Protein-nucleic acid interactions play a very important role in a variety of
biological activities. Accurate identification of nucleic acid-binding residues
is a critical step in understanding the interaction mechanisms. Although many
computationally based methods have been developed to predict nucleic
acid-binding residues, challenges remain. In this study, a fast and accurate
sequence-based method, called ESM-NBR, is proposed. In ESM-NBR, we first use
the large protein language model ESM2 to extract discriminative biological
properties feature representation from protein primary sequences; then, a
multi-task deep learning model composed of stacked bidirectional long
short-term memory (BiLSTM) and multi-layer perceptron (MLP) networks is
employed to explore common and private information of DNA- and RNA-binding
residues with ESM2 feature as input. Experimental results on benchmark data
sets demonstrate that the prediction performance of ESM2 feature representation
comprehensively outperforms evolutionary information-based hidden Markov model
(HMM) features. Meanwhile, the ESM-NBR obtains the MCC values for DNA-binding
residues prediction of 0.427 and 0.391 on two independent test sets, which are
18.61 and 10.45% higher than those of the second-best methods, respectively.
Moreover, by completely discarding the time-cost multiple sequence alignment
process, the prediction speed of ESM-NBR far exceeds that of existing methods
(5.52s for a protein sequence of length 500, which is about 16 times faster
than the second-fastest method). A user-friendly standalone package and the
data of ESM-NBR are freely available for academic use at:
this https URL

通过使用 ESM2 作为输入，ESM-NBR 提出了一种快速准确的序列 - based 方法，采用由双向长短期记忆和多层感知器网络组成的深度学习模型，探索 DNA 和 RNA 结合残基的共有和私有信息。在基准数据集上的实验证明，ESM2 特征表征的预测性能全面优于基于进化信息的隐马尔可夫模型（HMM）特征。此外，ESM-NBR 的预测速度远远超过现有方法（用于 500 个蛋白质序列的时间为 5.52 秒，比第二快的方法快约 16 倍）。