"Order from disordered": Potential role of intrinsically disordered regions in phytopathogenic oomycete intracellular effector proteins

There is a continuous arms race between pathogens and their host plants. However, successful pathogens, such as phytopathogenic oomycetes, secrete effector proteins to manipulate host defense responses for disease development. Structural analyses of these effector proteins reveal the existence of regions that fail to fold into three-dimensional structures, intrinsically disordered regions (IDRs). Because of their flexibility, these regions are involved in important biological functions of effector proteins, such as effector-host protein interactions that perturb host immune responses. Despite their significance, the role of IDRs in phytopathogenic oomycete effector-host protein interactions is not clear. This review, therefore, searched the literature for functionally characterized oomycete intracellular effectors with known host interactors. We further classify regions that mediate effector-host protein interactions into globular or disordered binding sites in these proteins. To fully appreciate the potential role of IDRs, five effector proteins encoding potential disordered binding sites were used as case studies. We also propose a pipeline that can be used to identify, classify as well as characterize potential binding regions in effector proteins. Understanding the role of IDRs in these effector proteins can aid in the development of new disease-control strategies.

Computational prediction of disordered binding regions

One of the key features of intrinsically disordered regions (IDRs) is their ability to interact with a broad range of partner molecules. Multiple types of interacting IDRs were identified including molecular recognition fragments (MoRFs), short linear sequence motifs (SLiMs), and protein-, nucleic acids- and lipid-binding regions. Prediction of binding IDRs in protein sequences is gaining momentum in recent years. We survey 38 predictors of binding IDRs that target interactions with a diverse set of partners, such as peptides, proteins, RNA, DNA and lipids. We offer a historical perspective and highlight key events that fueled efforts to develop these methods. These tools rely on a diverse range of predictive architectures that include scoring functions, regular expressions, traditional and deep machine learning and meta-models. Recent efforts focus on the development of deep neural network-based architectures and extending coverage to RNA, DNA and lipid-binding IDRs. We analyze availability of these methods and show that providing implementations and webservers results in much higher rates of citations/use. We also make several recommendations to take advantage of modern deep network architectures, develop tools that bundle predictions of multiple and different types of binding IDRs, and work on algorithms that model structures of the resulting complexes.

Computational Methods to Predict Intrinsically Disordered Regions and Functional Regions in Them

Intrinsically disordered regions (IDRs) are protein regions that do not adopt fixed tertiary structures. Since these regions lack ordered three-dimensional structures, they should be excluded from the target portions of homology modeling. IDRs can be predicted from the amino acid sequences, because their amino acid compositions are different from that of the structured domains. This chapter provides a review of the prediction methods of IDRs and a case study of IDR prediction.

Intrinsic disorder in proteins associated with oxidative stress-induced JNK signaling

The c-Jun N-terminal kinase (JNK) signaling cascade is a mitogen-activated protein kinase (MAPK) signaling pathway that can be activated in response to a wide range of environmental stimuli. Based on the type, degree, and duration of the stimulus, the JNK signaling cascade dictates the fate of the cell by influencing gene expression through its substrate transcription factors. Oxidative stress is a result of a disturbance in the pro-oxidant/antioxidant homeostasis of the cell and is associated with a large number of diseases, such as neurodegenerative disorders, cancer, diabetes, cardiovascular diseases, and disorders of the immune system, where it activates the JNK signaling pathway. Among different biological roles ascribed to the intrinsically disordered proteins (IDPs) and hybrid proteins containing ordered domains and intrinsically disordered protein regions (IDPRs) are signaling hub functions, as intrinsic disorder allows proteins to undertake multiple interactions, each with a different consequence. In order to ensure precise signaling, the cellular abundance of IDPs is highly regulated, and mutations or changes in abundance of IDPs/IDPRs are often associated with disease. In this study, we have used a combination of six disorder predictors to evaluate the presence of intrinsic disorder in proteins of the oxidative stress-induced JNK signaling cascade, and as per our findings, none of the 18 proteins involved in this pathway are ordered. The highest level of intrinsic disorder was observed in the scaffold proteins, JIP1, JIP2, JIP3; dual specificity phosphatases, MKP5, MKP7; 14-3-3ζ and transcription factor c-Jun. The MAP3Ks, MAP2Ks, MAPKs, TRAFs, and thioredoxin were the proteins that were predicted to be moderately disordered. Furthermore, to characterize the predicted IDPs/IDPRs in the proteins of the JNK signaling cascade, we identified the molecular recognition features (MoRFs), posttranslational modification (PTM) sites, and short linear motifs (SLiMs) associated with the disordered regions. These findings will serve as a foundation for experimental characterization of disordered regions in these proteins, which represents a crucial step for a better understanding of the roles of IDPRs in diseases associated with this important pathway.

