Deep Representation Learning Improves Prediction of LacI-mediated Transcriptional Repression
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PROVIDER: PRJNA732423 | ENA |
REPOSITORIES: ENA
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Project description:We analyze LacI mutations with the goal to understand and better predict basal transcriptional repression function after mutation. This study utilized lac repressor variants from the Taylor, et al. 2015 Nature Methods study including ten tiles of synthesized single mutations (NCBI GEO Series GSE75009), and an additional six tiles corresponding to higher-order mutations (this study).
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Project description: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.
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