DextMP: deep dive into text for predicting moonlighting proteins

Role of Moonlighting Proteins in Disease: Analyzing the Contribution of Canonical and Moonlighting Functions in Disease Progression

The term moonlighting proteins refers to those proteins that present alternative functions performed by a single polypeptide chain acquired throughout evolution (called canonical and moonlighting, respectively). Over 78% of moonlighting proteins are involved in human diseases, 48% are targeted by current drugs, and over 25% of them are involved in the virulence of pathogenic microorganisms. These facts encouraged us to study the link between the functions of moonlighting proteins and disease. We found a large number of moonlighting functions activated by pathological conditions that are highly involved in disease development and progression. The factors that activate some moonlighting functions take place only in pathological conditions, such as specific cellular translocations or changes in protein structure. Some moonlighting functions are involved in disease promotion while others are involved in curbing it. The disease-impairing moonlighting functions attempt to restore the homeostasis, or to reduce the damage linked to the imbalance caused by the disease. The disease-promoting moonlighting functions primarily involve the immune system, mesenchyme cross-talk, or excessive tissue proliferation. We often find moonlighting functions linked to the canonical function in a pathological context. Moonlighting functions are especially coordinated in inflammation and cancer. Wound healing and epithelial to mesenchymal transition are very representative. They involve multiple moonlighting proteins with a different role in each phase of the process, contributing to the current-phase phenotype or promoting a phase switch, mitigating the damage or intensifying the remodeling. All of this implies a new level of complexity in the study of pathology genesis, progression, and treatment. The specific protein function involved in a patient's progress or that is affected by a drug must be elucidated for the correct treatment of diseases.

A community resource to mass explore the wheat grain proteome and its application to the late-maturity alpha-amylase (LMA) problem

BACKGROUND

Late-maturity alpha-amylase (LMA) is a wheat genetic defect causing the synthesis of high isoelectric point alpha-amylase following a temperature shock during mid-grain development or prolonged cold throughout grain development, both leading to starch degradation. While the physiology is well understood, the biochemical mechanisms involved in grain LMA response remain unclear. We have applied high-throughput proteomics to 4,061 wheat flours displaying a range of LMA activities. Using an array of statistical analyses to select LMA-responsive biomarkers, we have mined them using a suite of tools applicable to wheat proteins.

RESULTS

We observed that LMA-affected grains activated their primary metabolisms such as glycolysis and gluconeogenesis; TCA cycle, along with DNA- and RNA- binding mechanisms; and protein translation. This logically transitioned to protein folding activities driven by chaperones and protein disulfide isomerase, as well as protein assembly via dimerisation and complexing. The secondary metabolism was also mobilized with the upregulation of phytohormones and chemical and defence responses. LMA further invoked cellular structures, including ribosomes, microtubules, and chromatin. Finally, and unsurprisingly, LMA expression greatly impacted grain storage proteins, as well as starch and other carbohydrates, with the upregulation of alpha-gliadins and starch metabolism, whereas LMW glutenin, stachyose, sucrose, UDP-galactose, and UDP-glucose were downregulated.

CONCLUSIONS

To our knowledge, this is not only the first proteomics study tackling the wheat LMA issue but also the largest plant-based proteomics study published to date. Logistics, technicalities, requirements, and bottlenecks of such an ambitious large-scale high-throughput proteomics experiment along with the challenges associated with big data analyses are discussed.