MedProDB: A database of Mediator proteins

•

Mediator complex is a key component of transcriptional regulation in eukaryotes.

•

Identification of Mediator subunits was done by using computational approaches.

•

Different physicochemical properties, and functions of Mediators were discussed.

•

We have developed first database of Mediator proteins e.g. MedProDB.

•

MedProDB contains different types of search and browse options, and various tools.

In the last three decades, the multi-subunit Mediator complex has emerged as the key component of transcriptional regulation of eukaryotic gene expression. Although there were initial hiccups, recent advancements in bioinformatics tools contributed significantly to in-silico prediction and characterization of Mediator subunits from several organisms belonging to different eukaryotic kingdoms. In this study, we have developed the first database of Mediator proteins named MedProDB with 33,971 Mediator protein entries. Out of those, 12531, 11545, and 9895 sequences belong to metazoans, plants, and fungi, respectively. Apart from the core information consisting of sequence, length, position, organism, molecular weight, and taxonomic lineage, additional information of each Mediator sequence like aromaticity, hydropathy, instability index, isoelectric point, functions, interactions, repeat regions, diseases, sequence alignment to Mediator subunit family, Intrinsically Disordered Regions (IDRs), Post-translation modifications (PTMs), and Molecular Recognition Features (MoRFs) may be of high utility to the users. Furthermore, different types of search and browse options with four different tools namely BLAST, Smith-Waterman Align, IUPred, and MoRF-Chibi_Light are provided at MedProDB to perform different types of analysis. Being a critical component of the transcriptional machinery and regulating almost all the aspects of transcription, it generated lots of interest in structural and functional studies of Mediator functioning. So, we think that the MedProDB database will be very useful for researchers studying the process of transcription. This database is freely available at www.nipgr.ac.in/MedProDB.

Prediction of MoRFs based on sequence properties and convolutional neural networks

Background

Intrinsically disordered proteins possess flexible 3-D structures, which makes them play an important role in a variety of biological functions. Molecular recognition features (MoRFs) act as an important type of functional regions, which are located within longer intrinsically disordered regions and undergo disorder-to-order transitions upon binding their interaction partners.

Results

We develop a method, MoRF_CNN, to predict MoRFs based on sequence properties and convolutional neural networks (CNNs). The sequence properties contain structural and physicochemical properties which are used to describe the differences between MoRFs and non-MoRFs. Especially, to highlight the correlation between the target residue and adjacent residues, three windows are selected to preprocess the selected properties. After that, these calculated properties are combined into the feature matrix to predict MoRFs through the constructed CNN. Comparing with other existing methods, MoRF_CNN obtains better performance.

Conclusions

MoRF_CNN is a new individual MoRFs prediction method which just uses protein sequence properties without evolutionary information. The simulation results show that MoRF_CNN is effective and competitive.

Prevalence and functionality of intrinsic disorder in human FG-nucleoporins

The nuclear-cytoplasmic transport of biomolecules is assisted by the nuclear pores composed of evolutionarily conserved proteins termed nucleoporins (Nups). The central Nups, characterized by multiple FG-repeats, are highly dynamic and contain a high level of intrinsically disordered regions (IDPRs). FG-Nups bind several protein partners and play critical roles in molecular interactions and the regulation of cellular functions through their IDPRs. In the present study, we performed a multiparametric bioinformatics analysis to characterize the prevalence and functionality of IDPRs in human FG-Nups. These analyses revealed that the sequence of all FG-Nups contained >50% IDPRs (except Nup54 and Nup358). Nup98, Nup153, and POM121 were extremely disordered with ~80% IDPRs. The functional disorder-based binding regions in the FG-Nups were identified. The phase separation behavior of FG-Nups indicated that all FG-Nups have the potential to undergo liquid-to-liquid phase separation that could stabilize their liquid state. The inherent structural flexibility in FG-Nups is mechanistically and functionally advantageous. Since certain FG-Nups interact with disease-relevant protein aggregates, their complexes can be exploited for drug design. Furthermore, consideration of the FG-Nups from the intrinsic disorder perspective provides critical information that can guide future experimental studies to uncover novel pathways associated with diseases linked with protein misfolding and aggregation.

