Zhiwei Cao

Genome sequencing and comparative analysis of Saccharomyces cerevisiae strain YJM789

We sequenced the genome of Saccharomyces cerevisiae strain YJM789, which was derived from a yeast isolated from the lung of an AIDS patient with pneumonia. The strain is used for studies of fungal infections and quantitative genetics because of its extensive phenotypic differences to the laboratory reference strain, including growth at high temperature and deadly virulence in mouse models. Here we show that the ≈12-Mb genome of YJM789 contains ≈60,000 SNPs and ≈6,000 indels with respect to the reference S288c genome, leading to protein polymorphisms with a few known cases of phenotypic changes. Several ORFs are found to be unique to YJM789, some of which might have been acquired through horizontal transfer. Localized regions of high polymorphism density are scattered over the genome, in some cases spanning multiple ORFs and in others concentrated within single genes. The sequence of YJM789 contains clues to pathogenicity and spurs the development of more powerful approaches to dissecting the genetic basis of complex hereditary traits.

HIT: linking herbal active ingredients to targets

The information of protein targets and small molecule has been highly valued by biomedical and pharmaceutical research. Several protein target databases are available online for FDA-approved drugs as well as the promising precursors that have largely facilitated the mechanistic study and subsequent research for drug discovery. However, those related resources regarding to herbal active ingredients, although being unusually valued as a precious resource for new drug development, is rarely found. In this article, a comprehensive and fully curated database for Herb Ingredients’ Targets (HIT, http://lifecenter.sgst.cn/hit/) has been constructed to complement above resources. Those herbal ingredients with protein target information were carefully curated. The molecular target information involves those proteins being directly/indirectly activated/inhibited, protein binders and enzymes whose substrates or products are those compounds. Those up/down regulated genes are also included under the treatment of individual ingredients. In addition, the experimental condition, observed bioactivity and various references are provided as well for users reference. Derived from more than 3250 literatures, it currently contains 5208 entries about 1301 known protein targets (221 of them are described as direct targets) affected by 586 herbal compounds from more than 1300 reputable Chinese herbs, overlapping with 280 therapeutic targets from Therapeutic Targets Database (TTD), and 445 protein targets from DrugBank corresponding to 1488 drug agents. The database can be queried via keyword search or similarity search. Crosslinks have been made to TTD, DrugBank, KEGG, PDB, Uniprot, Pfam, NCBI, TCM-ID and other databases.

SEPPA: a computational server for spatial epitope prediction of protein antigens

In recent years, a lot of efforts have been made in conformational epitope prediction as antigen proteins usually bind antibodies with an assembly of sequentially discontinuous and structurally compact surface residues. Currently, only a few methods for spatial epitope prediction are available with focus on single residue propensity scales or continual segments clustering. In the method of SEPPA, a concept of ‘unit patch of residue triangle’ was introduced to better describe the local spatial context in protein surface. Besides that, SEPPA incorporated clustering coefficient to describe the spatial compactness of surface residues. Validated by independent testing datasets, SEPPA gave an average AUC value over 0.742 and produced a successful pick-up rate of 96.64%. Comparing with peers, SEPPA shows significant improvement over other popular methods like CEP, DiscoTope and BEpro. In addition, the threshold scores for certain accuracy, sensitivity and specificity are provided online to give the confidence level of the spatial epitope identification. The web server can be accessed at http://lifecenter.sgst.cn/seppa/index.php. Batch query is supported.

Hierarchical modularity of nested bow-ties in metabolic networks

BackgroundThe exploration of the structural topology and the organizing principles of genome-based large-scale metabolic networks is essential for studying possible relations between structure and functionality of metabolic networks. Topological analysis of graph models has often been applied to study the structural characteristics of complex metabolic networks.ResultsIn this work, metabolic networks of 75 organisms were investigated from a topological point of view. Network decomposition of three microbes (Escherichia coli, Aeropyrum pernix and Saccharomyces cerevisiae) shows that almost all of the sub-networks exhibit a highly modularized bow-tie topological pattern similar to that of the global metabolic networks. Moreover, these small bow-ties are hierarchically nested into larger ones and collectively integrated into a large metabolic network, and important features of this modularity are not observed in the random shuffled network. In addition, such a bow-tie pattern appears to be present in certain chemically isolated functional modules and spatially separated modules including carbohydrate metabolism, cytosol and mitochondrion respectively.ConclusionThe highly modularized bow-tie pattern is present at different levels and scales, and in different chemical and spatial modules of metabolic networks, which is likely the result of the evolutionary process rather than a random accident. Identification and analysis of such a pattern is helpful for understanding the design principles and facilitate the modelling of metabolic networks.

Modular co-evolution of metabolic networks

BackgroundThe architecture of biological networks has been reported to exhibit high level of modularity, and to some extent, topological modules of networks overlap with known functional modules. However, how the modular topology of the molecular network affects the evolution of its member proteins remains unclear.ResultsIn this work, the functional and evolutionary modularity of Homo sapiens (H. sapiens) metabolic network were investigated from a topological point of view. Network decomposition shows that the metabolic network is organized in a highly modular core-periphery way, in which the core modules are tightly linked together and perform basic metabolism functions, whereas the periphery modules only interact with few modules and accomplish relatively independent and specialized functions. Moreover, over half of the modules exhibit co-evolutionary feature and belong to specific evolutionary ages. Peripheral modules tend to evolve more cohesively and faster than core modules do.ConclusionThe correlation between functional, evolutionary and topological modularity suggests that the evolutionary history and functional requirements of metabolic systems have been imprinted in the architecture of metabolic networks. Such systems level analysis could demonstrate how the evolution of genes may be placed in a genome-scale network context, giving a novel perspective on molecular evolution.

