Mass spectrometry-based functional proteomics: from molecular machines to protein networks

Investigating Anion Effects on Metal Ion Binding Interactions With Amyloid β Peptide by Ion Mobility Mass Spectrometry

The study of metal ion's role in the biological processes of Alzheimer's disease has spurred investigations into the coordination chemistry of amyloid beta peptide and its fragments. Nano-electrospray ionization mass spectrometry (nESI-MS) has been utilized to examine the stabilization of bound anions on multiprotein complexes without bulk solvent. However, the effects of anions on metal ion binding interactions with amyloid beta peptide have not been explored. This study directly examined metal-peptide complexes using nESI-MS and investigated the effects of various anions on the binding ratio and stability of these complexes from ammonium salt solutions. The results indicate that different anions have distinct effects on the binding ratio and stability of various metal-peptide complexes. Of these, the bicarbonate ion exhibits the highest binding ratios for metal-peptide complexes, while binding ratios for these complexes in phosphate are comparatively low. Our results suggest that acetate, formate, bicarbonate, and phosphate have weak affinities and act as weak stabilizers of the metal-peptide complex structure in the gas phase. Intriguingly, chloride and sulfate act as stabilizers of the metal-peptide complex in the gas phase. The rank order determined from these data is substantially different from the Hofmeister salt series in solution. Although this outcome was anticipated due to the reduced influence of anions and water solvation, our findings correlate well with expected anion binding in solution and emphasize the importance of both hydration layer and anion-metal-peptide binding effects for Hofmeister-type stabilization in solution. This approach proved useful in examining the interactions between metal ions and amyloid beta peptide, which are relevant to Alzheimer's disease, using direct ESI-MS.

A computational pipeline elucidating functions of conserved hypothetical Trypanosoma cruzi proteins based on public proteomic data

The proteome is complex, dynamic, and functionally diverse. Functional proteomics aims to characterize the functions of proteins in biological systems. However, there is a delay in annotating the function of proteins, even in model organisms. This gap is even greater in other organisms, including Trypanosoma cruzi, the causative agent of the parasitic, systemic, and sometimes fatal disease called Chagas disease. About 99.8% of Trypanosoma cruzi proteome is not manually annotated (unreviewed), among which>25% are conserved hypothetical proteins (CHPs), calling attention to the knowledge gap on the protein content of this organism. CHPs are conserved proteins among different species of various evolutionary lineages; however, they lack functional validation. This study describes a bioinformatics pipeline applied to public proteomic data to infer possible biological functions of conserved hypothetical Trypanosoma cruzi proteins. Here, the adopted strategy consisted of collecting differentially expressed proteins between the epimastigote and metacyclic trypomastigotes stages of Trypanosoma cruzi; followed by the functional characterization of these CHPs applying a manifold learning technique for dimension reduction and 3D structure homology analysis (Spalog). We found a panel of 25 and 26 upregulated proteins in the epimastigote and metacyclic trypomastigote stages, respectively; among these, 18 CHPs (8 in the epimastigote stage and 10 in the metacyclic stage) were characterized. The data generated corroborate the literature and complement the functional analyses of differentially regulated proteins at each stage, as they attribute potential functions to CHPs, which are frequently identified in Trypanosoma cruzi proteomics studies. However, it is important to point out that experimental validation is required to deepen our understanding of the CHPs.

MATR3 is an endogenous inhibitor of DUX4 in FSHD muscular dystrophy

Facioscapulohumeral muscular dystrophy (FSHD) is one of the most common neuromuscular disorders and has no cure. Due to an unknown molecular mechanism, FSHD displays overlapping manifestations with the neurodegenerative disease amyotrophic lateral sclerosis (ALS). FSHD is caused by aberrant gain of expression of the transcription factor double homeobox 4 (DUX4), which triggers a pro-apoptotic transcriptional program resulting in inhibition of myogenic differentiation and muscle wasting. Regulation of DUX4 activity is poorly known. We identify Matrin 3 (MATR3), whose mutation causes ALS and dominant distal myopathy, as a cellular factor controlling DUX4 expression and activity. MATR3 binds to the DUX4 DNA-binding domain and blocks DUX4-mediated gene expression, rescuing cell viability and myogenic differentiation of FSHD muscle cells, without affecting healthy muscle cells. Finally, we characterize a shorter MATR3 fragment that is necessary and sufficient to directly block DUX4-induced toxicity to the same extent as the full-length protein. Collectively, our data suggest MATR3 as a candidate for developing a treatment for FSHD.

Proteomic Alteration in the Progression of Multiple Myeloma: A Comprehensive Review

Multiple myeloma (MM) is an incurable hematologic malignancy. Most MM patients are diagnosed at a late stage because the early symptoms of the disease can be uncertain and nonspecific, often resembling other, more common conditions. Additionally, MM patients are commonly associated with rapid relapse and an inevitable refractory phase. MM is characterized by the abnormal proliferation of monoclonal plasma cells in the bone marrow. During the progression of MM, massive genomic alterations occur that target multiple signaling pathways and are accompanied by a multistep process involving differentiation, proliferation, and invasion. Moreover, the transformation of healthy plasma cell biology into genetically heterogeneous MM clones is driven by a variety of post-translational protein modifications (PTMs), which has complicated the discovery of effective treatments. PTMs have been identified as the most promising candidates for biomarker detection, and further research has been recommended to develop promising surrogate markers. Proteomics research has begun in MM, and a comprehensive literature review is available. However, proteomics applications in MM have yet to make significant progress. Exploration of proteomic alterations in MM is worthwhile to improve understanding of the pathophysiology of MM and to search for new treatment targets. Proteomics studies using mass spectrometry (MS) in conjunction with robust bioinformatics tools are an excellent way to learn more about protein changes and modifications during disease progression MM. This article addresses in depth the proteomic changes associated with MM disease transformation.

Allergy, asthma, and proteomics: opportunities with immediate impact

Allergy is widely discussed by researchers due to its complex mechanism that leads to disorders and injuries, but the reason behind the allergic status remains unclear. Current treatments are insufficient to improve the patient's quality of life significantly. New technologies in scientific and technological development are emerging. For instance, the union between allergy and peptidomics and bioinformatics tools may help fill the gaps in this field, diagnosis, and treatment. In this review, we look at peptidomics and address some findings, such as target proteins or biomarkers that help better understand mechanisms that lead to inflammation, organ damage, and, consequently, poor quality of life or even death.

