  Background: Disulfide bonds are crucial to protein structural formation.
Developing an effective method topredict disulfide bonding formation is
important for protein structural modeling and functional study. Mostcurrent
methods still have shortcomings, including low accuracy and strict requirements
for the selection ofdiscriminative features. Results: In this study, we
introduced a nested structure of Bidirectional Long-short Term
Memory(BLSTM)neural network called Global-Local-BLSTM (GL-BLSTM) for disulfide
bonding state prediction. Based on thepatterns of disulfide bond formation, a
BLSTM network called Local-BLSTM is used to extract context-basedfeatures
around every Cys residue. Another BLSTM network called Global-BLSTM is
introduced aboveLocal-BLSTM layer to integrate context-based features of all
Cys residues in the same protein chain, therebyinvolving inter-residue
relationships in the training process. According to our experimental
results,GL-BLSTMnetwork performs much better than other methods, GL-BLSTM
reached 90.26% accuracy at residue-level and 83.66% at protein-level. This
model has reached the state of the art in this field. Conclusion: GL-BLSTMs
special structure and mechanisms are beneficial to disulfide bonding state
prediction.By applying bidirectional LSTM, it can extract context based
features by processing protein sequences, therebyobtain more discriminative
information than traditional machine learning methods. Whats more,
GL-BLSTMsspecial two-layer structure enables it to extract both local and
global features, in which global features areplaying important roles in
improving prediction accuracy, especially at protein-level. Keywords: Disulfide
bonds; Prediction; Deep learning; Bidirectional long short term memory