Bioinformatics analysis of the epitope regions for norovirus capsid protein

BackgroundNorovirus is the major cause of nonbacterial epidemic gastroenteritis, being highly prevalent in both developing and developed countries. Despite of the available monoclonal antibodies (MAbs) for different sub-genogroups, a comprehensive epitope analysis based on various bioinformatics technology is highly desired for future potential antibody development in clinical diagonosis and treatment.MethodsA total of 18 full-length human norovirus capsid protein sequences were downloaded from GenBank. Protein modeling was performed with program Modeller 9.9. The modeled 3D structures of capsid protein of norovirus were submitted to the protein antigen spatial epitope prediction webserver (SEPPA) for predicting the possible spatial epitopes with the default threshold. The results were processed using the Biosoftware.ResultsCompared with GI, we found that the GII genogroup had four deletions and two special insertions in the VP1 region. The predicted conformational epitope regions mainly concentrated on N-terminal (1~96), Middle Part (298~305, 355~375) and C-terminal (560~570). We find two common epitope regions on sequences for GI and GII genogroup, and also found an exclusive epitope region for GII genogroup.ConclusionsThe predicted conformational epitope regions of norovirus VP1 mainly concentrated on N-terminal, Middle Part and C-terminal. We find two common epitope regions on sequences for GI and GII genogroup, and also found an exclusive epitope region for GII genogroup. The overlapping with experimental epitopes indicates the important role of latest computational technologies. With the fast development of computational immunology tools, the bioinformatics pipeline will be more and more critical to vaccine design.

Therapeutic effects of astragaloside IV on myocardial injuries: multi-target identification and network analysis.

Astragaloside IV (AGS-IV) is a main active ingredient of Astragalus membranaceus Bunge, a medicinal herb used for cardiovascular diseases (CVD). In this work, we investigated the therapeutic mechanisms of AGS-IV at a network level by computer-assisted target identification with the in silico inverse docking program (INVDOCK). Targets included in the analysis covered all signaling pathways thought to be implicated in the therapeutic actions of all CVD drugs approved by US FDA. A total of 39 putative targets were identified. Three of these targets, calcineurin (CN), angiotensin-converting enzyme (ACE), and c-Jun N-terminal kinase (JNK), were experimentally validated at a molecular level. Protective effects of AGS-IV were also compared with the CN inhibitor cyclosporin A (CsA) in cultured cardiomyocytes exposed to adriamycin. Network analysis of protein-protein interactions (PPI) was carried out with reference to the therapeutic profiles of approved CVD drugs. The results suggested that the therapeutic effects of AGS-IV are based upon a combination of blocking calcium influx, vasodilation, anti-thrombosis, anti-oxidation, anti-inflammation and immune regulation.

Potassium Channels: Structures, Diseases, and Modulators

Potassium channels participate in many critical biological functions and play important roles in a variety of diseases. In recent years, many significant discoveries have been made which motivate us to review these achievements. The focus of our review is mainly on three aspects. Firstly, we try to summarize the latest developments in structure determinants and regulation mechanism of all types of potassium channels. Secondly, we review some diseases induced by or related to these channels. Thirdly, both qualitative and quantitative approaches are utilized to analyze structural features of modulators of potassium channels. Our analyses further prove that modulators possess some certain natural‐product scaffolds. And pharmacokinetic parameters are important properties for organic molecules. Besides, with in silico methods, some features that can be used to differentiate modulators are derived. There is no doubt that all these studies on potassium channels as possible pharmaceutical targets will facilitate future translational research. All the strategies developed in this review could be extended to studies on other ion channels and proteins as well.

Brief communication: Operon prediction based on SVM

The operon is a specific functional organization of genes found in bacterial genomes. Most genes within operons share common features. The support vector machine (SVM) approach is here used to predict operons at the genomic level. Four features were chosen as SVM input vectors: the intergenic distances, the number of common pathways, the number of conserved gene pairs and the mutual information of phylogenetic profiles. The analysis reveals that these common properties are indeed characteristic of the genes within operons and are different from that of non-operonic genes. Jackknife testing indicates that these input feature vectors, employed with RBF kernel SVM, achieve high accuracy. To validate the method, Escherichia coli K12 and Bacillus subtilis were taken as benchmark genomes of known operon structure, and the prediction results in both show that the SVM can detect operon genes in target genomes efficiently and offers a satisfactory balance between sensitivity and specificity.

Insight into potential toxicity mechanisms of melamine: an in silico study.

The toxicity of melamine has attracted much attention since the outbreak of nephrolithiasis among children ingesting melamine-contaminated infant formula in China. However, there is little knowledge about the molecular mechanisms underlying the melamine-induced toxicity. In this paper, a ligand-protein docking method (INVDOCK) was applied to predict the toxicity-related target proteins for melamine and its metabolite, cyanuric acid. As a result, 23 and 35 proteins were finally identified as the potential target proteins for melamine and cyanuric acid, respectively. Through an enrichment analysis, it was found that nephrotoxicity and lung toxicity might be the most significant toxicities induced by melamine and cyanuric acid. Four target proteins (glutathione peroxidase 1, beta-hexosaminidase subunit beta, L-lactate dehydrogenase and lysozyme C) may be related to the molecular basis of the nephrotoxicity induced by melamine except for known kidney crystals formation. After mapping all these toxicity-related target proteins onto cellular pathways, it was indicated that the toxicities of melamine and cyanuric acid might also be caused by breaking down redox balance, perturbing the arginine and proline metabolism and damaging the homeostasis of energy production system. To further explore the mechanisms underlying the toxicities of melamine and cyanuric acid, a biological signal cascades network constructed by some of the toxicity-related target proteins was discussed.