A network view of human immune system and virus-human interaction

The immune system is highly networked and complex, which is continuously changing as encountering old and new pathogens. However, reductionism-based researches do not give a systematic understanding of the molecular mechanism of the immune response and viral pathogenesis. Here, we present HUMPPI-2022, a high-quality human protein-protein interaction (PPI) network, containing > 11,000 protein-coding genes with > 78,000 interactions. The network topology and functional characteristics analyses of the immune-related genes (IRGs) reveal that IRGs are mostly located in the center of the network and link genes of diverse biological processes, which may reflect the gene pleiotropy phenomenon. Moreover, the virus-human interactions reveal that pan-viral targets are mostly hubs, located in the center of the network and enriched in fundamental biological processes, but not for coronavirus. Finally, gene age effect was analyzed from the view of the host network for IRGs and virally-targeted genes (VTGs) during evolution, with IRGs gradually became hubs and integrated into host network through bridging functionally differentiated modules. Briefly, HUMPPI-2022 serves as a valuable resource for gaining a better understanding of the composition and evolution of human immune system, as well as the pathogenesis of viruses.

Omics Technologies for High-Throughput-Screening of Cell-Biomaterial Interactions

Recent research effort in biomaterial development has largely focused on engineering bio-instructive materials to stimulate specific cell signaling. Assessing the biological performance of these materials using time-consuming and trial-and-error traditional...

Current status and future perspectives of computational studies on human-virus protein-protein interactions

The protein-protein interactions (PPIs) between human and viruses mediate viral infection and host immunity processes. Therefore, the study of human-virus PPIs can help us understand the principles of human-virus relationships and can thus guide the development of highly effective drugs to break the transmission of viral infectious diseases. Recent years have witnessed the rapid accumulation of experimentally identified human-virus PPI data, which provides an unprecedented opportunity for bioinformatics studies revolving around human-virus PPIs. In this article, we provide a comprehensive overview of computational studies on human-virus PPIs, especially focusing on the method development for human-virus PPI predictions. We briefly introduce the experimental detection methods and existing database resources of human-virus PPIs, and then discuss the research progress in the development of computational prediction methods. In particular, we elaborate the machine learning-based prediction methods and highlight the need to embrace state-of-the-art deep-learning algorithms and new feature engineering techniques (e.g. the protein embedding technique derived from natural language processing). To further advance the understanding in this research topic, we also outline the practical applications of the human-virus interactome in fundamental biological discovery and new antiviral therapy development.

Proteomics for Target Identification in Psychiatric and Neurodegenerative Disorders

Psychiatric and neurodegenerative disorders such as schizophrenia (SCZ), Parkinson's disease (PD), and Alzheimer's disease (AD) continue to grow around the world with a high impact on health, social, and economic outcomes for the patient and society. Despite efforts, the etiology and pathophysiology of these disorders remain unclear. Omics technologies have contributed to the understanding of the molecular mechanisms that underlie these complex disorders and have suggested novel potential targets for treatment and diagnostics. Here, we have highlighted the unique and common pathways shared between SCZ, PD, and AD and highlight the main proteomic findings over the last 5 years using in vitro models, postmortem brain samples, and cerebrospinal fluid (CSF) or blood of patients. These studies have identified possible therapeutic targets and disease biomarkers. Further studies including target validation, the use of large sample sizes, and the integration of omics findings with bioinformatics tools are required to provide a better comprehension of pharmacological targets.

Navigating the Global Protein-Protein Interaction Landscape Using iRefWeb

iRefWeb is a resource that provides web interface to a large collection of protein-protein interactions aggregated from major primary databases. The underlying data-consolidation process, called iRefIndex, implements a rigorous methodology of identifying redundant protein sequences and integrating disparate data records that reference the same peptide sequences, despite many potential differences in data identifiers across various source databases. iRefWeb offers a unified user interface to all interaction records and associated information collected by iRefIndex, in addition to a number of data filters and visual features that present the supporting evidence. Users of iRefWeb can explore the consolidated landscape of protein-protein interactions, establish the provenance and reliability of each data record, and compare annotations performed by different data curator teams. The iRefWeb portal is freely available at http://wodaklab.org/iRefWeb .

Proteomics-inspired precision medicine for treating and understanding multiple myeloma

Introduction

Remarkable progress in molecular characterization methods has led to significant improvements in how we manage multiple myeloma (MM). The introduction of novel therapies has led to significant improvements in overall survival over the past 10 years. However, MM remains incurable and treatment choice is largely based on outdated risk-adaptive strategies that do not factor in improved treatment outcomes in the context of modern therapies.

Areas covered

This review discusses current risk-adaptive strategies in MM and the clinical application of proteomics in the monitoring of treatment response, disease progression, and minimal residual disease (MRD). We also discuss promising biomarkers of disease progression, treatment response, and chemoresistance. Finally, we will discuss an immunomics-based approach to monoclonal antibody (mAb), vaccine, and CAR-T cell development.

Expert opinion

It is an exciting era in oncology with basic scientific knowledge translating in novel therapeutic approaches to improve patient outcomes. With the advent of effective immunotherapies and targeted therapies, it has become crucial to identify biomarkers to aid in the stratification of patients based on anticipated sensitivity to chemotherapy. As a paradigm of diseases highly dependent on protein homeostasis, multiple myeloma provides the perfect opportunity to investigate the use of proteomics to aid in precision medicine.

Proteomics and Leishmaniasis: Potential Clinical Applications

Leishmaniases are diseases caused by protozoan parasites of the genus Leishmania. They are endemic in 98 countries, affect around 12 million people worldwide and may present several distinct clinical forms. Unfortunately, there are only a few drugs available for treatment of leishmaniasis, which are toxic and not always effective. Different parasite species and different clinical forms require optimization of the treatment or more specific therapies, which are not available. The emergence of resistance is also a matter of concern. Besides, diagnosis can sometimes be complicated due to atypical manifestations and associations with other pathologies. In this review, proteomic data are presented and discussed in terms of their application in important issues in leishmaniasis such as parasite resistance to chemotherapy, diagnosis of active disease in patients and dogs, markers for different clinical forms, identification of virulence factors, and their potential use in vaccination. It is shown that proteomics has contributed to the discovery of potential biomarkers for prognosis, diagnosis, therapeutics, monitoring of disease progression, treatment follow-up and identification of vaccine candidates for specific diseases. However, the authors believe its capabilities have not yet been fully explored for routine clinical analysis for several reasons, which will be presented in this review.

Quantitative proteomics analysis of sporadic parathyroid adenoma tissue samples

OBJECTIVE

Molecular pathogenesis of parathyroid tumors is incompletely understood. Identification of novel molecules and understanding their role in parathyroid tumorigenesis by proteomics approach would be informative with potential clinical implications.

