Discovering proteinbinding RNA motifs with a generative model of RNA sequences Abstract   Recent advances in highthroughput experimental technologies have generated a huge amount of data on interactions between proteins and nucleic acids. Motivated by the big experimental data, several computational methods have been developed either to predict binding sites in a sequence or to determine if an interaction exists between protein and nucleic acid sequences. However, most of the methods cannot be used to discover new nucleic acid sequences that bind to a target protein because they are classifiers rather than generators. In this paper we propose a generative model for constructing proteinbinding RNA sequences and motifs using a long shortterm memory (LSTM) neural network. Testing the model for several target proteins showed that RNA sequences generated by the model have high binding affinity and specificity for their target proteins and that the proteinbinding motifs derived from the generated RNA sequences are comparable to the motifs from experimentally validated proteinbinding RNA sequences. The results are promising and we believe this approach will help design more efficient in vitro or in vivo experiments by suggesting potential RNA aptamers for a target protein.

A deep neural network approach using distributed representations of RNA sequence and structure for identifying binding site of RNAbinding proteins RNAbinding proteins (RBPs) play a crucial role in the posttranscriptional regulation of RNAs. Identification of RBP binding sites is a key step to understand the biological mechanism of posttranscriptional regulation. Although many computational methods have been developed for predicting RNAprotein binding sites, few study considers the kmer embedding representation of RNA primary sequence and secondary structure specificities. In this paper, we develop a general deep learning framework, named deepRKE, to predict RNAprotein binding sites. deepRKE takes an unsupervised shallow twolayer neural network to automatically learn the distributed representation of kmers by taking their neighbor context into account. Compared to conventional kmers approach, distributed representations effectively detect the latent relationship and similarity between kmers. The distributed representations of the sequences and secondary structures are fed into CNN convolutional neural network (CNN) and a bidirectional long short term memory network (BLSTM) to discriminate the RBP binding sites from unbound sites. We comprehensively evaluate deepRKE on two largescale RBP binding sites datasets, and the experimental results show that deepRKE achieves better performance than five competitive methods.