High-throughput prediction of RNA, DNA and protein binding regions mediated by intrinsic disorder

By Zhenling Peng, Lukasz Kurgan. 2015

164 citations recorded by the source corpus.

Intrinsically disordered proteins and regions (IDPs and IDRs) lack stable 3D structure under physiological conditions in-vitro, are common in eukaryotes, and facilitate interactions with RNA, DNA and proteins. Current methods for prediction of IDPs and IDRs do not provide insights into their functions, except for a handful of methods that address predictions of protein-binding regions. We report first-of-its-kind computational method DisoRDPbind for high-throughput prediction of RNA, DNA and protein binding residues located in IDRs from protein sequences. DisoRDPbind is implemented using a runtime-efficient multi-layered design that utilizes information extracted from physiochemical properties of amino acids, sequence complexity, putative secondary structure and disorder and sequence alignment. Empirical tests demonstrate that it provides accurate predictions that are competitive with other predictors of disorder-mediated protein binding regions and complementary to the methods that predict RNA- and DNA-binding residues annotated based on crystal structures. Application in Homo sapiens, Mus musculus, Caenorhabditis elegans and Drosophila melanogaster proteomes reveals that RNA- and DNA-binding proteins predicted by DisoRDPbind complement and overlap with the corresponding known binding proteins collected from several sources. Also, the number of the putative protein-binding regions predicted with DisoRDPbind correlates with the promiscuity of proteins in the corresponding protein–protein interaction networks. Webserver: http://biomine.ece.ualberta.ca/DisoRDPbind/

Subfields: Molecular Biology

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Citations in the corpus

SPOT-Disorder2: Improved Protein Intrinsic Disorder Prediction by Ensembled Deep Learning (2019, methodology): “For comparison with other models, besides the web servers of MoRFpred, fMoRFpred, and Dis-oRDPbind [58,59] (http://biomine.cs.vcu.edu/#webservers), we also used ANCHOR2 [60] (https://iupred2a.elte.hu/), (https://gsponerlab.msl.ubc.ca/software/morf_chibi/), and the local version of MoRFPred-plus…”
Comprehensive review of methods for prediction of intrinsic disorder and its molecular functions (2017, methodology): “DisoRDPbind [121] was authored by Peng and Kurgan at the University of Alberta.”
DFLpred: High-throughput prediction of disordered flexible linker regions in protein sequences (2016, unknown): “Following (Disfani et al., 2012; Peng and Kurgan, 2015; Yan et al., 2015), we tested statistical significance of differences in predictive quality offered by DFLpred and other methods, and between the selected design of DFLpred and other considered designs.”
APOD: accurate sequence-based predictor of disordered flexible linkers (2020, methodology): “…including MoRFs (Disfani et al., 2012; Hanson et al., 2019; Katuwawala et al., 2019a,b,c; Malhis et al., 2016; Oldfield et al., 2019a,b; Peng and Kurgan, 2015; Sharma et al., 2019), IDRs that interact with nucleic acids (Peng and Kurgan, 2015), IDR that interact with proteins…”
MoRFchibi SYSTEM: software tools for the identification of MoRFs in protein sequences (2016, methodology): “Furthermore, the recently developed DisoRDPbind method has an extended target space that covers intrinsically disordered regions involved in interactions with any type of partner including protein, RNA or DNA (18).”