Protein–Protein Interactions Prediction Base on Multiple Information Fusion via Graph Representation Learning

It is a critical impact on the processing of biological cells to protein–protein interactions (PPIs) in nature. Traditional PPIs predictive biological experiments consume a lot of human and material costs and time. Therefore, there is a great need to use computational methods to forecast PPIs. Most of the existing calculation methods are based on the sequence characteristics or internal structural characteristics of proteins, and most of them have the singleness of features. Therefore, we propose a novel method to predict PPIs base on multiple information fusion through graph representation learning. Specifically, firstly, the known protein sequences are calculated, and the properties of each protein are obtained by k-mer. Then, the known protein relationship pairs were constructed into an adjacency graph, and the graph representation learning method–graph convolution network was used to fuse the attributes of each protein with the graph structure information to obtain the features containing a variety of information. Finally, we put the multi-information features into the random forest classifier species for prediction and classification. Experimental results indicate that our method has high accuracy and AUC of 78.83% and 86.10%, respectively. In conclusion, our method has an excellent application prospect for predicting unknown PPIs.

Flud: A Hybrid Crowd–Algorithm Approach for Visualizing Biological Networks

Modern experiments in many disciplines generate large quantities of network (graph) data. Researchers require aesthetic layouts of these networks that clearly convey the domain knowledge and meaning. However, the problem remains challenging due to multiple conflicting aesthetic criteria and complex domain-specific constraints. In this article, we present a strategy for generating visualizations that can help network biologists understand the protein interactions that underlie processes that take place in the cell. Specifically, we have developed Flud, a crowd-powered system that allows humans with no expertise to design biologically meaningful graph layouts with the help of algorithmically generated suggestions. Furthermore, we propose a novel hybrid approach for graph layout wherein crowd workers and a simulated annealing algorithm build on each other’s progress. A study of about 2,000 crowd workers on Amazon Mechanical Turk showed that the hybrid crowd–algorithm approach outperforms the crowd-only approach and state-of-the-art techniques when workers were asked to lay out complex networks that represent signaling pathways. Another study of seven participants with biological training showed that Flud layouts are more effective compared to those created by state-of-the-art techniques. We also found that the algorithmically generated suggestions guided the workers when they are stuck and helped them improve their score. Finally, we discuss broader implications for mixed-initiative interactions in layout design tasks beyond biology.

Mass Spectrometry-Based Structural Proteomics for Metal Ion/Protein Binding Studies

Metal ions are critical for the biological and physiological functions of many proteins. Mass spectrometry (MS)-based structural proteomics is an ever-growing field that has been adopted to study protein and metal ion interactions. Native MS offers information on metal binding and its stoichiometry. Footprinting approaches coupled with MS, including hydrogen/deuterium exchange (HDX), “fast photochemical oxidation of proteins” (FPOP) and targeted amino-acid labeling, identify binding sites and regions undergoing conformational changes. MS-based titration methods, including “protein–ligand interactions by mass spectrometry, titration and HD exchange” (PLIMSTEX) and “ligand titration, fast photochemical oxidation of proteins and mass spectrometry” (LITPOMS), afford binding stoichiometry, binding affinity, and binding order. These MS-based structural proteomics approaches, their applications to answer questions regarding metal ion protein interactions, their limitations, and recent and potential improvements are discussed here. This review serves as a demonstration of the capabilities of these tools and as an introduction to wider applications to solve other questions.

Phase Separation of Shell Protein and Enzyme: An Insight into the Biogenesis of a Prokaryotic Metabolosome

Bacterial microcompartments are substrate specific metabolic modules that are conditionally expressed in certain bacterial species. These all protein structures have size in the range of 100-150 nm and are formed by the self-assembly of thousands of protein subunits, all encoded by genes belonging to a single operon. The operon contains genes that encode for both enzymes and shell proteins. The shell proteins self-assemble to form the outer coat of the compartment and enzymes are encapsulated within. A perplexing question in MCP biology is to understand the mechanism which governs the formation of these small yet complex assemblages of proteins. In this work we use 1,2-propanediol utilization microcompartments (PduMCP) as a paradigm to identify the factors that drive the self-assembly of MCP proteins. We find that a major shell protein PduBB tend to self-assemble under macromolecular crowded environment and suitable ionic strength. Microscopic visualization and biophysical studies reveal phase separation to be the principle mechanism behind the self-association of shell protein in the presence of salts and macromolecular crowding. The shell protein PduBB interacts with the enzyme diol-dehydratase PduCDE and co-assemble into phase separated liquid droplets. The co-assembly of PduCDE and PduBB results in the enhancement of catalytic activity of the enzyme. A combination of spectroscopic and biochemical techniques shows the relevance of divalent cation Mg2+ in providing stability to intact PduMCP in vivo. Together our results suggest a combination of protein-protein interactions and phase separation guiding the self-assembly of Pdu shell protein and enzyme in solution phase.

