Prediction of motif-mediated viral mimicry through the integration of host-pathogen interactions

One of the mechanisms viruses use in hijacking host cellular machinery is mimicking Short Linear Motifs (SLiMs) in host proteins to maintain their life cycle inside host cells. In the face of the escalating volume of virus-host protein-protein interactions (vhPPIs) documented in databases; the accurate prediction of molecular mimicry remains a formidable challenge due to the inherent degeneracy of SLiMs. Consequently, there is a pressing need for computational methodologies to predict new instances of viral mimicry. Our present study introduces a DMI-de-novo pipeline, revealing that vhPPIs catalogued in the VirHostNet3.0 database effectively capture domain-motif interactions (DMIs). Notably, both affinity purification coupled mass spectrometry and yeast two-hybrid assays emerged as good approaches for delineating DMIs. Furthermore, we have identified new vhPPIs mediated by SLiMs across different viruses. Importantly, the de-novo prediction strategy facilitated the recognition of several potential mimicry candidates implicated in the subversion of host cellular proteins. The insights gleaned from this research not only enhance our comprehension of the mechanisms by which viruses co-opt host cellular machinery but also pave the way for the development of novel therapeutic interventions.

"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.

Functions of intrinsically disordered proteins through evolutionary lenses

Protein sequences are the result of an evolutionary process that involves the balancing act of experimenting with novel mutations and selecting out those that have an undesirable functional outcome. In the case of globular proteins, the function relies on a well-defined conformation, therefore, there is a strong evolutionary pressure to preserve the structure. However, different evolutionary rules might apply for the group of intrinsically disordered regions and proteins (IDR/IDPs) that exist as an ensemble of fluctuating conformations. The function of IDRs can directly originate from their disordered state or arise through different types of molecular recognition processes. There is an amazing variety of ways IDRs can carry out their functions, and this is also reflected in their evolutionary properties. In this chapter we give an overview of the different types of evolutionary behavior of disordered proteins and associated functions in normal and disease settings.

Intrinsic disorder in proteins: Relevance to protein assemblies, drug design and host-pathogen interactions

Intrinsic disorder in proteins resulting in considerable variation in structure can lead to multiple functions including multi-specificity and diverse pathologies. Protein interfaces can involve disordered regions that assemble through a concerted-fold-and-bind mechanism. The binding involves both enthalpic and entropic gains by exploiting 'hot spots' on the partner and displacing water molecules placed in thermodynamically unfavorable situations. The examples of Rad51-BRCA2 and Artemis-DNA-PKCs/LigIV complexes illustrate this in the context of drug design. This overview tracks the seamless involvement of protein disorder in multi-specificity of biocatalysts, protein assembly formations and host-pathogen interactions, where intrinsic disorder can in Mycobacteria, compensate for genome reduction by carrying out multiple functions and in some RNA viruses facilitate adaption to the host. These present challenging opportunities for designing new drugs and interventions.

Interaction of human dynein light chain 1 (DYNLL1) with enterochelin esterase (Salmonella typhimurium) and protective antigen (Bacillus anthraci) might be the potential cause of human infection

The cytoplasmic dynein light chain 1 (DYNLL1) is an important constituent of motor proteins complex. In human it is encoded by DYNLL1 gene. It is involved in cargo transport functions and interacts with many viral proteins with the help of short linear consensus motif sequence (K/R) XTQT. Viral proteins bind to DYNLL1 through its consensus short linear motif (SLiM) sequence to reach the target site in the cell and cause different infections in the host. It is still unknown if bacterial proteins also contain the same conserved SLiMs sequence through which they bind to this motor protein and cause infections. So, it is important to investigate the role of DYNLL1 in human bacterial infections. The interaction partner proteins of DYNLL1 against conserved viral motif sequences were predicted through PDBSum. Pairwise sequence alignment, between viral motif sequence and that of predicted proteins, was performed to identify conserved region in predicted interaction partners. Docking between the DYNLL1 and new pathogenic interaction partners was performed, by using PatchDock, to explore the protein-protein binding quality. Interactions of docked complexes were visualized by DimPlot. Three pathogenic bacterial proteins i.e., enterochelin esterase (3MGA), protective antigen (3J9C) and putative lipoprotein (4KT3) were selected as candidate interaction partners of DYNLL1. The putative lipoprotein (4KT3) showed low quality binding with DYNLL1. So, enterochelin esterase (3MGA) and protective antigen (3J9C) were speculated to be involved in human bacterial infections by using DYNLL1 to reach their target sites.

