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3696 KiB

Open AccessArticle

Compartmentalization and Functionality of Nuclear Disorder: Intrinsic Disorder and Protein-Protein Interactions in Intra-Nuclear Compartments

by Fanchi Meng, Insung Na, Lukasz Kurgan and Vladimir N. Uversky

Int. J. Mol. Sci. 2016, 17(1), 24; https://doi.org/10.3390/ijms17010024 - 25 Dec 2015

Cited by 90 | Viewed by 9494

Abstract

The cell nucleus contains a number of membrane-less organelles or intra-nuclear compartments. These compartments are dynamic structures representing liquid-droplet phases which are only slightly denser than the bulk intra-nuclear fluid. They possess different functions, have diverse morphologies, and are typically composed of RNA [...] Read more.

The cell nucleus contains a number of membrane-less organelles or intra-nuclear compartments. These compartments are dynamic structures representing liquid-droplet phases which are only slightly denser than the bulk intra-nuclear fluid. They possess different functions, have diverse morphologies, and are typically composed of RNA (or, in some cases, DNA) and proteins. We analyzed 3005 mouse proteins localized in specific intra-nuclear organelles, such as nucleolus, chromatin, Cajal bodies, nuclear speckles, promyelocytic leukemia (PML) nuclear bodies, nuclear lamina, nuclear pores, and perinuclear compartment and compared them with ~29,863 non-nuclear proteins from mouse proteome. Our analysis revealed that intrinsic disorder is enriched in the majority of intra-nuclear compartments, except for the nuclear pore and lamina. These compartments are depleted in proteins that lack disordered domains and enriched in proteins that have multiple disordered domains. Moonlighting proteins found in multiple intra-nuclear compartments are more likely to have multiple disordered domains. Protein-protein interaction networks in the intra-nuclear compartments are denser and include more hubs compared to the non-nuclear proteins. Hubs in the intra-nuclear compartments (except for the nuclear pore) are enriched in disorder compared with non-nuclear hubs and non-nuclear proteins. Therefore, our work provides support to the idea of the functional importance of intrinsic disorder in the cell nucleus and shows that many proteins associated with sub-nuclear organelles in nuclei of mouse cells are enriched in disorder. This high level of disorder in the mouse nuclear proteins defines their ability to serve as very promiscuous binders, possessing both large quantities of potential disorder-based interaction sites and the ability of a single such site to be involved in a large number of interactions. Full article

(This article belongs to the Special Issue In-Silico Prediction and Characterization of Intrinsic Disorder in Proteins)

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503 KiB

Open AccessArticle

Accurate Ab Initio and Template-Based Prediction of Short Intrinsically-Disordered Regions by Bidirectional Recurrent Neural Networks Trained on Large-Scale Datasets

by Viola Volpato, Badr Alshomrani and Gianluca Pollastri

Int. J. Mol. Sci. 2015, 16(8), 19868-19885; https://doi.org/10.3390/ijms160819868 - 21 Aug 2015

Cited by 1 | Viewed by 5791

Abstract

Intrinsically-disordered regions lack a well-defined 3D structure, but play key roles in determining the function of many proteins. Although predictors of disorder have been shown to achieve relatively high rates of correct classification of these segments, improvements over the the years have been [...] Read more.

Intrinsically-disordered regions lack a well-defined 3D structure, but play key roles in determining the function of many proteins. Although predictors of disorder have been shown to achieve relatively high rates of correct classification of these segments, improvements over the the years have been slow, and accurate methods are needed that are capable of accommodating the ever-increasing amount of structurally-determined protein sequences to try to boost predictive performances. In this paper, we propose a predictor for short disordered regions based on bidirectional recurrent neural networks and tested by rigorous five-fold cross-validation on a large, non-redundant dataset collected from MobiDB, a new comprehensive source of protein disorder annotations. The system exploits sequence and structural information in the forms of frequency profiles, predicted secondary structure and solvent accessibility and direct disorder annotations from homologous protein structures (templates) deposited in the Protein Data Bank. The contributions of sequence, structure and homology information result in large improvements in predictive accuracy. Additionally, the large scale of the training set leads to low false positive rates, making our systems a robust and efficient way to address high-throughput disorder prediction. Full article

(This article belongs to the Special Issue In-Silico Prediction and Characterization of Intrinsic Disorder in Proteins)

