In this post-genomic era, protein network can be used as a complementary way to shed light on the growing amount of data generated from current high-throughput technologies. Protein network is a powerful approach to describe the molecular mechanisms of the biological events through protein-protein interactions. Here, we describe the computational methods used to construct the protein network using expression data. We provide a list of available tools and databases that can be used in constructing the network.
Protein-protein interactions are fundamental to various aspects of cell biology with many protein complexes participating in numerous fundamental biological processes such as transcription, translation and cell cycle. MS-based proteomics techniques are routinely applied for characterising the interactome, such as affinity purification coupled to mass spectrometry that has been used to selectively enrich and identify interacting partners of a bait protein. In recent years, many orthogonal MS-based techniques and approaches have surfaced including proximity-dependent labelling of neighbouring proteins, chemical cross-linking of two interacting proteins, as well as inferring PPIs from the co-behaviour of proteins such as the co-fractionating profiles and the thermal solubility profiles of proteins. This review discusses the underlying principles, advantages, limitations and experimental considerations of these emerging techniques. In addition, a brief account on how MS-based techniques are used to investigate the structural and functional properties of protein complexes, including their topology, stoichiometry, copy number and dynamics, are discussed.
Matched MeSH terms: Protein Interaction Mapping/methods*
Most proteins function by forming complexes within a dynamic interconnected network that underlies various biological mechanisms. To systematically investigate such interactomes, high-throughput techniques, including CF-MS, have been developed to capture, identify, and quantify protein-protein interactions (PPIs) on a large scale. Compared to other techniques, CF-MS allows the global identification and quantification of native protein complexes in one setting, without genetic manipulation. Furthermore, quantitative CF-MS can potentially elucidate the distribution of a protein in multiple co-elution features, informing the stoichiometries and dynamics of a target protein complex. In this issue, Youssef et al. (Proteomics 2023, 00, e2200404) combined multiplex CF-MS and a new algorithm to study the dynamics of the PPI network for Escherichia coli grown under ten different conditions. Although the results demonstrated that most proteins remained stable, the authors were able to detect disrupted interactions that were growth condition specific. Further bioinformatics analyses also revealed the biophysical properties and structural patterns that govern such a response.
Matched MeSH terms: Protein Interaction Mapping/methods
Mosquito-borne flaviviruses, including dengue virus (DENV) and Zika virus (ZIKV), are a growing public health concern. Systems-level analysis of how flaviviruses hijack cellular processes through virus-host protein-protein interactions (PPIs) provides information about their replication and pathogenic mechanisms. We used affinity purification-mass spectrometry (AP-MS) to compare flavivirus-host interactions for two viruses (DENV and ZIKV) in two hosts (human and mosquito). Conserved virus-host PPIs revealed that the flavivirus NS5 protein suppresses interferon stimulated genes by inhibiting recruitment of the transcription complex PAF1C and that chemical modulation of SEC61 inhibits DENV and ZIKV replication in human and mosquito cells. Finally, we identified a ZIKV-specific interaction between NS4A and ANKLE2, a gene linked to hereditary microcephaly, and showed that ZIKV NS4A causes microcephaly in Drosophila in an ANKLE2-dependent manner. Thus, comparative flavivirus-host PPI mapping provides biological insights and, when coupled with in vivo models, can be used to unravel pathogenic mechanisms.
Networks have been an excellent framework for modeling complex biological information, but the methodological details of network-based tools are often described for a technical audience. We have developed Graphery, an interactive tutorial webserver that illustrates foundational graph concepts frequently used in network-based methods. Each tutorial describes a graph concept along with executable Python code that can be interactively run on a graph. Users navigate each tutorial using their choice of real-world biological networks that highlight the diverse applications of network algorithms. Graphery also allows users to modify the code within each tutorial or write new programs, which all can be executed without requiring an account. Graphery accepts ideas for new tutorials and datasets that will be shaped by both computational and biological researchers, growing into a community-contributed learning platform. Graphery is available at https://graphery.reedcompbio.org/.
Sperm-egg interaction defect is a significant cause of in-vitro fertilization failure for infertile cases. Numerous molecular interactions in the form of protein-protein interactions mediate the sperm-egg membrane interaction process. Recent studies have demonstrated that in addition to experimental techniques, computational methods, namely protein interaction network approach, can address protein-protein interactions between human sperm and egg. Up to now, no drugs have been detected to treat sperm-egg interaction disorder, and the initial step in drug discovery research is finding out essential proteins or drug targets for a biological process. The main purpose of this study is to identify putative drug targets for human sperm-egg interaction deficiency and consider if the detected essential proteins are targets for any known drugs using protein-protein interaction network and ingenuity pathway analysis.
