September 22, 2023

Accurate Proteome-Wide Missense Variant Effect Prediction with AlphaMissense 

In human genetics, interpreting these variations is a significant and continual challenge. Just 2% of the more than 4 million identified missense variations have been clinically categorized as benign or harmful.
– Jun Cheng, Guido Novati, Joshua Pan, Clare Bycroft, Akvilė Žemgulytė, Taylor Applebaum, Alexander Pritzel, Lai Hong Wong, Michal Zielinski, Tobias Sargeant, Rosalia G Schneider, Andrew W Senior, John Jumper, Demis Hassabis, Pushmeet Kohli, Žiga Avsec

Human populations exhibit a wide range of genetic variation, as evidenced by genome sequencing. Missense variants, a type of genetic variation, alter the sequence of amino acids in proteins. These variants involve substitutions in nucleotides, leading to the replacement of one amino acid with another in the protein structure. Benign missense variants have little impact, whereas pathogenic missense variants interfere with protein function and lower organismal fitness. 

 

Variant Impact with AlphaMissense in Human Proteome 

In human genetics, interpreting these variations is a significant and continual challenge. Just 2% of the more than 4 million identified missense variations have been clinically categorized as benign or harmful, with the great majority having unclear clinical significance. This hinders the advancement or implementation of clinical treatments that address the underlying genetic etiology as well as the identification of uncommon disorders. Machine learning techniques have the potential to bridge the gap in variant assessment by utilizing biological data patterns to estimate the disease-causing potential of unannotated variations. In particular, the disease-causing potential of protein variations may be predicted using AlphaFold, which precisely estimates structure of proteins from the amino acid sequence. 

 

DeepMind Technologies Limited created Alphafold

 

Using a novel method called AlphaMissense, pathogenicity of missense variants may be successfully predicted by using unsupervised protein language modeling, structural context from AlphaFold system, and adjustments on weak tags from population frequency information. It exceeds in clinical, de novo, and experimental scenarios without direct training on such datasets.  AlphaMissense provides a comprehensive database that spans all potential single amino acid substitutions in the human proteome, designating 32% as likely pathogenic and 57% as likely benign, with a 90% precision cutoff on the ClinVar dataset. This resource has the potential to expedite research across a wide range of disciplines. It can help geneticists evaluate the significance of genes, aid molecular biologists in designing experiments, and support clinicians to prioritize de novo variants that have been classified as pathogenic for rare diseases, as well as inform complex trait geneticists on rare potentially causative variants. 

 

In conclusion, AlphaMissense estimations have the potential to shed light on the molecular consequences of variants on protein function, aid in the discovery of causative missense variants and formerly unidentified gene-phenotype associations, as well as improve the diagnostic yield of uncommon genetic illnesses. The ongoing improvement of specific protein variant effect predictors using structure estimation models will also be supported by AlphaMissense. 

 

Learn more about using Pharmacogenomics (PGx) in Real World Applications

 

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