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2835 KiB

Open AccessArticle

In Silico Analysis of Correlations between Protein Disorder and Post-Translational Modifications in Algae

by Atsushi Kurotani and Tetsuya Sakurai

Int. J. Mol. Sci. 2015, 16(8), 19812-19835; https://doi.org/10.3390/ijms160819812 - 20 Aug 2015

Cited by 21 | Viewed by 6314

Abstract

Recent proteome analyses have reported that intrinsically disordered regions (IDRs) of proteins play important roles in biological processes. In higher plants whose genomes have been sequenced, the correlation between IDRs and post-translational modifications (PTMs) has been reported. The genomes of various eukaryotic algae [...] Read more.

Recent proteome analyses have reported that intrinsically disordered regions (IDRs) of proteins play important roles in biological processes. In higher plants whose genomes have been sequenced, the correlation between IDRs and post-translational modifications (PTMs) has been reported. The genomes of various eukaryotic algae as common ancestors of plants have also been sequenced. However, no analysis of the relationship to protein properties such as structure and PTMs in algae has been reported. Here, we describe correlations between IDR content and the number of PTM sites for phosphorylation, glycosylation, and ubiquitination, and between IDR content and regions rich in proline, glutamic acid, serine, and threonine (PEST) and transmembrane helices in the sequences of 20 algae proteomes. Phosphorylation, O-glycosylation, ubiquitination, and PEST preferentially occurred in disordered regions. In contrast, transmembrane helices were favored in ordered regions. N-glycosylation tended to occur in ordered regions in most of the studied algae; however, it correlated positively with disordered protein content in diatoms. Additionally, we observed that disordered protein content and the number of PTM sites were significantly increased in the species-specific protein clusters compared to common protein clusters among the algae. Moreover, there were specific relationships between IDRs and PTMs among the algae from different groups. Full article

(This article belongs to the Special Issue In-Silico Prediction and Characterization of Intrinsic Disorder in Proteins)

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1549 KiB

Open AccessArticle

How Common Is Disorder? Occurrence of Disordered Residues in Four Domains of Life

by Mikhail Yu. Lobanov and Oxana V. Galzitskaya

Int. J. Mol. Sci. 2015, 16(8), 19490-19507; https://doi.org/10.3390/ijms160819490 - 18 Aug 2015

Cited by 21 | Viewed by 5069

Abstract

Disordered regions play important roles in protein adaptation to challenging environmental conditions. Flexible and disordered residues have the highest propensities to alter the protein packing. Therefore, identification of disordered/flexible regions is important for structural and functional analysis of proteins. We used the IsUnstruct [...] Read more.

Disordered regions play important roles in protein adaptation to challenging environmental conditions. Flexible and disordered residues have the highest propensities to alter the protein packing. Therefore, identification of disordered/flexible regions is important for structural and functional analysis of proteins. We used the IsUnstruct program to predict the ordered or disordered status of residues in 122 proteomes, including 97 eukaryotic and 25 large bacterial proteomes larger than 2,500,000 residues. We found that bacterial and eukaryotic proteomes contain comparable fraction of disordered residues, which was 0.31 in the bacterial and 0.38 in the eukaryotic proteomes. Additional analysis of the total of 1540 bacterial proteomes of various sizes yielded a smaller fraction of disordered residues, which was only 0.26. Together, the results showed that the larger is the size of the proteome, the larger is the fraction of the disordered residues. A continuous dependence of the fraction of disordered residues on the size of the proteome is observed for four domains of life: Eukaryota, Bacteria, Archaea, and Viruses. Furthermore, our analysis of 122 proteomes showed that the fraction of disordered residues increased with increasing the length of homo-repeats for polar, charged, and small residues, and decreased for hydrophobic residues. The maximal fraction of disordered residues was obtained for proteins containing lysine and arginine homo-repeats. The minimal fraction was found in valine and leucine homo-repeats. For 15-residue long homo-repeats these values were 0.2 (for Val and Leu) and 0.7 (for Lys and Arg). Full article

(This article belongs to the Special Issue In-Silico Prediction and Characterization of Intrinsic Disorder in Proteins)

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997 KiB

Open AccessArticle

DeepCNF-D: Predicting Protein Order/Disorder Regions by Weighted Deep Convolutional Neural Fields

by Sheng Wang, Shunyan Weng, Jianzhu Ma and Qingming Tang

Int. J. Mol. Sci. 2015, 16(8), 17315-17330; https://doi.org/10.3390/ijms160817315 - 29 Jul 2015

Cited by 58 | Viewed by 7885

Abstract

Intrinsically disordered proteins or protein regions are involved in key biological processes including regulation of transcription, signal transduction, and alternative splicing. Accurately predicting order/disorder regions ab initio from the protein sequence is a prerequisite step for further analysis of functions and mechanisms for [...] Read more.

