Molecular BioSystems最新文献

ADP-ribosylation is a protein post-translational modification catalyzed by ADP-ribose transferases (ARTs). ART activity is critical in mediating many cellular processes, and is required for DNA damage repair. All five histone proteins are extensively ADP-ribosylated by ARTs upon induction of DNA damage. However, how these modifications aid in repair processes is largely unknown, primarily due to lack of knowledge about where they site-specifically occur on histones. Here, we conduct a comprehensive analysis of histone Asp/Glu ADP-ribosylation sites upon DNA damage induced by dimethyl sulfate (DMS). We also demonstrate that incubation of cell nuclei with NAD⁺, as has been done previously in the literature, leads to spurious ADP-ribosylation levels of histone proteins. Altogether, we were able to identify 30 modification sites, 20 of which are novel. We also quantify the abundance of these modification sites during the course of DNA damage insult to identify which sites are critical for mediating repair. We found that every quantifiable site increases in abundance over time and that each identified ADP-ribosylation site is located on the surface of the nucleosome. Together, the data suggest specific Asp/Glu residues are unlikely to be critical for DNA damage repair and rather that this process is likely dependent on ADP-ribosylation of the nucleosomal surface in general.

Prediction of new associations between drugs and targeting pathways can provide valuable clues for drug discovery & development. However, information integration and a class-imbalance problem are important challenges for available prediction methods. This paper proposes a prediction of potential associations between drugs and pathways based on a disease-related LSA-PU-KNN method. Firstly, we built a drug–disease–pathway network and combined the drug–disease and pathway–disease features obtained by different types of feature profiles. Then we applied a latent semantic analysis (LSA) method to perform dimension reduction by combining positive-unlabeled (PU) learning and k nearest neighbors (KNN) method. The experimental results showed that our method can achieve a higher AUC (the area under the ROC curve) and AUPR (the area under the PR curve) than other typical methods. Furthermore, some interesting drug–pathway interaction pairs were identified and validated.

Regulation of protein translation constitutes a crucial step in control of gene expression. In comparison to transcriptional regulation, however, translational control has remained a significantly under-studied layer of gene expression. This trend is now beginning to shift thanks to recent advances in next-generation sequencing, proteomics, and microscopy based methodologies which allow accurate monitoring of protein translation rates, from single target messenger RNA molecules to genome-wide scale studies. In this review, we summarize these recent advances, and discuss how they are enabling researchers to study translational regulation in a wide variety of in vitro and in vivo biological systems, with unprecedented depth and spatiotemporal resolution.

In all organisms, DNA glycosylases initiate base excision repair pathways resulting in removal of aberrant bases from DNA. Human SMUG1 belongs to the superfamily of uracil-DNA glycosylases catalyzing the hydrolysis of the N-glycosidic bond of uridine and uridine lesions bearing oxidized groups at C5: 5-hydroxymethyluridine (5hmU), 5-formyluridine (5fU), and 5-hydroxyuridine (5hoU). An apurinic/apyrimidinic (AP) site formed as the product of an N-glycosylase reaction is tightly bound to hSMUG1, thus inhibiting the downstream action of AP-endonuclease APE1. The steady-state kinetic parameters (k_cat and K_M; obtained from the literature) correspond to the enzyme turnover process limited by the release of hSMUG1 from the complex with the AP-site. In the present study, our objective was to carry out a stopped-flow fluorescence analysis of the interaction of hSMUG1 with a DNA substrate containing a dU:dG base pair to follow the pre-steady-state kinetics of conformational changes in both molecules. A comparison of kinetic data obtained by means of Trp and 2-aminopurine fluorescence and F?rster resonance energy transfer (FRET) detection allowed us to elucidate the stages of specific and nonspecific DNA binding, to propose the mechanism of damaged base recognition by hSMUG1, and to determine the true rate of the catalytic step. Our results shed light on the kinetic mechanism underlying the initiation of base excision repair by hSMUG1 using the “wedge” strategy for DNA lesion search.

Expression level provides important clues about gene function. Previously, various efforts have been undertaken to profile human genes according to their expression level. Intrinsically disordered proteins (IDPs) do not adopt any rigid conformation under physiological conditions, however, are considered as an important functional class in all domains of life. Based on a human tissue-averaged gene expression level, previous studies showed that IDPs are expressed at a lower level than ordered globular proteins. Here, we examined the gene expression pattern of human ordered and disordered proteins in 32 normal tissues. We noticed that in most of the tissues, ordered and disordered proteins are expressed at a similar level. Moreover, in a number of tissues IDPs were found to be expressed at a higher level than ordered proteins. Rigorous statistical analyses suggested that the lower tissue-averaged gene expression level of IDPs (reported earlier) may be the consequence of their biased gene expression in some specific tissues and higher protein length. When we considered the gene repertory of each tissue we noticed that a number of human tissues (brain, testes, etc.) selectively express a higher fraction of disordered proteins, which help them to maintain higher protein connectivity by forming disordered binding motifs and to sustain their functional specificities. Our results demonstrated that the disordered proteins are indispensable in these tissues for their functional advantages.