Understanding protein multifunctionality: from short linear motifs to cellular functions

Moonlighting proteins perform multiple unrelated functions without any change in polypeptide sequence. They can coordinate cellular activities, serving as switches between pathways and helping to respond to changes in the cellular environment. Therefore, regulation of the multiple protein activities, in space and time, is likely to be important for the homeostasis of biological systems. Some moonlighting proteins may perform their multiple functions simultaneously while others alternate between functions due to certain triggers. The switch of the moonlighting protein's functions can be regulated by several distinct factors, including the binding of other molecules such as proteins. We here review the approaches used to identify moonlighting proteins and existing repositories. We particularly emphasise the role played by short linear motifs and PTMs as regulatory switches of moonlighting functions.

Deciphering the intrinsic properties of fungal proteases in optimizing phytopathogenic interaction

Phytopathogenic fungi secrete a wide range of enzymes to penetrate and colonize host tissues. Of them protease activity is reported to increase disease aggressiveness in the plant. With the aim to explore the reason of the higher infection potential of proteases, we have compared several genomic and proteomic attributes among different hydrolytic enzymes coded by five pathogenic fungal species which are the potent infectious agents of plant. Categorizing the enzymes into four major groups, namely protease, lipase, amylase and cell-wall degraders, we observed that proteases are evolutionary more conserved, have higher expression levels, contain more hydrophobic buried residues, short linear motifs and post-translational modified (PTM) sites than the other three groups of enzymes. Again, comparing these features of protease between pathogenic and non-pathogenic Aspergillus sps, we have hypothesized that protein structural properties could play significant roles in imposing infection potency to the fungal proteases.

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.

SLiM-Enrich: computational assessment of protein-protein interaction data as a source of domain-motif interactions

Many important cellular processes involve protein–protein interactions (PPIs) mediated by a Short Linear Motif (SLiM) in one protein interacting with a globular domain in another. Despite their significance, these domain-motif interactions (DMIs) are typically low affinity, which makes them challenging to identify by classical experimental approaches, such as affinity pulldown mass spectrometry (AP-MS) and yeast two-hybrid (Y2H). DMIs are generally underrepresented in PPI networks as a result. A number of computational methods now exist to predict SLiMs and/or DMIs from experimental interaction data but it is yet to be established how effective different PPI detection methods are for capturing these low affinity SLiM-mediated interactions. Here, we introduce a new computational pipeline (SLiM-Enrich) to assess how well a given source of PPI data captures DMIs and thus, by inference, how useful that data should be for SLiM discovery. SLiM-Enrich interrogates a PPI network for pairs of interacting proteins in which the first protein is known or predicted to interact with the second protein via a DMI. Permutation tests compare the number of known/predicted DMIs to the expected distribution if the two sets of proteins are randomly associated. This provides an estimate of DMI enrichment within the data and the false positive rate for individual DMIs. As a case study, we detect significant DMI enrichment in a high-throughput Y2H human PPI study. SLiM-Enrich analysis supports Y2H data as a source of DMIs and highlights the high false positive rates associated with naïve DMI prediction. SLiM-Enrich is available as an R Shiny app. The code is open source and available via a GNU GPL v3 license at: https://github.com/slimsuite/SLiMEnrich. A web server is available at: http://shiny.slimsuite.unsw.edu.au/SLiMEnrich/.

Short linear motifs in intrinsically disordered regions modulate HOG signaling capacity

Background

The effort to characterize intrinsically disordered regions of signaling proteins is rapidly expanding. An important class of disordered interaction modules are ubiquitous and functionally diverse elements known as short linear motifs (SLiMs).

