Natural products play a significant role in drug discovery and development. Many topological pharmacophore patterns are common between natural products and commercial drugs. A better understanding of the specific physicochemical and structural features of natural products is important for corresponding drug development. Several encyclopedias of natural compounds have been composed, but the information remains scattered or not freely available. The first version of the Supernatural database containing ?50 000 compounds was published in 2006 to face these challenges. Here we present a new, updated and expanded version of natural product database, Super Natural II (http://bioinformatics.charite.de/supernatural), comprising ?326 000 molecules. It provides all corresponding 2D structures, the most important structural and physicochemical properties, the predicted toxicity class for ?170 000 compounds and the vendor information for the vast majority of compounds. The new version allows a template-based search for similar compounds as well as a search for compound names, vendors, specific physical properties or any substructures. Super Natural II also provides information about the pathways associated with synthesis and degradation of the natural products, as well as their mechanism of action with respect to structurally similar drugs and their target proteins.
The SuperPred web server connects chemical similarity of drug-like compounds with molecular targets and the therapeutic approach based on the similar property principle. Since the first release of this server, the number of known compound-target interactions has increased from 7000 to 665,000, which allows not only a better prediction quality but also the estimation of a confidence. Apart from the addition of quantitative binding data and the statistical consideration of the similarity distribution in all drug classes, new approaches were implemented to improve the target prediction. The 3D similarity as well as the occurrence of fragments and the concordance of physico-chemical properties is also taken into account. In addition, the effect of different fingerprints on the prediction was examined. The retrospective prediction of a drug class (ATC code of the WHO) allows the evaluation of methods and descriptors for a well-characterized set of approved drugs. The prediction is improved by 7.5% to a total accuracy of 75.1%. For query compounds with sufficient structural similarity, the web server allows prognoses about the medical indication area of novel compounds and to find new leads for known targets. SuperPred is publicly available without registration at: http://prediction.charite.de.
Animal trials are currently the major method for determining the possible toxic effects of drug candidates and cosmetics. In silico prediction methods represent an alternative approach and aim to rationalize the preclinical drug development, thus enabling the reduction of the associated time, costs and animal experiments. Here, we present ProTox, a web server for the prediction of rodent oral toxicity. The prediction method is based on the analysis of the similarity of compounds with known median lethal doses (LD50) and incorporates the identification of toxic fragments, therefore representing a novel approach in toxicity prediction. In addition, the web server includes an indication of possible toxicity targets which is based on an in-house collection of protein-ligand-based pharmacophore models ('toxicophores') for targets associated with adverse drug reactions. The ProTox web server is open to all users and can be accessed without registration at: http://tox.charite.de/tox. The only requirement for the prediction is the two-dimensional structure of the input compounds. All ProTox methods have been evaluated based on a diverse external validation set and displayed strong performance (sensitivity, specificity and precision of 76, 95 and 75%, respectively) and superiority over other toxicity prediction tools, indicating their possible applicability for other compound classes.
Searches for sex and gender-specific publications are complicated by the absence of a specific algorithm within search engines and by the lack of adequate archives to collect the retrieved results. We previously addressed this issue by initiating the first systematic archive of medical literature containing sex and/or gender-specific analyses. This initial collection has now been greatly enlarged and re-organized as a free user-friendly database with multiple functions: GenderMedDB (http://gendermeddb.charite.de).
As the number of prescribed drugs is constantly rising, drug-drug interactions are an important issue. The simultaneous administration of several drugs can cause severe adverse effects based on interactions with the same metabolizing enzyme(s). The Transformer database (http://bioinformatics.charite.de/transformer) contains integrated information on the three phases of biotransformation (modification, conjugation and excretion) of 3000 drugs and >350 relevant food ingredients (e.g. grapefruit juice) and herbs, which are catalyzed by 400 proteins. A total of 100 000 interactions were found through text mining and manual validation. The 3D structures of 200 relevant proteins are included. The database enables users to search for drugs with a visual display of known interactions with phase I (Cytochrome P450) and phase II enzymes, transporters, food and herbs. For each interaction, PubMed references are given. To detect mutual impairments of drugs, the drug-cocktail tool displays interactions between selected drugs. By choosing the indication for a drug, the tool offers suggestions for alternative medications to avoid metabolic conflicts. Drug interactions can also be visualized in an interactive network view. Additionally, prodrugs, including their mechanisms of activation, and further information on enzymes of biotransformation, including 3D models, can be viewed.