Matched MeSH terms: Protein Interaction Mapping/methods*
Based on clinical observations, women with polycystic ovarian syndrome (PCOS) are prone to developing several other diseases, such as metabolic and cardiovascular diseases. However, the molecular association between PCOS and these diseases remains poorly understood. Recent studies showed that the information from protein-protein interaction (PPI) network analysis are useful in understanding the disease association in detail. This study utilized this approach to deepen the knowledge on the association between PCOS and other diseases. A PPI network for PCOS was constructed using PCOS-related proteins (PCOSrp) obtained from PCOSBase. MCODE was used to identify highly connected regions in the PCOS network, known as subnetworks. These subnetworks represent protein families, where their molecular information is used to explain the association between PCOS and other diseases. Fisher's exact test and comorbidity data were used to identify PCOS-disease subnetworks. Pathway enrichment analysis was performed on the PCOS-disease subnetworks to identify significant pathways that are highly involved in the PCOS-disease associations. Migraine, schizophrenia, depressive disorder, obesity, and hypertension, along with twelve other diseases, were identified to be highly associated with PCOS. The identification of significant pathways, such as ribosome biogenesis, antigen processing and presentation, and mitophagy, suggest their involvement in the association between PCOS and migraine, schizophrenia, and hypertension.
Surface plasmon resonance (SPR) sensing is recently emerging as a valuable technique for measuring the binding constants, association and dissociation rate constants, and stoichimetry for a binding interaction kinetics in a number of emerging biological areas. This technique can be applied to the study of immune system diseases in order to contribute to improved understanding and evaluation of binding parameters for a variety of interactions between antigens and antibodies biochemically and clinically. Since the binding constants determination of an anti-protein dengue antibody (Ab) to a protein dengue antigen (Ag) is mostly complicated, the SPR technique aids a determination of binding parameters directly for a variety of particular dengue Ag_Ab interactions in the real-time. The study highlights the doctrine of real-time dengue Ag_Ab interaction kinetics as well as to determine the binding parameters that is performed with SPR technique. In addition, this article presents a precise prediction as a reference curve for determination of dengue sample concentration.
Matched MeSH terms: Protein Interaction Mapping/instrumentation; Protein Interaction Mapping/methods
The pressing need for SARS-CoV-2 controls has led to a reassessment of strategies to identify and develop natural product inhibitors of zoonotic, highly virulent, and rapidly emerging viruses. This review article addresses how contemporary approaches involving computational chemistry, natural product (NP) and protein databases, and mass spectrometry (MS) derived target-ligand interaction analysis can be utilized to expedite the interrogation of NP structures while minimizing the time and expense of extraction, purification, and screening in BioSafety Laboratories (BSL)3 laboratories. The unparalleled structural diversity and complexity of NPs is an extraordinary resource for the discovery and development of broad-spectrum inhibitors of viral genera, including Betacoronavirus, which contains MERS, SARS, SARS-CoV-2, and the common cold. There are two key technological advances that have created unique opportunities for the identification of NP prototypes with greater efficiency: (1) the application of structural databases for NPs and target proteins and (2) the application of modern MS techniques to assess protein-ligand interactions directly from NP extracts. These approaches, developed over years, now allow for the identification and isolation of unique antiviral ligands without the immediate need for BSL3 facilities. Overall, the goal is to improve the success rate of NP-based screening by focusing resources on source materials with a higher likelihood of success, while simultaneously providing opportunities for the discovery of novel ligands to selectively target proteins involved in viral infection.
INTRODUCTION: Dengue virus (DENV) is principally transmitted by the Aedes aegypti mosquito. To date, mosquito population control remains the key strategy for reducing the continuing spread of DENV. The focus on the development of new vector control strategies through an understanding of the mosquito-virus relationship is essential, especially targeting the midgut, which is the first mosquito organ exposed to DENV infection.
METHODOLOGY: A cDNA library derived from female adult A. aegypti mosquito midgut cells was established using the switching mechanism at the 5' end of the RNA transcript (SMART), in combination with a highly potent recombination machinery of Saccharomyces cerevisiae. Gal4-based yeast two-hybrid (Y2H) assays were performed against DENV-2 proteins (E, prM, M, and NS1). Mammalian two-hybrid (M2H) and double immunofluorescence assays (IFA) were conducted to validate the authenticity of the three selected interactions.