Understanding COVID-19 via comparative analysis of dark proteomes of SARS-CoV-2, human SARS and bat SARS-like coronaviruses

The recently emerged coronavirus designated as SARS-CoV-2 (also known as 2019 novel coronavirus (2019-nCoV) or Wuhan coronavirus) is a causative agent of coronavirus disease 2019 (COVID-19), which is rapidly spreading throughout the world now. More than 1.21 million cases of SARS-CoV-2 infection and more than 67,000 COVID-19-associated mortalities have been reported worldwide till the writing of this article, and these numbers are increasing every passing hour. The World Health Organization (WHO) has declared the SARS-CoV-2 spread as a global public health emergency and admitted COVID-19 as a pandemic now. Multiple sequence alignment data correlated with the already published reports on SARS-CoV-2 evolution indicated that this virus is closely related to the bat severe acute respiratory syndrome-like coronavirus (bat SARS-like CoV) and the well-studied human SARS coronavirus (SARS-CoV). The disordered regions in viral proteins are associated with the viral infectivity and pathogenicity. Therefore, in this study, we have exploited a set of complementary computational approaches to examine the dark proteomes of SARS-CoV-2, bat SARS-like, and human SARS CoVs by analysing the prevalence of intrinsic disorder in their proteins. According to our findings, SARS-CoV-2 proteome contains very significant levels of structural order. In fact, except for nucleocapsid, Nsp8, and ORF6, the vast majority of SARS-CoV-2 proteins are mostly ordered proteins containing less intrinsically disordered protein regions (IDPRs). However, IDPRs found in SARS-CoV-2 proteins are functionally important. For example, cleavage sites in its replicase 1ab polyprotein are found to be highly disordered, and almost all SARS-CoV-2 proteins contains molecular recognition features (MoRFs), which are intrinsic disorder-based protein-protein interaction sites that are commonly utilized by proteins for interaction with specific partners. The results of our extensive investigation of the dark side of SARS-CoV-2 proteome will have important implications in understanding the structural and non-structural biology of SARS or SARS-like coronaviruses.

MoRFPred_en: Sequence-based prediction of MoRFs using an ensemble learning strategy

Molecular recognition features (MoRFs) usually act as "hub" sites in the interaction networks of intrinsically disordered proteins (IDPs). Because an increasing number of serious diseases have been found to be associated with disordered proteins, identifying MoRFs has become increasingly important. In this study, we propose an ensemble learning strategy, named MoRFPred_en, to predict MoRFs from protein sequences. This approach combines four submodels that utilize different sequence-derived features for the prediction, including a multichannel one-dimensional convolutional neural network (CNN_1D multichannel) based model, two deep two-dimensional convolutional neural network (DCNN_2D) based models, and a support vector machine (SVM) based model. When compared with other methods on the same datasets, the MoRFPred_en approach produced better results than existing state-of-the-art MoRF prediction methods, achieving an AUC of 0.762 on the VALIDATION419 dataset, 0.795 on the TEST45 dataset, and 0.776 on the TEST49 dataset. Availability: http://vivace.bi.a.u-tokyo.ac.jp:8008/fang/MoRFPred_en.php.

Understanding COVID-19 via comparative analysis of dark proteomes of SARS-CoV-2, human SARS and bat SARS-like coronaviruses

The recently emerged coronavirus designated as SARS-CoV-2 (also known as 2019 novel coronavirus (2019-nCoV) or Wuhan coronavirus) is a causative agent of coronavirus disease 2019 (COVID-19), which is rapidly spreading throughout the world now. More than 1.21 million cases of SARS-CoV-2 infection and more than 67,000 COVID-19-associated mortalities have been reported worldwide till the writing of this article, and these numbers are increasing every passing hour. The World Health Organization (WHO) has declared the SARS-CoV-2 spread as a global public health emergency and admitted COVID-19 as a pandemic now. Multiple sequence alignment data correlated with the already published reports on SARS-CoV-2 evolution indicated that this virus is closely related to the bat severe acute respiratory syndrome-like coronavirus (bat SARS-like CoV) and the well-studied human SARS coronavirus (SARS-CoV). The disordered regions in viral proteins are associated with the viral infectivity and pathogenicity. Therefore, in this study, we have exploited a set of complementary computational approaches to examine the dark proteomes of SARS-CoV-2, bat SARS-like, and human SARS CoVs by analysing the prevalence of intrinsic disorder in their proteins. According to our findings, SARS-CoV-2 proteome contains very significant levels of structural order. In fact, except for nucleocapsid, Nsp8, and ORF6, the vast majority of SARS-CoV-2 proteins are mostly ordered proteins containing less intrinsically disordered protein regions (IDPRs). However, IDPRs found in SARS-CoV-2 proteins are functionally important. For example, cleavage sites in its replicase 1ab polyprotein are found to be highly disordered, and almost all SARS-CoV-2 proteins contains molecular recognition features (MoRFs), which are intrinsic disorder-based protein–protein interaction sites that are commonly utilized by proteins for interaction with specific partners. The results of our extensive investigation of the dark side of SARS-CoV-2 proteome will have important implications in understanding the structural and non-structural biology of SARS or SARS-like coronaviruses.

