Alignment-based protein mutational landscape prediction: doing more with less

The wealth of genomic data has boosted the development of computational
methods predicting the phenotypic outcomes of missense variants. The most
accurate ones exploit multiple sequence alignments, which can be costly to
generate. Recent efforts for democratizing protein structure prediction
have overcome this bottleneck by leveraging the fast homology
search of MMseqs2. Here, we show the usefulness of this strategy
for mutational outcome prediction through a large-scale assessment of 1.5M
missense variants across 72 protein families. Our study demonstrates the
feasibility of producing alignment-based mutational landscape predictions
that are both high-quality and compute-efficient for entire proteomes. We
provide the community with the whole human proteome mutational landscape
and simplified access to our predictive pipeline.