Intrinsically disordered proteins or protein regions are involved in key biological processes including regulation of transcription, signal transduction, and alternative splicing. Accurately predicting order/disorder regions ab initio from the protein sequence is a prerequisite step for further analysis of functions and mechanisms for these disordered regions. This work presents a learning method, weighted DeepCNF (Deep Convolutional Neural Fields), to improve the accuracy of order/disorder prediction by exploiting the long-range sequential information and the interdependency between adjacent order/disorder labels and by assigning different weights for each label during training and prediction to solve the label imbalance issue. Evaluated by the CASP9 and CASP10 targets, our method obtains 0.855 and 0.898 AUC values, which are higher than the state-of-the-art single ab initio predictors. Full article

(This article belongs to the Special Issue In-Silico Prediction and Characterization of Intrinsic Disorder in Proteins)

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1821 KiB

Open AccessArticle

A Method for Systematic Assessment of Intrinsically Disordered Protein Regions by NMR

by Natsuko Goda, Kana Shimizu, Yohta Kuwahara, Takeshi Tenno, Tamotsu Noguchi, Takahisa Ikegami, Motonori Ota and Hidekazu Hiroaki

Int. J. Mol. Sci. 2015, 16(7), 15743-15760; https://doi.org/10.3390/ijms160715743 - 10 Jul 2015

Cited by 7 | Viewed by 8405

Abstract

Intrinsically disordered proteins (IDPs) that lack stable conformations and are highly flexible have attracted the attention of biologists. Therefore, the development of a systematic method to identify polypeptide regions that are unstructured in solution is important. We have designed an “indirect/reflected” detection system [...] Read more.

Intrinsically disordered proteins (IDPs) that lack stable conformations and are highly flexible have attracted the attention of biologists. Therefore, the development of a systematic method to identify polypeptide regions that are unstructured in solution is important. We have designed an “indirect/reflected” detection system for evaluating the physicochemical properties of IDPs using nuclear magnetic resonance (NMR). This approach employs a “chimeric membrane protein”-based method using the thermostable membrane protein PH0471. This protein contains two domains, a transmembrane helical region and a C-terminal OB (oligonucleotide/oligosaccharide binding)-fold domain (named NfeDC domain), connected by a flexible linker. NMR signals of the OB-fold domain of detergent-solubilized PH0471 are observed because of the flexibility of the linker region. In this study, the linker region was substituted with target IDPs. Fifty-three candidates were selected using the prediction tool POODLE and 35 expression vectors were constructed. Subsequently, we obtained ¹⁵N-labeled chimeric PH0471 proteins with 25 IDPs as linkers. The NMR spectra allowed us to classify IDPs into three categories: flexible, moderately flexible, and inflexible. The inflexible IDPs contain membrane-associating or aggregation-prone sequences. This is the first attempt to use an indirect/reflected NMR method to evaluate IDPs and can verify the predictions derived from our computational tools. Full article

(This article belongs to the Special Issue In-Silico Prediction and Characterization of Intrinsic Disorder in Proteins)

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6445 KiB

Open AccessArticle

Structural Disorder within Paramyxoviral Nucleoproteins and Phosphoproteins in Their Free and Bound Forms: From Predictions to Experimental Assessment

by Johnny Habchi and Sonia Longhi

Int. J. Mol. Sci. 2015, 16(7), 15688-15726; https://doi.org/10.3390/ijms160715688 - 10 Jul 2015

Cited by 17 | Viewed by 6758

Abstract

We herein review available computational and experimental data pointing to the abundance of structural disorder within the nucleoprotein (N) and phosphoprotein (P) from three paramyxoviruses, namely the measles (MeV), Nipah (NiV) and Hendra (HeV) viruses. We provide a detailed molecular description of the [...] Read more.

