  Native contacts between residues could be predicted from the amino acid
sequence of proteins, and the predicted contact information could assist the de
novo protein structure prediction. Here, we present a novel pipeline of a
residue contact predictor AmoebaContact and a contact-assisted folder GDFold
for rapid protein structure prediction. Unlike mainstream contact predictors
that utilize human-designed neural networks, AmoebaContact adopts a set of
network architectures that are found as optimal for contact prediction through
automatic searching and predicts the residue contacts at a series of cutoffs.
Different from conventional contact-assisted folders that only use top-scored
contact pairs, GDFold considers all residue pairs from the prediction results
of AmoebaContact in a differentiable loss function and optimizes the atom
coordinates using the gradient descent algorithm. Combination of AmoebaContact
and GDFold allows quick reconstruction of the protein structure, with
comparable model quality to the state-of-the-art protein structure prediction
methods.
