Deep convolutional networks for quality assessment of protein folds

Understand

The computational prediction of a protein structure from its sequence generally relies on a method to assess the quality of protein models.

• Most assessment methods rank candidate models using heavily engineered structural features, defined as complex functions of the atomic coordinates.
• However, very few methods have attempted to learn these features directly from the data.
• We show that deep convolutional networks can be used to predict the ranking of model structures solely on the basis of their raw three-dimensional atomic densities, without any feature tuning.