We herein review available computational and experimental data pointing to the abundance of structural disorder within the nucleoprotein (N) and phosphoprotein (P) from three paramyxoviruses, namely the measles (MeV), Nipah (NiV) and Hendra (HeV) viruses. We provide a detailed molecular description of the mechanisms governing the disorder-to-order transition that the intrinsically disordered C-terminal domain (N_TAIL) of their N proteins undergoes upon binding to the C-terminal X domain (P_XD) of the homologous P proteins. We also show that N_TAIL–P_XD complexes are “fuzzy”, i.e., they possess a significant residual disorder, and discuss the possible functional significance of this fuzziness. Finally, we emphasize the relevance of N–P interactions involving intrinsically disordered proteins as promising targets for new antiviral approaches, and end up summarizing the general functional advantages of disorder for viruses. Full article

(This article belongs to the Special Issue In-Silico Prediction and Characterization of Intrinsic Disorder in Proteins)

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1708 KiB

Open AccessArticle

An Overview of Practical Applications of Protein Disorder Prediction and Drive for Faster, More Accurate Predictions

by Xin Deng, Jordan Gumm, Suman Karki, Jesse Eickholt and Jianlin Cheng

Int. J. Mol. Sci. 2015, 16(7), 15384-15404; https://doi.org/10.3390/ijms160715384 - 7 Jul 2015

Cited by 18 | Viewed by 6369

Abstract

Protein disordered regions are segments of a protein chain that do not adopt a stable structure. Thus far, a variety of protein disorder prediction methods have been developed and have been widely used, not only in traditional bioinformatics domains, including protein structure prediction, [...] Read more.

Protein disordered regions are segments of a protein chain that do not adopt a stable structure. Thus far, a variety of protein disorder prediction methods have been developed and have been widely used, not only in traditional bioinformatics domains, including protein structure prediction, protein structure determination and function annotation, but also in many other biomedical fields. The relationship between intrinsically-disordered proteins and some human diseases has played a significant role in disorder prediction in disease identification and epidemiological investigations. Disordered proteins can also serve as potential targets for drug discovery with an emphasis on the disordered-to-ordered transition in the disordered binding regions, and this has led to substantial research in drug discovery or design based on protein disordered region prediction. Furthermore, protein disorder prediction has also been applied to healthcare by predicting the disease risk of mutations in patients and studying the mechanistic basis of diseases. As the applications of disorder prediction increase, so too does the need to make quick and accurate predictions. To fill this need, we also present a new approach to predict protein residue disorder using wide sequence windows that is applicable on the genomic scale. Full article

(This article belongs to the Special Issue In-Silico Prediction and Characterization of Intrinsic Disorder in Proteins)

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4412 KiB

Open AccessArticle

Effect of pH on the Aggregation of α-syn12 Dimer in Explicit Water by Replica-Exchange Molecular Dynamics Simulation

by Zanxia Cao, Xiumei Zhang, Lei Liu, Liling Zhao, Haiyan Li and Jihua Wang

Int. J. Mol. Sci. 2015, 16(7), 14291-14304; https://doi.org/10.3390/ijms160714291 - 24 Jun 2015

Cited by 2 | Viewed by 6255

Abstract

The dimeric structure of the N-terminal 12 residues drives the interaction of α-synuclein protein with membranes. Moreover, experimental studies indicated that the aggregation of α-synuclein is faster at low pH than neutral pH. Nevertheless, the effects of different pH on the structural characteristics [...] Read more.

The dimeric structure of the N-terminal 12 residues drives the interaction of α-synuclein protein with membranes. Moreover, experimental studies indicated that the aggregation of α-synuclein is faster at low pH than neutral pH. Nevertheless, the effects of different pH on the structural characteristics of the α-syn12 dimer remain poorly understood. We performed 500 ns temperature replica exchange molecular dynamics (T-REMD) simulations of two α-syn12 peptides in explicit solvent. The free energy surfaces contain ten highly populated regions at physiological pH, while there are only three highly populated regions contained at acidic pH. The anti-parallel β-sheet conformations were found as the lowest free energy state. Additionally, these states are nearly flat with a very small barrier which indicates that these states can easily transit between themselves. The dimer undergoes a disorder to order transition from physiological pH to acidic pH and the α-syn12 dimer at acidic pH involves a faster dimerization process. Further, the Lys6–Asp2 contact may prevent the dimerization. Full article

