Structural insights into the design of small molecule inhibitors that selectively antagonize Mcl-1.

The screening of a small focused library of rhodanine derivatives as inhibitors of Bcl-2 proteins led to the discovery of two structurally related compounds with different binding profiles against the Bcl-XL and the Mcl-1 proteins. Subsequent NMR studies with mutant proteins and in silico docking studies provide a possible rationale for the observed specificity.

Recent advances in computer-aided drug design.

Modern drug discovery is characterized by the production of vast quantities of compounds and the need to examine these huge libraries in short periods of time. The need to store, manage and analyze these rapidly increasing resources has given rise to the field known as computer-aided drug design (CADD). CADD represents computational methods and resources that are used to facilitate the design and discovery of new therapeutic solutions. Digital repositories, containing detailed information on drugs and other useful compounds, are goldmines for the study of chemical reactions capabilities. Design libraries, with the potential to generate molecular variants in their entirety, allow the selection and sampling of chemical compounds with diverse characteristics. Fold recognition, for studying sequence-structure homology between protein sequences and structures, are helpful for inferring binding sites and molecular functions. Virtual screening, the in silico analog of high-throughput screening, offers great promise for systematic evaluation of huge chemical libraries to identify potential lead candidates that can be synthesized and tested. In this article, we present an overview of the most important data sources and computational methods for the discovery of new molecular entities. The workflow of the entire virtual screening campaign is discussed, from data collection through to post-screening analysis.

Meta-analysis of genetic polymorphisms and gastric cancer risk: variability in associations according to race.

The goal of this study was to consolidate information on genetic risk factors for gastric cancer. An additional aim was to investigate the influence of race on these genetic risk associations. Relevant studies were identified from PubMed and references of retrieved articles. Meta-analysis techniques were used to summarise associations between genetic polymorphisms and gastric cancer. A total of 203 relevant studies were identified, assessing 225 polymorphisms across 95 genes. Subgroup analysis indicated that Chinese, Japanese and Korean data were consistent and could be pooled. However, 6 of 13 polymorphisms (ACE I/D, CCND1 870G>A, CDH1 -160C>A, IL1B -511C>T, IL4 -590C>T, IL10 -592A>C) displayed conflicting effects between Asian and Caucasian populations, three of which (ACE I/D, CCND1 870G>A, IL1B -511C>T) had significantly different odds ratios between the two racial groups. In total, 37 polymorphisms across 27 genes were found to be significantly associated with gastric cancer in Asians, and 12 polymorphisms across 11 genes in Caucasians. Consolidated panels of polymorphisms associated with gastric cancer risk were identified in Asians and Caucasians. The results caution against the assumption that genetic risk factors are consistent between races.

CLEVER: pipeline for designing in silico chemical libraries.

Advances in virtual screening have created new channels for expediting the process of discovering novel drugs. Of particular relevance and interest are in silico techniques that enable the enumeration of combinatorial chemical libraries, generation of 3D coordinates and assessment of their propensity for drug-likeness. In a bid to provide an integrated pipeline that encompasses the common components functional for designing, managing and analyzing combinatorial chemical libraries, we describe a platform-independent, standalone Java application entitled CLEVER (Chemical Library Editing, Visualizing and Enumerating Resource). CLEVER supports chemical library creation and manipulation, combinatorial chemical library enumeration using user-specified chemical components, chemical format conversion and visualization, as well as chemical compounds analysis and filtration with respect to drug-likeness, lead-likeness and fragment-likeness based on the physicochemical properties computed from the derived molecules. Also provided is an integrated property-based graphing component that visually depicts the diversity, coverage and distribution of selected compound collections. When deployed in conjunction with large-scale virtual screening campaigns, CLEVER can offer insights into what chemical compounds to synthesize, and more importantly, what not to synthesize. The software is available at http://datam.i2r.a-star.edu.sg/clever/.

BEID: database for sequence-structure-function information on antigen-antibody interactions.

