ESM-NBR: fast and accurate nucleic acid-binding residue prediction via
  protein language model feature representation and multi-task learning
Wenwu Zeng Dafeng Lv Wenjuan Liu Shaoliang Peng
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:
https://github.com/wwzll123/ESM-NBR.