Exploration of the dynamic properties of protein complexes predicted from spatially constrained protein-protein interaction networks

The Application of High-Throughput Approaches in Identifying Novel Therapeutic Targets and Agents to Treat Diabetes

During the past decades, unprecedented progress in technologies has revolutionized traditional research methodologies. Among these, advances in high-throughput drug screening approaches have permitted the rapid identification of potential therapeutic agents from drug libraries that contain thousands or millions of molecules. Moreover, high-throughput-based therapeutic target discovery strategies can comprehensively interrogate relationships between biomolecules (e.g., gene, RNA, and protein) and diseases and significantly increase the authors' knowledge of disease mechanisms. Diabetes is a chronic disease primarily characterized by the incapacity of the body to maintain normoglycemia. The prevalence of diabetes in modern society has become a severe public health issue that threatens the well-being of millions of patients. Although a number of pharmacological treatments are available, there is no permanent cure for diabetes, and discovering novel therapeutic targets and agents continues to be an urgent need. The present review discusses the technical details of high-throughput screening approaches in drug discovery, followed by introducing the applications of such approaches to diabetes research. This review aims to provide an example of the applicability of high-throughput technologies in facilitating different aspects of disease research.

Modeling the MreB-CbtA Interaction to Facilitate the Prediction and Design of Candidate Antibacterial Peptides

Protein-protein interactions (PPIs) have emerged as promising targets for PPI modulators as alternative drugs because they are essential for most biochemical processes in living organisms. In recent years, a spotlight has been put on the development of peptide-based PPI inhibitors as the next-generation therapeutics to combat antimicrobial resistance taking cognizance of protein-based PPI-modulators that interact with target proteins to inhibit function. Although protein-based PPI inhibitors are not effective therapeutic agents because of their high molecular weights, they could serve as sources for peptide-based pharmaceutics if the target-inhibitor complex is accessible and well characterized. The Escherichia coli (E. coli) toxin protein, CbtA, has been identified as a protein-based PPI modulator that binds to the bacterial actin homolog MreB leading to the perturbation of its polymerization dynamics; and consequently has been suggested to have antibacterial properties. Unfortunately, however, the three-dimensional structures of CbtA and the MreB-CbtA complex are currently not available to facilitate the optimization process of the pharmacological properties of CbtA. In this study, computer modeling strategies were used to predict key MreB-CbtA interactions to facilitate the design of antiMreB peptide candidates. A model of the E. coli CbtA was built using the trRosetta software and its stability was assessed through molecular dynamics (MD) simulations. The modeling and simulations data pointed to a model with reasonable quality and stability. Also, the HADDOCK software was used to predict a possible MreB-CbtA complex, which was characterized through MD simulations and compared with MreB-MreB dimmer. The results suggest that CbtA inhibits MreB through the competitive mechanism whereby CbtA competes with MreB monomers for the interprotofilament interface leading to interference with double protofilament formation. Additionally, by using the antiBP software to predict antibacterial peptides in CbtA, and the MreB-CbtA complex as the reference structure to determine important interactions and contacts, candidate antiMreB peptides were suggested. The peptide sequences could be useful in a rational antimicrobial peptide hybridization strategy to design novel antibiotics. All-inclusive, the data reveal the molecular basis of MreB inhibition by CbtA and can be incorporated in the design/development of the next-generation antibacterial peptides targeting MreB.