METHOD

Adenomatous (n = 5) and normal (n = 2) parathyroid tissue lysates were analyzed for protein profile by LC-MS/MS method and the proteins were classified using bioinformatics tools such as PANTHER and toppfun functional enrichment tool. Identified proteins were further validated by western blotting and qRT-PCR (n = 20).

RESULT

Comparative proteomics analysis revealed that a total of 206 proteins (74 upregulated and 132 downregulated) were differentially expressed (≥ twofold change) in adenomas. Bioinformatics analysis revealed that 48 proteins were associated with plasma membrane, 49 with macromolecular complex, 39 were cytoplasm, 38 were organelle related, 21 were cell junction and 10 were extracellular proteins. These proteins belonged to a diverse protein family such as enzymes, transcription factors, cell signalling, cell adhesion, cytoskeleton proteins, receptors, and calcium-binding proteins. The major biological processes predicted for the proteins were a cellular, metabolic and developmental process, cellular localization, and biological regulation. The differentially expressed proteins were found to be associated with MAPK, phospholipase C (PLC) and phosphatidylinositol (PI) signalling pathways, and with chromatin organization. Western blot and qRT-PCR analysis of three proteins (DNAJC2, ACO2, and PRDX2) validated the LC-MS/MS findings.

CONCLUSION

This exploratory study demonstrates the feasibility of proteomics approach in finding the dysregulated proteins in benign parathyroid adenomas, and our preliminary results suggest that MAPK, PLC and PI signalling pathways and chromatin organization are involved in parathyroid tumorigenesis.

Functional Proteomics of Nuclear Proteins in Tetrahymena thermophila: A Review

Identification and characterization of protein complexes and interactomes has been essential to the understanding of fundamental nuclear processes including transcription, replication, recombination, and maintenance of genome stability. Despite significant progress in elucidation of nuclear proteomes and interactomes of organisms such as yeast and mammalian systems, progress in other models has lagged. Protists, including the alveolate ciliate protozoa with Tetrahymena thermophila as one of the most studied members of this group, have a unique nuclear biology, and nuclear dimorphism, with structurally and functionally distinct nuclei in a common cytoplasm. These features have been important in providing important insights about numerous fundamental nuclear processes. Here, we review the proteomic approaches that were historically used as well as those currently employed to take advantage of the unique biology of the ciliates, focusing on Tetrahymena, to address important questions and better understand nuclear processes including chromatin biology of eukaryotes.

Mass Spectrometry for Proteomics and Recent Developments in ESI, MALDI and other Ionization Methodologies

Background:

Mass spectrometry is a tool used in analytical chemistry to identify components in a chemical compound and it is of tremendous importance in the field of biology for high throughput analysis of biomolecules, among which protein is of great interest.

Objective:

Advancement in proteomics based on mass spectrometry has led the way to quantify multiple protein complexes, and proteins interactions with DNA/RNA or other chemical compounds which is a breakthrough in the field of bioinformatics.

Methods:

Many new technologies have been introduced in electrospray ionization (ESI) and Matrixassisted Laser Desorption/Ionization (MALDI) techniques which have enhanced sensitivity, resolution and many other key features for the characterization of proteins.

Results:

The advent of ambient mass spectrometry and its different versions like Desorption Electrospray Ionization (DESI), DART and ELDI has brought a huge revolution in proteomics research. Different imaging techniques are also introduced in MS to map proteins and other significant biomolecules. These drastic developments have paved the way to analyze large proteins of >200kDa easily.

Conclusion:

Here, we discuss the recent advancement in mass spectrometry, which is of great importance and it could lead us to further deep analysis of the molecules from different perspectives and further advancement in these techniques will enable us to find better ways for prediction of molecules and their behavioral properties.

Emerging roles of allosteric modulators in the regulation of protein-protein interactions (PPIs): A new paradigm for PPI drug discovery

Protein-protein interactions (PPIs) are closely implicated in various types of cellular activities and are thus pivotal to health and disease states. Given their fundamental roles in a wide range of biological processes, the modulation of PPIs has enormous potential in drug discovery. However, owing to the general properties of large, flat, and featureless interfaces of PPIs, previous attempts have demonstrated that the generation of therapeutic agents targeting PPI interfaces is challenging, rendering them almost "undruggable" for decades. To date, rapid progress in chemical and structural biology techniques has promoted the exploitation of allostery as a novel approach in drug discovery. By attaching to allosteric sites that are topologically and spatially distinct from PPI interfaces, allosteric modulators can achieve improved physiochemical properties. Thus, allosteric modulators may represent an alternative strategy to target intractable PPIs and have attracted intense pharmaceutical interest. In this review, we first briefly introduce the characteristics of PPIs and then present different approaches for investigating PPIs, as well as the latest methods for modulating PPIs. Importantly, we comprehensively review the recent progress in the development of allosteric modulators to inhibit or stabilize PPIs. Finally, we conclude with future perspectives on the discovery of allosteric PPI modulators, especially the application of computational methods to aid in allosteric PPI drug discovery.

Proteomic analysis of cholera toxin adjuvant-stimulated human monocytes identifies Thrombospondin-1 and Integrin-β1 as strongly upregulated molecules involved in adjuvant activity

Cholera Toxin (CT) as well as its related non-toxic mmCT and dmLT mutant proteins have been shown to be potent adjuvants for mucosally administered vaccines. Their adjuvant activity involves activation of cAMP/protein kinase A (PKA) signaling and inflammasome/IL-1β pathways in antigen presenting cells (APC). To get a further understanding of the signal transduction and downstream pathways activated in APCs by this group of adjuvants we have, employing quantitative proteomic analytic tools, investigated human monocytes at various time points after treatment with CT. We report the activation of three main biological pathways among upregulated proteins, peaking at 16 hours of CT treatment: cellular organization, metabolism, and immune response. Specifically, in the further analyzed immune response pathway we note a strong upregulation of thrombospondin 1 (THBS1) and integrin β1 (ITGB1) in response to CT as well as to mmCT and dmLT, mediated via cAMP/PKA and NFKB signaling. Importantly, inhibition in vitro of THSB1 and ITGB1 in monocytes or primary dendritic cells using siRNA abrogated the ability of the treated APCs to promote an adjuvant-stimulated Th17 cell response when co-cultured with peripheral blood lymphocytes indicating the involvement of these molecules in the adjuvant action on APCs by CT, mmCT and dmLT.

