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摘要
Prediction of the spatial structure or function of biological macromolecules based on their sequences remains an important challenge in bioinformatics. When modeling biological sequences using traditional sequencing models, long-range interaction, complicated and variable output of labeled structures, and variable length of biological sequences usually lead to different solutions on a case-by-case basis. This study proposed a unified deep learning architecture based on long short-term memory or a gated recurrent unit to capture long-range interactions. The architecture designs the optional reshape operator to adapt to the diversity of the output labels and implements a training algorithm to support the training of sequence models capable of processing variable-length sequences. The merging and pooling operators enhances the ability of capturing short-range interactions between basic units of biological sequences. The proposed deep-learning architecture and its training algorithm might be capable of solving currently variable biological sequence-modeling problems under a unified framework. We validated the model on one of the most difficult biological sequence-modeling problems, protein residue interaction prediction. The results indicate that the accuracy of obtaining the residue interactions of the model exceeded popular approaches by 10 percent on multiple widely-used benchmarks.
