Pokharel Suresh, Barasa Kepha, Pratyush Pawel, Kc Dukka B
Golisano College of Computing and Information Sciences, Rochester Institute of Technology, Rochester 14623, NY, United States.
College of Computing, Michigan Technological University, Houghton 49931, MI, United States.
Brief Bioinform. 2025 May 1;26(3). doi: 10.1093/bib/bbaf245.
DNA-binding proteins (DBPs) play a crucial role in gene regulation, development, and environmental responses across plants, animals, and microorganisms. Existing DBP prediction methods are largely limited to sequence information, whether through handcrafted features or sequence-based protein language models (PLMs), overlooking structural cues critical to protein function. In addition, most existing tools are trained for general DBP predictions, which are often not accurate for plant-specific DBPs due to the unique structural and functional properties of plant proteins. Our work introduces PLM-DBPs, a deep learning framework that integrates both sequence-based and structure-aware representations to enhance DBP prediction in plants. We evaluated several state-of-the-art PLMs to extract high-dimensional protein representations and experimented with various fusion strategies to validate the complementary information between the various representations. Our final model, a fusion of sequence-based and structure-aware ANN models, achieves a notable improvement in predicting DBPs in plants outperforming previous state-of-the-art models. Although sequence-based PLMs already demonstrate strong performance in DBP prediction, our findings show that the integration of structural information further enhances predictive accuracy. This underscores the complementary nature of structural representations and establishes PLM-DBPs as a robust tool for advancing plant research and agricultural innovation. The proposed model and other resources are publicly available at https://github.com/suresh-pokharel/PLM-DBPs.
DNA结合蛋白（DBP）在植物、动物和微生物的基因调控、发育及环境响应中发挥着关键作用。现有的DBP预测方法很大程度上局限于序列信息，无论是通过手工制作的特征还是基于序列的蛋白质语言模型（PLM），都忽略了对蛋白质功能至关重要的结构线索。此外，大多数现有工具是针对一般DBP预测进行训练的，由于植物蛋白质独特的结构和功能特性，这些工具对植物特异性DBP的预测往往不准确。我们的工作引入了PLM-DBPs，这是一个深度学习框架，它整合了基于序列的表示和结构感知表示，以增强对植物中DBP的预测。我们评估了几种先进的PLM，以提取高维蛋白质表示，并试验了各种融合策略，以验证不同表示之间的互补信息。我们的最终模型是基于序列的和结构感知的人工神经网络模型的融合，在预测植物中的DBP方面取得了显著改进，优于先前的先进模型。尽管基于序列的PLM在DBP预测中已经表现出强大的性能，但我们的研究结果表明，结构信息的整合进一步提高了预测准确性。这突出了结构表示的互补性，并将PLM-DBPs确立为推进植物研究和农业创新的强大工具。所提出的模型和其他资源可在https://github.com/suresh-pokharel/PLM-DBPs上公开获取。
