  Nucleic acid-based drugs like aptamers have recently demonstrated great
therapeutic potential. However, experimental platforms for aptamer screening
are costly, and the scarcity of labeled data presents a challenge for
supervised methods to learn protein-aptamer binding. To this end, we develop an
unsupervised learning approach based on the predicted pairwise contact map
between a protein and a nucleic acid and demonstrate its effectiveness in
protein-aptamer binding prediction. Our model is based on FAFormer, a novel
equivariant transformer architecture that seamlessly integrates frame averaging
(FA) within each transformer block. This integration allows our model to infuse
geometric information into node features while preserving the spatial semantics
of coordinates, leading to greater expressive power than standard FA models.
Our results show that FAFormer outperforms existing equivariant models in
contact map prediction across three protein complex datasets, with over 10%
relative improvement. Moreover, we curate five real-world protein-aptamer
interaction datasets and show that the contact map predicted by FAFormer serves
as a strong binding indicator for aptamer screening.
