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arXiv 2406.09586q-bio.BM
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基于帧平均Transformer的蛋白质-核酸复合物建模

Protein-Nucleic Acid Complex Modeling with Frame Averaging Transformer

Tinglin Huang, Zhenqiao Song, Rex Ying, Wengong Jin

AI总结:

针对适配体筛选实验成本高、标记数据稀缺的问题,提出集成帧平均的新型等变Transformer架构FAFormer,通过无监督学习预测蛋白质-核酸接触图,在多个数据集上性能优于现有模型,可作为适配体筛选的有效结合指示。

摘要:

适配体等核酸类药物近期展现出巨大的治疗潜力。然而,适配体筛选的实验平台成本高昂,且标记数据稀缺,给监督学习方法学习蛋白质-适配体结合模式带来了挑战。为此,我们提出了一种基于蛋白质与核酸之间预测的成对接触图的无监督学习方法,并验证了其在蛋白质-适配体结合预测中的有效性。我们的模型基于FAFormer——一种新型等变Transformer架构,它将帧平均(frame averaging,FA)无缝集成到每个Transformer块中。这种集成使模型能够在保留坐标空间语义的同时,将几何信息注入节点特征,从而获得比标准FA模型更强的表达能力。实验结果表明,在三个蛋白质复合物数据集的接触图预测任务中,FAFormer的性能优于现有等变模型,相对提升超过10%。此外,我们整理了五个真实世界的蛋白质-适配体相互作用数据集,并证明FAFormer预测的接触图可作为适配体筛选的强结合指示因子。

英文摘要:

Nucleic acid-based drugs like aptamers have recently demonstrated great therapeutic potential. However, experimental platforms for aptamer screening are costly, and the scarcity of labeled data presents a challenge for supervised methods to learn protein-aptamer binding. To this end, we develop an unsupervised learning approach based on the predicted pairwise contact map between a protein and a nucleic acid and demonstrate its effectiveness in protein-aptamer binding prediction. Our model is based on FAFormer, a novel equivariant transformer architecture that seamlessly integrates frame averaging (FA) within each transformer block. This integration allows our model to infuse geometric information into node features while preserving the spatial semantics of coordinates, leading to greater expressive power than standard FA models. Our results show that FAFormer outperforms existing equivariant models in contact map prediction across three protein complex datasets, with over 10% relative improvement. Moreover, we curate five real-world protein-aptamer interaction datasets and show that the contact map predicted by FAFormer serves as a strong binding indicator for aptamer screening.

机构、总结、中文摘要均由AI分析而来,可能存在错误,请以论文正文为准。

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