Scents are well known to be emitted from flowers and animals. In nature, these volatiles are responsible for inter- and intra-organismic communication, e.g. attraction and defence. Consequently, they influence and improve the establishment of organisms and populations in ecological niches by acting as single compounds or in mixtures. Despite the known wealth of volatile organic compounds (VOCs) from species of the plant and animal kingdom, in the past, less attention has been focused on volatiles of microorganisms. Although fast and affordable sequencing methods facilitate the detection of microbial diseases, however, the analysis of signature or fingerprint volatiles will be faster and easier. Microbial VOCs (mVOCs) are presently used as marker to detect human diseases, food spoilage or moulds in houses. Furthermore, mVOCs exhibited antagonistic potential against pathogens in vitro, but their biological roles in the ecosystems remain to be investigated. Information on volatile emission from bacteria and fungi is presently scattered in the literature, and no public and up-to-date collection on mVOCs is available. To address this need, we have developed mVOC, a database available online at http://bioinformatics.charite.de/mvoc.
Pain is more than an unpleasant sensory experience associated with actual or potential tissue damage: it is the most common reason for physician consultation and often dramatically affects quality of life. The management of pain is often difficult and new targets are required for more effective and specific treatment. SuperPain (http://bioinformatics.charite.de/superpain/) is freely available database for pain-stimulating and pain-relieving compounds, which bind or potentially bind to ion channels that are involved in the transmission of pain signals to the central nervous system, such as TRPV1, TRPM8, TRPA1, TREK1, TRESK, hERG, ASIC, P2X and voltage-gated sodium channels. The database consists of ?8700 ligands, which are characterized by experimentally measured binding affinities. Additionally, 100 000 putative ligands are included. Moreover, the database provides 3D structures of receptors and predicted ligand-binding poses. These binding poses and a structural classification scheme provide hints for the design of new analgesic compounds. A user-friendly graphical interface allows similarity searching, visualization of ligands docked into the receptor, etc.
The type I ATP-binding cassette (ABC) importer for positively charged amino acids of the thermophilic bacterium Geobacillus stearothermophilus consists of the extracellular solute binding protein, ArtJ, and a homodimer each of the transmembrane subunit, ArtM, and the nucleotide-binding and -hydrolyzing subunit, ArtP. We have investigated the functional consequences of mutations affecting conserved residues from two peptide regions in ArtM, recently proposed to form a gate by which access of a substrate to the translocation path is controlled (Hollenstein et al., 2007 ). Transporter variants were reconstituted into proteoliposomes and assayed for ArtJ/arginine-stimulated ATPase activity. Replacement of residues from region 1 (Arg-63, Pro-66) caused no or only moderate reduction in ATPase activity. In contrast, mutating residues from gate region 2 (Lys-159, Leu-163) resulted in a substantial increase in ATPase activity which, however, as demonstrated for variants ArtM(K159I) and ArtM(K159E), is not coupled to transport. Replacing homologous residues in the closely related histidine transporter of Salmonella enterica serovar Typhimurium (HisJ-QMP2) caused different phenotypes. Mutation to isoleucine of HisQ(K163) or HisM(H172), both homologous to ArtM(K159), abolished ATPase activity. The mutations most likely caused a structural change as revealed by limited proteolysis. In contrast, substantial, albeit reduced, enzymatic activity was observed with variants of HisQ(L167?G) or HisM(L176?G), both homologous to ArtM(L163). Our study provides the first experimental evidence in favor of a crucial role of residues from the proposed gate region in type I ABC importer function.
Despite progress in conventional cancer therapies, cancer is still one of the leading causes of death in industrial nations. Therefore, an urgent need of progress in fighting cancer remains. A promising alternative to conventional methods is immune therapy. This relies on the fact that low-immunogenic tumours can be eradicated if an immune response against them is induced. Peptide vaccination is carried out by injecting tumour peptides into a patient to trigger a specific immune response against the tumour in its entirety. However, peptide vaccination is a highly complicated treatment and currently many factors like the optimal number of epitopes are not known precisely. Therefore, it is necessary to evaluate how certain parameters influence the therapy.
To perform their various functions, protein surfaces often have to interact with each other in a specific way. Usually, only parts of a protein are accessible and can act as binding sites. Because proteins consist of polypeptide chains that fold into complex three-dimensional shapes, binding sites can be divided into two different types: linear sites that follow the primary amino acid sequence and discontinuous binding sites, which are made up of short peptide fragments that are adjacent in spatial proximity. Such discontinuous binding sites dominate protein-protein interactions, but are difficult to identify. To meet this challenge, we combined a computational, structure-based approach and an experimental, high-throughput method. SUPERFICIAL is a program that uses protein structures as input and generates peptide libraries to represent the proteins surface. A large number of the predicted peptides can be simultaneously synthesised applying the SPOT technology. The results of a binding assay subsequently help to elucidate protein-protein interactions; the approach is applicable to any kind of protein. The crystal structure of the complex of hen egg lysozyme with the well-characterised murine IgG1 antibody HyHEL-5 is available, and the complex is known to have a discontinuous binding site. Using SUPERFICIAL, the entire surface of lysozyme was translated into a peptide library that was synthesised on a cellulose membrane using the SPOT technology and tested against the HyHEL-5 antibody. In this way, it was possible to identify two peptides (longest common sequence and peptide 19) that represented the discontinuous epitope of lysozyme.
