A Max-Margin Model for Predicting Residue—Base Contacts in Protein–RNA Interactions

Abstract

Protein–RNA interactions (PRIs) are essential for many biological processes, so understanding aspects of the sequences and structures involved in PRIs is important for unraveling such processes. Because of the expensive and time-consuming techniques 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 RNA-binding 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 the identification of 3D structures or homologous sequences, which are not available for all protein and RNA sequences. Here, we propose a prediction method for predicting residue–base contacts between proteins and RNAs using only sequence information and structural information predicted from sequences. The method can be applied to any protein–RNA pair, even when rich information such as its 3D structure, is not available. In this method, 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-mers of sequences and the predicted secondary structure. The scoring function is trained using 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.

Recent trends in RNA informatics: a review of machine learning and deep learning for RNA secondary structure prediction and RNA drug discovery

“…[ 107 ] proposed a BERT (Bidirectional Encoder Representations from Transformers)-based model for predicting RNA–protein interactions with biological implications, and Kashiwagi et al. [ 108 ] introduced a max-margin model for predicting residue-level contact in RNA–protein interactions. These studies on natural RBPs could potentially inform the design of artificial RNA aptamers that target proteins; for further details, see the reference [ 109 ].…”

Role of Optimization in RNA–Protein-Binding Prediction

“…Our focus in this study is the task of recognizing RNA sequences with RBP binding sites. However, it is worth mentioning other related tasks, such as identifying RNA–protein interactions that detect protein sites binding to RNA [ 31 , 32 , 33 , 34 , 35 ] and the prediction of residue–base contacts between proteins and RNAs [ 36 , 37 ]. The distinction between our task and the other two related tasks lies primarily in the directionality of the interaction being analyzed.…”