Bioinformatics and Moonlighting Proteins

The distal-proximal relationships among the human moonlighting proteins: Evolutionary hotspots and Darwinian checkpoints

Moonlighting proteins, known for their ability to perform multiple, often unrelated functions within a single polypeptide chain, challenge the traditional "one gene, one protein, one function" paradigm. As organisms evolved, their genomes remained relatively stable in size, but the introduction of post-translational modifications and sub-strategies like protein promiscuity and intrinsic disorder enabled multifunctionality. Enzymes, in particular, exemplify this phenomenon, engaging in unrelated processes alongside their primary catalytic roles. This study employs a systematic, quantitative informatics approach to shed light on human moonlighting protein sequences. Phylogenetic analyses of human moonlighting proteins are presented, elucidating the distal-proximal relationships among these proteins based on sequence-derived quantitative features. The findings unveil the captivating world of human moonlighting proteins, urging further investigations in the emerging field of moonlighting proteomics, with the potential for significant contributions to our understanding of multifunctional proteins and their roles in diverse cellular processes and diseases.

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.

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

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.

Bacterial low-abundant taxa are key determinants of a healthy airway metagenome in the early years of human life

The default removal of low-abundance (rare) taxa from microbial community analyses may lead to an incomplete picture of the taxonomic and functional microbial potential within the human habitat. Publicly available shotgun metagenomics data of healthy children and children with cystic fibrosis (CF) were reanalysed to study the development of the rare species biosphere, which was here defined by either the 15th, 25th or 35th species abundance percentile. We found that healthy children contained an age-independent network of abundant (core) and rare species with both entities being essential in maintaining the network structure. The protein sequence usage for more than 100 bacterial metabolic pathways differed between the core and rare species biosphere. In CF children, the background structure was underdeveloped and random forest bootstrapping based on all constituents of the early airway metagenome and host-associated factors indicated that rare taxa were the most important variables in deciding whether a child was healthy or suffered from the life-limiting CF disease. Attempts failed to make the age-independent CF network as robust as the healthy structure when an increasing number of bacterial taxa from the healthy network was incorporated into the CF structure by computer-based model simulations. However, the transfer of a key combination of taxa from the healthy to the CF network structure with high species diversity and low species dominance, correlated with a more robust CF network and a topological approximation of CF and healthy graph structures. Rothia mucilaginosa, Streptococci and rare species were essential in improving the underdeveloped CF network.

Structural Predictive Model of Presenilin-2 Protein and Analysis of Structural Effects of Familial Alzheimer's Disease Mutations

Alzheimer's disease manifests itself in brain tissue by neuronal death, due to aggregation of β-amyloid, produced by senile plaques, and hyperphosphorylation of the tau protein, which produces neurofibrillary tangles. One of the genetic markers of the disease is the gene that translates the presenilin-2 protein, which has mutations that favor the appearance of the disease and has no reported crystallographic structure. In view of this, protein modeling is performed using prediction and structural refinement tools followed by an energetic and stereochemical characterization for its validation. For the simulation, four reported mutations are chosen, which are Met239Ile, Met239Val, Ser130Leu, and Thr122Arg, all associated with various functional responses. From a theoretical analysis, a preliminary bioinformatic study is made to find the phosphorylation patterns in the protein and the hydropathic index according to the polarity and chemical environment. Molecular visualization was carried out with the Chimera 1.14 software, and the theoretical calculation with the hybrid quantum mechanics/molecular mechanics system from the semi-empirical method, with Spartan18 software and an AustinModel1 basis. These relationships allow for studying the system from a structural approach with the determination of small distance changes, potential surfaces, electrostatic maps, and angle changes, which favor the comparison between wild-type and mutant systems. With the results obtained, it is expected to complement experimental data reported in the literature from models that would allow us to understand the effects of the selected mutations.

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.

Pathogen Moonlighting Proteins: From Ancestral Key Metabolic Enzymes to Virulence Factors

Moonlighting and multitasking proteins refer to proteins with two or more functions performed by a single polypeptide chain. An amazing example of the Gain of Function (GoF) phenomenon of these proteins is that 25% of the moonlighting functions of our Multitasking Proteins Database (MultitaskProtDB-II) are related to pathogen virulence activity. Moreover, they usually have a canonical function belonging to highly conserved ancestral key functions, and their moonlighting functions are often involved in inducing extracellular matrix (ECM) protein remodeling. There are three main questions in the context of moonlighting proteins in pathogen virulence: (A) Why are a high percentage of pathogen moonlighting proteins involved in virulence? (B) Why do most of the canonical functions of these moonlighting proteins belong to primary metabolism? Moreover, why are they common in many pathogen species? (C) How are these different protein sequences and structures able to bind the same set of host ECM protein targets, mainly plasminogen (PLG), and colonize host tissues? By means of an extensive bioinformatics analysis, we suggest answers and approaches to these questions. There are three main ideas derived from the work: first, moonlighting proteins are not good candidates for vaccines. Second, several motifs that might be important in the adhesion to the ECM were identified. Third, an overrepresentation of GO codes related with virulence in moonlighting proteins were seen.