The dark side of Alzheimer's disease: unstructured biology of proteins from the amyloid cascade signaling pathway

Alzheimer's disease (AD) is a leading cause of age-related dementia worldwide. Despite more than a century of intensive research, we are not anywhere near the discovery of a cure for this disease or a way to prevent its progression. Among the various molecular mechanisms proposed for the description of the pathogenesis and progression of AD, the amyloid cascade hypothesis, according to which accumulation of a product of amyloid precursor protein (APP) cleavage, amyloid β (Aβ) peptide, induces pathological changes in the brain observed in AD, occupies a unique niche. Although multiple proteins have been implicated in this amyloid cascade signaling pathway, their structure-function relationships are mostly unexplored. However, it is known that two major proteins related to AD pathology, Aβ peptide, and microtubule-associated protein tau belong to the category of intrinsically disordered proteins (IDPs), which are the functionally important proteins characterized by a lack of fixed, ordered three-dimensional structure. IDPs and intrinsically disordered protein regions (IDPRs) play numerous vital roles in various cellular processes, such as signaling, cell cycle regulation, macromolecular recognition, and promiscuous binding. However, the deregulation and misfolding of IDPs may lead to disturbed signaling, interactions, and disease pathogenesis. Often, molecular recognition-related IDPs/IDPRs undergo disorder-to-order transition upon binding to their biological partners and contain specific disorder-based binding motifs, known as molecular recognition features (MoRFs). Knowing the intrinsic disorder status and disorder-based functionality of proteins associated with amyloid cascade signaling pathway may help to untangle the mechanisms of AD pathogenesis and help identify therapeutic targets. In this paper, we have used multiple computational tools to evaluate the presence of intrinsic disorder and MoRFs in 27 proteins potentially relevant to the amyloid cascade signaling pathway. Among these, BIN1, APP, APOE, PICALM, PSEN1 and CD33 were found to be highly disordered. Furthermore, their disorder-based binding regions and associated short linear motifs have also been identified. These findings represent important foundation for the future research, and experimental characterization of disordered regions in these proteins is required to better understand their roles in AD pathogenesis.

Computational prediction of MoRFs based on protein sequences and minimax probability machine

Background

Molecular recognition features (MoRFs) are one important type of disordered segments that can promote specific protein-protein interactions. They are located within longer intrinsically disordered regions (IDRs), and undergo disorder-to-order transitions upon binding to their interaction partners. The functional importance of MoRFs and the limitation of experimental identification make it necessary to predict MoRFs accurately with computational methods.

Results

In this study, a new sequence-based method, named as MoRF_MPM, is proposed for predicting MoRFs. MoRF_MPM uses minimax probability machine (MPM) to predict MoRFs based on 16 features and 3 different windows, which neither relying on other predictors nor calculating the properties of the surrounding regions of MoRFs separately. Comparing with ANCHOR, MoRFpred and MoRF_CHiBi on the same test sets, MoRF_MPM not only obtains higher AUC, but also obtains higher TPR at low FPR.

Conclusions

The features used in MoRF_MPM can effectively predict MoRFs, especially after preprocessing. Besides, MoRF_MPM uses a linear classification algorithm and does not rely on results of other predictors which makes it accessible and repeatable.

