ProtNote: a multimodal method for protein-function annotation

Samir Char, Nathaniel Corley, Sarah Alamdari, Kevin K. Yang, Ava P. Amini
2024 · doi:10.1101/2024.10.17.618952
preprint OA: closed CC-BY-4.0
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Abstract

Understanding the protein sequence-function relationship is essential for advancing protein biology and engineering. However, fewer than 1% of known protein sequences have human-verified functions. While deep learning methods have demonstrated promise for protein function prediction, current models are limited to predicting only those functions on which they were trained. Here, we introduce ProtNote, a multimodal deep learning model that leverages free-form text to enable both supervised and zero-shot protein function prediction. ProtNote not only maintains near state-of-the-art performance for annotations in its train set, but also generalizes to unseen and novel functions in zero-shot test settings. We envision that ProtNote will enhance protein function discovery by enabling scientists to use free text inputs, without restriction to predefined labels – a necessary capability for navigating the dynamic landscape of protein biology.

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License: CC-BY-4.0