Genome Wide Re-Annotation of Caldicellulosiruptor saccharolyticus with New Insights into Genes Involved in Biomass Degradation and Hydrogen Production.

Caldicellulosiruptor saccharolyticus has proven itself to be an excellent candidate for biological hydrogen (H2) production, but still it has major drawbacks like sensitivity to high osmotic pressure and low volumetric H2 productivity, which should be considered before it can be used industrially. A whole genome re-annotation work has been carried out as an attempt to update the incomplete genome information that causes gap in the knowledge especially in the area of metabolic engineering, to improve the H2 producing capabilities of C. saccharolyticus. Whole genome re-annotation was performed through manual means for 2,682 Coding Sequences (CDSs). Bioinformatics tools based on sequence similarity, motif search, phylogenetic analysis and fold recognition were employed for re-annotation. Our methodology could successfully add functions for 409 hypothetical proteins (HPs), 46 proteins previously annotated as putative and assigned more accurate functions for the known protein sequences. Homology based gene annotation has been used as a standard method for assigning function to novel proteins, but over the past few years many non-homology based methods such as genomic context approaches for protein function prediction have been developed. Using non-homology based functional prediction methods, we were able to assign cellular processes or physical complexes for 249 hypothetical sequences. Our re-annotation pipeline highlights the addition of 231 new CDSs generated from MicroScope Platform, to the original genome with functional prediction for 49 of them. The re-annotation of HPs and new CDSs is stored in the relational database that is available on the MicroScope web-based platform. In parallel, a comparative genome analyses were performed among the members of genus Caldicellulosiruptor to understand the function and evolutionary processes. Further, with results from integrated re-annotation studies (homology and genomic context approach), we strongly suggest that Csac_0437 and Csac_0424 encode for glycoside hydrolases (GH) and are proposed to be involved in the decomposition of recalcitrant plant polysaccharides. Similarly, HPs: Csac_0732, Csac_1862, Csac_1294 and Csac_0668 are suggested to play a significant role in biohydrogen production. Function prediction of these HPs by using our integrated approach will considerably enhance the interpretation of large-scale experiments targeting this industrially important organism.

TaxKB: a knowledge base for new taxane-related drug discovery.

BACKGROUND: Taxanes are naturally occurring compounds which belong to a powerful group of chemotherapeutic drugs with anticancer properties. Their current use, clinical efficacy, and unique mechanism of action indicate their potentiality for cancer drug discovery and development thereby promising to reduce the high economy associated with cancer worldwide. Extensive research has been carried out on taxanes with the aim to combat issues of drug resistance, side effects, limited natural supply, and also to increase the therapeutic index of these molecules. These efforts have led to the isolation of many naturally occurring compounds belonging to this family (more than 350 different kinds), and the synthesis of semisynthetic analogs of the naturally existing molecules (>500), and has also led to the characterization of many (>1000) of them. A web-based database system on clinically exploitable taxanes, providing a link between the structure and the pharmacological property of these molecules could help to reduce the druggability gap for these molecules. RESULTS: Taxane knowledge base (TaxKB, http://bioinfo.au-kbc.org.in/taxane/Taxkb/), is an online multi-tier relational database that currently holds data on 42 parameters of 250 natural and 503 semisynthetic analogs of taxanes. This database provides researchers with much-needed information necessary for drug development. TaxKB enables the user to search data on the structure, drug-likeness, and physicochemical properties of both natural and synthetic taxanes with a "General Search" option in addition to a "Parameter Specific Search." It displays 2D structure and allows the user to download the 3D structure (a PDB file) of taxanes that can be viewed with any molecular visualization tool. The ultimate aim of TaxKB is to provide information on Absorption, Distribution, Metabolism, and Excretion/Toxicity (ADME/T) as well as data on bioavailability and target interaction properties of candidate anticancer taxanes, ahead of expensive clinical trials. CONCLUSION: This first web-based single-information portal will play a central role and help researchers to move forward in taxane-based cancer drug research.

NGS meta data analysis for identification of SNP and INDEL patterns in human airway transcriptome: A preliminary indicator for lung cancer.

High-throughput sequencing of RNA (RNA-Seq) was developed primarily to analyze global gene expression in different tissues. It is also an efficient way to discover coding SNPs and when multiple individuals with different genetic backgrounds were used, RNA-Seq is very effective for the identification of SNPs. The objective of this study was to perform SNP and INDEL discoveries in human airway transcriptome of healthy never smokers, healthy current smokers, smokers without lung cancer and smokers with lung cancer. By preliminary comparative analysis of these four data sets, it is expected to get SNP and INDEL patterns responsible for lung cancer. A total of 85,028 SNPs and 5738 INDELs in healthy never smokers, 32,671 SNPs and 1561 INDELs in healthy current smokers, 50,205 SNPs and 3008 INDELs in smokers without lung cancer and 51,299 SNPs and 3138 INDELs in smokers with lung cancer were identified. The analysis of the SNPs and INDELs in genes that were reported earlier as differentially expressed was also performed. It has been found that a smoking person has SNPs at position 62,186,542 and 62,190,293 in SCGB1A1 gene and 180,017,251, 180,017,252, and 180,017,597 in SCGB3A1 gene and INDELs at position 35,871,168 in NFKBIA gene and 180,017,797 in SCGB3A1 gene. The SNPs identified in this study provides a resource for genetic studies in smokers and shall contribute to the development of a personalized medicine. This study is only a preliminary kind and more vigorous data analysis and wet lab validation are required.