Predictive modeling of moonlighting DNA-binding proteins

Moonlighting proteins are multifunctional, single-polypeptide chains capable of performing multiple autonomous functions. Most moonlighting proteins have been discovered through work unrelated to their multifunctionality. We believe that prediction of moonlighting proteins from first principles, that is, using sequence, predicted structure, evolutionary profiles, and global gene expression profiles, for only one functional class of proteins in a single organism at a time will significantly advance our understanding of multifunctional proteins. In this work, we investigated human moonlighting DNA-binding proteins (mDBPs) in terms of properties that distinguish them from other (non-moonlighting) proteins with the same DNA-binding protein (DBP) function. Following a careful and comprehensive analysis of discriminatory features, a machine learning model was developed to assess the predictability of mDBPs from other DBPs (oDBPs). We observed that mDBPs can be discriminated from oDBPs with high accuracy of 74% AUC of ROC using these first principles features. A number of novel predicted mDBPs were found to have literature support for their being moonlighting and others are proposed as candidates, for which the moonlighting function is currently unknown. We believe that this work will help in deciphering and annotating novel moonlighting DBPs and scale up other functions. The source codes and data sets used for this work are freely available at https://zenodo.org/record/7299265#.Y2pO3ctBxPY.

A method for identifying moonlighting proteins based on linear discriminant analysis and bagging-SVM

Moonlighting proteins have at least two independent functions and are widely found in animals, plants and microorganisms. Moonlighting proteins play important roles in signal transduction, cell growth and movement, tumor inhibition, DNA synthesis and repair, and metabolism of biological macromolecules. Moonlighting proteins are difficult to find through biological experiments, so many researchers identify moonlighting proteins through bioinformatics methods, but their accuracies are relatively low. Therefore, we propose a new method. In this study, we select SVMProt-188D as the feature input, and apply a model combining linear discriminant analysis and basic classifiers in machine learning to study moonlighting proteins, and perform bagging ensemble on the best-performing support vector machine. They are identified accurately and efficiently. The model achieves an accuracy of 93.26% and an F-sorce of 0.946 on the MPFit dataset, which is better than the existing MEL-MP model. Meanwhile, it also achieves good results on the other two moonlighting protein datasets.

Moonlighting in Rickettsiales: Expanding Virulence Landscape

The order Rickettsiales includes species that cause a range of human diseases such as human granulocytic anaplasmosis (Anaplasma phagocytophilum), human monocytic ehrlichiosis (Ehrlichia chaffeensis), scrub typhus (Orientia tsutsugamushi), epidemic typhus (Rickettsia prowazekii), murine typhus (R. typhi), Mediterranean spotted fever (R. conorii), or Rocky Mountain spotted fever (R. rickettsii). These diseases are gaining a new momentum given their resurgence patterns and geographical expansion due to the overall rise in temperature and other human-induced pressure, thereby remaining a major public health concern. As obligate intracellular bacteria, Rickettsiales are characterized by their small genome sizes due to reductive evolution. Many pathogens employ moonlighting/multitasking proteins as virulence factors to interfere with multiple cellular processes, in different compartments, at different times during infection, augmenting their virulence. The utilization of this multitasking phenomenon by Rickettsiales as a strategy to maximize the use of their reduced protein repertoire is an emerging theme. Here, we provide an overview of the role of various moonlighting proteins in the pathogenicity of these species. Despite the challenges that lie ahead to determine the multiple potential faces of every single protein in Rickettsiales, the available examples anticipate this multifunctionality as an essential and intrinsic feature of these obligates and should be integrated into available moonlighting repositories.

Moonlighting Biochemistry of Cysteine Synthase: A Species-specific Global Regulator

Cysteine Synthase (CS), the enzyme that synthesizes cysteine, performs non-canonical regulatory roles by binding and modulating functions of disparate proteins. Beyond its role in catalysis and regulation in the cysteine biosynthesis pathway, it exerts its moonlighting effect by binding to few other proteins which possess a C-terminal "CS-binding motif", ending with a terminal ILE. Therefore, we hypothesized that CS might regulate many other disparate proteins with the "CS-binding motif". In this study, we developed an iterative sequence matching method for mapping moonlighting biochemistry of CS and validated our prediction by analytical and structural approaches. Using a minimal protein-peptide interaction system, we show that five previously unknown CS-binder proteins that participate in diverse metabolic processes interact with CS in a species-specific manner. Furthermore, results show that signatures of protein-protein interactions, including thermodynamic, competitive-inhibition, and structural features, highly match the known CS-Binder, serine acetyltransferase (SAT). Together, the results presented in this study allow us to map the extreme multifunctional space (EMS) of CS and reveal the biochemistry of moonlighting space, a subset of EMS. We believe that the integrated computational and experimental workflow developed here could be further modified and extended to study protein-specific moonlighting properties of multifunctional proteins.

