Transfer Learning for Protein Structure Classification at Low Resolution

Structure determination is key to understanding protein function at a molecular level. Whilst significant advances have been made in predicting structure and function from amino acid sequence, researchers must still rely on expensive, time-consuming analytical methods to visualise detailed protein conformation. In this study, we demonstrate that it is possible to make accurate ($\geq$80%) predictions of protein class and architecture from structures determined at low ($>$3A) resolution, using a deep convolutional neural network trained on high-resolution ($\leq$3A) structures represented as 2D matrices. Thus, we provide proof of concept for high-speed, low-cost protein structure classification at low resolution, and a basis for extension to prediction of function. We investigate the impact of the input representation on classification performance, showing that side-chain information may not be necessary for fine-grained structure predictions. Finally, we confirm that high-resolution, low-resolution and NMR-determined structures inhabit a common feature space, and thus provide a theoretical foundation for boosting with single-image super-resolution.

Transfer Learning for Protein Structure Classification and Function Inference at Low Resolution

Structure determination is key to understanding protein function at a molecular level. Whilst significant advances have been made in predicting structure and function from amino acid sequence, researchers must still rely on expensive, time-consuming analytical methods to visualise detailed protein conformation. In this study, we demonstrate that it is possible to make accurate predictions of protein fold taxonomy from structures determined at low ($>$3 Angstroms) resolution, using a deep convolutional neural network trained on high-resolution structures ($\leq$3 Angstroms). Thus, we provide proof of concept for high-speed, low-cost protein structure classification at low resolution. We explore the relationship between the information content of the input image and the predictive power of the model, achieving state of the art performance on homologous superfamily prediction with maps of interatomic distance. Our findings contribute further evidence that inclusion of both amino acid alpha and beta carbon geometry in these maps improves classification performance over purely alpha carbon representations, and show that side-chain information may not be necessary for fine-grained structure predictions. Finally, we confirm that high-resolution, low-resolution and NMR-determined structures inhabit a common feature space, and thus provide a theoretical basis for mapping between domains to boost resolution.