Inferring Protein Variant Impacts Across Contexts

Multiplexed assays of variant effects (MAVEs) measure the functional impact of many protein sequence variants in parallel, potentially covering all possible single amino acid substitutions. Unlike current computational variant effect predictors, MAVEs can reveal the effects of variants under different genetic and environmental contexts. However, whereas the space of possible contexts is effectively infinite, contextual MAVE studies are limited by finite experimental budgets. To maximize coverage across contexts, one strategy is to carry out sub-saturation contextual MAVEs and then fill in the gaps via imputation. Here, we categorize and compare different imputation challenges, explore a collection of multi-context imputation solutions, including linear mixed-effects models, random forests, and autoencoders, and provide insight into how best to proceed for a given imputation task. We find that the optimal method depends on the imputation task and how densely the contexts have been measured. More flexible models excel when measurements are plentiful, whereas the simplest models prove most reliable when measurements are sparse. However, the simple source-to-target regression models, although well suited to imputing scores for variants measured in the source context, cannot impute scores for variants that were not measured in either context. This is a major limitation when both maps are sparsely measured. We provide a conceptual framework and an initial evaluation of multi-context imputation methods that can extend the scope of large-scale studies of context-dependent variant effects.