Machine learning modeling of family wide enzyme-substrate specificity screens
Fig 1
Enzyme-substrate interaction modeling strategies.
(A) Current machine learning-directed evolution strategies, which involve design-build-test-model-learn cycles measuring protein variant activity on a single substrate of interest. (B) The “dense screen” setting where homologous enzyme variants from one protein family are profiled against multiple substrates. In this setting, we can aim to generalize to either new enzymes (“enzyme discovery”) or new substrates (“substrate discovery”). (C) Three different styles of models evaluated in this study, where single task models independently build predictive models for rows and columns from panel (B), whereas a CPI model takes both substrates and enzymes as input. (D) An example CPI model architecture where pretrained neural networks extract features from the substrate and enzyme to be fed into a top-level feed forward model for activity prediction.