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Accurate elucidation of genome wide protein–protein interactions is crucial for understanding the regulatory processes of the cell. High-throughput techniques, such as the yeast-2-hybrid (Y2H) assay, co-immunoprecipitation (co-IP), mass spectrometric (MS) protein complex identification, affinity purification (AP) etc., are generally relied upon to determine protein interactions. Unfortunately, each type of method is inherently subject to different types of noise and results in false positive interactions. On the other hand, precise understanding of proteins, especially knowledge of their functional associations is necessary for understanding how complex molecular machines function. To solve this problem, computational techniques are generally relied upon to precisely predict protein interactions. In this work, we present a novel method that combines structural and non-structural biological data to precisely predict protein interactions. The conceptual novelty of our approach lies in identifying and precisely associating biological information that provides substantial interaction clues. Our model combines structural and non-structural information using Bayesian statistics to calculate the likelihood of each interaction. The proposed model is tested on Saccharomyces cerevisiae's interactions extracted from the DIP and IntAct databases and provides substantial improvements in terms of accuracy, precision, recall and F1 score, as compared with the most widely used related state-of-the-art techniques.

The lifetimes of protein-DNA adducts are strictly related to the various protein functions. This feature must be encoded by the amino acids located at the protein-DNA interface. The large number of structurally characterized protein-DNA complexes now available from the Protein Data Bank (PDB) allows extensive structural bioinformatics investigations on protein-DNA interfaces. The modes of protein binding to DNA have been explored by dividing 629 non-redundant PDB files of protein-DNA complexes into separate classes for structural proteins, transcription factors and DNA-related enzymes. From the selected PDB structures, we could define 2953 protein-DNA contact regions. A systematic analysis of amino acid occurrences at these protein-DNA contact regions yielded composition profiles, which are typical for each of the three protein classes. The critical role of some amino acids to influence intermolecular contact lifetimes is discussed here. The occurrence of arginine at the protein-DNA interface, by far the most abundant amino acid in this protein moiety, is found to be the main feature that differentiates proteins from the three classes. Structural proteins and, to a lesser extent, transcription factors exhibit the highest Arg occurrence at protein-DNA contact regions. Reduced Arg/Lys ratios together with increased contents of Asp and Glu are observed in all the DNA-interacting enzymes. The amount of negatively charged side chains, highly conserved among homologous DNA-related enzymes at protein-DNA interfaces, is suggested as a tool to modulate protein mobility along DNA chains. Arg/Lys, Asp/Asn and Glu/Gln substitutions at protein-DNA interfaces may represent a very feasible way to control protein motion on DNA rails.

Correction for ‘Dynamic properties of dipeptidyl peptidase III from Bacteroides thetaiotaomicron and the structural basis for its substrate specificity – a computational study’ by M. Tomin et al., Mol. BioSyst., 2017, 13, 2407–2417.

A cationic terminal extension or tail is a common feature of many DNA-binding proteins. We show that a particular type of tail rich in proline, alanine and lysine belongs to the class of ‘flexible disorder’ and consists of characteristic pentapeptide repeats. Our designed peptides, (AAKKA)1–4 and (PAKKA)1–4, represent the tails of several bacterial DNA-binding proteins. Enhanced conformational sampling of these representative peptides using accelerated molecular dynamic simulations supported by circular dichroism spectroscopy and nuclear magnetic resonance studies demonstrates the role of frequent and interspersed prolines in augmenting conformational heterogeneity of the peptide backbone. Analysis of circular variance of backbone dihedral angles indicates alternating regions of relative rigidity and flexibility along the peptide sequence due to prolines. Preferred placement of lysines in the regions of higher backbone flexibility might improve DNA-binding by conformational selection. Our results could be relevant for rational de novo design of disordered peptides.