Moonlighting Proteins: The Case of the Hexokinases

Moonlighting proteins are defined as proteins with two or more functions that are unrelated and independent to each other, so that inactivation of one of them should not affect the second one and vice versa. Intriguingly, all the glycolytic enzymes are described as moonlighting proteins in some organisms. Hexokinase (HXK) is a critical enzyme in the glycolytic pathway and displays a wide range of functions in different organisms such as fungi, parasites, mammals, and plants. This review discusses HXKs moonlighting functions in depth since they have a profound impact on the responses to nutritional, environmental, and disease challenges. HXKs’ activities can be as diverse as performing metabolic activities, as a gene repressor complexing with other proteins, as protein kinase, as immune receptor and regulating processes like autophagy, programmed cell death or immune system responses. However, most of those functions are particular for some organisms while the most common moonlighting HXK function in several kingdoms is being a glucose sensor. In this review, we also analyze how different regulation mechanisms cause HXK to change its subcellular localization, oligomeric or conformational state, the response to substrate and product concentration, and its interactions with membrane, proteins, or RNA, all of which might impact the HXK moonlighting functions.

Moonlighting protein prediction using physico-chemical and evolutional properties via machine learning methods

Background

Moonlighting proteins (MPs) are a subclass of multifunctional proteins in which more than one independent or usually distinct function occurs in a single polypeptide chain. Identification of unknown cellular processes, understanding novel protein mechanisms, improving the prediction of protein functions, and gaining information about protein evolution are the main reasons to study MPs. They also play an important role in disease pathways and drug-target discovery. Since detecting MPs experimentally is quite a challenge, most of them are detected randomly. Therefore, introducing an appropriate computational approach to predict MPs seems reasonable.

Results

In this study, we introduced a competent model for detecting moonlighting and non-MPs through extracted features from protein sequences. We attempted to set up a well-judged scheme for detecting outlier proteins. Consequently, 37 distinct feature vectors were utilized to study each protein’s impact on detecting MPs. Furthermore, 8 different classification methods were assessed to find the best performance. To detect outliers, each one of the classifications was executed 100 times by tenfold cross-validation on feature vectors; proteins which misclassified 90 times or more were grouped. This process was applied to every single feature vector and eventually the intersection of these groups was determined as the outlier proteins. The results of tenfold cross-validation on a dataset of 351 samples (containing 215 moonlighting and 136 non-moonlighting proteins) reveal that the SVM method on all feature vectors has the highest performance among all methods in this study and other available methods. Besides, the study of outliers showed that 57 of 351 proteins in the dataset could be an appropriate candidate for the outlier. Among the outlier proteins, there were non-MPs (such as P69797) that have been misclassified in 8 different classification methods with 16 different feature vectors. Because these proteins have been obtained by computational methods, the results of this study could reduce the likelihood of hypothesizing whether these proteins are non-moonlighting at all.

Conclusions

MPs are difficult to be identified through experimentation. Using distinct feature vectors, our method enabled identification of novel moonlighting proteins. The study also pinpointed that a number of non-MPs are likely to be moonlighting.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12859-021-04194-5.

Structural Insights into the Interactions of Candidal Enolase with Human Vitronectin, Fibronectin and Plasminogen

Significant amounts of enolase—a cytosolic enzyme involved in the glycolysis pathway—are exposed on the cell surface of Candida yeast. It has been hypothesized that this exposed enolase form contributes to infection-related phenomena such as fungal adhesion to human tissues, and the activation of fibrinolysis and extracellular matrix degradation. The aim of the present study was to characterize, in structural terms, the protein-protein interactions underlying these moonlighting functions of enolase. The tight binding of human vitronectin, fibronectin and plasminogen by purified C. albicans and C. tropicalis enolases was quantitatively analyzed by surface plasmon resonance measurements, and the dissociation constants of the formed complexes were determined to be in the 10⁻⁷–10⁻⁸ M range. In contrast, the binding of human proteins by the S.cerevisiae enzyme was much weaker. The chemical cross-linking method was used to map the sites on enolase molecules that come into direct contact with human proteins. An internal motif ₂₃₅DKAGYKGKVGIAMDVASSEFYKDGK₂₅₉ in C. albicans enolase was suggested to contribute to the binding of all three human proteins tested. Models for these interactions were developed and revealed the sites on the enolase molecule that bind human proteins, extensively overlap for these ligands, and are well-separated from the catalytic activity center.

Integrative functional genomics decodes herpes simplex virus 1

The predicted 80 open reading frames (ORFs) of herpes simplex virus 1 (HSV-1) have been intensively studied for decades. Here, we unravel the complete viral transcriptome and translatome during lytic infection with base-pair resolution by computational integration of multi-omics data. We identify a total of 201 transcripts and 284 ORFs including all known and 46 novel large ORFs. This includes a so far unknown ORF in the locus deleted in the FDA-approved oncolytic virus Imlygic. Multiple transcript isoforms expressed from individual gene loci explain translation of the vast majority of ORFs as well as N-terminal extensions (NTEs) and truncations. We show that NTEs with non-canonical start codons govern the subcellular protein localization and packaging of key viral regulators and structural proteins. We extend the current nomenclature to include all viral gene products and provide a genome browser that visualizes all the obtained data from whole genome to single-nucleotide resolution.

