A Survey of Current Resources to Study lncRNA-Protein Interactions

Abstract

Phenotypes are driven by regulated gene expression, which in turn are mediated by complex interactions between diverse biological molecules. Protein–DNA interactions such as histone and transcription factor binding are well studied, along with RNA–RNA interactions in short RNA silencing of genes. In contrast, lncRNA-protein interaction (LPI) mechanisms are comparatively unknown, likely directed by the difficulties in studying LPI. However, LPI are emerging as key interactions in epigenetic mechanisms, playing a role in development and disease. Their importance is further highlighted by their conservation across kingdoms. Hence, interest in LPI research is increasing. We therefore review the current state of the art in lncRNA-protein interactions. We specifically surveyed recent computational methods and databases which researchers can exploit for LPI investigation. We discovered that algorithm development is heavily reliant on a few generic databases containing curated LPI information. Additionally, these databases house information at gene-level as opposed to transcript-level annotations. We show that early methods predict LPI using molecular docking, have limited scope and are slow, creating a data processing bottleneck. Recently, machine learning has become the strategy of choice in LPI prediction, likely due to the rapid growth in machine learning infrastructure and expertise. While many of these methods have notable limitations, machine learning is expected to be the basis of modern LPI prediction algorithms.

HLA complex P5 upregulation is correlated with poor prognosis and tumor progression in esophageal squamous cell carcinoma

“…The role of lncRNA depends on its subcellular localization, in which cytoplasmic lncRNA plays an important role in a variety of molecular mechanisms, including mRNA stability and translation regulation, protein modification, as a precursor of miRNA or as a competitive endogenous RNA sponging miRNA [ 20 ]. The interaction mode of lncRNA and protein is critical for cancer initiation, development and progression [ 21 ]. For example, AC073352.1 was reported as a binding partner of YBX1, and increased YBX1 protein stability, promoting breast cancer metastasis and angiogenesis [ 22 ].…”

Biophysical characterisation of human LincRNA-p21 sense and antisense Alu inverted repeats

“…This was statistically significant, although hnRNP-K localisation in the nucleus still occurred to a lesser extent which is hypothesised to be the result of the LincRNA-p21’s 5′-end interacting with hnRNP-K ( 15 ). Employing our three-dimensional structures as a starting framework, future studies can investigate the effects of deleting the arms or introducing point mutations which can also include additional computational techniques such as molecular docking software to visualise the LincRNA-p21-hnRNP-K interaction ( 102 ). While these mutations and deletions may not change the structure dramatically, our structure clearly demonstrates the presence of RCCTCC consensus sequences that provide easy access to hnRNP-K interaction.…”

Functional Relationships between Long Non-Coding RNAs and Estrogen Receptor Alpha: A New Frontier in Hormone-Responsive Breast Cancer Management

“…The data obtained downstream of the applications of LPI determination methods are collected in databases of lncRNA-protein interactions (Starbase [ 65 ], RNAInter [ 66 ], POSTAR [ 67 ], NPInter [ 68 ], and RAIN [ 69 ]) or RNA-binding motifs (ATtRACT [ 70 ] and oRNAment [ 71 ]). These curated databases are further exploited by LPI prediction algorithms that use molecular docking or machine learning approaches that were extensively described by Philip et al [ 72 ].…”

“…Cross-linking immunoprecipitation (CLIP) [49,50] Targets of RNA-binding proteins identified by editing (TRIBE) [51] Digestion-optimized RNA immunoprecipitation cDNA library sequencing (DO-RIP-seq) [52] RNA-affinity purification followed by mass spectrometry [53][54][55][56] RNA-affinity purification followed by protein microarrays [57] The isolation of target RNA molecules by biotinylated antisense probes [54,58] The isolation of target RNA molecules by peptide nucleic acid oligomers [55] HB-tag-based affinity RNA purification [56] Chromatin isolation with RNA purification (ChIRP) [59,60] RNA chromosome conformation capture (R3C) [61] LPI computational prediction [72] The biophysical characterization of quantitative and qualitative LPIs Electrophoretic mobility shift assay (EMSA) [62] Filter-binding assays [82] Surface plasmon resonance [63,64] The evaluation of the coding capacity of lncRNAs…”