RESULTS: The cDNA library was of good quality based on its transformation efficiency, cell density, titer, and the percentage of insert size. A total of 36 midgut proteins interacting with DENV-2 proteins were identified, some involved in nucleic acid transcription, oxidoreductase activity, peptidase activity, and ion binding. Positive outcomes were obtained from the three selected interactions validated using M2H and double IFA assays.
CONCLUSIONS: The identified proteins have different biological activities that may aid in the virus replication pathway. Therefore, the midgut cDNA library is a valuable tool for identifying DENV-2 interacting proteins. The positive outcomes of the three selected proteins validated supported the quality of the cDNA library and the robustness of the Y2H mechanisms.
Paramyxoviruses and other negative-strand RNA viruses encode matrix proteins that coordinate the virus assembly process. The matrix proteins link the viral glycoproteins and the viral ribonucleoproteins at virus assembly sites and often recruit host machinery that facilitates the budding process. Using a co-affinity purification strategy, we have identified the beta subunit of the AP-3 adapter protein complex, AP3B1, as a binding partner for the M proteins of the zoonotic paramyxoviruses Nipah virus and Hendra virus. Binding function was localized to the serine-rich and acidic Hinge domain of AP3B1, and a 29-amino-acid Hinge-derived polypeptide was sufficient for M protein binding in coimmunoprecipitation assays. Virus-like particle (VLP) production assays were used to assess the relationship between AP3B1 binding and M protein function. We found that for both Nipah virus and Hendra virus, M protein expression in the absence of any other viral proteins led to the efficient production of VLPs in transfected cells, and this VLP production was potently inhibited upon overexpression of short M-binding polypeptides derived from the Hinge region of AP3B1. Both human and bat (Pteropus alecto) AP3B1-derived polypeptides were highly effective at inhibiting the production of VLPs. VLP production was also impaired through small interfering RNA (siRNA)-mediated depletion of AP3B1 from cells. These findings suggest that AP-3-directed trafficking processes are important for henipavirus particle production and identify a new host protein-virus protein binding interface that could become a useful target in future efforts to develop small molecule inhibitors to combat paramyxoviral infections.
Protein-protein interactions (PPIs) play a significant role in many crucial cellular operations such as metabolism, signaling and regulations. The computational methods for predicting PPIs have shown tremendous growth in recent years, but problem such as huge false positive rates has contributed to the lack of solid PPI information. We aimed at enhancing the overlap between computational predictions and experimental results in an effort to partially remove PPIs falsely predicted. The use of protein function predictor named PFP() that are based on shared interacting domain patterns is introduced in this study with the purpose of aiding the Gene Ontology Annotations (GOA). We used GOA and PFP() as agents in a filtering process to reduce false positive pairs in the computationally predicted PPI datasets. The functions predicted by PFP() were extracted from cross-species PPI data in order to assign novel functional annotations for the uncharacterized proteins and also as additional functions for those that are already characterized by the GO (Gene Ontology). The implementation of PFP() managed to increase the chances of finding matching function annotation for the first rule in the filtration process as much as 20%. To assess the capability of the proposed framework in filtering false PPIs, we applied it on the available S. cerevisiae PPIs and measured the performance in two aspects, the improvement made indicated as Signal-to-Noise Ratio (SNR) and the strength of improvement, respectively. The proposed filtering framework significantly achieved better performance than without it in both metrics.
Matched MeSH terms: Protein Interaction Mapping/methods*
While protein-protein interaction is the first step of the SARS-CoV-2 infection, recent comparative proteomic profiling enabled the identification of over 11,000 protein dynamics, thus providing a comprehensive reflection of the molecular mechanisms underlying the cellular system in response to viral infection. Here we summarize and rationalize the results obtained by various mass spectrometry (MS)-based proteomic approaches applied to the functional characterization of proteins and pathways associated with SARS-CoV-2-mediated infections in humans. Comparative analysis of cell-lines versus tissue samples indicates that our knowledge in proteome profile alternation in response to SARS-CoV-2 infection is still incomplete and the tissue-specific response to SARS-CoV-2 infection can probably not be recapitulated efficiently by in vitro experiments. However, regardless of the viral infection period, sample types, and experimental strategies, a thorough cross-comparison of the recently published proteome, phosphoproteome, and interactome datasets led to the identification of a common set of proteins and kinases associated with PI3K-Akt, EGFR, MAPK, Rap1, and AMPK signaling pathways. Ephrin receptor A2 (EPHA2) was identified by 11 studies including all proteomic platforms, suggesting it as a potential future target for SARS-CoV-2 infection mechanisms and the development of new therapeutic strategies. We further discuss the potentials of future proteomics strategies for identifying prognostic SARS-CoV-2 responsive age-, gender-dependent, tissue-specific protein targets.
