Assessing the similarity of ligand binding conformations with the Contact Mode Score

© 2016 Elsevier Ltd Structural and computational biologists often need to measure the similarity of ligand binding conformations. The commonly used root-mean-square deviation (RMSD) is not only ligand-size dependent, but also may fail to capture biologically meaningful binding features. To address these issues, we developed the Contact Mode Score (CMS), a new metric to assess the conformational similarity based on intermolecular protein-ligand contacts. The CMS is less dependent on the ligand size and has the ability to include flexible receptors. In order to effectively compare binding poses of non-identical ligands bound to different proteins, we further developed the eXtended Contact Mode Score (XCMS). We believe that CMS and XCMS provide a meaningful assessment of the similarity of ligand binding conformations. CMS and XCMS are freely available at http://brylinski.cct.lsu.edu/content/contact-mode-score and http://geaux-computational-bio.github.io/contact-mode-score/

Methanation Catalyst for Low CO Concentration

A Ni-based catalyst supported by γ-Al2O3 was prepared by impregnation method, and the catalyst was used in a low CO and CO2 concentration methanation system. The effect of temperature, pressure and space velocity on the methanation reaction was investigated in an experimental fixed-bed reactor. The methanation reaction was operated at the conditions of 190-240°C, 3000-24000ml•g-1•h-1 and 1.5-3.5MPa. The results show that temperature and space velocity play important role on the reaction. With the increase of reaction temperature the CO and CO2 conversion increase and the selectivity of CH4 increase. And with the increase of the space velocity the conversion of CO and CO2 and the selectivity of CH4 decrease sharply

Acute small bowel obstruction: a rare initial presentation for the metastasis of the large-cell carcinoma of the lung

We present one case with symptom of paroxysmal abdominal pain for over 20 days. Abdominal computerized tomography (CT) scan revealed intestinal obstruction and a mass of 6.0 cm × 6.0 cm in size located at the left adrenal. Chest CT scan showed a lobulated mass of 2.7 cm × 2.7 cm in size at the upper left lung. Core needle biopsy of the lung mass confirmed the diagnosis of large cell carcinoma. The patient underwent an emergency abdominal laparotomy and received a chemotherapy regimen that consisted of pemetrexed and cisplatin postoperatively. In addition, we made a review of the literature of the occurrence, diagnosis and outcome of this manifestation

GeauxDock: A novel approach for mixed-resolution ligand docking using a descriptor-based force field

© 2015 Wiley Periodicals, Inc. Molecular docking is an important component of computer-aided drug discovery. In this communication, we describe GeauxDock, a new docking approach that builds on the ideas of ligand homology modeling. GeauxDock features a descriptor-based scoring function integrating evolutionary constraints with physics-based energy terms, a mixed-resolution molecular representation of protein-ligand complexes, and an efficient Monte Carlo sampling protocol. To drive docking simulations toward experimental conformations, the scoring function was carefully optimized to produce a correlation between the total pseudoenergy and the native-likeness of binding poses. Indeed, benchmarking calculations demonstrate that GeauxDock has a strong capacity to identify near-native conformations across docking trajectories with the area under receiver operating characteristics of 0.85. By excluding closely related templates, we show that GeauxDock maintains its accuracy at lower levels of homology through the increased contribution from physics-based energy terms compensating for weak evolutionary constraints. GeauxDock is available at http://www.institute.Loni.org/lasigma/package/dock/

Constructing a robust protein-protein interaction network by integrating multiple public databases

<p>Abstract</p> <p>Background</p> <p>Protein-protein interactions (PPIs) are a critical component for many underlying biological processes. A PPI network can provide insight into the mechanisms of these processes, as well as the relationships among different proteins and toxicants that are potentially involved in the processes. There are many PPI databases publicly available, each with a specific focus. The challenge is how to effectively combine their contents to generate a robust and biologically relevant PPI network.</p> <p>Methods</p> <p>In this study, seven public PPI databases, BioGRID, DIP, HPRD, IntAct, MINT, REACTOME, and SPIKE, were used to explore a powerful approach to combine multiple PPI databases for an integrated PPI network. We developed a novel method called <it>k</it>-votes to create seven different integrated networks by using values of <it>k</it> ranging from 1-7. Functional modules were mined by using SCAN, a Structural Clustering Algorithm for Networks. Overall module qualities were evaluated for each integrated network using the following statistical and biological measures: (1) modularity, (2) similarity-based modularity, (3) clustering score, and (4) enrichment.</p> <p>Results</p> <p>Each integrated human PPI network was constructed based on the number of votes (<it>k</it>) for a particular interaction from the committee of the original seven PPI databases. The performance of functional modules obtained by SCAN from each integrated network was evaluated. The optimal value for <it>k</it> was determined by the functional module analysis. Our results demonstrate that the <it>k</it>-votes method outperforms the traditional union approach in terms of both statistical significance and biological meaning. The best network is achieved at <it>k</it>=2, which is composed of interactions that are confirmed in at least two PPI databases. In contrast, the traditional union approach yields an integrated network that consists of all interactions of seven PPI databases, which might be subject to high false positives.</p> <p>Conclusions</p> <p>We determined that the k-votes method for constructing a robust PPI network by integrating multiple public databases outperforms previously reported approaches and that a value of k=2 provides the best results. The developed strategies for combining databases show promise in the advancement of network construction and modeling.</p