The cytochrome P450 (CYP) enzymes are major players in drug metabolism. More than 2,000 mutations have been described, and certain single nucleotide polymorphisms (SNPs) have been shown to have a large impact on CYP activity. Therefore, CYPs play an important role in inter-individual drug response and their genetic variability should be factored into personalized medicine. To identify the most relevant polymorphisms in human CYPs, a text mining approach was used. We investigated their frequencies in different ethnic groups, the number of drugs that are metabolized by each CYP, the impact of CYP SNPs, as well as CYP expression patterns in different tissues. The most important polymorphic CYPs were found to be 1A2, 2D6, 2C9 and 2C19. Thirty-four common allele variants in Caucasians led to altered enzyme activity. To compare the relevant Caucasian SNPs with those of other ethnicities a search in 1,000 individual genomes was undertaken. We found 199 non-synonymous SNPs with frequencies over one percent in the 1,000 genomes, many of them not described so far. With knowledge of frequent mutations and their impact on CYP activities, it may be possible to predict patient response to certain drugs, as well as adverse side effects. With improved availability of genotyping, our data may provide a resource for an understanding of the effects of specific SNPs in CYPs, enabling the selection of a more personalized treatment regimen.
MicroRNAs (miRNAs) are small non-coding RNAs that regulate gene expression. It has been proposed that miRNAs play an important role in cancer development and progression. Their ability to affect multiple gene pathways by targeting various mRNAs makes them an interesting class of regulators.
There are at least two good reasons for the on-going interest in drug-target interactions: first, drug-effects can only be fully understood by considering a complex network of interactions to multiple targets (so-called off-target effects) including metabolic and signaling pathways; second, it is crucial to consider drug-target-pathway relations for the identification of novel targets for drug development. To address this on-going need, we have developed a web-based data warehouse named SuperTarget, which integrates drug-related information associated with medical indications, adverse drug effects, drug metabolism, pathways and Gene Ontology (GO) terms for target proteins. At present, the updated database contains >6000 target proteins, which are annotated with >330,000 relations to 196,000 compounds (including approved drugs); the vast majority of interactions include binding affinities and pointers to the respective literature sources. The user interface provides tools for drug screening and target similarity inclusion. A query interface enables the user to pose complex queries, for example, to find drugs that target a certain pathway, interacting drugs that are metabolized by the same cytochrome P450 or drugs that target proteins within a certain affinity range. SuperTarget is available at http://bioinformatics.charite.de/supertarget.
The identification of novel drug targets is one of the major challenges in proteomics. Computational methods developed over the last decade have enhanced the process of drug design in both terms of time and quality. The main task is the design of selective compounds, which bind targets more specifically, dependent on the desired mode of action of the particular drug. This makes it necessary to create compounds, which either exhibit their functions on one single protein to exclude undesired cross-reactivity or to use the advantageous effect of less selective drugs that target numerous proteins and therefore exhibit their functions on whole protein classes. Main aspects in the assignment of interactions between ligands and putative targets involve the amino acid composition of the binding site, evolutionary conservation and similarity in sequence and structure of known targets. Similarities or differences within classified protein families can be the key to their function and give first hints to functional drug design. Hereby, binding site-based classification outnumbers sequence-based classifications since similar binding sites can also be found in more distant proteins. Membrane proteins are difficult targets, because of their special physicochemical characteristics and the general lack of structural information. Here, we describe recent advances in modeling methods dedicated to membrane proteins. Different descriptors of similarity between compounds and the similarity between binding sites are under development and elucidate important aspects like dynamics or entropy. The importance of computational drug design is undisputable. Nevertheless, the process of design is complicated by increasing complexity, which underlines the importance of accurate knowledge about the addressed target class(es) and particularly their binding sites. One main objective by considering named topics is to predict putative side effects and errant functions (off-target effects) of novel drugs, which requires a holistic (systems biology) view on drug-target-pathway relations. In the following, we give a brief summary about the recent discussion on drug-target interactions with emphasis on membrane proteins.
The sheer volume of non-synonymous single nucleotide polymorphisms that have been generated in recent years from projects such as the Human Genome Project, the HapMap Project and Genome-Wide Association Studies means that it is not possible to characterize all mutations experimentally on the gene products, i.e. elucidate the effects of mutations on protein structure and function. However, automatic methods that can predict the effects of mutations will allow a reduced set of mutations to be studied. Site Directed Mutator (SDM) is a statistical potential energy function that uses environment-specific amino-acid substitution frequencies within homologous protein families to calculate a stability score, which is analogous to the free energy difference between the wild-type and mutant protein. Here, we present a web server for SDM (http://www-cryst.bioc.cam.ac.uk/~sdm/sdm.php), which has obtained more than 10,000 submissions since being online in April 2008. To run SDM, users must upload a wild-type structure and the position and amino acid type of the mutation. The results returned include information about the local structural environment of the wild-type and mutant residues, a stability score prediction and prediction of disease association. Additionally, the wild-type and mutant structures are displayed in a Jmol applet with the relevant residues highlighted.