Interactome mapping defines BRG1, a component of the SWI/SNF chromatin remodeling complex, as a new partner of the transcriptional regulator CTCF

The highly conserved zinc finger CCCTC-binding factor (CTCF) regulates genomic imprinting and gene expression by acting as a transcriptional activator or repressor of promoters and insulator of enhancers. The multiple functions of CTCF are accomplished by co-association with other protein partners and are dependent on genomic context and tissue specificity. Despite the critical role of CTCF in the organization of genome structure, to date, only a subset of CTCF interaction partners have been identified. Here we present a large-scale identification of CTCF-binding partners using affinity purification and high-resolution LC-MS/MS analysis. In addition to functional enrichment of specific protein families such as the ribosomal proteins and the DEAD box helicases, we identified novel high-confidence CTCF interactors that provide a still unexplored biochemical context for CTCF's multiple functions. One of the newly validated CTCF interactors is BRG1, the major ATPase subunit of the chromatin remodeling complex SWI/SNF, establishing a relationship between two master regulators of genome organization. This work significantly expands the current knowledge of the human CTCF interactome and represents an important resource to direct future studies aimed at uncovering molecular mechanisms modulating CTCF pleiotropic functions throughout the genome.

Endogenous epitope tagging of heat shock protein 70 isoform Hsc70 using CRISPR/Cas9

Heat shock protein 70 (Hsp70) is an evolutionarily well-conserved molecular chaperone involved in several cellular processes such as folding of proteins, modulating protein-protein interactions, and transport of proteins across the membrane. Binding partners of Hsp70 (known as "clients") are identified on an individual basis as researchers discover their particular protein of interest binds to Hsp70. A full complement of Hsp70 interactors under multiple stress conditions remains to be determined. A promising approach to characterizing the Hsp70 "interactome" is the use of protein epitope tagging and then affinity purification followed by mass spectrometry (AP-MS/MS). AP-MS analysis is a widely used method to decipher protein-protein interaction networks and identifying protein functions. Conventionally, the proteins are overexpressed ectopically which interferes with protein complex stoichiometry, skewing AP-MS/MS data. In an attempt to solve this issue, we used CRISPR/Cas9-mediated gene editing to integrate a tandem-affinity (TAP) epitope tag into the genomic locus of HSC70. This system offers several benefits over existing expression systems including native expression, no requirement for selection, and homogeneity between cells. This cell line, freely available to chaperone researchers, will aid in small and large-scale protein interaction studies as well as the study of biochemical activities and structure-function relationships of the Hsc70 protein.

Analyses of Protein Interaction Networks Using Computational Tools

The knowledge of protein-protein interactions (PPIs) and PPI networks (PPINs) is the key to starting to understand the biological processes inside the cell. Many computational tools have been designed to help explore PPIs and PPINs, such as those for interaction detection, reliability assessment and interaction network construction. Here, the application of computational tools is reviewed from three perspectives: PPI database construction, PPI prediction, and interaction network construction and analysis. This overview will provide researchers guidance on choosing appropriate methods for exploring PPIs.

Protein complexes, big data, machine learning and integrative proteomics: lessons learned over a decade of systematic analysis of protein interaction networks

OVERVIEW

Elucidation of the networks of physical (functional) interactions present in cells and tissues is fundamental for understanding the molecular organization of biological systems, the mechanistic basis of essential and disease-related processes, and for functional annotation of previously uncharacterized proteins (via guilt-by-association or -correlation). After a decade in the field, we felt it timely to document our own experiences in the systematic analysis of protein interaction networks. Areas covered: Researchers worldwide have contributed innovative experimental and computational approaches that have driven the rapidly evolving field of 'functional proteomics'. These include mass spectrometry-based methods to characterize macromolecular complexes on a global-scale and sophisticated data analysis tools - most notably machine learning - that allow for the generation of high-quality protein association maps. Expert commentary: Here, we recount some key lessons learned, with an emphasis on successful workflows, and challenges, arising from our own and other groups' ongoing efforts to generate, interpret and report proteome-scale interaction networks in increasingly diverse biological contexts.

Identification of α-enolase as a prognostic and diagnostic precancer biomarker in oral submucous fibrosis

AIMS

Diagnostic ambiguities regarding the malignant potentiality of oral submucous fibrosis (OSF), an oral precancerous condition having dysplastic and non-dysplastic isoforms are the major failure for early intervention of oral squamous cell carcinoma (OSCC) patients. Our goal is to identify proteomic signatures from biopsies that can be used as precancer diagnostic marker for patient suffering from OSF.

METHODS

The high throughput techniques adopting de novo peptide sequencing (1D SDS-PAGE coupled nanoLC MALDI tandem mass spectrometry (MS/MS)-based peptide mass fingerprint), immunohistochemistry (IHC), Western blot (WB) and real-time PCR (RT-PCR) analysis are considered for such biomarker identification and multilevel validations.

RESULTS

Alpha-enolase is identified as an overexpressed protein in biopsies of oral submucous fibrosis with dysplasia (OSFWD) compared with oral submucous fibrosis without dysplasia (OSFWT) and normal oral mucosa (NOM). Total proteome analysis of an overexpressed protein band around 47 kDa of OSFWD identifies 334 peptides corresponding to 61 human proteins. Among them α-enolase is identified as a prime protein with highest number of peptides (44 out of 334 peptides) and sequence coverage (66.4%). Furthermore, RT-PCR, WB and IHC analysis also show mRNA and tissue level upregulation of α-enolase in OSFWD validating α-enolase as precancer marker.

CONCLUSIONS

This study for the first time identifies and validates α-enolase as a novel biomarker for early diagnosis of malignant potentiality of OSF. Hence, the identified protein marker, α-enolase can help in early therapeutic intervention of OSF patients leading to the reduction of patient's pain, treatment cost and enhancement of patient's quality of life.

Proton Dynamics in Protein Mass Spectrometry

Native electrospray ionization/ion mobility-mass spectrometry (ESI/IM-MS) allows an accurate determination of low-resolution structural features of proteins. Yet, the presence of proton dynamics, observed already by us for DNA in the gas phase, and its impact on protein structural determinants, have not been investigated so far. Here, we address this issue by a multistep simulation strategy on a pharmacologically relevant peptide, the N-terminal residues of amyloid-β peptide (Aβ(1-16)). Our calculations reproduce the experimental maximum charge state from ESI-MS and are also in fair agreement with collision cross section (CCS) data measured here by ESI/IM-MS. Although the main structural features are preserved, subtle conformational changes do take place in the first ∼0.1 ms of dynamics. In addition, intramolecular proton dynamics processes occur on the picosecond-time scale in the gas phase as emerging from quantum mechanics/molecular mechanics (QM/MM) simulations at the B3LYP level of theory. We conclude that proton transfer phenomena do occur frequently during fly time in ESI-MS experiments (typically on the millisecond time scale). However, the structural changes associated with the process do not significantly affect the structural determinants.

