Language Models (LMs) excel in understanding textual descriptions of
proteins, as evident in biomedical question-answering tasks. However, their
capability falters with raw protein data, such as amino acid sequences, due to
a deficit in pretraining on such data. Conversely, Protein Language Models
(PLMs) can understand and convert protein data into high-quality
representations, but struggle to process texts. To address their limitations,
we introduce ProtT3, a framework for Protein-to-Text Generation for Text-based
Protein Understanding. ProtT3 empowers an LM to understand protein sequences of
amino acids by incorporating a PLM as its protein understanding module,
enabling effective protein-to-text generation. This collaboration between PLM
and LM is facilitated by a cross-modal projector (i.e., Q-Former) that bridges
the modality gap between the PLM's representation space and the LM's input
space. Unlike previous studies focusing on protein property prediction and
protein-text retrieval, we delve into the largely unexplored field of
protein-to-text generation. To facilitate comprehensive benchmarks and promote
future research, we establish quantitative evaluations for protein-text
modeling tasks, including protein captioning, protein question-answering, and
protein-text retrieval. Our experiments show that ProtT3 substantially
surpasses current baselines, with ablation studies further highlighting the
efficacy of its core components. Our code is available at
this https URL

ProtT3 是一个框架，通过将 Protein Language Models 作为其蛋白质理解模块，使语言模型能够理解氨基酸序列，并实现有效的蛋白质到文本生成。