Cobweb is a Java applet for real-time network visualization; its strength lies in enabling the interactive exploration of networks. Therefore, it allows new nodes to be interactively added to a network by querying a database on a server. The network constantly rearranges to provide the most meaningful topological view.
Peptide vaccination in cancer therapy is a promising alternative to conventional methods. However, the parameters for this personalized treatment are difficult to access experimentally. In this respect, in silico models can help to narrow down the parameter space or to explain certain phenomena at a systems level. Herein, we develop two empirical interaction potentials specific to B-cell and T-cell receptor complexes and validate their applicability in comparison to a more general potential. The interaction potentials are applied to the model VaccImm which simulates the immune response against solid tumors under peptide vaccination therapy. This multi-agent system is derived from another immune system simulator (C-ImmSim) and now includes a module that enables the amino acid sequence of immune receptors and their ligands to be taken into account. The multi-agent approach is combined with approved methods for prediction of major histocompatibility complex (MHC)-binding peptides and the newly developed interaction potentials. In the analysis, we critically assess the impact of the different modules on the simulation with VaccImm and how they influence each other. In addition, we explore the reasons for failures in inducing an immune response by examining the activation states of the immune cell populations in detail.In summary, the present work introduces immune-specific interaction potentials and their application to the agent-based model VaccImm which simulates peptide vaccination in cancer therapy.
The procedure of drug approval is time-consuming, costly and risky. Accidental findings regarding multi-specificity of approved drugs led to block-busters in new indication areas. Therefore, the interest in systematically elucidating new areas of application for known drugs is rising. Furthermore, the knowledge, understanding and prediction of so-called off-target effects allow a rational approach to the understanding of side-effects. With PROMISCUOUS we provide an exhaustive set of drugs (25,000), including withdrawn or experimental drugs, annotated with drug-protein and protein-protein relationships (21,500/104,000) compiled from public resources via text and data mining including manual curation. Measures of structural similarity for drugs as well as known side-effects can be easily connected to protein-protein interactions to establish and analyse networks responsible for multi-pharmacology. This network-based approach can provide a starting point for drug-repositioning. PROMISCUOUS is publicly available at http://bioinformatics.charite.de/promiscuous.
Consideration of biomolecules in terms of their molecular building blocks provides valuable new information regarding their synthesis, degradation and similarity. Here, we present the FragmentStore, a resource for the comparison of fragments found in metabolites, drugs or toxic compounds. Starting from 13,000 metabolites, 16,000 drugs and 2200 toxic compounds we generated 35,000 different building blocks (fragments), which are not only relevant to their biosynthesis and degradation but also provide important information regarding side-effects and toxicity. The FragmentStore provides a variety of search options such as 2D structure, molecular weight, rotatable bonds, etc. Various analysis tools have been implemented including the calculation of amino acid preferences of fragments binding sites, classification of fragments based on the enzyme classification class of the enzyme(s) they bind to and small molecule library generation via a fragment-assembler tool. Using the FragmentStore, it is now possible to identify the common fragments of different classes of molecules and generate hypotheses about the effects of such intersections. For instance, the co-occurrence of fragments in different drugs may indicate similar targets and possible off-target interactions whereas the co-occurrence of fragments in a drug and a toxic compound/metabolite could be indicative of side-effects. The database is publicly available at:?http://bioinformatics.charite.de/fragment_store.
During the development of methods for cancer diagnosis and treatment, a vast amount of information is generated. Novel cancer target proteins have been identified and many compounds that activate or inhibit cancer-relevant target genes have been developed. This knowledge is based on an immense number of experimentally validated compound-target interactions in the literature, and excerpts from literature text mining are spread over numerous data sources. Our own analysis shows that the overlap between important existing repositories such as Comparative Toxicogenomics Database (CTD), Therapeutic Target Database (TTD), Pharmacogenomics Knowledge Base (PharmGKB) and DrugBank as well as between our own literature mining for cancer-annotated entries is surprisingly small. In order to provide an easy overview of interaction data, it is essential to integrate this information into a single, comprehensive data repository. Here, we present CancerResource, a database that integrates cancer-relevant relationships of compounds and targets from (i) our own literature mining and (ii) external resources complemented with (iii) essential experimental and supporting information on genes and cellular effects. In order to facilitate an overview of existing and supporting information, a series of novel information connections have been established. CancerResource addresses the spectrum of research on compound-target interactions in natural sciences as well as in individualized medicine; CancerResource is available at: http://bioinformatics.charite.de/cancerresource/.
