Learning protein fitness models from evolutionary and assay-labeled data

A simple machine learning algorithm combines evolutionary and experimental data for improved protein fitness prediction. Machine learning-based models of protein fitness typically learn from either unlabeled, evolutionarily related sequences or variant sequences with experimentally measured labels. For regimes where only limited experimental data are available, recent work has suggested methods for combining both sources of information. Toward that goal, we propose a simple combination approach that is competitive with, and on average outperforms more sophisticated methods. Our approach uses ridge regression on site-specific amino acid features combined with one probability density feature from modeling the evolutionary data. Within this approach, we find that a variational autoencoder-based probability density model showed the best overall performance, although any evolutionary density model can be used. Moreover, our analysis highlights the importance of systematic evaluations and sufficient baselines.

Automated summary

This study proposes a simple machine learning algorithm that combines evolutionary and experimental data for improved protein fitness prediction. They find that using ridge regression on site-specific amino acid features combined with a probability density feature from modeling the evolutionary data performs well on this task.