Protein solubility is a critical determinant of biologic candidates' developability, stability, and therapeutic efficacy. However, accurate solubility prediction remains a central challenge in computational protein engineering due to the inherent complexity within protein sequences. In this work, we propose a multimodal prompt learning framework, called MPSol, for protein solubility prediction that integrates complementary representations derived from primary sequences, structural proxies, and textual descriptions generated by large language models (LLMs). MPSol is built upon a unified multimodal backbone with a dedicated cross-modal fusion module that captures fine-grained interactions across modalities. In addition, we design label-aware prompts that encode solubility-specific semantic cues associated with each class. These prompts provide semantic supervision, guiding the alignment of fused protein representations to promote semantic consistency. Extensive experiments demonstrate that MPSol achieves state-of-the-art performance, reaching an accuracy of 0.815, AUC of 0.867 and MCC of 0.642 on the standard PDBSol test set, and generalizes well to the external out-of-distribution test dataset with an accuracy of 0.632, AUC of 0.653 and MCC of 0.332. These results underscore the potential of prompt-driven multimodal learning for interpretable and effective protein property prediction.
Zhang et al. (Mon,) studied this question.