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Utilization of protein intrinsic disorder knowledge in structural proteomics.

ABSTRACT: Intrinsically disordered proteins (IDPs) and proteins with long disordered regions are highly abundant in various proteomes. Despite their lack of well-defined ordered structure, these proteins and regions are frequently involved in crucial biological processes. Although in recent years these proteins have attracted the attention of many researchers, IDPs represent a significant challenge for structural characterization since these proteins can impact many of the processes in the structure determination pipeline. Here we investigate the effects of IDPs on the structure determination process and the utility of disorder prediction in selecting and improving proteins for structural characterization. Examination of the extent of intrinsic disorder in existing crystal structures found that relatively few protein crystal structures contain extensive regions of intrinsic disorder. Although intrinsic disorder is not the only cause of crystallization failures and many structured proteins cannot be crystallized, filtering out highly disordered proteins from structure-determination target lists is still likely to be cost effective. Therefore it is desirable to avoid highly disordered proteins from structure-determination target lists and we show that disorder prediction can be applied effectively to enrich structure determination pipelines with proteins more likely to yield crystal structures. For structural investigation of specific proteins, disorder prediction can be used to improve targets for structure determination. Finally, a framework for considering intrinsic disorder in the structure determination pipeline is proposed.

SUBMITTER: Oldfield CJ

PROVIDER: S-EPMC3951342 | biostudies-literature | 2013 Feb

REPOSITORIES: biostudies-literature

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Utilization of protein intrinsic disorder knowledge in structural proteomics.

Oldfield Christopher J CJ Xue Bin B Van Ya-Yue YY Ulrich Eldon L EL Markley John L JL Dunker A Keith AK Uversky Vladimir N VN

Biochimica et biophysica acta 20121208 2

Intrinsically disordered proteins (IDPs) and proteins with long disordered regions are highly abundant in various proteomes. Despite their lack of well-defined ordered structure, these proteins and regions are frequently involved in crucial biological processes. Although in recent years these proteins have attracted the attention of many researchers, IDPs represent a significant challenge for structural characterization since these proteins can impact many of the processes in the structure deter ...[more]

PMID: 23232152

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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:BackgroundDue 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.ResultsWe 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.ConclusionThe 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.

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Utilization of protein intrinsic disorder knowledge in structural proteomics.

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Utilization of protein intrinsic disorder knowledge in structural proteomics.

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