  MOTIVATION: Proteins fold into complex structures that are crucial for their
biological functions. Experimental determination of protein structures is
costly and therefore limited to a small fraction of all known proteins. Hence,
different computational structure prediction methods are necessary for the
modelling of the vast majority of all proteins. In most structure prediction
pipelines, the last step is to select the best available model and to estimate
its accuracy. This model quality estimation problem has been growing in
importance during the last decade, and progress is believed to be important for
large scale modelling of proteins. The current generation of model quality
estimation programs performs well at separating incorrect and good models, but
fails to consistently identify the best possible model. State-of-the-art model
quality assessment methods use a combination of features that describe a model
and the agreement of the model with features predicted from the protein
sequence.
  RESULTS: We first introduce a deep neural network architecture to predict
model quality using significantly fewer input features than state-of-the-art
methods. Thereafter, we propose a methodology to train the deep network that
leverages the comparative structure of the problem. We also show the
possibility of applying transfer learning on databases of known protein
structures. We demonstrate its viability by reaching state-of-the-art
performance using only a reduced set of input features and a coarse description
of the models.
  AVAILABILITY: The code will be freely available for download at
github.com/ElofssonLab/ProQ4.