Results

To further examine the role of SLiMs in signal transduction, we used a previously devised bioinformatics method to predict evolutionarily conserved SLiMs within a well-characterized pathway in S. cerevisiae. Using a single cell, reporter-based flow cytometry assay in conjunction with a fluorescent reporter driven by a pathway-specific promoter, we quantitatively assessed pathway output via systematic deletions of individual motifs. We found that, when deleted, 34% (10/29) of predicted SLiMs displayed a significant decrease in pathway output, providing evidence that these motifs play a role in signal transduction. Assuming that mutations in SLiMs have quantitative effects on mechanisms of signaling, we show that perturbations of parameters in a previously published stochastic model of HOG signaling could reproduce the quantitative effects of 4 out of 7 mutations in previously unknown SLiMs.

Conclusions

Our study suggests that, even in well-characterized pathways, large numbers of functional elements remain undiscovered, and that challenges remain for application of systems biology models to interpret the effects of mutations in signaling pathways.

Electronic supplementary material

The online version of this article (10.1186/s12918-018-0597-3) contains supplementary material, which is available to authorized users.

Perturbed human sub-networks by Fusobacterium nucleatum candidate virulence proteins

BACKGROUND

Fusobacterium nucleatum is a gram-negative anaerobic species residing in the oral cavity and implicated in several inflammatory processes in the human body. Although F. nucleatum abundance is increased in inflammatory bowel disease subjects and is prevalent in colorectal cancer patients, the causal role of the bacterium in gastrointestinal disorders and the mechanistic details of host cell functions subversion are not fully understood.

RESULTS

We devised a computational strategy to identify putative secreted F. nucleatum proteins (FusoSecretome) and to infer their interactions with human proteins based on the presence of host molecular mimicry elements. FusoSecretome proteins share similar features with known bacterial virulence factors thereby highlighting their pathogenic potential. We show that they interact with human proteins that participate in infection-related cellular processes and localize in established cellular districts of the host-pathogen interface. Our network-based analysis identified 31 functional modules in the human interactome preferentially targeted by 138 FusoSecretome proteins, among which we selected 26 as main candidate virulence proteins, representing both putative and known virulence proteins. Finally, six of the preferentially targeted functional modules are implicated in the onset and progression of inflammatory bowel diseases and colorectal cancer.

CONCLUSIONS

Overall, our computational analysis identified candidate virulence proteins potentially involved in the F. nucleatum-human cross-talk in the context of gastrointestinal diseases.

Bioinformatics Approaches for Predicting Disordered Protein Motifs

Short, linear motifs (SLiMs) in proteins are functional microdomains consisting of contiguous residue segments along the protein sequence, typically not more than 10 consecutive amino acids in length with less than 5 defined positions. Many positions are 'degenerate' thus offering flexibility in terms of the amino acid types allowed at those positions. Their short length and degenerate nature confers evolutionary plasticity meaning that SLiMs often evolve convergently. Further, SLiMs have a propensity to occur within intrinsically unstructured protein segments and this confers versatile functionality to unstructured regions of the proteome. SLiMs mediate multiple types of protein interactions based on domain-peptide recognition and guide functions including posttranslational modifications, subcellular localization of proteins, and ligand binding. SLiMs thus behave as modular interaction units that confer versatility to protein function and SLiM-mediated interactions are increasingly being recognized as therapeutic targets. In this chapter we start with a brief description about the properties of SLiMs and their interactions and then move on to discuss algorithms and tools including several web-based methods that enable the discovery of novel SLiMs (de novo motif discovery) as well as the prediction of novel occurrences of known SLiMs. Both individual amino acid sequences as well as sets of protein sequences can be scanned using these methods to obtain statistically overrepresented sequence patterns. Lists of putatively functional SLiMs are then assembled based on parameters such as evolutionary sequence conservation, disorder scores, structural data, gene ontology terms and other contextual information that helps to assess the functional credibility or significance of these motifs. These bioinformatics methods should certainly guide experiments aimed at motif discovery.