Matched MeSH terms: Protein Interaction Mapping/methods
Binding of dengue virus 2 (DENV-2) to C6/36 mosquito cells protein was investigated. A 48 kDa DENV-2-binding C6/36 cells protein (D2BP) was detected in a virus overlay protein-binding assay. The binding occurred only to the C6/36 cells cytosolic protein fraction and it was inhibited by free D2BP. D2BP was shown to bind to DENV-2 E in the far-Western-binding studies and using mass spectrometry (MS) and MS/MS, peptide masses of the D2BP that matched to beta-tubulin and alpha-tubulin chains were identified. These findings suggest that DENV-2 through DENV-2 E binds directly to a 48 kDa tubulin or tubulin-like protein of C6/36 mosquito cells.
Matched MeSH terms: Protein Interaction Mapping/methods*
Non-obstructive azoospermia is a severe infertility factor. Currently, the etiology of this condition remains elusive with several possible molecular pathway disruptions identified in the post-meiotic spermatozoa. In the presented study, in order to identify all possible candidate genes associated with azoospermia and to map their relationship, we present the first protein-protein interaction network related to azoospermia and analyze the complex effects of the related genes systematically. Using Online Mendelian Inheritance in Man, the Human Protein Reference Database and Cytoscape, we created a novel network consisting of 209 protein nodes and 737 interactions. Mathematical analysis identified three proteins, ar, dazap2, and esr1, as hub nodes and a bottleneck protein within the network. We also identified new candidate genes, CREBBP and BCAR1, which may play a role in azoospermia. The gene ontology analysis suggests a genetic link between azoospermia and liver disease. The KEGG analysis also showed 45 statistically important pathways with 31 proteins associated with colorectal, pancreatic, chronic myeloid leukemia and prostate cancer. Two new genes and associated diseases are promising for further experimental validation.
Matched MeSH terms: Protein Interaction Mapping/methods
Jumonji C (JmjC) domain-containing proteins have been shown to regulate cellular processes by hydroxylating or demethylating histone and non-histone targets. JMJD8 belongs to the JmjC domain-only family that was recently shown to be involved in angiogenesis and TNF-induced NF-κB signaling. Here, we employed bioinformatic analysis and immunofluorescence microscopy to examine the physiological properties of JMJD8. We demonstrated that JMJD8 localizes to the lumen of endoplasmic reticulum and that JMJD8 forms dimers or oligomers in vivo. Furthermore, we identified potential JMJD8-interacting proteins that are known to regulate protein complex assembly and protein folding. Taken together, this work demonstrates that JMJD8 is the first JmjC domain-containing protein found in the lumen of the endoplasmic reticulum that may function in protein complex assembly and protein folding.
GalNAc-T1, a key candidate of GalNac-transferases genes family that is involved in mucin-type O-linked glycosylation pathway, is expressed in most biological tissues and cell types. Despite the reported association of GalNAc-T1 gene mutations with human disease susceptibility, the comprehensive computational analysis of coding, noncoding and regulatory SNPs, and their functional impacts on protein level, still remains unknown. Therefore, sequence- and structure-based computational tools were employed to screen the entire listed coding SNPs of GalNAc-T1 gene in order to identify and characterize them. Our concordant in silico analysis by SIFT, PolyPhen-2, PANTHER-cSNP, and SNPeffect tools, identified the potential nsSNPs (S143P, G258V, and Y414D variants) from 18 nsSNPs of GalNAc-T1. Additionally, 2 regulatory SNPs (rs72964406 and #x26; rs34304568) were also identified in GalNAc-T1 by using FastSNP tool. Using multiple computational approaches, we have systematically classified the functional mutations in regulatory and coding regions that can modify expression and function of GalNAc-T1 enzyme. These genetic variants can further assist in better understanding the wide range of disease susceptibility associated with the mucin-based cell signalling and pathogenic binding, and may help to develop novel therapeutic elements for associated diseases.