The B-cell Epitope Interaction Database (BEID; http://datam.i2r.a-star.edu.sg/BEID) is an open-access database describing sequence-structure-function information on immunoglobulin (Ig)-antigen interactions. The current version of the database contains 164 antigens, 126 Ig and 189 Ig-antigen complexes extracted from the Protein Data Bank (PDB). Each entry is manually verified, classified, and analyzed for intermolecular interactions between antigens and the corresponding bound Ig molecules. Ig-antigen interaction information that is stored in BEID includes solvent accessibility, hydrogen bonds, non-hydrogen bonds, gap volume, gap index, interface area and contact residues. The database can be searched with a user-friendly search tool and schematic diagrams for Ig-antigen interactions are available for download in PDF format. The ultimate purpose of BEID is to enhance the understanding of the rules of engagement between antigen and the corresponding bound Ig molecules. It is also a precious data source for developing computational predictors for B-cell epitopes.

Pleiotrophin enhances clonal growth and long-term expansion of human embryonic stem cells.

To identify additional growth factors for optimizing propagation of human embryonic stem cells (hESCs), we mined publicly available data sets for the transcriptomes of murine and human ESCs and feeder cells, thereby generating a list of growth factors and complementary receptors. We identified the major pathways previously reported to be important, as well as several new ones. One pathway is the Pleiotrophin (PTN)-Pleiotrophin receptor (PTPRZ1) axis. Murine fibroblasts secrete Ptn, whereas hESCs expressed PTPRZ1, which is downregulated upon differentiation. Depletion of PTPRZ1 resulted in decreased colony formation and lower recovery of hESCs. Supplementation of chemically defined medium for feeder-free propagation of hESCs with PTN allowed higher recovery of hESCs without loss of pluripotency. PTN-PTPRZ1 functions here predominantly via an antiapoptotic effect mediated in part by the activation of Akt. These findings reveal the underlying importance of PTN in hESC survival and its usefulness in the clonal manipulation and large-scale propagation of hESCs. Disclosure of potential conflicts of interest is found at the end of this article.

Gastric Cancer (Biomarkers) Knowledgebase (GCBKB): A Curated and Fully Integrated Knowledgebase of Putative Biomarkers Related to Gastric Cancer.

The Gastric Cancer (Biomarkers) Knowledgebase (GCBKB) (http://biomarkers.bii.a-star.edu.sg/background/gastricCancerBiomarkersKb.php) is a curated and fully integrated knowledgebase that provides data relating to putative biomarkers that may be used in the diagnosis and prognosis of gastric cancer. It is freely available to all users. The data contained in the knowledgebase was derived from a large literature source and the putative biomarkers therein have been annotated with data from the public domain. The knowledgebase is maintained by a curation team who update the data from a defined source. As well as mining data from the literature, the knowledgebase will also be populated with unpublished experimental data from investigators working in the gastric cancer biomarker discovery field. Users can perform searches to identify potential markers defined by experiment type, tissue type and disease state. Search results may be saved, manipulated and retrieved at a later date. As far as the authors are aware this is the first open access database dedicated to the discovery and investigation of gastric cancer biomarkers.

iHAP--integrated haplotype analysis pipeline for characterizing the haplotype structure of genes.

BACKGROUND: The advent of genotype data from large-scale efforts that catalog the genetic variants of different populations have given rise to new avenues for multifactorial disease association studies. Recent work shows that genotype data from the International HapMap Project have a high degree of transferability to the wider population. This implies that the design of genotyping studies on local populations may be facilitated through inferences drawn from information contained in HapMap populations. RESULTS: To facilitate analysis of HapMap data for characterizing the haplotype structure of genes or any chromosomal regions, we have developed an integrated web-based resource, iHAP. In addition to incorporating genotype and haplotype data from the International HapMap Project and gene information from the UCSC Genome Browser Database, iHAP also provides capabilities for inferring haplotype blocks and selecting tag SNPs that are representative of haplotype patterns. These include block partitioning algorithms, block definitions, tag SNP definitions, as well as SNPs to be "force included" as tags. Based on the parameters defined at the input stage, iHAP performs on-the-fly analysis and displays the result graphically as a webpage. To facilitate analysis, intermediate and final result files can be downloaded. CONCLUSION: The iHAP resource, available at http://ihap.bii.a-star.edu.sg, provides a convenient yet flexible approach for the user community to analyze HapMap data and identify candidate targets for genotyping studies.