The protein arginine methyltransferase (PRMT) family of enzymes comprises nine family members in mammals. They catalyze arginine methylation, either monomethylation or symmetric/asymmetric dimethylation of histone and non-histone proteins. PRMT methylation of its substrate proteins modulates cellular processes such as signal transduction, transcription, and mRNA splicing. Recent studies have linked overexpression of PRMT5, a member of the PRMT superfamily, to oncogenesis, making it a potential target for cancer therapy. In this study, we developed a highly sensitive (Z′ score = 0.7) robotic high throughput screening (HTS) platform to discover small molecule inhibitors of PRMT5 by adapting the AlphaLISA? technology. Using biotinylated histone H4 as a substrate, and S-adenosyl-L-methionine as a methyl donor, PRMT5 symmetrically dimethylated H4 at arginine (R) 3. Highly specific acceptor beads for symmetrically dimethylated H4R3 and streptavidin-coated donor beads bound the substrate, emitting a signal that is proportional to the methyltransferase activity. Using this powerful approach, we identified specific PRMT5 inhibitors P1608K04 and P1618J22, and further validated their efficacy and specificity for inhibiting PRMT5. Importantly, these two compounds exhibited much more potent efficacy than the commercial PRMT5 inhibitor EPZ015666 in both pancreatic and colorectal cancer cells. Overall, our work highlights a novel, powerful, and sensitive approach to identify specific PRMT5 inhibitors. The general principle of this HTS screening method can not only be applied to PRMT5 and the PRMT superfamily, but may also be extended to other epigenetic targets. This approach allows us to identify compounds that inhibit the activity of their respective targets, and screening hits like P1608K04 and P1618J22 may serve as the basis for novel drug development to treat cancer and/or other diseases.

Mass spectrometry (MS) has become an increasingly important technique to analyze proteins. In popular bottom-up MS-based proteomics, reduction and alkylation are routine steps to facilitate peptide identification. However, incomplete reactions and side reactions may occur, which compromise the experimental results. In this work, we systematically evaluated the reduction step with commonly used reagents, i.e., dithiothreitol, 2-mercaptoethanol, tris(2-carboxyethyl)phosphine, or tris(3-hydroxypropyl)phosphine, and alkylation with iodoacetamide, acrylamide, N-ethylmaleimide, or 4-vinylpyridine. By using digested peptides from a yeast whole-cell lysate, the number of proteins and peptides identified were very similar using four different reducing reagents. The results from four alkylating reagents, however, were dramatically different with iodoacetamide giving the highest number of peptides with alkylated cysteine and the lowest number of peptides with incomplete cysteine alkylation and side reactions. Alkylation conditions with iodoacetamide were further optimized. To identify more peptides with cysteine, thiopropyl-sepharose 6B resins were used to enrich them, and the optimal conditions were employed for the reduction and alkylation. The enrichment resulted in over three times more cysteine-containing peptides than without enrichment. Systematic evaluation of the reduction and alkylation with different reagents can aid in a better design of bottom-up proteomic experiments.

Infectious diseases caused by bacterial pathogens pose a major concern to public health and, thus, greater attention must be given to providing insightful knowledge on host–pathogen interactions. There are several theories addressing the dynamics of complex mechanisms of host–pathogen interactions. The availability of an ample number of universally accepted model systems, including vertebrates, invertebrates, and mammalian cells, provides in-depth transcriptomics data to evaluate these complex mechanisms during host–pathogen interactions. Recent model system based proteomic studies have addressed the issues related to human diseases by establishing the protein profile of model animals that closely resemble the environment. As a result, model system based proteomics has been widely accepted as a powerful and effective approach to understand the highly complex host–pathogen interfaces at their protein levels. This review offers a snapshot of the contributions of selective model systems on host–bacterial pathogen interactions through proteomic approaches.

