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Unfoldomics of human diseases: linking protein intrinsic disorder with diseases.

ABSTRACT: Intrinsically disordered proteins (IDPs) and intrinsically disordered regions (IDRs) lack stable tertiary and/or secondary structure yet fulfills key biological functions. The recent recognition of IDPs and IDRs is leading to an entire field aimed at their systematic structural characterization and at determination of their mechanisms of action. Bioinformatics studies showed that IDPs and IDRs are highly abundant in different proteomes and carry out mostly regulatory functions related to molecular recognition and signal transduction. These activities complement the functions of structured proteins. IDPs and IDRs were shown to participate in both one-to-many and many-to-one signaling. Alternative splicing and posttranslational modifications are frequently used to tune the IDP functionality. Several individual IDPs were shown to be associated with human diseases, such as cancer, cardiovascular disease, amyloidoses, diabetes, neurodegenerative diseases, and others. This raises questions regarding the involvement of IDPs and IDRs in various diseases.IDPs and IDRs were shown to be highly abundant in proteins associated with various human maladies. As the number of IDPs related to various diseases was found to be very large, the concepts of the disease-related unfoldome and unfoldomics were introduced. Novel bioinformatics tools were proposed to populate and characterize the disease-associated unfoldome. Structural characterization of the members of the disease-related unfoldome requires specialized experimental approaches. IDPs possess a number of unique structural and functional features that determine their broad involvement into the pathogenesis of various diseases.Proteins associated with various human diseases are enriched in intrinsic disorder. These disease-associated IDPs and IDRs are real, abundant, diversified, vital, and dynamic. These proteins and regions comprise the disease-related unfoldome, which covers a significant part of the human proteome. Profound association between intrinsic disorder and various human diseases is determined by a set of unique structural and functional characteristics of IDPs and IDRs. Unfoldomics of human diseases utilizes unrivaled bioinformatics and experimental techniques, paves the road for better understanding of human diseases, their pathogenesis and molecular mechanisms, and helps develop new strategies for the analysis of disease-related proteins.

SUBMITTER: Uversky VN

PROVIDER: S-EPMC2709268 | biostudies-literature | 2009 Jul

REPOSITORIES: biostudies-literature

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Unfoldomics of human diseases: linking protein intrinsic disorder with diseases.

Uversky Vladimir N VN Oldfield Christopher J CJ Midic Uros U Xie Hongbo H Xue Bin B Vucetic Slobodan S Iakoucheva Lilia M LM Obradovic Zoran Z Dunker A Keith AK

BMC genomics 20090707

<h4>Background</h4>Intrinsically disordered proteins (IDPs) and intrinsically disordered regions (IDRs) lack stable tertiary and/or secondary structure yet fulfills key biological functions. The recent recognition of IDPs and IDRs is leading to an entire field aimed at their systematic structural characterization and at determination of their mechanisms of action. Bioinformatics studies showed that IDPs and IDRs are highly abundant in different proteomes and carry out mostly regulatory functions ...[more]

PMID: 19594884

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Project description:Allostery is an intrinsic property of many globular proteins and enzymes that is indispensable for cellular regulatory and feedback mechanisms. Recent theoretical and empirical observations indicate that allostery is also manifest in intrinsically disordered proteins, which account for a substantial proportion of the proteome. Many intrinsically disordered proteins are promiscuous binders that interact with multiple partners and frequently function as molecular hubs in protein interaction networks. The adenovirus early region 1A (E1A) oncoprotein is a prime example of a molecular hub intrinsically disordered protein. E1A can induce marked epigenetic reprogramming of the cell within hours after infection, through interactions with a diverse set of partners that include key host regulators such as the general transcriptional coactivator CREB binding protein (CBP), its paralogue p300, and the retinoblastoma protein (pRb; also called RB1). Little is known about the allosteric effects at play in E1A-CBP-pRb interactions, or more generally in hub intrinsically disordered protein interaction networks. Here we used single-molecule fluorescence resonance energy transfer (smFRET) to study coupled binding and folding processes in the ternary E1A system. The low concentrations used in these high-sensitivity experiments proved to be essential for these studies, which are challenging owing to a combination of E1A aggregation propensity and high-affinity binding interactions. Our data revealed that E1A-CBP-pRb interactions have either positive or negative cooperativity, depending on the available E1A interaction sites. This striking cooperativity switch enables fine-tuning of the thermodynamic accessibility of the ternary versus binary E1A complexes, and may permit a context-specific tuning of associated downstream signalling outputs. Such a modulation of allosteric interactions is probably a common mechanism in molecular hub intrinsically disordered protein function.

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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Unfoldomics of human diseases: linking protein intrinsic disorder with diseases.

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Unfoldomics of human diseases: linking protein intrinsic disorder with diseases.

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