A vast number of sweet tasting molecules are known, encompassing small compounds, carbohydrates, d-amino acids and large proteins. Carbohydrates play a particularly big role in human diet. The replacement of sugars in food with artificial sweeteners is common and is a general approach to prevent cavities, obesity and associated diseases such as diabetes and hyperlipidemia. Knowledge about the molecular basis of taste may reveal new strategies to overcome diet-induced diseases. In this context, the design of safe, low-calorie sweeteners is particularly important. Here, we provide a comprehensive collection of carbohydrates, artificial sweeteners and other sweet tasting agents like proteins and peptides. Additionally, structural information and properties such as number of calories, therapeutic annotations and a sweetness-index are stored in SuperSweet. Currently, the database consists of more than 8000 sweet molecules. Moreover, the database provides a modeled 3D structure of the sweet taste receptor and binding poses of the small sweet molecules. These binding poses provide hints for the design of new sweeteners. A user-friendly graphical interface allows similarity searching, visualization of docked sweeteners into the receptor etc. A sweetener classification tree and browsing features allow quick requests to be made to the database. The database is freely available at: http://bioinformatics.charite.de/sweet/.
The incorporation of sex and gender-specific analysis in medical research is increasing due to pressure from public agencies, funding bodies, and the clinical and research community. However, generations of knowledge and publication trends in this discipline are currently spread over distinct specialties and are difficult to analyze comparatively.
Currently, cancer is one of the leading causes of death in industrial nations. While conventional cancer treatment usually results in the patient suffering from severe side effects, immunotherapy is a promising alternative. Nevertheless, some questions remain unanswered with regard to using immunotherapy to treat cancer hindering it from being widely established. To help rectify this deficit in knowledge, experimental data, accumulated from a huge number of different studies, can be integrated into theoretical models of the tumor-immune system interaction. Many complex mechanisms in immunology and oncology cannot be measured in experiments, but can be analyzed by mathematical simulations. Using theoretical modeling techniques, general principles of tumor-immune system interactions can be explored and clinical treatment schedules optimized to lower both tumor burden and side effects. In this paper, we aim to explain the main mathematical and computational modeling techniques used in tumor immunology to experimental researchers and clinicians. In addition, we review relevant published work and provide an overview of its impact to the field.
Cancer is one of the most challenging diseases of today. Optimization of standard treatment protocols consisting of the main columns of chemo- and radiotherapy followed or preceded by surgical intervention is often limited by toxic side effects and induction of concomitant malignancies and/or development of resistant mechanisms. This requires the development of therapeutic strategies which are as effective as standard therapies but permit the patients a life without severe negative side effects. Along this line, the development of immunotherapy in general and the innovative concept of DNA vaccination in particular may provide a venue to achieve this goal. Using the patients own immune system by activation of humoral and cellular immune responses to target the cancer cells has shown first promising results in clinical trials and may allow reduced toxicity standard therapy regimen in the future. The main challenge of this concept is to transfer the plethora of convincing preclinical and early clinical results to an effective treatment of patients.
Tumor necrosis factor (alpha)-related apoptosis-inducing ligand (TRAIL) is a promising anticancer agent that preferentially kills tumor cells with limited cytotoxicity to nonmalignant cells. However, signaling from death receptors requires amplification via the mitochondrial apoptosis pathway (type II) in the majority of tumor cells. Thus, TRAIL-induced cell death entirely depends on the proapoptotic Bcl-2 family member Bax, which is often lost as a result of epigenetic inactivation or mutations. Consequently, Bax deficiency confers resistance against TRAIL-induced apoptosis. Despite expression of Bak, Bax-deficient cells are resistant to TRAIL-induced apoptosis. In this study, we show that the Bax dependency of TRAIL-induced apoptosis is determined by Mcl-1 but not Bcl-xL. Both are antiapoptotic Bcl-2 family proteins that keep Bak in check. Nevertheless, knockdown of Mcl-1 but not Bcl-xL overcame resistance to TRAIL, CD95/FasL and tumor necrosis factor (alpha) death receptor ligation in Bax-deficient cells, and enabled TRAIL to activate Bak, indicating that Mcl-1 rather than Bcl-xL is a major target for sensitization of Bax-deficient tumors for death receptor-induced apoptosis via the Bak pathway.
Solved structures of protein-protein complexes give fundamental insights into protein function and molecular recognition. Although the determination of protein-protein complexes is generally more difficult than solving individual proteins, the number of experimentally determined complexes increased conspicuously during the last decade. Here, the interfaces of 750 transient protein-protein interactions as well as 2,000 interactions between domains of the same protein chain (obligate interactions) were analyzed to obtain a better understanding of molecular recognition and to identify features applicable for protein binding site prediction. Calculation of knowledge-based potentials showed a preference of contacts between amino acids having complementary physicochemical properties. The analysis of amino acid conservation of the entire interface area showed a weak but significant tendency to a higher evolutionary conservation of protein binding sites compared to surface areas that are permanently exposed to solvent. Remarkably, contact frequencies between outstandingly conserved residues are much higher than expected confirming the so-called "hot spot" theory. The comparisons between obligate and transient domain contacts reveal differences and point out that structural diversification and molecular recognition of protein-protein interactions are subjected to other evolutionary aspects than obligate domain-domain interactions.
