ProtST: Multi-Modality Learning of Protein Sequences and Biomedical Texts

TL;DR

ProtST enhances protein language models by integrating biomedical texts and protein descriptions, improving function prediction and retrieval through multimodal pre-training tasks.

Contribution

This work introduces the ProtDescribe dataset and the ProtST framework, combining protein sequences and texts to improve protein representation learning.

Findings

ProtST outperforms previous models on diverse benchmarks.

Effective zero-shot protein classification achieved.

Enables functional protein retrieval without annotations.

Abstract

Current protein language models (PLMs) learn protein representations mainly based on their sequences, thereby well capturing co-evolutionary information, but they are unable to explicitly acquire protein functions, which is the end goal of protein representation learning. Fortunately, for many proteins, their textual property descriptions are available, where their various functions are also described. Motivated by this fact, we first build the ProtDescribe dataset to augment protein sequences with text descriptions of their functions and other important properties. Based on this dataset, we propose the ProtST framework to enhance Protein Sequence pre-training and understanding by biomedical Texts. During pre-training, we design three types of tasks, i.e., unimodal mask prediction, multimodal representation alignment and multimodal mask prediction, to enhance a PLM with protein property information with different granularities and, at the same time, preserve the PLM's original representation power. On downstream tasks, ProtST enables both supervised learning and zero-shot prediction. We verify the superiority of ProtST-induced PLMs over previous ones on diverse representation learning benchmarks. Under the zero-shot setting, we show the effectiveness of ProtST on zero-shot protein classification, and ProtST also enables functional protein retrieval from a large-scale database without any function annotation.