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

ABSTRACT: 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/.

SUBMITTER: Peng Z

PROVIDER: S-EPMC4605291 | biostudies-literature | 2015 Oct

REPOSITORIES: biostudies-literature

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

Peng Zhenling Z Kurgan Lukasz L

Nucleic acids research 20150624 18

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 pro ...[more]

PMID: 26109352

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Project description:RNA binding proteins (RBPs) are essential for critical biological processes such as translation regulation and mRNA processing, and misfunctions of these proteins are associated with diseases such as cancer and neurodegeneration. SERBP1 (SERPINE1 mRNA Binding Protein 1) is an RBP that comprises two RG/RGG repeat regions yet lacks other recognizable RNA-binding motifs. It is involved in mRNA maturation, and translational regulation. It was initially identified as a hyaluronic acid binding protein, but recent studies have identified central roles for SERBP1 in brain function and development, especially neurogenesis and synaptogenesis. SERBP1 regulates One-carbon metabolism and epigenetic modification of histones, and increased SERBP1 expression in cancers such as leukemia, ovarian, prostate, liver and glioblastoma is correlated with poor patient outcomes. Despite these important regulatory roles for SERBP1, little is known about its structural and dynamic properties, nor about the molecular mechanisms governing its interaction with mRNA. Here, we define SERBP1 as an intrinsically disordered protein, containing highly conserved elements that were shown to be functionally important. The RNA binding activity of SERBP1 was explored using solution NMR and other biophysical techniques. The outcome of these experiments revealed that SERBP1 preferentially samples compact conformations including a central, stable α-helix and show that SERBP1 recognizes G-rich RNA sequences at the C-terminus involving the RGG box and neighboring residues. Despite the role in RNA recognition, the RGG boxes do not seem to stabilize the central helix and the central helix does not participate in RNA binding. Further, SERBP1 undergoes liquid-liquid phase separation, mediated by salt and RNA, and both RGG boxes are necessary for the efficient formation of condensed phases. Together, these results provide a foundation for understanding the molecular mechanisms of SERBP1 functions in physiological and pathological processes.

Project description:Due to the functional importance of intrinsically disordered proteins or protein regions, prediction of intrinsic protein disorder from amino acid sequence has become an area of active research as witnessed in the 6th experiment on Critical Assessment of Techniques for Protein Structure Prediction (CASP6). Since the initial work by Romero et al. (Identifying disordered regions in proteins from amino acid sequences, IEEE Int. Conf. Neural Netw., 1997), our group has developed several predictors optimized for long disordered regions (>30 residues) with prediction accuracy exceeding 85%. However, these predictors are less successful on short disordered regions (< or =30 residues). A probable cause is a length-dependent amino acid compositions and sequence properties of disordered regions.We proposed two new predictor models, VSL2-M1 and VSL2-M2, to address this length-dependency problem in prediction of intrinsic protein disorder. These two predictors are similar to the original VSL1 predictor used in the CASP6 experiment. In both models, two specialized predictors were first built and optimized for short (< or = 30 residues) and long disordered regions (>30 residues), respectively. A meta predictor was then trained to integrate the specialized predictors into the final predictor model. As the 10-fold cross-validation results showed, the VSL2 predictors achieved well-balanced prediction accuracies of 81% on both short and long disordered regions. Comparisons over the VSL2 training dataset via 10-fold cross-validation and a blind-test set of unrelated recent PDB chains indicated that VSL2 predictors were significantly more accurate than several existing predictors of intrinsic protein disorder.The VSL2 predictors are applicable to disordered regions of any length and can accurately identify the short disordered regions that are often misclassified by our previous disorder predictors. The success of the VSL2 predictors further confirmed the previously observed differences in amino acid compositions and sequence properties between short and long disordered regions, and justified our approaches for modelling short and long disordered regions separately. The VSL2 predictors are freely accessible for non-commercial use at http://www.ist.temple.edu/disprot/predictorVSL2.php.

Dataset Information

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

Publications

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

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