Much of the information on the Cytochrome P450 enzymes (CYPs) is spread across literature and the internet. Aggregating knowledge about CYPs into one database makes the search more efficient. Text mining on 57 CYPs and drugs led to a mass of papers, which were screened manually for facts about metabolism, SNPs and their effects on drug degradation. Information was put into a database, which enables the user not only to look up a particular CYP and all metabolized drugs, but also to check tolerability of drug-cocktails and to find alternative combinations, to use metabolic pathways more efficiently. The SuperCYP database contains 1170 drugs with more than 3800 interactions including references. Approximately 2000 SNPs and mutations are listed and ordered according to their effect on expression and/or activity. SuperCYP (http://bioinformatics.charite.de/supercyp) is a comprehensive resource focused on CYPs and drug metabolism. Homology-modeled structures of the CYPs can be downloaded in PDB format and related drugs are available as MOL-files. Within the resource, CYPs can be aligned with each other, drug-cocktails can be mixed, SNPs, protein point mutations, and their effects can be viewed and corresponding PubMed IDs are given. SuperCYP is meant to be a platform and a starting point for scientists and health professionals for furthering their research.
The cellular fingerprint, a novel in silico screening approach, was developed to identify new biologically active compounds in combination with structural fingerprints. To this end, high-throughput screening (HTS) data from the National Cancer Institute have been used. To validate this method, we have selected the proapoptotic, natural compound betulinic acid (BA). Because of its antiproliferative effect on a variety of cancer cell lines, the identification of novel BA analogs is of great interest. Novel analogs have been identified and validated in different apoptosis assays. In addition, the novel approach exhibited a strong correlation between structural similarity and biological activity, so that it offers enormous potential for the identification of novel biologically active compounds.
Methyl salicylate and methyl benzoate have important roles in a variety of processes including pollinator attraction and plant defence. These compounds are synthesized by salicylic acid, benzoic acid and benzoic acid/salicylic acid carboxyl methyltransferases (SAMT, BAMT and BSMT) which are members of the SABATH gene family. Both SAMT and BSMT were isolated from Nicotiana suaveolens, Nicotiana alata, and Nicotiana sylvestris allowing us to discern levels of enzyme divergence resulting from gene duplication in addition to species divergence. Phylogenetic analyses showed that Nicotiana SAMTs and BSMTs evolved in separate clades and the latter can be differentiated into the BSMT1 and the newly established BSMT2 branch. Although SAMT and BSMT orthologs showed minimal change coincident with species divergences, substantial evolutionary change of enzyme activity and expression patterns occurred following gene duplication. After duplication, the BSMT enzymes evolved higher preference for benzoic acid (BA) than salicylic acid (SA) whereas SAMTs maintained ancestral enzymatic preference for SA over BA. Expression patterns are largely complementary in that BSMT transcripts primarily accumulate in flowers, leaves and stems whereas SAMT is expressed mostly in roots. A novel enzyme, nicotinic acid carboxyl methyltransferase (NAMT), which displays a high degree of activity with nicotinic acid was discovered to have evolved in N. gossei from an ancestral BSMT. Furthermore a SAM-dependent synthesis of methyl anthranilate via BSMT2 is reported and contrasts with alternative biosynthetic routes previously proposed. While BSMT in flowers is clearly involved in methyl benzoate synthesis to attract pollinators, its function in other organs and tissues remains obscure.
Apoptosis, the programmed cell death, is a highly regulated process, necessary for normal development and homeostasis of the functions of organisms. The Bcl-2 inhibitors BH3I-1 and BH3I-2 were used as lead compounds to find possible Bcl-2 or Bcl-X(L) inhibitors by using computer-assisted screening with our in-house database, containing more than four million commercially available molecules. Identified compounds were further investigated regarding their possible application as a drug.
SuperLooper provides the first online interface for the automatic, quick and interactive search and placement of loops in proteins (LIP). A database containing half a billion segments of water-soluble proteins with lengths up to 35 residues can be screened for candidate loops. A specified database containing 180,000 membrane loops in proteins (LIMP) can be searched, alternatively. Loop candidates are scored based on sequence criteria and the root mean square deviation (RMSD) of the stem atoms. Searching LIP, the average global RMSD of the respective top-ranked loops to the original loops is benchmarked to be <2 A, for loops up to six residues or <3 A for loops shorter than 10 residues. Other suitable conformations may be selected and directly visualized on the web server from a top-50 list. For user guidance, the sequence homology between the template and the original sequence, proline or glycine exchanges or close contacts between a loop candidate and the remainder of the protein are denoted. For membrane proteins, the expansions of the lipid bilayer are automatically modeled using the TMDET algorithm. This allows the user to select the optimal membrane protein loop concerning its relative orientation to the lipid bilayer. The server is online since October 2007 and can be freely accessed at URL: http://bioinformatics.charite.de/superlooper/.