Folylpoly-ɣ-glutamate synthetase association to the cytoskeleton: Implications to folate metabolon compartmentalization

Folates are essential for nucleotide biosynthesis, amino acid metabolism and cellular proliferation. Following carrier-mediated uptake, folates are polyglutamylated by folylpoly-ɣ-glutamate synthetase (FPGS), resulting in their intracellular retention. FPGS appears as a long isoform, directed to mitochondria via a leader sequence, and a short isoform reported as a soluble cytosolic protein (cFPGS). However, since folates are labile and folate metabolism is compartmentalized, we herein hypothesized that cFPGS is associated with the cytoskeleton, to couple folate uptake and polyglutamylation and channel folate polyglutamates to metabolon compartments. We show that cFPGS is a cytoskeleton-microtubule associated protein: Western blot analysis revealed that endogenous cFPGS is associated with the insoluble cellular fraction, i.e., cytoskeleton and membranes, but not with the cytosol. Mass spectrometry analysis identified the putative cFPGS interactome primarily consisting of microtubule subunits and cytoskeletal motor proteins. Consistently, immunofluorescence microscopy with cytosol-depleted cells demonstrated the association of cFPGS with the cytoskeleton and unconventional myosin-1c. Furthermore, since anti-microtubule, anti-actin cytoskeleton, and coatomer dissociation-inducing agents yielded perinuclear pausing of cFPGS, we propose an actin- and microtubule-dependent transport of cFPGS between the ER-Golgi and the plasma membrane. These novel findings support the coupling of folate transport with polyglutamylation and folate channeling to intracellular metabolon compartments. SIGNIFICANCE: FPGS, an essential enzyme catalyzing intracellular folate polyglutamylation and efficient retention, was described as a soluble cytosolic enzyme in the past 40 years. However, based on the lability of folates and the compartmentalization of folate metabolism and nucleotide biosynthesis, we herein hypothesized that cytoplasmic FPGS is associated with the cytoskeleton, to couple folate transport and polyglutamylation as well as channel folate polyglutamates to biosynthetic metabolon compartments. Indeed, using complementary techniques including Mass-spectrometry proteomics and fluorescence microscopy, we show that cytoplasmic FPGS is associated with the cytoskeleton and unconventional myosin-1c. This novel cytoskeletal localization of cytoplasmic FPGS supports the dynamic channeling of polyglutamylated folates to metabolon compartments to avoid oxidation and intracellular dilution of folates, while enhancing folate-dependent de novo biosynthesis of nucleotides and DNA/protein methylation.

Context-dependent prediction of protein complexes by SiComPre

Most cellular processes are regulated by groups of proteins interacting together to form protein complexes. Protein compositions vary between different tissues or disease conditions enabling or preventing certain protein-protein interactions and resulting in variations in the complexome. Quantitative and qualitative characterization of context-specific protein complexes will help to better understand context-dependent variations in the physiological behavior of cells. Here, we present SiComPre 1.0, a computational tool that predicts context-specific protein complexes by integrating multi-omics sources. SiComPre outperforms other protein complex prediction tools in qualitative predictions and is unique in giving quantitative predictions on the complexome depending on the specific interactions and protein abundances defined by the user. We provide tutorials and examples on the complexome prediction of common model organisms, various human tissues and how the complexome is affected by drug treatment.

Altered protein glycosylation predicts Alzheimer's disease and modulates its pathology in disease model Drosophila

The pathological hallmarks of Alzheimer's disease (AD) are pathogenic oligomers and fibrils of misfolded amyloidogenic proteins (e.g., β-amyloid and hyper-phosphorylated tau in AD), which cause progressive loss of neurons in the brain and nervous system. Although deviations from normal protein glycosylation have been documented in AD, their role in disease pathology has been barely explored. Here our analysis of available expression data sets indicates that many glycosylation-related genes are differentially expressed in brains of AD patients compared with healthy controls. The robust differences found enabled us to predict the occurrence of AD with remarkable accuracy in a test cohort and identify a set of key genes whose expression determines this classification. We then studied in vivo the effect of reducing expression of homologs of 6 of these genes in transgenic Drosophila overexpressing human tau, a well-established invertebrate AD model. These experiments have led to the identification of glycosylation genes that may augment or ameliorate tauopathy phenotypes. Our results indicate that OstDelta, l(2)not and beta4GalT7 are tauopathy suppressors, whereas pgnat5 and CG33303 are enhancers, of tauopathy. These results suggest that specific alterations in protein glycosylation may play a causal role in AD etiology.