  Protein structure prediction has been a grand challenge for over 50 years,
owing to its broad scientific and application interests. There are two primary
types of modeling algorithms, template-free modeling and template-based
modeling. The latter one is suitable for easy prediction tasks and is widely
adopted in computer-aided drug discoveries for drug design and screening.
Although it has been several decades since its first edition, the current
template-based modeling approach suffers from two critical problems: 1) there
are many missing regions in the template-query sequence alignment, and 2) the
accuracy of the distance pairs from different regions of the template varies,
and this information is not well introduced into the modeling. To solve these
two problems, we propose a structural optimization process based on template
modeling, introducing two neural network models to predict the distance
information of the missing regions and the accuracy of the distance pairs of
different regions in the template modeling structure. The predicted distances
and residue pairwise-specific deviations are incorporated into the potential
energy function for structural optimization, which significantly improves the
qualities of the original template modeling decoys.
