, or uncertain. By predicting the pathogenicity of all possible missense variants in the human genome, AlphaMissense provides a community resource for better understanding of the functional consequences of genetic variation.

In summary, as a significantly noteworthy advancement in the field of protein variant analysis, AlphaMissense predicts the possible impact of every amino acid substitution in the human proteome and classifies 89% of missense variants as either likely benign or likely pathogenic. In addition, AlphaMissense interprets the effects of every possible missense mutation in the human genome on protein structure and function based on the insights obtained from protein structure analysis. Overall, by incorporating the pathogenic classification of missense mutations of unknown significance into protein structure-predicting models, AlphaMissense holds promise to identify where disease-causing mutations are likely to occur in a protein.

Article Access: https://doi.org/10.1002/mef2.70

More about MedComm-Future Medicine: https://onlinelibrary.wiley.com/journal/27696456

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