An Inducible Retroviral Expression System for Tandem Affinity Purification Mass-Spectrometry-Based Proteomics Identifies Mixed Lineage Kinase Domain-like Protein (MLKL) as an Heat Shock Protein 90 (HSP90) Client

Tandem affinity purification–mass spectrometry (TAP-MS) is a popular strategy for the identification of protein–protein interactions, characterization of protein complexes, and entire networks. Its employment in cellular settings best fitting the relevant physiology is limited by convenient expression vector systems. We developed an easy-to-handle, inducible, dually selectable retroviral expression vector allowing dose- and time-dependent control of bait proteins bearing the efficient streptavidin-hemagglutinin (SH)-tag at their N- or C termini. Concomitant expression of a reporter fluorophore allows to monitor bait-expressing cells by flow cytometry or microscopy and enables high-throughput phenotypic assays. We used the system to successfully characterize the interactome of the neuroblastoma RAS viral oncogene homolog (NRAS) Gly12Asp (G12D) mutant and exploited the advantage of reporter fluorophore expression by tracking cytokine-independent cell growth using flow cytometry. Moreover, we tested the feasibility of studying cytotoxicity-mediating proteins with the vector system on the cell death-inducing mixed lineage kinase domain-like protein (MLKL) Ser358Asp (S358D) mutant. Interaction proteomics analysis of MLKL Ser358Asp (S358D) identified heat shock protein 90 (HSP90) as a high-confidence interacting protein. Further phenotypic characterization established MLKL as a novel HSP90 client. In summary, this novel inducible expression system enables SH-tag-based interaction studies in the cell line proficient for the respective phenotypic or signaling context and constitutes a valuable tool for experimental approaches requiring inducible or traceable protein expression.

TNIP2 is a Hub Protein in the NF-κB Network with Both Protein and RNA Mediated Interactions

The NF-κB family of transcription factors is pivotal in controlling cellular responses to environmental stresses; abnormal nuclear factor kappa-light-chain-enhancer of activated B cells (NF-κB) signaling features in many autoimmune diseases and cancers. Several components of the NF-κB signaling pathway have been reported to interact with the protein TNIP2 (also known as ABIN2), and TNIP2 can both positively and negatively regulate NF-κB- dependent transcription of target genes. However, the function of TNIP2 remains elusive and the cellular machinery associating with TNIP2 has not been systematically defined. Here we first used a broad MudPIT/Halo Affinity Purification Mass Spectrometry (AP-MS) approach to map the network of proteins associated with the NF-κB transcription factors, and establish TNIP2 as an NF-κB network hub protein. We then combined AP-MS with biochemical approaches in a more focused study of truncated and mutated forms of TNIP2 to map protein associations with distinct regions of TNIP2. NF-κB interacted with the N-terminal region of TNIP2. A central region of TNIP2 interacted with the endosomal sorting complex ESCRT-I via its TSG101 subunit, a protein essential for HIV-1 budding, and a single point mutant in TNIP2 disrupted this interaction. The major gene ontology category for TNIP2 associated proteins was mRNA metabolism, and several of these associations, like KHDRBS1, were lost upon depletion of RNA. Given the major association of TNIP2 with mRNA metabolism proteins, we analyzed the RNA content of affinity purified TNIP2 using RNA-Seq. Surprisingly, a specific limited number of mRNAs was associated with TNIP2. These RNAs were enriched for transcription factor binding, transcription factor cofactor activity, and transcription regulator activity. They included mRNAs of genes in the Sin3A complex, the Mediator complex, JUN, HOXC6, and GATA2. Taken together, our findings suggest an expanded role for TNIP2, establishing a link between TNIP2, cellular transport machinery, and RNA transcript processing.

Epigenetic targets in hepatocellular carcinoma cells: identification of chaperone protein complexes with histone deacetylases

AIMS

The study was designed to find out the protein complex(s) associated with HDAC3 in liver cancer using a modified form of affinity purification coupled with a mass spectrometry technique in HepG2 cells. The organ-specific requirement for HDAC1 and HDAC3 during liver formation in zebrafish and their altered expression in liver cancer tissues indicates they are indispensible for hepato-organogenesis and hepatocarcinogenesis. However, how they exert their function is unknown.

MATERIAL & METHODS

HepG2 cells were transfected with either mock or construct-containing HDAC3 using a C-terminal strepIII-HA tag as bait. The bait proteins were purified by double affinity columns and were analyzed on a Thermo LTQ Orbitrap™ (Thermo Scientific, MA, USA) chromatography system.

RESULTS

Affinity purification coupled with mass spectrometry resulted in the identification of 24 putative binders of HDAC3 in HepG2 cells. The majority (83%) of these are novel interactions are reported for the first time in this study.

CONCLUSION

This is the first study reporting the affinity purification and identification of protein complexes with two closely related proteins in one cell line. The novel HDAC1 and HDAC3 complexes identified in HepG2 cells could serve as a platform for the design of future therapeutic medicine for the treatment of liver cancer.

Rapid immunoprecipitation mass spectrometry of endogenous proteins (RIME) for analysis of chromatin complexes

Rapid immunoprecipitation mass spectrometry of endogenous protein (RIME) is a method that allows the study of protein complexes, in particular chromatin and transcription factor complexes, in a rapid and robust manner by mass spectrometry (MS). The method can be used in parallel with chromatin immunoprecipitation-sequencing (ChIP-seq) experiments to provide information on both the cistrome and interactome for a given protein. The method uses formaldehyde fixation to stabilize protein complexes. By using antibodies against the endogenous target, the cross-linked complex is immunoprecipitated, rigorously washed, and then digested into peptides while avoiding antibody contamination (on-bead digestion). By using this method, MS identification of the target protein and several dozen interacting proteins is possible using a 100-min LC-MS/MS run. The protocol does not require substantial proteomics expertise, and it typically takes 2-3 d from the collection of material to results.

A non-synonymous single-nucleotide polymorphism associated with multiple sclerosis risk affects the EVI5 interactome

Despite recent progress in the characterization of genetic loci associated with multiple sclerosis (MS) risk, the ubiquitous linkage disequilibrium operating across the genome has stalled efforts to distinguish causative variants from proxy single-nucleotide polymorphisms (SNPs). Here, we have identified through fine mapping and meta-analysis EVI5 as the most plausible disease risk gene within the 1p22.1 locus. We further show that an exonic SNP associated with risk induces changes in superficial hydrophobicity patterns of the coiled-coil domain of EVI5, which, in turns, affects the EVI5 interactome. Immunoprecipitation of wild-type and mutated EVI5 followed by mass spectrometry generated a roster of disease-specific interactors functionally linked to lipid metabolism. Among the exclusive binding partners of the risk variant, we describe the novel interaction with sphingosine 1-phosphate lyase (SGPL1)-a key enzyme for the creation of the sphingosine-1 phosphate gradient, which is relevant to the pathogenic process and therapeutic management of MS.

