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

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

URL: http://arxiv.org/abs/2301.12040v2
Date: Wed, 5 Jul 2023 03:17:48 GMT
Title: ProtST: Multi-Modality Learning of Protein Sequences and Biomedical Texts
Authors: Minghao Xu, Xinyu Yuan, Santiago Miret, Jian Tang
Abstract summary: We build a ProtST dataset to augment protein sequences with text descriptions of their functions and other important properties. During pre-training, we design three types of tasks, i.e., unimodal mask prediction, multimodal representation alignment and multimodal mask prediction. On downstream tasks, ProtST enables both supervised learning and zero-shot prediction.
Score: 22.870765825298268
License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
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.