Abstract: A central focus of clinical proteomics is to search for biomarkers in plasma for diagnostic and therapeutic use. We studied a set of plasma proteins accessed from the Healthy Human Individual's Integrated Plasma Proteome (HIP(2)) database, a larger set of curated human proteins, and a subset of inflammatory proteins, for overlap with sets of known protein biomarkers, drug targets, and secreted proteins. Most inflammatory proteins were found to occur in plasma, and over three times the level of biomarkers were found in inflammatory plasma proteins and their interacting protein neighbors compared to the sets of plasma and curated human proteins. Percentage overlaps with Gene Ontology terms were similar between the curated human set and plasma protein set, yet the set of inflammatory plasma proteins had a distinct ontology-based profile. Most of the major hub proteins within protein-protein interaction networks of tissue-specific sets of inflammatory proteins were found to occur in disease pathways. The present study presents a systematic approach for profiling a plasma subproteome's relationship to both its potential range of clinical application and its overlap with complex disease.

Abstract: The Comparative Toxicogenomics Database (CTD) is a curated database that promotes understanding about the effects of environmental chemicals on human health. Biocurators at CTD manually curate chemical-gene interactions, chemical-disease relationships and gene-disease relationships from the literature. This strategy allows data to be integrated to construct chemical-gene-disease networks. CTD is unique in numerous respects: curation focuses on environmental chemicals; interactions are manually curated; interactions are constructed using controlled vocabularies and hierarchies; additional gene attributes (such as Gene Ontology, taxonomy and KEGG pathways) are integrated; data can be viewed from the perspective of a chemical, gene or disease; results and batch queries can be downloaded and saved; and most importantly, CTD acts as both a knowledgebase (by reporting data) and a discovery tool (by generating novel inferences). Over 116,000 interactions between 3900 chemicals and 13,300 genes have been curated from 270 species, and 5900 gene-disease and 2500 chemical-disease direct relationships have been captured. By integrating these data, 350,000 gene-disease relationships and 77,000 chemical-disease relationships can be inferred. This wealth of chemical-gene-disease information yields testable hypotheses for understanding the effects of environmental chemicals on human health. CTD is freely available at http://ctd.mdibl.org.

Abstract: MMsINC (http://mms.dsfarm.unipd.it/MMsINC/search) is a database of non-redundant, richly annotated and biomedically relevant chemical structures. A primary goal of MMsINC is to guarantee the highest quality and the uniqueness of each entry. MMsINC then adds value to these entries by including the analysis of crucial chemical properties, such as ionization and tautomerization processes, and the in silico prediction of 24 important molecular properties in the biochemical profile of each structure. MMsINC is consequently a natural input for different chemoinformatics and virtual screening applications. In addition, MMsINC supports various types of queries, including substructure queries and the novel 'molecular scissoring' query. MMsINC is interfaced with other primary data collectors, such as PubChem, Protein Data Bank (PDB), the Food and Drug Administration database of approved drugs and ZINC.

Abstract: Many available methods aimed at incorporating the receptor flexibility in ligand docking are computationally expensive, require a high level of user intervention, and were tested only on benchmarks of limited size and diversity. Here we describe the four-dimensional (4D) docking approach that allows seamless incorporation of receptor conformational ensembles in a single docking simulation and reduces the sampling time while preserving the accuracy of traditional ensemble docking. The approach was tested on a benchmark of 99 therapeutically relevant proteins and 300 diverse ligands (half of them experimental or marketed drugs). The conformational variability of the binding pockets was represented by the available crystallographic data, with the total of 1113 receptor structures. The 4D docking method reproduced the correct ligand binding geometry in 77.3% of the benchmark cases, matching the success rate of the traditional approach but employed on average only one-fourth of the time during the ligand sampling phase.

Abstract: Dual-specificity phosphatases (DSPs) are important, but poorly understood, cell signaling enzymes that remove phosphate groups from tyrosine and serine/threonine residues on their substrate. Deregulation of DSPs has been implicated in cancer, obesity, diabetes, inflammation, and Alzheimer's disease. Due to their biological and biomedical significance, DSPs have increasingly become the subject of drug discovery high-throughput screening (HTS) and focused compound library development efforts. Progress in identifying selective and potent DSP inhibitors has, however, been restricted by the lack of sufficient structural data on inhibitor-bound DSPs. The shallow, almost flat, substrate binding sites in DSPs have been a major factor in hampering the rational design and the experimental development of active site inhibitors. Recent experimental and virtual HTS studies, as well as advances in molecular modeling, provide new insights into the potential mechanisms for substrate recognition and binding by this important class of enzymes. We present herein an overview of the progress, along with a brief description of applications to two types of DSPs: Cdc25 and MAP kinase phosphatase (MKP) family members. In particular, we focus on combined computational and experimental efforts for designing Cdc25B and MKP-1 inhibitors and understanding their mechanisms of interactions with their target proteins. These studies emphasize the utility of developing computational models and methods that meet the two major challenges currently faced in structure-based in silico design of lead compounds: the conformational flexibility of the target protein and the entropic contribution to the selection and stabilization of particular bound conformers.