Integrating Resonant Recognition Model and Stockwell Transform for Localization of Hotspots in Tubulin

Tubulin is a promising target for designing anti-cancer drugs. Identification of hotspots in multifunctional Tubulin protein provides insights for new drug discovery. Although machine learning techniques have shown significant results in prediction, they fail to identify the hotspots corresponding to a particular biological function. This paper presents a signal processing technique combining resonant recognition model (RRM) and Stockwell Transform (ST) for the identification of hotspots corresponding to a particular functionality. The characteristic frequency (CF) representing a specific biological function is determined using the RRM. Then the spectrum of the protein sequence is computed using ST. The CF is filtered from the ST spectrum using a time-frequency mask. The energy peaks in the filtered sequence represent the hotspots. The hotspots predicted by the proposed method are compared with the experimentally detected binding residues of Tubulin stabilizing drug Taxol and destabilizing drug Colchicine present in the Tubulin protein. Out of the 53 experimentally identified hotspots, 60% are predicted by the proposed method whereas around 20% are predicted by existing machine learning based methods. Additionally, the proposed method predicts some new hot spots, which may be investigated.

De novo Prediction of Moonlighting Proteins Using Multimodal Deep Ensemble Learning

Moonlighting proteins (MPs) are a special type of protein with multiple independent functions. MPs play vital roles in cellular regulation, diseases, and biological pathways. At present, very few MPs have been discovered by biological experiments. Due to the lack of data sample, computation-based methods to identify MPs are limited. Currently, there is no de-novo prediction method for MPs. Therefore, systematic research and identification of MPs are urgently required. In this paper, we propose a multimodal deep ensemble learning architecture, named MEL-MP, which is the first de novo computation model for predicting MPs. First, we extract four sequence-based features: primary protein sequence information, evolutionary information, physical and chemical properties, and secondary protein structure information. Second, we select specific classifiers for each kind of feature. Finally, we apply the stacked ensemble to integrate the output of each classifier. Through comprehensive model selection and cross-validation experiments, it is shown that specific classifiers for specific feature types can achieve superior performance. For validating the effectiveness of the fusion-based stacked ensemble, different feature fusion strategies including direct combination and a multimodal deep auto-encoder are used for comparative purposes. MEL-MP is shown to exhibit superior prediction performance (F-score = 0.891), surpassing the existing machine learning model, MPFit (F-score = 0.784). In addition, MEL-MP is leveraged to predict the potential MPs among all human proteins. Furthermore, the distribution of predicted MPs on different chromosomes, the evolution of MPs, the association of MPs with diseases, and the functional enrichment of MPs are also explored. Finally, for maximum convenience, a user-friendly web server is available at: http://ml.csbg-jlu.site/mel-mp/.

Moonlighting Proteins Shine New Light on Molecular Signaling Niches

Plants as sessile organisms face daily environmental challenges and have developed highly nuanced signaling systems to enable suitable growth, development, defense, or stalling responses. Moonlighting proteins have multiple tasks and contribute to cellular signaling cascades where they produce additional variables adding to the complexity or fuzziness of biological systems. Here we examine roles of moonlighting kinases that also generate 3',5'-cyclic guanosine monophosphate (cGMP) in plants. These proteins include receptor like kinases and lipid kinases. Their guanylate cyclase activity potentiates the development of localized cGMP-enriched nanodomains or niches surrounding the kinase and its interactome. These nanodomains contribute to allosteric regulation of kinase and other molecules in the immediate complex directly or indirectly modulating signal cascades. Effects include downregulation of kinase activity, modulation of other members of the protein complexes such as cyclic nucleotide gated channels and potential triggering of cGMP-dependent degradation cascades terminating signaling. The additional layers of information provided by the moonlighting kinases are discussed in terms of how they may be used to provide a layer of fuzziness to effectively modulate cellular signaling cascades.

