A Max-margin Model for Predicting Residue–base Contacts in Protein–RNA Interactions

Abstract

Motivation: Protein-RNA interactions (PRIs) are essential for many biological processes, so understanding aspects of the sequence and structure in PRIs is important for understanding those processes. Due to the expensive and time-consuming processes required for experimental determination of complex protein-RNA structures, various computational methods have been developed to predict PRIs. However, most of these methods focus on predicting only RNAbinding regions in proteins or only protein-binding motifs in RNA. Methods for predicting entire residue-base contacts in PRIs have not yet achieved sufficient accuracy. Furthermore, some of these methods require 3D structures or homologous sequences, which are not available for all protein and RNA sequences. Results: We propose a prediction method for residue-base contacts between proteins and RNAs using only sequence information and structural information predicted from only sequences. The method can be applied to any protein-RNA pair, even when rich information such as 3D structure is not available. Residue-base contact prediction is formalized as an integer programming problem. We predict a residue-base contact map that maximizes a scoring function based on sequence-based features such as k-mer of sequences and predicted secondary structure. The scoring function is trained by a max-margin framework from known PRIs with 3D structures. To verify our method, we conducted several computational experiments. The results suggest that our method, which is based on only sequence information, is comparable with RNA-binding residue prediction methods based on known binding data. Availability: The source code of our algorithm is available at https: //github.com/satoken/practip.