(This article belongs to the Special Issue In-Silico Prediction and Characterization of Intrinsic Disorder in Proteins)

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3793 KiB

Open AccessArticle

by Aidan Petrovich, Adam Borne, Vladimir N. Uversky and Bin Xue

Int. J. Mol. Sci. 2015, 16(6), 13829-13849; https://doi.org/10.3390/ijms160613829 - 16 Jun 2015

Cited by 7 | Viewed by 6254

Abstract

Computational methods are prevailing in identifying protein intrinsic disorder. The results from predictors are often given as per-residue disorder scores. The scores describe the disorder propensity of amino acids of a protein and can be further represented as a disorder curve. Many proteins [...] Read more.

Computational methods are prevailing in identifying protein intrinsic disorder. The results from predictors are often given as per-residue disorder scores. The scores describe the disorder propensity of amino acids of a protein and can be further represented as a disorder curve. Many proteins share similar patterns in their disorder curves. The similar patterns are often associated with similar functions and evolutionary origins. Therefore, finding and characterizing specific patterns of disorder curves provides a unique and attractive perspective of studying the function of intrinsically disordered proteins. In this study, we developed a new computational tool named IDalign using dynamic programming. This tool is able to identify similar patterns among disorder curves, as well as to present the distribution of intrinsic disorder in query proteins. The disorder-based information generated by IDalign is significantly different from the information retrieved from classical sequence alignments. This tool can also be used to infer functions of disordered regions and disordered proteins. The web server of IDalign is available at (http://labs.cas.usf.edu/bioinfo/service.html). Full article

(This article belongs to the Special Issue In-Silico Prediction and Characterization of Intrinsic Disorder in Proteins)

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444 KiB

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Conformational Ensembles Explored Dynamically from Disordered Peptides Targeting Chemokine Receptor CXCR4

by Marian Vincenzi, Susan Costantini, Stefania Scala, Diego Tesauro, Antonella Accardo, Marilisa Leone, Giovanni Colonna, Jean Guillon, Luigi Portella, Anna Maria Trotta, Luisa Ronga and Filomena Rossi

Int. J. Mol. Sci. 2015, 16(6), 12159-12173; https://doi.org/10.3390/ijms160612159 - 28 May 2015

Cited by 6 | Viewed by 5768

Abstract

This work reports on the design and the synthesis of two short linear peptides both containing a few amino acids with disorder propensity and an allylic ester group at the C-terminal end. Their structural properties were firstly analyzed by means of experimental [...] Read more.

This work reports on the design and the synthesis of two short linear peptides both containing a few amino acids with disorder propensity and an allylic ester group at the C-terminal end. Their structural properties were firstly analyzed by means of experimental techniques in solution such as CD and NMR methods that highlighted peptide flexibility. These results were further confirmed by MD simulations that demonstrated the ability of the peptides to assume conformational ensembles. They revealed a network of transient and dynamic H-bonds and interactions with water molecules. Binding assays with a well-known drug-target, i.e., the CXCR4 receptor, were also carried out in an attempt to verify their biological function and the possibility to use the assays to develop new specific targets for CXCR4. Moreover, our data indicate that these peptides represent useful tools for molecular recognition processes in which a flexible conformation is required in order to obtain an interaction with a specific target. Full article

(This article belongs to the Special Issue In-Silico Prediction and Characterization of Intrinsic Disorder in Proteins)

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Review

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An Overview of Predictors for Intrinsically Disordered Proteins over 2010–2014

by Jianzong Li, Yu Feng, Xiaoyun Wang, Jing Li, Wen Liu, Li Rong and Jinku Bao

Int. J. Mol. Sci. 2015, 16(10), 23446-23462; https://doi.org/10.3390/ijms161023446 - 29 Sep 2015

Cited by 40 | Viewed by 6363

Abstract

The sequence-structure-function paradigm of proteins has been changed by the occurrence of intrinsically disordered proteins (IDPs). Benefiting from the structural disorder, IDPs are of particular importance in biological processes like regulation and signaling. IDPs are associated with human diseases, including cancer, cardiovascular disease, [...] Read more.

