![]() Molecular technologists strive to engineer biologics that are safe and effective 1, design new genomes 2, 3, and develop ‘smart’ libraries of synthesized molecules 4, 5. Understanding the phenotypic effects of genetic variation is a central challenge for bioengineering and basic biology. We suggest EVmutation may be useful to assess the quantitative effects of mutations in genes of any organism and provide precomputed predictions for ~ 7000 human proteins. We find that it improves the prediction accuracies of a comprehensive collection of recent high-throughput experimental fitness landscapes, biochemical measurements and human disease mutations. We present an unsupervised method for predicting the effects of mutations (EVmutation) that explicitly captures residue dependencies between positions. Most computational methods have relied significantly on the signal of evolutionary conservation, but do not account for dependencies between positions in a sequence. However, since designing functional assays is challenging and systematic testing of combinations of mutations is intractable, there is a parallel need to develop more accurate computational predictions. Increasing interest in determining the effects of genetic variation for bioengineering, human health and basic biological research has propelled the development of technologies for high-throughput mutagenesis and selection.
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