Despite the reported association of adiponectin receptor 1 (ADIPOR1) gene mutations with vulnerability to several human metabolic diseases, there is lack of computational analysis on the functional and structural impacts of single nucleotide polymorphisms (SNPs) of the human ADIPOR1 at protein level. Therefore, sequence- and structure-based computational tools were employed in this study to functionally and structurally characterize the coding nsSNPs of ADIPOR1 gene listed in the dbSNP database. Our in silico analysis by SIFT, nsSNPAnalyzer, PolyPhen-2, Fathmm, I-Mutant 2.0, SNPs&GO, PhD-SNP, PANTHER, and SNPeffect tools identified the nsSNPs with distorting functional impacts, namely, rs765425383 (A348G), rs752071352 (H341Y), rs759555652 (R324L), rs200326086 (L224F), and rs766267373 (L143P) from 74 nsSNPs of ADIPOR1 gene. Finally the aforementioned five deleterious nsSNPs were introduced using Swiss-PDB Viewer package within the X-ray crystal structure of ADIPOR1 protein, and changes in free energy for these mutations were computed. Although increased free energy was observed for all the mutants, the nsSNP H341Y caused the highest energy increase amongst all. RMSD and TM scores predicted that mutants were structurally similar to wild type protein. Our analyses suggested that the aforementioned variants especially H341Y could directly or indirectly destabilize the amino acid interactions and hydrogen bonding networks of ADIPOR1.
Viruses have evolved unique strategies to evade or subvert autophagy machinery. Enterovirus A71 (EV-A71) induces autophagy during infection in vitro and in vivo. In this study, we report that EV-A71 triggers autolysosome formation during infection in human rhabdomyosarcoma (RD) cells to facilitate its replication. Blocking autophagosome-lysosome fusion with chloroquine inhibited virus RNA replication, resulting in lower viral titres, viral RNA copies and viral proteins. Overexpression of the non-structural protein 2BC of EV-A71 induced autolysosome formation. Yeast 2-hybrid and co-affinity purification assays showed that 2BC physically and specifically interacted with aN-ethylmaleimide-sensitive factor attachment receptor (SNARE) protein, syntaxin-17 (STX17). Co-immunoprecipitation assay further showed that 2BC binds to SNARE proteins, STX17 and synaptosome associated protein 29 (SNAP29). Transient knockdown of STX17, SNAP29, and microtubule-associated protein 1 light chain 3B (LC3B), crucial proteins in the fusion between autophagosomes and lysosomes) as well as the lysosomal-associated membrane protein 1 (LAMP1) impaired production of infectious EV-A71 in RD cells. Collectively, these results demonstrate that the generation of autolysosomes triggered by the 2BC non-structural protein is important for EV-A71 replication, revealing a potential molecular pathway targeted by the virus to exploit autophagy. This study opens the possibility for the development of novel antivirals that specifically target 2BC to inhibit formation of autolysosomes during EV-A71 infection.
PNMA2, a member of the Paraneoplastic Ma Family (PNMA), was identified through expression cloning by using anti-sera from patients with paraneoplastic disorder. Tissue expression studies showed that PNMA2 was predominantly expressed in normal human brain; however, the protein was shown to exhibit abnormal expression profile as it was found to be expressed in a number of tumour tissues obtained from paraneopalstic patients. The abnormal expression profile of PNMA2 suggests that it might play an important role in tumorigenesis; however, apart from protein expression and immunological studies, the physiological role of PNMA2 remains unclear. In order to determine potential role of PNMA2 in tumorigenesis, and its functional relationship with PNMA family members, MOAP-1 (PNMA4) and PNMA1, expression constructs encoding the respective proteins were generated for both in vitro and in vivo studies. Our investigations showed that over-expressed MOAP-1 and PNMA1 promoted apoptosis and chemo-sensitization in MCF-7 cells as evidenced by condensed nuclei and Annexin-V positive MCF-7 cells; however, the effects mediated by these proteins were significantly inhibited or abolished when co-expressed with PNMA2 in MCF-7 cells. Furthermore, co-immunoprecipitation study showed that PNMA1 and MOAP-1 failed to associate with each other but readily formed respective heterodimer with PNMA2, suggesting that PNMA2 functions as antagonist of MOAP-1 and PNMA1 through heterodimeric interaction.