Pairing interacting protein sequences using masked language modeling
Umberto Lupo Damiano Sgarbossa Anne-Florence Bitbol
Predicting which proteins interact together from amino-acid sequences is an
important task. We develop a method to pair interacting protein sequences which
leverages the power of protein language models trained on multiple sequence
alignments, such as MSA Transformer and the EvoFormer module of AlphaFold. We
formulate the problem of pairing interacting partners among the paralogs of two
protein families in a differentiable way. We introduce a method called DiffPALM
that solves it by exploiting the ability of MSA Transformer to fill in masked
amino acids in multiple sequence alignments using the surrounding context. MSA
Transformer encodes coevolution between functionally or structurally coupled
amino acids. We show that it captures inter-chain coevolution, while it was
trained on single-chain data, which means that it can be used
out-of-distribution. Relying on MSA Transformer without fine-tuning, DiffPALM
outperforms existing coevolution-based pairing methods on difficult benchmarks
of shallow multiple sequence alignments extracted from ubiquitous prokaryotic
protein datasets. It also outperforms an alternative method based on a
state-of-the-art protein language model trained on single sequences. Paired
alignments of interacting protein sequences are a crucial ingredient of
supervised deep learning methods to predict the three-dimensional structure of
protein complexes. DiffPALM substantially improves the structure prediction of
some eukaryotic protein complexes by AlphaFold-Multimer, without significantly
deteriorating any of those we tested. It also achieves competitive performance
with using orthology-based pairing.