Advances in coupling microfluidic chips to mass spectrometry

Microfluidic technology has shown advantages of low sample consumption, reduced analysis time, high throughput, and potential for integration and automation. Coupling microfluidic chips to mass spectrometry (Chip-MS) can greatly improve the overall analytical performance of MS-based approaches and expand their potential applications. In this article, we review the advances of Chip-MS in the past decade, covering innovations in microchip fabrication, microchips coupled to electrospray ionization (ESI)-MS and matrix-assisted laser desorption/ionization (MALDI)-MS. Development of integrated microfluidic systems for automated MS analysis will be further documented, as well as recent applications of Chip-MS in proteomics, metabolomics, cell analysis, and clinical diagnosis.

The Prion Protein Controls Polysialylation of Neural Cell Adhesion Molecule 1 during Cellular Morphogenesis

Despite its multi-faceted role in neurodegenerative diseases, the physiological function of the prion protein (PrP) has remained elusive. On the basis of its evolutionary relationship to ZIP metal ion transporters, we considered that PrP may contribute to the morphogenetic reprogramming of cells underlying epithelial-to-mesenchymal transitions (EMT). Consistent with this hypothesis, PrP transcription increased more than tenfold during EMT, and stable PrP-deficient cells failed to complete EMT in a mammalian cell model. A global comparative proteomics analysis identified the neural cell adhesion molecule 1 (NCAM1) as a candidate mediator of this impairment, which led to the observation that PrP-deficient cells fail to undergo NCAM1 polysialylation during EMT. Surprisingly, this defect was caused by a perturbed transcription of the polysialyltransferase ST8SIA2 gene. Proteomics data pointed toward β-catenin as a transcriptional regulator affected in PrP-deficient cells. Indeed, pharmacological blockade or siRNA-based knockdown of β-catenin mimicked PrP-deficiency in regards to NCAM1 polysialylation. Our data established the existence of a PrP-ST8SIA2-NCAM signaling loop, merged two mature fields of investigation and offer a simple model for explaining phenotypes linked to PrP.

Quantitative phosphoproteomics of the ataxia telangiectasia-mutated (ATM) and ataxia telangiectasia-mutated and rad3-related (ATR) dependent DNA damage response in Arabidopsis thaliana

The reversible phosphorylation of proteins on serine, threonine, and tyrosine residues is an important biological regulatory mechanism. In the context of genome integrity, signaling cascades driven by phosphorylation are crucial for the coordination and regulation of DNA repair. The two serine/threonine protein kinases ataxia telangiectasia-mutated (ATM) and Ataxia telangiectasia-mutated and Rad3-related (ATR) are key factors in this process, each specific for different kinds of DNA lesions. They are conserved across eukaryotes, mediating the activation of cell-cycle checkpoints, chromatin modifications, and regulation of DNA repair proteins. We designed a novel mass spectrometry-based phosphoproteomics approach to study DNA damage repair in Arabidopsis thaliana. The protocol combines filter aided sample preparation, immobilized metal affinity chromatography, metal oxide affinity chromatography, and strong cation exchange chromatography for phosphopeptide generation, enrichment, and separation. Isobaric labeling employing iTRAQ (isobaric tags for relative and absolute quantitation) was used for profiling the phosphoproteome of atm atr double mutants and wild type plants under either regular growth conditions or challenged by irradiation. A total of 10,831 proteins were identified and 15,445 unique phosphopeptides were quantified, containing 134 up- and 38 down-regulated ATM/ATR dependent phosphopeptides. We identified known and novel ATM/ATR targets such as LIG4 and MRE11 (needed for resistance against ionizing radiation), PIE1 and SDG26 (implicated in chromatin remodeling), PCNA1, WAPL, and PDS5 (implicated in DNA replication), and ASK1 and HTA10 (involved in meiosis).

Mapping protein complexes using covalently linked antibodies and isobaric mass tags

Affinity enrichment techniques in combination with quantitative proteomics enable the unbiased identification of protein-protein interaction, and thus the delineation of protein complexes and interaction networks. Here, we describe an immunoaffinity enrichment approach that employs covalently immobilized antibodies for the identification of protein-protein interactions of endogenously expressed proteins under near-to-physiological conditions. Specifically enriched proteins are identified using shotgun mass spectrometry and isobaric mass tag-based relative quantification.

Quantitative proteomics of the yeast Hsp70/Hsp90 interactomes during DNA damage reveal chaperone-dependent regulation of ribonucleotide reductase

The highly conserved molecular chaperones Hsp90 and Hsp70 are indispensible for folding and maturation of a significant fraction of the proteome, including many proteins involved in signal transduction and stress response. To examine the dynamics of chaperone-client interactions after DNA damage, we applied quantitative affinity-purification mass spectrometry (AP-MS) proteomics to characterize interactomes of the yeast Hsp70 isoform Ssa1 and Hsp90 isoform Hsp82 before and after exposure to methyl methanesulfonate. Of 256 proteins identified and quantified via (16)O(/18)O labeling and LC-MS/MS, 142 are novel Hsp70/90 interactors. Nearly all interactions remained unchanged or decreased after DNA damage, but 5 proteins increased interactions with Ssa1 and/or Hsp82, including the ribonucleotide reductase (RNR) subunit Rnr4. Inhibiting Hsp70 or 90 chaperone activity destabilized Rnr4 in yeast and its vertebrate homolog hRMM2 in breast cancer cells. In turn, pre-treatment of cancer cells with chaperone inhibitors sensitized cells to the RNR inhibitor gemcitabine, suggesting a novel chemotherapy strategy. All MS data have been deposited in the ProteomeXchange with identifier PXD001284.

BIOLOGICAL SIGNIFICANCE

This study provides the dynamic interactome of the yeast Hsp70 and Hsp90 under DNA damage which suggest key roles for the chaperones in a variety of signaling cascades. Importantly, the cancer drug target ribonucleotide reductase was shown to be a client of Hsp70 and Hsp90 in both yeast and breast cancer cells. As such, this study highlights the potential of a novel cancer therapeutic strategy that exploits the synergy of chaperone and ribonucleotide reductase inhibitors.