Resistance against apoptosis-inducing anti-cancer drugs remains a severe problem in therapy. One reason is the overexpression of inhibitors of apoptosis proteins (IAPs), a group of proteins responsible for the prevention of apoptosis induction by inactivation of initiator caspases. The natural inhibitor of the IAPs is the protein Smac, which impedes the binding to the caspases. Although Smac is a potent inhibitor, Smac peptides are not very stable in vivo and thus not applicable in therapy. Bioinformatical methods were applied to design Smac-derived peptides to break the therapy resistance in IAP high-expressing tumor cells. The exchange of amino acids in the Smac peptides AVPI and AVPF against unnatural amino acids leads to an improvement of the apoptosis sensitivity. The variety of Smac peptides was filtered by computational docking. Moreover, Smac-derived peptides with sufficient binding to the IAPs were tested in IAP-expressing Hodgkin Lymphoma cell lines.
The inhibitor of apoptosis protein survivin is highly expressed in neuroblastoma (NB) and survivin-specific T cells were identified in Stage 4 patients. Therefore, we generated a novel survivin minigene DNA vaccine (pUS-high) encoding exclusively for survivin-derived peptides with superior MHC class I (H2-K(k)) binding affinities and tested its efficacy to suppress tumor growth and metastases in a syngeneic NB mouse model. Vaccination was performed by oral gavage of attenuated Salmonella typhimurium SL7207 carrying pUS-high. Mice receiving the pUS-high in the prophylactic setting presented a 48-52% reduction in s.c. tumor volume, weight and liver metastasis level in contrast to empty vector controls. This response was as effective as a survivin full-length vaccine and was associated with an increased target cell lysis, increased presence of CD8(+) T-cells at the primary tumor site and enhanced production of proinflammatory cytokines by systemic CD8(+) T cells. Furthermore, depletion of CD8(+) but not CD4(+) T-cells completely abrogated the pUS-high mediated primary tumor growth suppression, demonstrating a CD8(+) T-cell mediated effect. Therapeutic vaccination with pUS-high led to complete NB eradication in over 50% of immunized mice and surviving mice showed an over 80% reduction in primary tumor growth upon rechallenge in contrast to controls. In summary, survivin-based DNA vaccination is effective against NB and the rational minigene design provides a promising approach to circumvent potentially hazardous effects of using full length antiapoptotic genes as DNA vaccines.
Within our everyday life, we are confronted with a variety of toxic substances of natural or artificial origin. Toxins are already used, e.g. in medicine, but there is still an increasing number of toxic compounds, representing a tremendous potential to extract new substances. Since predictive toxicology gains in importance, the careful and extensive investigation of known toxins is the basis to assess the properties of unknown substances. In order to achieve this aim, we have collected toxic compounds from literature and web sources in the database SuperToxic. The current version of this database compiles about 60,000 compounds and their structures. These molecules are classified according to their toxicity, based on more than 2 million measurements. The SuperToxic database provides a variety of search options like name, CASRN, molecular weight and measured values of toxicity. With the aid of implemented similarity searches, information about possible biological interactions can be gained. Furthermore, connections to the Protein Data Bank, UniProt and the KEGG database are available, to allow the identification of targets and those pathways, the searched compounds are involved in. This database is available online at: http://bioinformatics.charite.de/supertoxic.
The packing of protein atoms is an indicator for their stability and functionality, and applied in determining thermostability, in protein design, ligand binding and to identify flexible regions in proteins. Here, we present Voronoia, a database of atomic-scale packing data for protein 3D structures. It is based on an improved Voronoi Cell algorithm using hyperboloid interfaces to construct atomic volumes, and to resolve solvent-accessible and -inaccessible regions of atoms. The database contains atomic volumes, local packing densities and interior cavities calculated for 61 318 biological units from the PDB. A report for each structure summarizes the packing by residue and atom types, and lists the environment of interior cavities. The packing data are compared to a nonredundant set of structures from SCOP superfamilies. Both packing densities and cavities can be visualized in the 3D structures by the Jmol plugin. Additionally, PDB files can be submitted to the Voronoia server for calculation. This service performs calculations for most full-atomic protein structures within a few minutes. For batch jobs, a standalone version of the program with an optional PyMOL plugin is available for download. The database can be freely accessed at: http://bioinformatics.charite.de/voronoia.
