ESM-NBR: fast and accurate nucleic acid-binding residue prediction via
protein language model feature representation and multi-task learning
- URL: http://arxiv.org/abs/2312.00842v1
- Date: Fri, 1 Dec 2023 04:00:20 GMT
- Title: ESM-NBR: fast and accurate nucleic acid-binding residue prediction via
protein language model feature representation and multi-task learning
- Authors: Wenwu Zeng, Dafeng Lv, Wenjuan Liu, Shaoliang Peng
- Abstract summary: We propose a fast and accurate sequence-based method, called ESM-NBR, to predict nucleic acid-binding residues.
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.
By completely discarding the time-cost multiple sequence alignment process, the prediction speed of ESM-NBR far exceeds that of existing methods.
- Score: 1.6576008113462954
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: 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.
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