An increasing amount of evidence indicates that microRNAs (miRNAs) are closely related to many important biological processes and play a significant role in various human diseases. More and more researchers have begun to seek effective methods to predict potential miRNA–disease associations. However, reliable computational methods to predict potential disease-related miRNAs are lacking. In this study, we developed a new miRNA–disease association prediction model called Negative-Aware and rating-based Recommendation algorithm for miRNA–Disease Association prediction (NARRMDA) based on the known miRNA–disease associations in the HMDD database, miRNA functional similarity, disease semantic similarity and Gaussian interaction profile kernel similarity. NARRMDA combined a rating-based recommendation algorithm and a negative-aware algorithm to score and rank miRNAs without known associations with investigated diseases. Furthermore, we used leave-one-out cross validation to evaluate the accuracy of NARRMDA and compared NARRMDA with four previous classical prediction models (RLSMDA, HDMP, RWRMDA and MCMDA). As it turned out, NARRMDA and the other four prediction models achieved AUCs of 0.8053, 0.6953, 0.7702, 0.7891 and 0.7718, respectively, which proved that NARRMDA has superior performance of prediction accuracy. Furthermore, we verified the prediction results associated with colon neoplasms, esophageal neoplasms, lymphoma and breast neoplasms by two different validation schemas. In these case studies, 92%, 84%, 92%, and 100% of the top 50 potential miRNAs for these four diseases were confirmed by experimental discoveries, respectively. These results further show that NARRMDA has reliable performance of prediction ability.

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.

Hydrophobic surfaces are known to adsorb and unfold proteins, a process that has been studied only for a few proteins. Here we address the interaction of β2-microglobulin, a paradigmatic protein for the study of amyloidogenesis, with hydrophobic surfaces. A system with 27 copies of the protein surrounded by a model cubic hydrophobic box is studied by implicit solvent molecular dynamics simulations. Most proteins adsorb on the walls of the box without major distortions in local geometry, whereas free molecules maintain proper structures and fluctuations as observed in explicit solvent molecular dynamics simulations. The major conclusions from the simulations are as follows: (i) the adopted implicit solvent model is adequate to describe protein dynamics and thermodynamics; (ii) adsorption occurs readily and is irreversible on the simulated timescale; (iii) the regions most involved in molecular encounters and stable interactions with the walls are the same as those that are important in protein–protein and protein–nanoparticle interactions; (iv) unfolding following adsorption occurs at regions found to be flexible by both experiments and simulations; (v) thermodynamic analysis suggests a very large contribution from van der Waals interactions, whereas unfavorable electrostatic interactions are not found to contribute much to adsorption energy. Surfaces with different degrees of hydrophobicity may occur in vivo. Our simulations show that adsorption is a fast and irreversible process which is accompanied by partial unfolding. The results and the thermodynamic analysis presented here are consistent with and rationalize previous experimental work.

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.

Toxic cyanobacteria blooms populate water bodies by consuming external nutrients and releasing cyanotoxins that are detrimental for other aquatic species, producing a significant impact on the plankton ecosystem and food web. To exercise population-level control of toxin production, understanding the biochemical mechanisms that explain cyanotoxin regulation within a bacterial cell is of utmost importance. In this study, we explore the mechanistic events to investigate the dependence of toxin microcystin on external nitrogen, a known regulator of the toxin, and for the first time, propose a kinetic model that analyzes the intracellular conditions required to ensure nitrogen dependence on microcystin. We hypothesize that the GS–GOGAT cycle is manipulated by variable influx of different intracellular metabolites that can either disturb or promote the balance between the enzyme microcystin synthetase and substrate glutamate to produce variable microcystin levels. As opposed to the popular notion that nitrogen starvation increases microcystin synthesis, our analyses suggest that under certain intracellular metabolite regimes, this relationship can either be completely lost or reversed. External nitrogen can only complement the conditions fixed by intracellular glutamate, glutamine and 2-oxoglutarate. This mechanistic understanding can provide an experimentally testable hypothesis for exploring the less-known biology of microcystin synthesis and designing specific interventions.