RPIPLM: Prediction of ncRNA-protein interaction by post-training a dual-tower pretrained biological model with supervised contrastive learning

Yiwei Liu, Ting Bao, Peng Yin, Shumin Wang and Yanbin Wang

PLOS ONE, 2025, vol. 20, issue 8, 1-22

Abstract: The field of biological research has been profoundly impacted by the emergence of biological pre-trained models, which have resulted in remarkable advancements in life sciences and medicine. However, the current landscape of biological pre-trained language models suffers from a shortcoming, i.e., their inability to grasp the intricacies of molecular interactions, such as ncRNA-protein interactions. It is in this context that our paper introduces a two-tower computational framework, termed RPIPLM, which brings forth a new paradigm for the prediction of ncRNA-protein interactions. The core of RPIPLM lies in its harnessing of the pre-trained RNA language model and protein language model to process ncRNA and protein sequences, thereby enabling the transfer of the general knowledge gained from self-supervised learning of vast data to ncRNA-protein interaction tasks. Additionally, to learn the intricate interaction patterns between RNA and protein embeddings across diverse scales, we employ a fusion of scaled dot-product self-attention mechanism and Multi-scale convolution operations on the output of the dual-tower architecture, effectively capturing both global and local information. Furthermore, we introduce supervised contrastive learning into the training of RPIPLM, enabling the model to effectively capture discriminative information by distinguishing between interacting and non-interacting samples in the learned representations. Through extensive experiments and an interpretability study, we demonstrate the effectiveness of RPIPLM and its superiority over other methods, establishing new state-of-the-art performance. RPIPLM is a powerful and scalable computational framework that holds the potential to unlock enormous insights from vast biological data, thereby accelerating the discovery of molecular interactions.

Date: 2025
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Persistent link: https://EconPapers.repec.org/RePEc:plo:pone00:0329174

DOI: 10.1371/journal.pone.0329174

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