Computational Prediction of MoRFs, Short Disorder-to-order Transitioning Protein Binding Regions

Molecular recognition features (MoRFs) are short protein-binding regions that undergo disorder-to-order transitions (induced folding) upon binding protein partners. These regions are abundant in nature and can be predicted from protein sequences based on their distinctive sequence signatures. This first-of-its-kind survey covers 14 MoRF predictors and six related methods for the prediction of short protein-binding linear motifs, disordered protein-binding regions and semi-disordered regions. We show that the development of MoRF predictors has accelerated in the recent years. These predictors depend on machine learning-derived models that were generated using training datasets where MoRFs are annotated using putative disorder. Our analysis reveals that they generate accurate predictions. We identified eight methods that offer area under the ROC curve (AUC) ≥ 0.7 on experimentally-validated test datasets. We show that modern MoRF predictors accurately find experimentally annotated MoRFs even though they were trained using the putative disorder annotations. They are relatively highly-cited, particularly the methods available as webservers that on average secure three times more citations than methods without this option. MoRF predictions contribute to the experimental discovery of protein-protein interactions, annotation of protein functions and computational analysis of a variety of proteomes, protein families, and pathways. We outline future development and application directions for these tools, stressing the importance to develop novel tools that would target interactions of disordered regions with other types of partners.

Predicting Functions of Disordered Proteins with MoRFpred

Intrinsically disordered proteins and regions are involved in a wide range of cellular functions, and they often facilitate protein-protein interactions. Molecular recognition features (MoRFs) are segments of intrinsically disordered regions that bind to partner proteins, where binding is concomitant with a transition to a structured conformation. MoRFs facilitate translation, transport, signaling, and regulatory processes and are found across all domains of life. A popular computational tool, MoRFpred, accurately predicts MoRFs in protein sequences. MoRFpred is implemented as a user-friendly web server that is freely available at http://biomine.cs.vcu.edu/servers/MoRFpred/ . We describe this predictor, explain how to run the web server, and show how to interpret the results it generates. We also demonstrate the utility of this web server based on two case studies, focusing on the relevance of evolutionary conservation of MoRF regions.

Predicting MoRFs in protein sequences using HMM profiles

BACKGROUND

Intrinsically Disordered Proteins (IDPs) lack an ordered three-dimensional structure and are enriched in various biological processes. The Molecular Recognition Features (MoRFs) are functional regions within IDPs that undergo a disorder-to-order transition on binding to a partner protein. Identifying MoRFs in IDPs using computational methods is a challenging task.

METHODS

In this study, we introduce hidden Markov model (HMM) profiles to accurately identify the location of MoRFs in disordered protein sequences. Using windowing technique, HMM profiles are utilised to extract features from protein sequences and support vector machines (SVM) are used to calculate a propensity score for each residue. Two different SVM kernels with high noise tolerance are evaluated with a varying window size and the scores of the SVM models are combined to generate the final propensity score to predict MoRF residues. The SVM models are designed to extract maximal information between MoRF residues, its neighboring regions (Flanks) and the remainder of the sequence (Others).

RESULTS

To evaluate the proposed method, its performance was compared to that of other MoRF predictors; MoRFpred and ANCHOR. The results show that the proposed method outperforms these two predictors.

CONCLUSIONS

Using HMM profile as a source of feature extraction, the proposed method indicates improvement in predicting MoRFs in disordered protein sequences.

Abundance of intrinsic structural disorder in the histone H1 subtypes

The intrinsically disordered proteins consist of partially structured regions linked to the unstructured stretches, which consequently form the transient and dynamic conformational ensembles. They undergo disorder to order transition upon binding their partners. Intrinsic disorder is attributed to histones H1, perceived as assemblers of chromatin structure and the regulators of DNA and proteins activity. In this work, the comparison of intrinsic disorder abundance in the histone H1 subtypes was performed both by the analysis of their amino acid composition and by the prediction of disordered stretches, as well as by identifying molecular recognition features (MoRFs) and ANCHOR protein binding regions (APBR) that are responsible for recognition and binding. Both human and model organisms-animals, plants, fungi and protists-have H1 histone subtypes with the properties typical of disordered state. They possess a significantly higher content of hydrophilic and charged amino acid residues, arranged in the long regions, covering over half of the whole amino acid residues in chain. Almost complete disorder corresponds to histone H1 terminal domains, including MoRFs and ANCHOR. Those motifs were also identified in a more ordered histone H1 globular domain. Compared to the control (globular and fibrous) proteins, H1 histones demonstrate the increased folding rate and a higher proportion of low-complexity segments. The results of this work indicate that intrinsic disorder is an inherent structural property of histone H1 subtypes and it is essential for establishing a protein conformation which defines functional outcomes affecting on DNA- and/or partner protein-dependent cell processes.