Abstract: Some drugs are routinely prescribed for dementia that sets in either due to normal ageing or due to neurodegenerative disorders. We have studied the effect of three of these drugs, Donepezil hydrochloride, Rivastigmine tartrate and Nootropyl, on the activity of DNA polymerases beta, a crucial enzyme in the base excision repair pathway, the most important mode of DNA repair in brain. All the three drugs inhibited DNA polymerase beta activity to varying degrees although the affects of Donepezil being the least and inconsistent. The drugs preferentially bind to and inhibit the activities of 8 kDa domain of DNA polymerase beta that is known to possess the dRP lyase activity. The function of 31 kDa domain dealing with template driven addition of nucleotides at 3' end of the primer is not adversely affected. The inhibitory action of most widely used dementia drugs on DNA repair potential signifies that pharma sector needs to consider this aspect especially while designing drugs targeted towards brain.

Abstract: The PharmGKB is a publicly available online resource that aims to facilitate understanding how genetic variation contributes to variation in drug response. It is not only a repository of pharmacogenomics primary data, but it also provides fully curated knowledge including drug pathways, annotated pharmacogene summaries, and relationships among genes, drugs, and diseases. This unit describes how to navigate the PharmGKB Web site to retrieve detailed information on genes and important variants, as well as their relationship to drugs and diseases. It also includes protocols on our drug-centered pathway, annotated pharmacogene summaries, and our Web services for downloading the underlying data. Workflow on how to use PharmGKB to facilitate design of the pharmacogenomic study is also described in this unit.

Abstract: We describe a method for modeling chemical mutagenicity in terms of simple rules based on molecular features. A classification model was built using a rule-based ensemble method called RuleFit, developed by Friedman and Popescu. We show how performance compares favorably against literature methods. Performance was measured through the use of cross-validation and testing on external test sets. All data sets used are publicly available. The method automatically generated transparent rules in terms of molecular structure that agree well with known toxicology. While we have focused on chemical mutagenicity in demonstrating this method, we anticipate that it may be more generally useful in modeling other molecular properties such as other types of chemical toxicity.

Abstract: Biomolecule phosphorylation by protein kinases is a fundamental cell signaling process in all living cells. Following the comprehensive cataloguing of the protein kinase complement of the human genome (Manning, G., Whyte, D. B., Martinez, R., Hunter, T., and Sudarsanam, S. (2002) The protein kinase complement of the human genome. Science 298, 1912-1934), this review will detail the state-of-the-art human and mouse kinase proteomes as provided in the UniProtKB/Swiss-Prot protein knowledgebase. The sequences of the 480 classical and up to 24 atypical protein kinases now believed to exist in the human genome and 484 classical and up to 24 atypical kinases within the mouse genome have been reviewed and, where necessary, revised. Extensive annotation has been added to each entry. In an era when a wealth of new databases is emerging on the Internet, UniProtKB/Swiss-Prot makes available to the scientific community the most up-to-date and in-depth annotation of these proteins with access to additional external resources linked from within each entry. Incorrect sequence annotations resulting from errors and artifacts have been eliminated. Each entry will be constantly reviewed and updated as new information becomes available with the orthologous enzymes in related species being annotated in a parallel effort and complete kinomes being completed as sequences become available. This ensures that the mammalian kinomes available from UniProtKB/Swiss-Prot are of a consistently high standard with each separate entry acting both as a valuable information resource and a central portal to a wealth of further detail via extensive cross-referencing.

Abstract: BACKGROUND: New systems biology studies require researchers to understand how interplay among myriads of biomolecular entities is orchestrated in order to achieve high-level cellular and physiological functions. Many software tools have been developed in the past decade to help researchers visually navigate large networks of biomolecular interactions with built-in template-based query capabilities. To further advance researchers' ability to interrogate global physiological states of cells through multi-scale visual network explorations, new visualization software tools still need to be developed to empower the analysis. A robust visual data analysis platform driven by database management systems to perform bi-directional data processing-to-visualizations with declarative querying capabilities is needed. RESULTS: We developed ProteoLens as a JAVA-based visual analytic software tool for creating, annotating and exploring multi-scale biological networks. It supports direct database connectivity to either Oracle or PostgreSQL database tables/views, on which SQL statements using both Data Definition Languages (DDL) and Data Manipulation languages (DML) may be specified. The robust query languages embedded directly within the visualization software help users to bring their network data into a visualization context for annotation and exploration. ProteoLens supports graph/network represented data in standard Graph Modeling Language (GML) formats, and this enables interoperation with a wide range of other visual layout tools. The architectural design of ProteoLens enables the de-coupling of complex network data visualization tasks into two distinct phases: 1) creating network data association rules, which are mapping rules between network node IDs or edge IDs and data attributes such as functional annotations, expression levels, scores, synonyms, descriptions etc; 2) applying network data association rules to build the network and perform the visual annotation of graph nodes and edges according to associated data values. We demonstrated the advantages of these new capabilities through three biological network visualization case studies: human disease association network, drug-target interaction network and protein-peptide mapping network. CONCLUSION: The architectural design of ProteoLens makes it suitable for bioinformatics expert data analysts who are experienced with relational database management to perform large-scale integrated network visual explorations. ProteoLens is a promising visual analytic platform that will facilitate knowledge discoveries in future network and systems biology studies.