77Se-Enriched Selenoglycoside Enables Significant Enhancement in NMR Spectroscopic Monitoring of Glycan–Protein Interactions

Detailed investigation of ligand–protein interactions is essential for better understanding of biological processes at the molecular level. Among these binding interactions, the recognition of glycans by lectins is of particular importance in several diseases, such as cancer; therefore, inhibition of glycan-lectin/galectin interactions represents a promising perspective towards developing therapeutics controlling cancer development. The recent introduction of 77Se NMR spectroscopy for monitoring the binding of a selenoglycoside to galectins prompted interest to optimize the sensitivity by increasing the 77Se content from the natural 7.63% abundance to 99%. Here, we report a convenient synthesis of 77Se-enriched selenodigalactoside (SeDG), which is a potent ligand of the medically relevant human galectin-3 protein, and proof of the expected sensitivity gain in 2D 1H, 77Se correlation NMR experiments. Our work opens perspectives for adding isotopically enriched selenoglycans for rapid monitoring of lectin-binding of selenated as well as non-selenated ligands and for ligand screening in competition experiments.

Towards an Ideal In Cell Hybridization-Based Strategy to Discover Protein Interactomes of Selected RNA Molecules

RNA-binding proteins are crucial to the function of coding and non-coding RNAs. The disruption of RNA–protein interactions is involved in many different pathological states. Several computational and experimental strategies have been developed to identify protein binders of selected RNA molecules. Amongst these, ‘in cell’ hybridization methods represent the gold standard in the field because they are designed to reveal the proteins bound to specific RNAs in a cellular context. Here, we compare the technical features of different ‘in cell’ hybridization approaches with a focus on their advantages, limitations, and current and potential future applications.

FRET monitoring of transcription factor activities in living bacteria

Bacteria adapt to the constantly changing environments largely by transcriptional regulation through the activities of various transcription factors (TFs). However, techniques that monitor the in situ TF-promoter interactions in living bacteria are lacking. Herein, we developed a whole-cell TF-promoter binding assay based on the intermolecular Förster resonance energy transfer (FRET) between a fluorescent unnatural amino acid CouA which is genetically encoded into defined sites in TFs and the live cell fluorescent nucleic acid stain SYTO 9. We show that this new FRET pair monitors the intricate TF-promoter interactions elicited by various types of signal transduction systems with specificity and sensitivity. Furthermore, the assay is applicable to identify novel modulators of the regulatory systems of interest and monitor TF activities in bacteria colonized in C. elegans. In conclusion, we established a tractable and sensitive TF-promoter binding assay in living bacteria which not only complements currently available approaches for DNA-protein interactions but also provides novel opportunities for functional annotation of bacterial signal transduction systems and studies of the bacteria-host interface.

Time-lapsed proteomics reveals a role for the novel protein, SNED1, in modulating ECM composition and protein folding

The extracellular matrix (ECM) is a complex and dynamic meshwork of proteins providing structural support to cells. It also provides biochemical signals governing cellular processes including proliferation and migration. Alterations of ECM structure and/or composition has been shown to lead to, or accompany, many pathological processes including cancer and fibrosis. To understand how the ECM contributes to diseases, we first need to obtain a comprehensive characterization of the ECM of tissues and of its changes during disease progression. Over the past decade, mass-spectrometry-based proteomics has become the state-of-the-art method to profile the protein composition of ECMs. However, existing methods do not fully capture the broad dynamic range of protein abundance in the ECM, nor do they permit to achieve the high coverage needed to gain finer biochemical information, including the presence of isoforms or post-translational modifications. In addition, broadly adopted proteomic methods relying on extended trypsin digestion do not provide structural information on ECM proteins, yet, gaining insights into ECM protein structure is critical to better understanding protein functions. Here, we present the optimization of a time-lapsed proteomic method using limited proteolysis of partially denatured samples and the sequential release of peptides to achieve superior sequence coverage as compared to standard ECM proteomic workflow. Exploiting the spatio-temporal resolution of this method, we further demonstrate how 3-dimensional time-lapsed peptide mapping can identify protein regions differentially susceptible to trypsin and can thus identify sites of post-translational modifications, including protein-protein interactions. We further illustrate how this approach can be leveraged to gain insight on the role of the novel ECM protein SNED1 in ECM homeostasis. We found that the expression of SNED1 expression by mouse embryonic fibroblasts results in the alteration of overall ECM composition and the sequence coverage of certain ECM proteins, raising the possibility that SNED1 could modify accessibility to trypsin by engaging in protein-protein interactions.

Group I Metabotropic Glutamate Receptors and Interacting Partners: An Update

Group I metabotropic glutamate (mGlu) receptors (mGlu1/5 subtypes) are G protein-coupled receptors and are broadly expressed in the mammalian brain. These receptors play key roles in the modulation of normal glutamatergic transmission and synaptic plasticity, and abnormal mGlu1/5 signaling is linked to the pathogenesis and symptomatology of various mental and neurological disorders. Group I mGlu receptors are noticeably regulated via a mechanism involving dynamic protein–protein interactions. Several synaptic protein kinases were recently found to directly bind to the intracellular domains of mGlu1/5 receptors and phosphorylate the receptors at distinct amino acid residues. A variety of scaffolding and adaptor proteins also interact with mGlu1/5. Constitutive or activity-dependent interactions between mGlu1/5 and their interacting partners modulate trafficking, anchoring, and expression of the receptors. The mGlu1/5-associated proteins also finetune the efficacy of mGlu1/5 postreceptor signaling and mGlu1/5-mediated synaptic plasticity. This review analyzes the data from recent studies and provides an update on the biochemical and physiological properties of a set of proteins or molecules that interact with and thus regulate mGlu1/5 receptors.