Exploring the Potential Role of Moonlighting Function of the Surface-Associated Proteins From Mycobacterium bovis BCG Moreau and Pasteur by Comparative Proteomic

Surface-associated proteins from Mycobacterium bovis BCG Moreau RDJ are important components of the live Brazilian vaccine against tuberculosis. They are important targets during initial BCG vaccine stimulation and modulation of the host's immune response, especially in the bacterial-host interaction. These proteins might also be involved in cellular communication, chemical response to the environment, pathogenesis processes through mobility, colonization, and adherence to the host cell, therefore performing multiple functions. In this study, the proteomic profile of the surface-associated proteins from M. bovis BCG Moreau was compared to the BCG Pasteur reference strain. The methodology used was 2DE gel electrophoresis combined with mass spectrometry techniques (MALDI-TOF/TOF), leading to the identification of 115 proteins. Of these, 24 proteins showed differential expression between the two BCG strains. Furthermore, 27 proteins previously described as displaying moonlighting function were identified, 8 of these proteins showed variation in abundance comparing BCG Moreau to Pasteur and 2 of them presented two different domain hits. Moonlighting proteins are multifunctional proteins in which two or more biological functions are fulfilled by a single polypeptide chain. Therefore, the identification of such proteins with moonlighting predicted functions can contribute to a better understanding of the molecular mechanisms unleashed by live BCG Moreau RDJ vaccine components.

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.

The roles of moonlight ribosomal proteins in the development of human cancers

"Moonlighting protein" is a term used to define a single protein with multiple functions and different activities that are not derived from gene fusions, multiple RNA splicing, or the proteolytic activity of promiscuous enzymes. Different proteinous constituents of ribosomes have been shown to have important moonlighting extra-ribosomal functions. In this review, we introduce the impact of key moonlight ribosomal proteins and dependent signal transduction in the initiation and progression of various cancers. As a future perspective, the potential role of these moonlight ribosomal proteins in the diagnosis, prognosis, and development of novel strategies to improve the efficacy of therapies for human cancers has been suggested.

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.

Multifunctional Proteins: Involvement in Human Diseases and Targets of Current Drugs

Multifunctionality or multitasking is the capability of some proteins to execute two or more biochemical functions. The objective of this work is to explore the relationship between multifunctional proteins, human diseases and drug targeting. The analysis of the proportion of multitasking proteins from the MultitaskProtDB-II database shows that 78% of the proteins analyzed are involved in human diseases. This percentage is much higher than the 17.9% found in human proteins in general. A similar analysis using drug target databases shows that 48% of these analyzed human multitasking proteins are targets of current drugs, while only 9.8% of the human proteins present in UniProt are specified as drug targets. In almost 50% of these proteins, both the canonical and moonlighting functions are related to the molecular basis of the disease. A procedure to identify multifunctional proteins from disease databases and a method to structurally map the canonical and moonlighting functions of the protein have also been proposed here. Both of the previous percentages suggest that multitasking is not a rare phenomenon in proteins causing human diseases, and that their detailed study might explain some collateral drug effects.

Protein Moonlighting Revealed by Noncatalytic Phenotypes of Yeast Enzymes

A single gene can partake in several biological processes, and therefore gene deletions can lead to different-sometimes unexpected-phenotypes. However, it is not always clear whether such pleiotropy reflects the loss of a unique molecular activity involved in different processes or the loss of a multifunctional protein. Here, using Saccharomyces cerevisiae metabolism as a model, we systematically test the null hypothesis that enzyme phenotypes depend on a single annotated molecular function, namely their catalysis. We screened a set of carefully selected genes by quantifying the contribution of catalysis to gene deletion phenotypes under different environmental conditions. While most phenotypes were explained by loss of catalysis, slow growth was readily rescued by a catalytically inactive protein in about one-third of the enzymes tested. Such noncatalytic phenotypes were frequent in the Alt1 and Bat2 transaminases and in the isoleucine/valine biosynthetic enzymes Ilv1 and Ilv2, suggesting novel "moonlighting" activities in these proteins. Furthermore, differential genetic interaction profiles of gene deletion and catalytic mutants indicated that ILV1 is functionally associated with regulatory processes, specifically to chromatin modification. Our systematic study shows that gene loss phenotypes and their genetic interactions are frequently not driven by the loss of an annotated catalytic function, underscoring the moonlighting nature of cellular metabolism.

MPFit: Computational Tool for Predicting Moonlighting Proteins

An increasing number of proteins have been found which are capable of performing two or more distinct functions. These proteins, known as moonlighting proteins, have drawn much attention recently as they may play critical roles in disease pathways and development. However, because moonlighting proteins are often found serendipitously, our understanding of moonlighting proteins is still quite limited. In order to lay the foundation for systematic moonlighting proteins studies, we developed MPFit, a software package for predicting moonlighting proteins from their omics features including protein-protein and gene interaction networks. Here, we describe and demonstrate the algorithm of MPFit, the idea behind it, and provide instruction for using the software.