In order to foster innovation and improve the effectiveness of drug discovery, there is a considerable interest in exploring unknown ‘chemical space’ to identify new bioactive compounds with novel and diverse scaffolds. Hence, fragment-based drug discovery (FBDD) was developed rapidly due to its advanced expansive search for ‘chemical space’, which can lead to a higher hit rate and ligand efficiency (LE). However, computational screening of fragments is always hampered by the promiscuous binding model. In this study, we developed a new web server Auto Core Fragment in silico Screening (ACFIS). It includes three computational modules, PARA_GEN, CORE_GEN and CAND_GEN. ACFIS can generate core fragment structure from the active molecule using fragment deconstruction analysis and perform in silico screening by growing fragments to the junction of core fragment structure. An integrated energy calculation rapidly identifies which fragments fit the binding site of a protein. We constructed a simple interface to enable users to view top-ranking molecules in 2D and the binding mode in 3D for further experimental exploration. This makes the ACFIS a highly valuable tool for drug discovery. The ACFIS web server is free and open to all users at http://chemyang.ccnu.edu.cn/ccb/server/ACFIS/.

Integrative genomics predictors, which score highly in predicting bacterial essential genes, would be unfeasible in most species because the data sources are limited. We developed a universal approach and tool designated Geptop, based on orthology and phylogeny, to offer gene essentiality annotations. In a series of tests, our Geptop method yielded higher area under curve (AUC) scores in the receiver operating curves than the integrative approaches. In the ten-fold cross-validations among randomly upset samples, Geptop yielded an AUC of 0.918, and in the cross-organism predictions for 19 organisms Geptop yielded AUC scores between 0.569 and 0.959. A test applied to the very recently determined essential gene dataset from the Porphyromonas gingivalis, which belongs to a phylum different with all of the above 19 bacterial genomes, gave an AUC of 0.77. Therefore, Geptop can be applied to any bacterial species whose genome has been sequenced. Compared with the essential genes uniquely identified by the lethal screening, the essential genes predicted only by Gepop are associated with more protein-protein interactions, especially in the three bacteria with lower AUC scores (<0.7). This may further illustrate the reliability and feasibility of our method in some sense. The web server and standalone version of Geptop are available at http://cefg.uestc.edu.cn/geptop/ free of charge. The tool has been run on 968 bacterial genomes and the results are accessible at the website.

BackgroundEssential genes are indispensable for the survival of living entities. They are the cornerstones of synthetic biology, and are potential candidate targets for antimicrobial and vaccine design.DescriptionHere we describe the Cluster of Essential Genes (CEG) database, which contains clusters of orthologous essential genes. Based on the size of a cluster, users can easily decide whether an essential gene is conserved in multiple bacterial species or is species-specific. It contains the similarity value of every essential gene cluster against human proteins or genes. The CEG_Match tool is based on the CEG database, and was developed for prediction of essential genes according to function. The database is available at http://cefg.uestc.edu.cn/ceg.ConclusionsProperties contained in the CEG database, such as cluster size, and the similarity of essential gene clusters against human proteins or genes, are very important for evolutionary research and drug design. An advantage of CEG is that it clusters essential genes based on function, and therefore decreases false positive results when predicting essential genes in comparison with using the similarity alignment method.Electronic supplementary materialThe online version of this article (doi:10.1186/1471-2164-14-769) contains supplementary material, which is available to authorized users.

Laribacter hongkongensis is a fish-borne pathogen associated with invasive infections and gastroenteritis. Its adaptive mechanisms to oxygen-limiting conditions in various environmental niches remain unclear. In this study, we compared the transcriptional profiles of L. hongkongensis under aerobic and anaerobic conditions using RNA-sequencing. Expression of genes involved in arginine metabolism significantly increased under anoxic conditions. Arginine was exploited as the sole energy source in L. hongkongensis for anaerobic respiration via the arginine catabolism pathway: specifically via the arginine deiminase (ADI) pathway. A transcriptional regulator FNR was identified to coordinate anaerobic metabolism by tightly regulating the expression of arginine metabolism genes. FNR executed its regulatory function by binding to FNR boxes in arc operons promoters. Survival of isogenic fnr mutant in macrophages decreased significantly when compared with wild-type; and expression level of fnr increased 8 h post-infection. Remarkably, FNR directly interacted with ArgR, another regulator that influences the biological fitness and intracellular survival of L. hongkongensis by regulating arginine metabolism genes. Our results demonstrated that FNR and ArgR work in coordination to respond to oxygen changes in both extracellular and intracellular environments, by finely regulating the ADI pathway and arginine anabolism pathways, thereby optimizing bacterial fitness in various environmental niches.

Knowledge of an organism’s fitness for survival is important for a complete understanding of microbial genetics and effective drug design. Current essential gene databases provide only binary essentiality data from genome-wide experiments. We therefore developed a new database that Integrates quantitative Fitness Information for Microbial genes (IFIM). The IFIM database currently contains data from 16 experiments and 2186 theoretical predictions. The highly significant correlation between the experiment-derived fitness data and our computational simulations demonstrated that the computer-generated predictions were often as reliable as the experimental data. The data in IFIM can be accessed easily, and the interface allows users to browse through the gene fitness information that it contains. IFIM is the first resource that allows easy access to fitness data of microbial genes. We believe this database will contribute to a better understanding of microbial genetics and will be useful in designing drugs to resist microbial pathogens, especially when experimental data are unavailable.Database URL: http://cefg.uestc.edu.cn/ifim/ or http://cefg.cn/ifim/