Protective Effect of Akkermansia muciniphila against Immune-Mediated Liver Injury in a Mouse Model

Accumulating evidence indicates that gut microbiota participates in the pathogenesis and progression of liver diseases. The severity of immune-mediated liver injury is associated with different microbial communities. Akkermansia muciniphila can regulate immunologic and metabolic functions. However, little is known about its effects on gut microbiota structure and function. This study investigated the effect of A. muciniphila on immune-mediated liver injury and potential underlying mechanisms. Twenty-two C57BL/6 mice were assigned to three groups (N = 7–8 per group) and continuously administrated A. muciniphila MucT or PBS by oral gavage for 14 days. Mouse feces were collected for gut microbiota analysis on the 15th day, and acute liver injury was induced by Concanavalin A (Con A, 15 mg/kg) injection through the tail vein. Samples (blood, liver, ileum, colon) were assessed for liver injury, systemic inflammation, and intestinal barrier function. We found that oral administration of A. muciniphila decreased serum ALT and AST and alleviated liver histopathological damage induced by Con A. Serum levels of pro-inflammatory cytokines and chemokines (IL-2, IFN-γ, IL-12p40, MCP-1, MIP-1a, MIP-1b) were substantially attenuated. A. muciniphila significantly decreased hepatocellular apoptosis; Bcl-2 expression increased, but Fas and DR5 decreased. Further investigation showed that A. muciniphila enhanced expression of Occludin and Tjp-1 and inhibited CB1 receptor, which strengthened intestinal barriers and reduced systemic LPS level. Fecal 16S rRNA sequence analysis indicated that A. muciniphila increased microbial richness and diversity. The community structure of the Akk group clustered distinctly from that of mice pretreated with PBS. Relative abundance of Firmicutes increased, and Bacteroidetes abundance decreased. Correlation analysis showed that injury-related factors (IL-12p40, IFN-γ, DR5) were negatively associated with specific genera (Ruminococcaceae_UCG_009, Lachnospiraceae_UCG_001, Akkermansia), which were enriched in mice pretreated with A. muciniphila. Our results suggested that A. muciniphila MucT had beneficial effects on immune-mediated liver injury by alleviating inflammation and hepatocellular death. These effects may be driven by the protective profile of the intestinal community induced by the bacteria. The results provide a new perspective on the immune function of gut microbiota in host diseases

Diversity and structure of soil bacterial communities in the Fildes Region (maritime Antarctica) as revealed by 454 pyrosequencing

This study assessed the diversity and composition of bacterial communities in four different soils (human-, penguin-, seal-colony impacted soils and pristine soil) in the Fildes Region (King George Island, Antarctica) using 454 pyrosequencing with bacterial-specific primers targeting the 16S rRNA gene. Proteobacteria, Actinobacteria, Acidobacteria, and Verrucomicrobia were abundant phyla in almost all the soil samples. The four types of soils were significantly different in geochemical properties and bacterial community structure. Thermotogae, Cyanobacteria, Fibrobacteres, Deinococcus-Thermus, and Chlorobi obviously varied in their abundance among the 4 soil types. Considering all the samples together, members of the genera Gaiella, Chloracidobacterium, Nitrospira, Polaromonas, Gemmatimonas, Sphingomonas and Chthoniobacter were found to predominate, whereas members of the genera Chamaesiphon, Herbaspirillum, Hirschia, Nevskia, Nitrosococcus, Rhodococcus, Rhodomicrobium, and Xanthomonas varied obviously in their abundance among the four soil types. Distance-based redundancy analysis revealed that pH (p < 0.01), phosphate phosphorus (p < 0.01), organic carbon (p < 0.05), and organic nitrogen (p < 0.05) were the most significant factors that correlated with the community distribution of soil bacteria. To our knowledge, this is the first study to explore the soil bacterial communities in human-, penguin-, and seal- colony impacted soils from ice-free areas in maritime Antarctica using high-throughput pyrosequencing