  Rapid progress in deep learning has spurred its application to bioinformatics
problems including protein structure prediction and design. In classic machine
learning problems like computer vision, progress has been driven by
standardized data sets that facilitate fair assessment of new methods and lower
the barrier to entry for non-domain experts. While data sets of protein
sequence and structure exist, they lack certain components critical for machine
learning, including high-quality multiple sequence alignments and insulated
training / validation splits that account for deep but only weakly detectable
homology across protein space. We have created the ProteinNet series of data
sets to provide a standardized mechanism for training and assessing data-driven
models of protein sequence-structure relationships. ProteinNet integrates
sequence, structure, and evolutionary information in programmatically
accessible file formats tailored for machine learning frameworks. Multiple
sequence alignments of all structurally characterized proteins were created
using substantial high-performance computing resources. Standardized data
splits were also generated to emulate the difficulty of past CASP (Critical
Assessment of protein Structure Prediction) experiments by resetting protein
sequence and structure space to the historical states that preceded six prior
CASPs. Utilizing sensitive evolution-based distance metrics to segregate
distantly related proteins, we have additionally created validation sets
distinct from the official CASP sets that faithfully mimic their difficulty.
ProteinNet thus represents a comprehensive and accessible resource for training
and assessing machine-learned models of protein structure.