Characterization of Lovastatin biosynthetic cluster proteins in Aspergillus terreus strain ATCC 20542.

Aspergillus terreus is a filamentous ascomycota, which is prominent for its production of lovastatin, an antihypercholesterolemic drug. The commercial importance of lovastatin with annual sales of billions of dollars made us to focus on lovastatin biosynthetic cluster proteins. The analysis of these lovastatin biosynthetic cluster proteins with different perspectives such as physicochemical property, structure based analysis and functional studies were done to find out the role and function of every protein involved in the lovastatin biosynthesis pathway. Several computational tools are used to predict the physicochemical properties, secondary structural features, topology, patterns, domains and cellular location. There are 8 unidentified proteins in lovastatin biosynthetic cluster, in which 6 proteins have homologous partners, and annotation transfer is done based on the closely related homologous genes, and their structures are also modeled. The two other proteins that do not have homologous partners are predicted as PQ loop repeat protein that may be involved in glycosylation machinery and as thiolase-acyl activity by the integrated functional analysis approach.

BCDB - A database for breast cancer research and information.

UNLABELLED: In pursuit of a better updated source including 'omics' information for breast cancer, Breast Cancer Database (BCDB) has been developed to provide the researcher with the quick overview of the Breast cancer disease and other relevant information. This database comprises of myriad of information about genes involved in breast cancer, its functions and drug molecules which are currently being used in the treatment of breast cancer. The data available in BCDB is retrieved from the biomedical research literature. It facilitates the user to search information on gene, its location in chromosome, functions and its importance in cancer diseases. Broadly, this can be queried by giving gene name, protein name and drug name. This database is platform independent, user friendly and freely accessible through internet. The data present in BCDB is directly linked to other on-line resources such as NCBI, PDB and PubMed. Hence, it can act as a complete web resource comprising gene sequences, drug structures and literature information related to breast cancer, which is not available in any other breast cancer database. AVAILABILITY: The database is freely available at http://122.165.25.137/bioinfo/breastcancerdb/

A novel strategy for mechanism based computational drug discovery.

Glioma, the common brain tumor, which arises from the glial cells, offers worse prognosis and therapy than any other tumors. Despite the genetic and pathological diversities of malignant gliomas, common signaling pathways that drive cellular proliferation, survival, invasion and angiogenesis have been identified. Very often, various tyrosine kinase receptors are inappropriately activated in human brain tumors and contribute to tumor malignancy. During such tumourous states where multiple pathways are involved, a few of them are responsbile for cell differentiation, proliferation and anti-apoptosis. Computational simulation studies of normal EGFR signaling in glioma together with the mutant EGFR mediated signaling and the MAPK signaling in glioma were carried out. There were no significant cross talks observed between the mutant EGFR and the MAPK pathways and thus from the simulation results, we propose a novel concept of 'multiple-targeting' that combines EGFR and Ras targeted therapy thereby providing a better therapeutic value against glioma. Diallyl Disulfide (DADS) that has been commonly used for Ras inhibition in glioma was taken for analyses and the effect of inhibiting the EGFR downstream signaling protein with this DADS was analyzed using the simulation and docking studies.

CanGeneBase (CGB)--a database on cancer related genes.

UNLABELLED: The advent of genomic and proteomic technologies in this post-genomic era has urged the researchers to develop novel research strategies against cancer by targeting the human genes that would greatly facilitate to identify more promising treatment and to develop accurate early diagnosis for cancer. To harness the power of cancer genetic information towards better treatment we have developed a cancer gene database called CanGeneBase (CGB). It is a comprehensive data collection of cancer-related genes with the intention of helping the researchers to stay on a single platform to gain exclusive information on the genes of their interest. According to the Cancer Gene Data Curation Project, about 4,700 genes have been identified as being related to cancer. The present CanGeneBase covers about 12 different types of cancer which includes 190 unique gene entries. Each entry encompasses about 33 useful parameters to provide detailed information about specific gene. CanGeneBase is made in such a way that it can be easily accessed by either gene symbol or by the type of cancer. AVAILABILITY: The database is freely available at http://122.165.25.137/bioinfo/cancerdb/

Assessment for the identification of better HDAC inhibitor class through binding energy calculations and descriptor analysis.

Histone Deacetylase (HDAC) inhibitors represent a budding class of targeted anti-cancer agents. This structurally diverse group of molecules can induce growth arrest, differentiation, apoptosis, and autophagocytic cell death of cancer cells. Of the different classes of HDAC the class I and Class II are considered the main targets for cancer. For the two classes of HDAC, only a few compounds have emerged as preferential inhibitors and even fewer are able to discriminate efficiently among HDACs in the same class. This limitation has diminutive relevance to the use of HDAC inhibitors as potential anti-tumor drugs. Hence, the four HDACs of class I was modeled and about twelve known inhibitors which are currently under the phase I/II trials were docked using an efficient shape-based search algorithm and the AScore scoring function, to each of the class I HDAC members in order to identify the inhibitor or group with better pharmacological action. The molecular descriptors study and the drug score, drug likeness prediction helped in the identification of potential compounds targeting specific enzymes of HDAC family. The ranking of various groups of ligands helped in the identification of potential groups and better compound that can better target class I HDAC in an effective way.