MULAN: Multimodal Protein Language Model for Sequence and Structure Encoding

Frolova, D.; Pak, M.; Litvin, A.; Sharov, I.; Ivankov, D.; Oseledets, I.

doi:10.1101/2024.05.30.596565

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bioRxiv · 10.1101/2024.05.30.596565

MULAN: Multimodal Protein Language Model for Sequence and Structure Encoding

Frolova, D.·Pak, M.·Litvin, A.·Sharov, I.·Ivankov, D.·Oseledets, I.

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Abstract

Most protein language models (PLMs), which are used to produce high-quality protein representations, use only protein sequences during training. However, the known protein structure is crucial in many protein property prediction tasks, so there is a growing interest in incorporating the knowledge about the protein structure into a PLM. In this study, we propose MULAN, a MULtimodal PLM for both sequence and ANgle-based structure encoding. MULAN has a pre-trained sequence encoder and an introduced Structure Adapter, which are then fused and trained together. According to the evaluation on 7 downstream tasks of various nature, both small and medium-sized MULAN models show consistent improvement in quality compared to both sequence-only ESM-2 and structure-aware SaProt. Importantly, our model offers a cheap increase in the structural awareness of the protein representations due to finetuning of existing PLMs instead of training from scratch. We perform a detailed analysis of the proposed model and demonstrate its awareness of the protein structure. The implementation, training data and model checkpoints are available at https://github.com/DFrolova/MULAN.

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Frolova, D., Pak, M., Litvin, A., Sharov, I., Ivankov, D., Oseledets, I.. 2024-06-02. MULAN: Multimodal Protein Language Model for Sequence and Structure Encoding. https://doi.org/10.1101/2024.05.30.596565

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