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
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*
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
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/methods