Moonlighting Proteins

The single gene, single protein, single function hypothesis is increasingly becoming obsolete. Numerous studies have demonstrated that individual proteins can moonlight, meaning they can have multiple functions based on their cellular or developmental context. In this review, we discuss moonlighting proteins, highlighting the biological pathways where this phenomenon may be particularly relevant. In addition, we combine genetic, cell biological, and evolutionary perspectives so that we can better understand how, when, and why moonlighting proteins may take on multiple roles.

Spatial proteomics: a powerful discovery tool for cell biology

Protein subcellular localization is tightly controlled and intimately linked to protein function in health and disease. Capturing the spatial proteome - that is, the localizations of proteins and their dynamics at the subcellular level - is therefore essential for a complete understanding of cell biology. Owing to substantial advances in microscopy, mass spectrometry and machine learning applications for data analysis, the field is now mature for proteome-wide investigations of spatial cellular regulation. Studies of the human proteome have begun to reveal a complex architecture, including single-cell variations, dynamic protein translocations, changing interaction networks and proteins localizing to multiple compartments. Furthermore, several studies have successfully harnessed the power of comparative spatial proteomics as a discovery tool to unravel disease mechanisms. We are at the beginning of an era in which spatial proteomics finally integrates with cell biology and medical research, thereby paving the way for unbiased systems-level insights into cellular processes. Here, we discuss current methods for spatial proteomics using imaging or mass spectrometry and specifically highlight global comparative applications. The aim of this Review is to survey the state of the field and also to encourage more cell biologists to apply spatial proteomics approaches.

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.

The Use of Proteomics Studies in Identifying Moonlighting Proteins

Proteomics studies that characterize hundreds or thousands of proteins in parallel can play an important part in the identification of moonlighting proteins, proteins that perform two or more distinct and physiologically relevant biochemical or biophysical functions. Functional assays, including ligand-binding assays, can find a surprising second function for a protein that was previously identified as performing a different function, for example, a DNA-binding ability for an enzyme in amino acid metabolism. The results of large-scale assays of protein-protein interactions, gene knockouts, or subcellular protein localizations, or bioinformatics analysis of amino acid sequences and three-dimensional structures, can also be used to predict that a protein has additional functions, but in these cases it is important to use biochemical and biophysical methods to confirm the protein can perform each function.

Identification of Moonlighting Proteins in Genomes Using Text Mining Techniques

Moonlighting proteins is an emerging concept for considering protein functions, which indicate proteins with two or more independent and distinct functions. An increasing number of moonlighting proteins have been reported in the past years; however, a systematic study of the topic has been hindered because the secondary functions of proteins are usually found serendipitously by experiments. Toward systematic identification and study of moonlighting proteins, computational methods for identifying moonlighting proteins from several different information sources, database entries, literature, and large-scale omics data have been developed. In this study, an overview for finding moonlighting proteins is discussed. Then, the literature-mining method, DextMP, is applied to find new moonlighting proteins in three genomes, Arabidopsis thaliana, Caenorhabditis elegans, and Drosophila melanogaster. Potential moonlighting proteins identified by DextMP are further examined by a two-step manual literature checking procedure, which finally yielded 13 new moonlighting proteins. Identified moonlighting proteins are categorized into two classes based on the clarity of the distinctness of two functions of the proteins. A few cases of the identified moonlighting proteins are described in detail. Further direction for improving the DextMP algorithm is also discussed.