Extracellular microRNAs (miRNAs) carried by exosomes can play a key role in cell-to-cell communication. Deregulation of miRNA expression and exosome secretion have been related to pathological conditions such as cancer. While it is known that circulating miRNAs can alter gene expression in recipient cells, it remains unclear how significant the dynamical impact of these extracellular miRNAs is. To shed light on this issue, we propose a model for the spatio-temporal evolution of the protein expression in a cell tissue altered by abnormal miRNA expression in a donor cell. This results in a nonhomogeneous cellular response in the tissue, which we quantify by studying the range of action of the donor cell on the surrounding cells. Key model parameters that control the range of action are identified. Based on a model for a heterogeneous cell population, we show that the dynamics of gene expression in the tissue is robust to random changes of the parameter values. Furthermore, we study the propagation of gene expression oscillations in a tissue induced by extracellular miRNAs. In the donor cell, the miRNA inhibits its own transcription which can give rise to local oscillations in gene expression. The resulting oscillations of the concentration of extracellular miRNA induce oscillations of the protein concentration in recipient cells. We analyse the nonmonotonic spatial evolution of the oscillation amplitude of the protein concentration in the tissue which may have implications for the propagation of oscillations in biological rhythms such as the circadian clock.

Renal cell carcinoma (RCC) is the leading cause of death in renal malignancies. MicroRNA-590-5p (miR-590-5p) is of great importance in the processes of many cancers regarding regulation of cancer cell invasion and proliferation. In our study, alternation of miR-590-5p expression in RCC cell lines through transfection with pre-miR-590-5p (up-regulation) or anti-miR-590-5p (down-regulation) was performed. Apoptosis and viability of RCC cell lines were measured by flow cytometry and CCK-8 analysis, respectively. Cell invasion and migration were estimated by Transwell assay. The association of miR-590-5p with ARHGAP24 expression was evaluated using luciferase assays, real-time PCR and western blot assay. The expressions of apoptosis and migration-related protein were also measured by western blotting. We found that pre-miR-590-5p transfection in Caki-2 and 786-O cells showed significant increases in cell viability, invasion and migration, which were accompanied by decreased cell apoptosis, while anti-miR-590-5p transfection obviously inhibited the cell viability, migration and invasion of Caki-2 and 786-O cells as well as induced apoptosis, compared with the negative control group. Furthermore, bioinformatics combined with luciferase reporter assays indicated that ARHGAP24 is directly targeted by miR-590-5p. ARHGAP24 overexpression in 786-O and Caki-2 cells phenocopied the effects of anti-miR-590-5p transfection along with enhanced expression of active Caspase-3 and Bax/Bcl-2 ratio as well as decreased expression of MMP-2 and MMP-9. These findings suggested that miR-590-5p/ARHGAP24 seems to function as a potentially beneficial target for RCC treatment.

Diabetic kidney disease (DKD) is the leading cause of ESRD; however, early intervention can greatly prevent the progression of DKD; thus, sensitive biomarkers for DKD are still required. This study was aimed at the identification of potential biomarkers and revelation of underlying pathways in DKD patients by non-targeted metabolomics. Gas chromatography-mass spectrometry was used to analyze urine obtained from the control and type 2 diabetes mellitus (T2DM) and DKD patients, and the renal histological changes in DKD patients were assessed. The DKD group showed increased level of uric acid, 1,5-anhydroglucitol, hippuric acid, stearic acid, and palmitic acid and reduced level of uracil, glycine, aconitic acid, isocitric acid, 4-hydroxybutyrate, 2-deoxyerythritol, and glycolic acid as compared to the control and T2DM groups. Further analysis indicated that many of the changed metabolites were involved in mitochondrial and fatty acid (FA) metabolism, and combined mitochondrial and FA metabolites showed better diagnosis values for DKD. Histological results confirmed that renal expression of key proteins was reduced in DKD patients with respect to mitochondrial biogenesis (PGC-1α, p-AMPK) and FA oxidation (PPAR-α, CPT-1) as compared to that in the control and T2DM groups. This study highlighted that both mitochondrial and FA metabolism were disturbed in DKD, and thus, they could serve as combined biomarkers for the prediction of DKD.