A new in vivo cross-linking mass spectrometry platform to define protein-protein interactions in living cells

Protein-protein interactions (PPIs) are fundamental to the structure and function of protein complexes. Resolving the physical contacts between proteins as they occur in cells is critical to uncovering the molecular details underlying various cellular activities. To advance the study of PPIs in living cells, we have developed a new in vivo cross-linking mass spectrometry platform that couples a novel membrane-permeable, enrichable, and MS-cleavable cross-linker with multistage tandem mass spectrometry. This strategy permits the effective capture, enrichment, and identification of in vivo cross-linked products from mammalian cells and thus enables the determination of protein interaction interfaces. The utility of the developed method has been demonstrated by profiling PPIs in mammalian cells at the proteome scale and the targeted protein complex level. Our work represents a general approach for studying in vivo PPIs and provides a solid foundation for future studies toward the complete mapping of PPI networks in living systems.

Molecular simulation-based structural prediction of protein complexes in mass spectrometry: the human insulin dimer

Protein electrospray ionization (ESI) mass spectrometry (MS)-based techniques are widely used to provide insight into structural proteomics under the assumption that non-covalent protein complexes being transferred into the gas phase preserve basically the same intermolecular interactions as in solution. Here we investigate the applicability of this assumption by extending our previous structural prediction protocol for single proteins in ESI-MS to protein complexes. We apply our protocol to the human insulin dimer (hIns2) as a test case. Our calculations reproduce the main charge and the collision cross section (CCS) measured in ESI-MS experiments. Molecular dynamics simulations for 0.075 ms show that the complex maximizes intermolecular non-bonded interactions relative to the structure in water, without affecting the cross section. The overall gas-phase structure of hIns2 does exhibit differences with the one in aqueous solution, not inferable from a comparison with calculated CCS. Hence, care should be exerted when interpreting ESI-MS proteomics data based solely on NMR and/or X-ray structural information.

Identification of histone deacetylase 1 protein complexes in liver cancer cells

BACKGROUND

Hepatocellular carcinoma is one of the leading causes of mortalities worldwide. The search for new therapeutic targets is of utmost importance for improved treatment. Altered expression of HDAC1 in hepatocellular carcinoma (HCC) and its requirement for liver formation in zebrafish, suggest that it may regulate key events in liver carcinogenesis and organogenesis. However, molecular mechanisms of HDAC1 action in liver carcinogenesis are largely unknown. The present study was conducted to identify HDAC1 interacting proteins in HepG2 cells using modified SH-double-affinity purification coupled with liquid mass spectrophotemetery.

MATERIALS AND METHODS

HepG2 cells were transfected with a construct containing HDAC1 with a C-terminal strepIII-HA tag as bait. Bait proteins were confirmed to be expressed in HepG2 cells by western blotting and purified by double affinity columns and protein complexes for analysis on a Thermo LTQ Orbitrap XL using a C18 nano flow ESI liquid chromatography system.

RESULTS

There were 27 proteins which showed novel interactions with HDAC1 identified only in this study, while 14 were among the established interactors. Various subunits of T complex proteins (TCP1) and prefoldin proteins (PFDN) were identified as interacting partners that showed high affinity with HDAC1 in HepG2 cells.

CONCLUSIONS

The double affinity purification method adopted in this study was very successful in terms of specificity and reproducibility. The novel HDAC1 complex identified in this study could be better therapeutic target for treatment of hepatocellular carcinoma.

A novel immuno-competitive capture mass spectrometry strategy for protein-protein interaction profiling reveals that LATS kinases regulate HCV replication through NS5A phosphorylation

Mapping protein-protein interactions is essential to fully characterize the biological function of a protein and improve our understanding of diseases. Affinity purification coupled to mass spectrometry (AP-MS) using selective antibodies against a target protein has been commonly applied to study protein complexes. However, one major limitation is a lack of specificity as a substantial part of the proposed binders is due to nonspecific interactions. Here, we describe an innovative immuno-competitive capture mass spectrometry (ICC-MS) method to allow systematic investigation of protein-protein interactions. ICC-MS markedly increases the specificity of classical immunoprecipitation (IP) by introducing a competition step between free and capturing antibody prior to IP. Instead of comparing only one experimental sample with a control, the methodology generates a 12-concentration antibody competition profile. Label-free quantitation followed by a robust statistical analysis of the data is then used to extract the cellular interactome of a protein of interest and to filter out background proteins. We applied this new approach to specifically map the interactome of hepatitis C virus (HCV) nonstructural protein 5A (NS5A) in a cellular HCV replication system and uncovered eight new NS5A-interacting protein candidates along with two previously validated binding partners. Follow-up biological validation experiments revealed that large tumor suppressor homolog 1 and 2 (LATS1 and LATS2, respectively), two closely related human protein kinases, are novel host kinases responsible for NS5A phosphorylation at a highly conserved position required for optimal HCV genome replication. These results are the first illustration of the value of ICC-MS for the analysis of endogenous protein complexes to identify biologically relevant protein-protein interactions with high specificity.

Mass spectrometry based proteomic analysis of salivary glands of urban malaria vector Anopheles stephensi

Salivary gland proteins of Anopheles mosquitoes offer attractive targets to understand interactions with sporozoites, blood feeding behavior, homeostasis, and immunological evaluation of malaria vectors and parasite interactions. To date limited studies have been carried out to elucidate salivary proteins of An. stephensi salivary glands. The aim of the present study was to provide detailed analytical attributives of functional salivary gland proteins of urban malaria vector An. stephensi. A proteomic approach combining one-dimensional electrophoresis (1DE), ion trap liquid chromatography mass spectrometry (LC/MS/MS), and computational bioinformatic analysis was adopted to provide the first direct insight into identification and functional characterization of known salivary proteins and novel salivary proteins of An. stephensi. Computational studies by online servers, namely, MASCOT and OMSSA algorithms, identified a total of 36 known salivary proteins and 123 novel proteins analysed by LC/MS/MS. This first report describes a baseline proteomic catalogue of 159 salivary proteins belonging to various categories of signal transduction, regulation of blood coagulation cascade, and various immune and energy pathways of An. stephensi sialotranscriptome by mass spectrometry. Our results may serve as basis to provide a putative functional role of proteins in concept of blood feeding, biting behavior, and other aspects of vector-parasite host interactions for parasite development in anopheline mosquitoes.

