Combination of deep neural network with attention mechanism enhances the
explainability of protein contact prediction
Deep learning has emerged as a revolutionary technology for protein
residue-residue contact prediction since the 2012 CASP10 competition.
Considerable advancements in the predictive power of the deep
learning-based contact predictions have been achieved since then.
However, little effort has been put into interpreting the black-box deep
learning methods. Algorithms that can interpret the relationship between
predicted contact maps and the internal mechanism of the deep learning
architectures are needed to explore the essential components of contact
inference and improve their explainability. In this study, we present an
attention-based convolutional neural network for protein contact
prediction, which consists of two attention mechanism-based modules:
sequence attention and regional attention. Our benchmark results on the
CASP13 free-modeling (FM) targets demonstrate that the two attention
modules added on top of existing typical deep learning models exhibit a
complementary effect that contributes to predictive improvements. More
importantly, the inclusion of the attention mechanism provides
interpretable patterns that contain useful insights into the key
fold-determining residues in proteins. We expect the attention-based
model can provide a reliable and practically interpretable technique
that helps break the current bottlenecks in explaining deep neural
networks for contact prediction. The source code of our method is
available at https://github.com/jianlin-cheng/InterpretContactMap.