Journal Article

Deep representation learning improves prediction of LacI-mediated transcriptional repression

Garruss, Alexander S., Collins, Katherine M., Church, George M.

2021

Overview

Recent progress in DNA synthesis and sequencing technology has enabled systematic studies of protein function at a massive scale. We explore a deep mutational scanning study that measured the transcriptional repression function of 43,669 variants of the Escherichia coli LacI protein. We analyze structural and evolutionary aspects that relate to how the function of this protein is maintained, including an in-depth look at the C-terminal domain. We develop a deep neural network to predict transcriptional repression mediated by the lac repressor of Escherichia coli using experimental measurements of variant function. When measured across 10 separate training and validation splits using 5,009 single mutations of the lac repressor, our best-performing model achieved a median Pearson correlation of 0.79, exceeding any previous model. We demonstrate that deep representation learning approaches, first trained in an unsupervised manner across millions of diverse proteins, can be fine-tuned in a supervised fashion using lac repressor experimental datasets to more effectively predict a variant’s effect on repression. These findings suggest a deep representation learning model may improve the prediction of other important properties of proteins.

• Publisher: National Academy of Sciences,Proceedings of the National Academy of Sciences
• Subject: Artificial neural networks / BASIC BIOLOGICAL SCIENCES / Biological Sciences / Biophysics and Computational Biology / Deep Learning / deep representation learning / DNA biosynthesis / DNA sequencing / E coli / ENGINEERING / Epistasis, Genetic / Escherichia coli / Escherichia coli Proteins - genetics / Escherichia coli Proteins - metabolism / Gene silencing / lac repressor / Lac Repressors - genetics / Lac Repressors - metabolism / LacI protein / Lactose repressor / Learning / Machine learning / MATHEMATICS AND COMPUTING / Mutation / Mutation - genetics / Neural networks / Protein Domains / Proteins / Representation learning / Reproducibility of Results / Science & Technology - Other Topics / Transcription, Genetic