  Predicting the structure of interacting chains is crucial for understanding
biological systems and developing new drugs. Large-scale pre-trained Protein
Language Models (PLMs), such as ESM2, have shown impressive abilities in
extracting biologically meaningful representations for protein structure
prediction. In this paper, we show that ESMFold, which has been successful in
computing accurate atomic structures for single-chain proteins, can be adapted
to predict the heterodimer structures in a lightweight manner. We propose
Linker-tuning, which learns a continuous prompt to connect the two chains in a
dimer before running it as a single sequence in ESMFold. Experiment results
show that our method successfully predicts 56.98% of interfaces on the i.i.d.
heterodimer test set, with an absolute improvement of +12.79% over the
ESMFold-Linker baseline. Furthermore, our model can generalize well to the
out-of-distribution (OOD) test set HeteroTest2 and two antibody test sets Fab
and Fv while being $9\times$ faster than AF-Multimer.
