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Mechanisms of Macromolecular Interactions Mediated by Protein Intrinsic Disorder.

ABSTRACT: Intrinsically disordered proteins or regions (IDPs or IDRs) are widespread in the eukaryotic proteome. Although lacking stable three-dimensional structures in the free forms, IDRs perform critical functions in various cellular processes. Accordingly, mutations and altered expression of IDRs are associated with many pathological conditions. Hence, it is of great importance to understand at the molecular level how IDRs interact with their binding partners. In particular, discovering the unique interaction features of IDRs originating from their dynamic nature may reveal uncharted regulatory mechanisms of specific biological processes. Here we discuss the mechanisms of the macromolecular interactions mediated by IDRs and present the relevant cellular processes including transcription, cell cycle progression, signaling, and nucleocytoplasmic transport. Of special interest is the multivalent binding nature of IDRs driving assembly of multicomponent macromolecular complexes. Integrating the previous theoretical and experimental investigations, we suggest that such IDR-driven multiprotein complexes can function as versatile allosteric switches to process diverse cellular signals. Finally, we discuss the future challenges and potential medical applications of the IDR research.

SUBMITTER: Hong S

PROVIDER: S-EPMC7700844 | biostudies-literature | 2020 Nov

REPOSITORIES: biostudies-literature

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Mechanisms of Macromolecular Interactions Mediated by Protein Intrinsic Disorder.

Hong Sunghyun S Choi Sangmin S Kim Ryeonghyeon R Koh Junseock J

Molecules and cells 20201101 11

Intrinsically disordered proteins or regions (IDPs or IDRs) are widespread in the eukaryotic proteome. Although lacking stable three-dimensional structures in the free forms, IDRs perform critical functions in various cellular processes. Accordingly, mutations and altered expression of IDRs are associated with many pathological conditions. Hence, it is of great importance to understand at the molecular level how IDRs interact with their binding partners. In particular, discovering the unique int ...[more]

PMID: 33243935

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Project description:Macromolecular interactions are formed between proteins, DNA and RNA molecules. Being a principle building block in macromolecular assemblies and pathways, the interactions underlie most of cellular functions. Malfunctioning of macromolecular interactions is also linked to a number of diseases. Structural knowledge of the macromolecular interaction allows one to understand the interaction's mechanism, determine its functional implications and characterize the effects of genetic variations, such as single nucleotide polymorphisms, on the interaction. Unfortunately, until now the interactions mediated by different types of macromolecules, e.g. protein-protein interactions or protein-DNA interactions, are collected into individual and unrelated structural databases. This presents a significant obstacle in the analysis of macromolecular interactions. For instance, the homogeneous structural interaction databases prevent scientists from studying structural interactions of different types but occurring in the same macromolecular complex. Here, we introduce DOMMINO 2.0, a structural Database Of Macro-Molecular INteractiOns. Compared to DOMMINO 1.0, a comprehensive database on protein-protein interactions, DOMMINO 2.0 includes the interactions between all three basic types of macromolecules extracted from PDB files. DOMMINO 2.0 is automatically updated on a weekly basis. It currently includes ?1,040,000 interactions between two polypeptide subunits (e.g. domains, peptides, termini and interdomain linkers), ?43,000 RNA-mediated interactions, and ?12,000 DNA-mediated interactions. All protein structures in the database are annotated using SCOP and SUPERFAMILY family annotation. As a result, protein-mediated interactions involving protein domains, interdomain linkers, C- and N- termini, and peptides are identified. Our database provides an intuitive web interface, allowing one to investigate interactions at three different resolution levels: whole subunit network, binary interaction and interaction interface. Database URL: http://dommino.org.

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: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.

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Mechanisms of Macromolecular Interactions Mediated by Protein Intrinsic Disorder.

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Mechanisms of Macromolecular Interactions Mediated by Protein Intrinsic Disorder.

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