RNA is the key player in many cellular processes such as signal transduction, replication, transport, cell division, transcription, and translation. These diverse functions are accomplished through interactions of RNA with proteins. However, protein-RNA interactions are still poorly derstood in contrast to protein-protein and protein-DNA interactions. This knowledge gap can be attributed to the limited availability of protein-RNA structures along with the experimental difficulties in studying these complexes. Recent progress in computational resources has expanded the number of tools available for studying protein-RNA interactions at various molecular levels. These include tools for predicting interacting residues from primary sequences, modelling of protein-RNA complexes, predicting hotspots in these complexes and insights into derstanding in the dynamics of their interactions. Each of these tools has its strengths and limitations, which makes it significant to select an optimal approach for the question of interest. Here we present a mini review of computational tools to study different aspects of protein-RNA interactions, with focus on overall application, development of the field and the future perspectives.

Biological networks are characterized by diverse interactions and dynamics in time and space. Many regulatory modules operate in parallel and are interconnected with each other. Some pathways are functionally known and annotated accordingly, e.g., endocytosis, migration, or cytoskeletal rearrangement. However, many interactions are not so well characterized. For reconstructing the biological complexity in cellular networks, we combine here existing experimentally confirmed and analyzed interactions with a protein-interaction inference framework using as basis experimentally confirmed interactions from other organisms. Prediction scoring includes sequence similarity, evolutionary conservation of interactions, the coexistence of interactions in the same pathway, orthology as well as structure similarity to rank and compare inferred interactions. We exemplify our inference method by studying host-pathogen interactions during infection of Mus musculus (phagolysosomes in alveolar macrophages) with Aspergillus fumigatus (conidia, airborne, asexual spores). Three of nine predicted critical host-pathogen interactions could even be confirmed by direct experiments. Moreover, we suggest drugs that manipulate the host-pathogen interaction.

Given their central role in translation, splicing, localization and stability of transcripts, RNA binding proteins (RBPs) are key regulators of several cellular processes. While experimental efforts have been put to study how RBPs bind to transcripts, very little is known about the RNA contributions to the interaction. Here, we review the most common RNA-centric methods to reveal interactions with RBPs: both in vitro (SELEX, SEQR, RNA-compete and RBNS) and in silico (MEME, SeAMotE, GLAM2, iDeep, MEMERIS, RNA context, RCK, RNApromo and GraphProt). We emphasize the main advantages and disadvantages of each technique and highlight the key physico-chemical features contributing to the identification of RNA motifs involved in RBP recognition. We discuss extrinsic determinants influencing protein-RNA binding, such as post-transcriptional and post-translational modifications as well as expression and location of transcripts.