PNAB: Prediction of proteinnucleic acid binding affinity using heterogeneous ensemble models Proteinnucleic acid interactions play critical roles in many biological processes. Quantifying the binding affinity of proteinnucleic acid complexes is helpful to the understanding of proteinnucleic acid recognition mechanism and identification of reliable binding partners. In this paper, we propose a computational approach, PNAB, which can effectively predict proteinnucleic acid binding affinity using heterogeneous ensemble models based on sequence. We build a dataset of proteinnucleic acid binding affinity that includes 103 proteinRNA complex and 100 proteinDNA complexes manually collected from related literature. We find that the binding affinity mainly depends on the structure of nucleic acid molecules. According to the type of nucleic acid associated with proteins composed of the proteinnucleic acid complex, we classify the complexes divide all the complexes into 11 categories (six classes for proteinRNA complexes and five classes for proteinDNA complexes). Then, we extract sequence features from the proteinnucleic acid complexes and build a stacking heterogeneous ensemble model based on the generated features for each category. We perform a comprehensive evaluation for the proposed method on the binding affinity dataset using leaveoneout crossvalidation, and we show that PNAB achieves correlations ranging from 0.84 to 0.95 among all of the categories, which is significantly better than other typical regression methods and the pioneer proteinnucleic acid binding affinity predictor. Also, a userfriendly web server has been developed to predict the binding affinity of proteinRNA complexes. The PNAB web server is freely available at http:pnab.denglab.org.

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.