The sequence-structure-function paradigm of proteins has been changed by the occurrence of intrinsically disordered proteins (IDPs). Benefiting from the structural disorder, IDPs are of particular importance in biological processes like regulation and signaling. IDPs are associated with human diseases, including cancer, cardiovascular disease, neurodegenerative diseases, amyloidoses, and several other maladies. IDPs attract a high level of interest and a substantial effort has been made to develop experimental and computational methods. So far, more than 70 prediction tools have been developed since 1997, within which 17 predictors were created in the last five years. Here, we presented an overview of IDPs predictors developed during 2010–2014. We analyzed the algorithms used for IDPs prediction by these tools and we also discussed the basic concept of various prediction methods for IDPs. The comparison of prediction performance among these tools is discussed as well. Full article

(This article belongs to the Special Issue In-Silico Prediction and Characterization of Intrinsic Disorder in Proteins)

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760 KiB

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Disorder Prediction Methods, Their Applicability to Different Protein Targets and Their Usefulness for Guiding Experimental Studies

by Jennifer D. Atkins, Samuel Y. Boateng, Thomas Sorensen and Liam J. McGuffin

Int. J. Mol. Sci. 2015, 16(8), 19040-19054; https://doi.org/10.3390/ijms160819040 - 13 Aug 2015

Cited by 55 | Viewed by 6998

Abstract

The role and function of a given protein is dependent on its structure. In recent years, however, numerous studies have highlighted the importance of unstructured, or disordered regions in governing a protein’s function. Disordered proteins have been found to play important roles in [...] Read more.

The role and function of a given protein is dependent on its structure. In recent years, however, numerous studies have highlighted the importance of unstructured, or disordered regions in governing a protein’s function. Disordered proteins have been found to play important roles in pivotal cellular functions, such as DNA binding and signalling cascades. Studying proteins with extended disordered regions is often problematic as they can be challenging to express, purify and crystallise. This means that interpretable experimental data on protein disorder is hard to generate. As a result, predictive computational tools have been developed with the aim of predicting the level and location of disorder within a protein. Currently, over 60 prediction servers exist, utilizing different methods for classifying disorder and different training sets. Here we review several good performing, publicly available prediction methods, comparing their application and discussing how disorder prediction servers can be used to aid the experimental solution of protein structure. The use of disorder prediction methods allows us to adopt a more targeted approach to experimental studies by accurately identifying the boundaries of ordered protein domains so that they may be investigated separately, thereby increasing the likelihood of their successful experimental solution. Full article

(This article belongs to the Special Issue In-Silico Prediction and Characterization of Intrinsic Disorder in Proteins)

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1256 KiB

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Identification of Inhibitors of Biological Interactions Involving Intrinsically Disordered Proteins

by Daniela Marasco and Pasqualina Liana Scognamiglio

Int. J. Mol. Sci. 2015, 16(4), 7394-7412; https://doi.org/10.3390/ijms16047394 - 2 Apr 2015

Cited by 42 | Viewed by 6560

Abstract

Protein–protein interactions involving disordered partners have unique features and represent prominent targets in drug discovery processes. Intrinsically Disordered Proteins (IDPs) are involved in cellular regulation, signaling and control: they bind to multiple partners and these high-specificity/low-affinity interactions play crucial roles in many human [...] Read more.

Protein–protein interactions involving disordered partners have unique features and represent prominent targets in drug discovery processes. Intrinsically Disordered Proteins (IDPs) are involved in cellular regulation, signaling and control: they bind to multiple partners and these high-specificity/low-affinity interactions play crucial roles in many human diseases. Disordered regions, terminal tails and flexible linkers are particularly abundant in DNA-binding proteins and play crucial roles in the affinity and specificity of DNA recognizing processes. Protein complexes involving IDPs are short-lived and typically involve short amino acid stretches bearing few “hot spots”, thus the identification of molecules able to modulate them can produce important lead compounds: in this scenario peptides and/or peptidomimetics, deriving from structure-based, combinatorial or protein dissection approaches, can play a key role as hit compounds. Here, we propose a panoramic review of the structural features of IDPs and how they regulate molecular recognition mechanisms focusing attention on recently reported drug-design strategies in the field of IDPs. Full article

(This article belongs to the Special Issue In-Silico Prediction and Characterization of Intrinsic Disorder in Proteins)

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