In Silico Prediction and Validation of Novel RNA Binding Proteins and Residues in the Human Proteome

Abstract

Deciphering a complete landscape of protein-RNA interactions in the human proteome remains an elusive challenge. We computationally elucidate RNA binding proteins (RBPs) using an approach that complements previous efforts. We employ two modern complementary sequence-based methods that provide accurate predictions from the structured and the intrinsically disordered sequences, even in the absence of sequence similarity to the known RBPs. We generate and analyze putative RNA binding residues on the whole proteome scale. Using a conservative setting that ensures low, 5% false positive rate, we identify 1511 putative RBPs that include 281 known RBPs and 166 RBPs that were previously predicted. We empirically demonstrate that these overlaps are statistically significant. We also validate the putative RBPs based on two major hallmarks of their RNA binding residues: high levels of evolutionary conservation and enrichment in charged amino acids. Moreover, we show that the novel RBPs are significantly under-annotated functionally which coincides with the fact that they were not yet found to interact with RNAs. We provide two examples of our novel putative RBPs for which there is recent evidence of their interactions with RNAs. The dataset of novel putative RBPs and RNA binding residues for the future hypothesis generation is provided in the Supporting Information.

Computational Prediction of MoRFs, Short Disorder-to-order Transitioning Protein Binding Regions

“…Besides predicting DPBRs, this is the first method that provides predictions of disordered RNA-binding and disordered DNA-binding regions. The RNA-binding regions generated with DisoRDPbind were recently used to derive the arguably most complete to date collection of putative RNA-binding proteins in the human proteome [ 110 ].…”

A Comprehensive Review of Computation‐Based Metal‐Binding Prediction Approaches at the Residue Level

“…These multiple sequence alignments, also named conservation profiles, include aligning families of homologous sequences and having knowledge of their evolutionary relationships [ 84 ]. For an unknown protein, although its accurate function is not available, it is expected that we can use its homologous proteins to speculate the function since they share the similar evolutionary profile [ 85 ]. Many studies use position-specific scoring matrix (PSSM), which is computed from PSI-BLAST [ 62 ], to quantify the evolutionary conservation.…”

Computational prediction of disordered binding regions

“…Being involved in key regulatory pathways, mis-regulation of IDPs and IDRs was shown to be associated with several human diseases 23 , 24 , 25 , 26 . Many of functions of IDPs involve interactions with a broad spectrum of partner molecules, including proteins, nucleic acids, lipids, metals, ions, carbohydrates and small-molecules 19 , 21 , 27 , 28 , 29 , 30 , 31 . In that context, conformational plasticity of IDRs provides them with certain advantages compared to structured regions, such as ability of a single IDR to interact with multiple different partners, leading to an enrichment of IDPs among the hub proteins in the protein interaction networks 32 , 33 , 34 , 35 .…”