Volatiles are efficient mediators of chemical communication acting universally as attractant, repellent or warning signal in all kingdoms of life. Beside this broad impact volatiles have in nature, scents are also widely used in pharmaceutical, food and cosmetic industries, so the identification of new scents is of great industrial interest. Despite this importance as well as the vast number and diversity of volatile compounds, there is currently no comprehensive public database providing information on structure and chemical classification of volatiles. Therefore, the database SuperScent was established to supply users with detailed information on the variety of odor components. The version of the database presented here comprises the 2D/3D structures of approximately 2100 volatiles and around 9200 synonyms as well as physicochemical properties, commercial availability and references. The volatiles are classified according to their origin, functionality and odorant groups. The information was extracted from the literature and web resources. SuperScent offers several search options, e.g. name, Pubchem ID number, species, functional groups, or molecular weight. SuperScent is available online at: http://bioinformatics.charite.de/superscent.
The increasing structural information about target-bound compounds provide a rich basis to study the binding mechanisms of metabolites and drugs. SuperSite is a database, which combines the structural information with various tools for the analysis of molecular recognition. The main data is made up of 8000 metabolites including 1300 drugs, bound to about 290,000 different receptor binding sites. The analysis tools include features, like the highlighting of evolutionary conserved receptor residues, the marking of putative binding pockets and the superpositioning of different binding sites of the same ligand. User-defined compounds can be edited or uploaded and will be superimposed with the most similar co-crystallized ligand. The user can examine all results online with the molecule viewer Jmol. An implemented search algorithm allows the screening of uploaded proteins, in order to detect potential drug binding sites, which are similar to known binding pockets. The huge data set of target-bound compounds in combination with the provided analysis tools allow to inspect the characteristics of molecular recognition, especially for drug target interactions. SuperSite is publicly available at: http://bioinformatics.charite.de/supersite.
The increasing number of solved macromolecules provides a solid number of 3D interfaces, if all types of molecular contacts are being considered. JAIL annotates three different kinds of macromolecular interfaces, those between interacting protein domains, interfaces of different protein chains and interfaces between proteins and nucleic acids. This results in a total number of about 184,000 database entries. All the interfaces can easily be identified by a detailed search form or by a hierarchical tree that describes the protein domain architectures classified by the SCOP database. Visual inspection of the interfaces is possible via an interactive protein viewer. Furthermore, large scale analyses are supported by an implemented sequential and by a structural clustering. Similar interfaces as well as non-redundant interfaces can be easily picked out. Additionally, the sequential conservation of binding sites was also included in the database and is retrievable via Jmol. A comprehensive download section allows the composition of representative data sets with user defined parameters. The huge data set in combination with various search options allow a comprehensive view on all interfaces between macromolecules included in the Protein Data Bank (PDB). The download of the data sets supports numerous further investigations in macromolecular recognition. JAIL is publicly available at http://bioinformatics.charite.de/jail.
In general, drug metabolism has to be considered to avoid adverse effects and ineffective therapy. In particular, chemotherapeutic drug cocktails strain drug metabolizing enzymes especially the cytochrome P450 family (CYP). Furthermore, a number of important chemotherapeutic drugs such as cyclophosphamide, ifosfamide, tamoxifen or procarbazine are administered as prodrugs and have to be activated by CYP. Therefore, the genetic variability of these enzymes should be taken into account to design appropriate therapeutic regimens to avoid inadequate drug administration, toxicity and inefficiency.
We created SynSysNet, available online at http://bioinformatics.charite.de/synsysnet, to provide a platform that creates a comprehensive 4D network of synaptic interactions. Neuronal synapses are fundamental structures linking nerve cells in the brain and they are responsible for neuronal communication and information processing. These processes are dynamically regulated by a network of proteins. New developments in interaction proteomics and yeast two-hybrid methods allow unbiased detection of interactors. The consolidation of data from different resources and methods is important to understand the relation to human behaviour and disease and to identify new therapeutic approaches. To this end, we established SynSysNet from a set of ?1000 synapse specific proteins, their structures and small-molecule interactions. For two-thirds of these, 3D structures are provided (from Protein Data Bank and homology modelling). Drug-target interactions for 750 approved drugs and 50 000 compounds, as well as 5000 experimentally validated protein-protein interactions, are included. The resulting interaction network and user-selected parts can be viewed interactively and exported in XGMML. Approximately 200 involved pathways can be explored regarding drug-target interactions. Homology-modelled structures are downloadable in Protein Data Bank format, and drugs are available as MOL-files. Protein-protein interactions and drug-target interactions can be viewed as networks; corresponding PubMed IDs or sources are given.
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Journal of Visualized Experiments
What is Visualize?
JoVE Visualize is a tool created to match the last 5 years of PubMed publications to methods in JoVE's video library.
How does it work?
We use abstracts found on PubMed and match them to JoVE videos to create a list of 10 to 30 related methods videos.
Video X seems to be unrelated to Abstract Y...
In developing our video relationships, we compare around 5 million PubMed articles to our library of over 4,500 methods videos. In some cases the language used in the PubMed abstracts makes matching that content to a JoVE video difficult. In other cases, there happens not to be any content in our video library that is relevant to the topic of a given abstract. In these cases, our algorithms are trying their best to display videos with relevant content, which can sometimes result in matched videos with only a slight relation.