Integrating the interactome and the transcriptome of Drosophila

Background

Networks of interacting genes and gene products mediate most cellular and developmental processes. High throughput screening methods combined with literature curation are identifying many of the protein-protein interactions (PPI) and protein-DNA interactions (PDI) that constitute these networks. Most of the detection methods, however, fail to identify the in vivo spatial or temporal context of the interactions. Thus, the interaction data are a composite of the individual networks that may operate in specific tissues or developmental stages. Genome-wide expression data may be useful for filtering interaction data to identify the subnetworks that operate in specific spatial or temporal contexts. Here we take advantage of the extensive interaction and expression data available for Drosophila to analyze how interaction networks may be unique to specific tissues and developmental stages.

Results

We ranked genes on a scale from ubiquitously expressed to tissue or stage specific and examined their interaction patterns. Interestingly, ubiquitously expressed genes have many more interactions among themselves than do non-ubiquitously expressed genes both in PPI and PDI networks. While the PDI network is enriched for interactions between tissue-specific transcription factors and their tissue-specific targets, a preponderance of the PDI interactions are between ubiquitous and non-ubiquitously expressed genes and proteins. In contrast to PDI, PPI networks are depleted for interactions among tissue- or stage- specific proteins, which instead interact primarily with widely expressed proteins. In light of these findings, we present an approach to filter interaction data based on gene expression levels normalized across tissues or developmental stages. We show that this filter (the percent maximum or pmax filter) can be used to identify subnetworks that function within individual tissues or developmental stages.

Conclusions

These observations suggest that protein networks are frequently organized into hubs of widely expressed proteins to which are attached various tissue- or stage-specific proteins. This is consistent with earlier analyses of human PPI data and suggests a similar organization of interaction networks across species. This organization implies that tissue or stage specific networks can be best identified from interactome data by using filters designed to include both ubiquitously expressed and specifically expressed genes and proteins.

Interactome analysis of AMP-activated protein kinase (AMPK)-α1 and -β1 in INS-1 pancreatic beta-cells by affinity purification-mass spectrometry

The heterotrimeric enzyme AMP-activated protein kinase (AMPK) is a major metabolic factor that regulates the homeostasis of cellular energy. In particular, AMPK mediates the insulin resistance that is associated with type 2 diabetes. Generally, cellular processes require tight regulation of protein kinases, which is effected through their formation of complex with other proteins and substrates. Despite their critical function in regulation and pathogenesis, there are limited data on the interaction of protein kinases. To identify proteins that interact with AMPK, we performed large-scale affinity purification (AP)-mass spectrometry (MS) of the AMPK-α1 and -β1 subunits. Through a comprehensive analysis, using a combination of immunoprecipitaion and ion trap mass spectrometry, we identified 381 unique proteins in the AMPKα/β interactomes: 325 partners of AMPK-α1 and 243 for AMPK-β1. Further, we identified 196 novel protein-protein interactions with AMPK-α1 and AMPK-β1. Notably, in our bioinformatics analysis, the novel interaction partners mediated functions that are related to the regulation of actin organization. Specifically, several such proteins were linked to pancreatic beta-cell functions, including glucose-stimulated insulin secretion, beta-cell development, beta-cell differentiation, and cell-cell communication.

Elution profile analysis of SDS-induced subcomplexes by quantitative mass spectrometry

Analyzing the molecular architecture of native multiprotein complexes via biochemical methods has so far been difficult and error prone. Protein complex isolation by affinity purification can define the protein repertoire of a given complex, yet, it remains difficult to gain knowledge of its substructure or modular composition. Here, we introduce SDS concentration gradient induced decomposition of protein complexes coupled to quantitative mass spectrometry and in silico elution profile distance analysis. By applying this new method to a cellular transport module, the IFT/lebercilin complex, we demonstrate its ability to determine modular composition as well as sensitively detect known and novel complex components. We show that the IFT/lebercilin complex can be separated into at least five submodules, the IFT complex A, the IFT complex B, the 14-3-3 protein complex and the CTLH complex, as well as the dynein light chain complex. Furthermore, we identify the protein TULP3 as a potential new member of the IFT complex A and showed that several proteins, classified as IFT complex B-associated, are integral parts of this complex. To further demonstrate EPASIS general applicability, we analyzed the modular substructure of two additional complexes, that of B-RAF and of 14-3-3-ε. The results show, that EPASIS provides a robust as well as sensitive strategy to dissect the substructure of large multiprotein complexes in a highly time- as well as cost-effective manner.

A decade of proteomics studies of glaucomatous neurodegeneration

Glaucoma is a leading cause of blindness; however, limited understanding of the molecular mechanisms involved in optic nerve degeneration hinders the development of improved treatment strategies. Proteomics techniques that combine the protein chemistry, MS, and bioinformatics offer the opportunity to shed light on molecular mechanisms so that new treatment strategies can be developed for immunomodulation, neuroprotection, neurorescue, neuroregeneration, and function gain in glaucoma. The proteomics technologies also hold great promise for biomarker discovery, another important goal of glaucoma research. As much as developing new treatment strategies, molecular biomarkers are strongly needed for early diagnosis of glaucoma, prediction of its prognosis, and monitoring the responses to new treatments. It is now a decade that the proteomics analysis techniques have been using to move glaucoma research forward. This review will focus on valuable applications of proteomics in the field of glaucoma research and highlight the power of this analytical toolbox in translational and clinical research toward better characterization and improved treatment of glaucomatous neurodegeneration and discovery of glaucoma-related molecular biomarkers.

PRIMOS: an integrated database of reassessed protein-protein interactions providing web-based access to in silico validation of experimentally derived data

Steady improvements in proteomics present a bioinformatic challenge to retrieve, store, and process the accumulating and often redundant amount of information. In particular, a large-scale comparison and analysis of protein-protein interaction (PPI) data requires tools for data interpretation as well as validation. At this juncture, the Protein Interaction and Molecule Search (PRIMOS) platform represents a novel web portal that unifies six primary PPI databases (BIND, Biomolecular Interaction Network Database; DIP, Database of Interacting Proteins; HPRD, Human Protein Reference Database; IntAct; MINT, Molecular Interaction Database; and MIPS, Munich Information Center for Protein Sequences) into a single consistent repository, which currently includes more than 196,700 redundancy-removed PPIs. PRIMOS supports three advanced search strategies centering on disease-relevant PPIs, on inter- and intra-organismal crosstalk relations (e.g., pathogen-host interactions), and on highly connected protein nodes analysis ("hub" identification). The main novelties distinguishing PRIMOS from other secondary PPI databases are the reassessment of known PPIs, and the capacity to validate personal experimental data by our peer-reviewed, homology-based validation. This article focuses on definite PRIMOS use cases (presentation of embedded biological concepts, example applications) to demonstrate its broad functionality and practical value. PRIMOS is publicly available at http://primos.fh-hagenberg.at.