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Edgar Jacoby

Publications and source records attributed to Edgar Jacoby.

At least 19 recordsLinked to original sources
PubMed2007

Complementarity of hydrophobic properties in ATP-protein binding: a new criterion to rank docking solutions.

ATP is an important substrate of numerous biochemical reactions in living cells. Molecular recognition of this ligand by proteins is very important for understanding enzymatic mechanisms. Considerable insight into the problem may be gained via molecular docking simulations. At the same time, standard docking protocols are often insufficient to predict correct conformations for protein-ATP complexes. Thus, in most cases the native-like solutions can be found among the docking poses, but current scoring functions have only limited ability to discriminate them from false positives. To improve the selection of correct docking solutions obtained with the GOLD software, we developed a new ranking criterion specific for ATP-protein binding. The method is based on detailed analysis of the intermolecular interactions in 40 high-resolution 3D structures of ATP-protein complexes (the training set). We found that the most important factors governing this recognition are hydrogen-bonding, stacking between adenine and aromatic protein residues, and hydrophobic contacts between adenine and protein residues. To address the latter, we applied the formalism of 3D molecular hydrophobicity potential. The results obtained were used to construct an ATP-oriented scoring criterion as a linear combination of the terms describing these intermolecular interactions. The criterion was then validated using the test set of 10 additional ATP-protein complexes. As compared with the standard scoring functions, the new ranking criterion significantly improved the selection of correct docking solutions in both sets and allowed considerable enrichment at the top of the list containing docking poses with correct solutions.

Adenine↗
PubMed2006

Chemogenomics: drug discovery's panacea?

Chemogenomics aims towards the systematic identification of small molecules that interact with the products of the genome and modulate their biological function. This Opinion article summarizes the different knowledge-based chemogenomics strategies that are followed and outlines the challenges and opportunities that will impact drug discovery. Chemogenomics aims towards the systematic identification of small molecules that interact with the products of the genome and modulate their biological function. While historically the approach is based on efforts that systematically explore target gene families like kinases, today additional knowledge-based systematization principles are followed within early drug discovery projects which aim to biologically validate the targets and to identify starting points for chemical lead optimization. While the expectations of chemogenomics are very high, the reality of drug discovery is quite sobering with very high project attrition rates. This article summarizes the different knowledge-based chemogenomics strategies that are followed and outlines the challenges and potential opportunities that will impact drug discovery.

Chemistry, Pharmaceutical↗
PubMed2006

The 7 TM G-protein-coupled receptor target family.

Chemical biology approaches have a long history in the exploration of the G-protein-coupled receptor (GPCR) family, which represents the largest and most important group of targets for therapeutics. The analysis of the human genome revealed a significant number of new members with unknown physiological function which are today the focus of many reverse pharmacology drug-discovery programs. As the seven hydrophobic transmembrane segments are a defining common structural feature of these receptors, and as signaling through heterotrimeric G proteins is not demonstrated in all cases, these proteins are also referred to as seven transmembrane (7 TM) or serpentine receptors. This review summarizes important historic milestones of GPCR research, from the beginning, when pharmacology was mainly descriptive, to the age of modern molecular biology, with the cloning of the first receptor and now the availability of the entire human GPCR repertoire at the sequence and protein level. It shows how GPCR-directed drug discovery was initially based on the careful testing of a few specifically made chemical compounds and is today pursued with modern drug-discovery approaches, including combinatorial library design, structural biology, molecular informatics, and advanced screening technologies for the identification of new compounds that activate or inhibit GPCRs specifically. Such compounds, in conjunction with other new technologies, allow us to study the role of receptors in physiology and medicine, and will hopefully result in novel therapies. We also outline how basic research on the signaling and regulatory mechanisms of GPCRs is advancing, leading to the discovery of new GPCR-interacting proteins and thus opening new perspectives for drug development. Practical examples from GPCR expression studies, HTS (high-throughput screening), and the design of monoamine-related GPCR-focused combinatorial libraries illustrate ongoing GPCR chemical biology research. Finally, we outline future progress that may relate today's discoveries to the development of new medicines.

Animals↗
PubMed2006

On scaffolds and hopping in medicinal chemistry.

The molecular scaffold is an oft-cited concept in medicinal chemistry suggesting that the definition of what makes a scaffold is rigorous and objective. However, this is far from the case with the definition of a scaffold being highly dependent on the particular viewpoint of a given scientist. It follows, therefore, that the definition of scaffold hopping and, more importantly, the detection of what constitutes a scaffold hop, is also ill-defined and highly subjective. Essentially, it is agreed that scaffolds should be substantially different from each other, although significantly similar to each other, to constitute a hop. In the latter, the scaffolds must permit a similar geometric arrangement of functional groups to permit the mode of action. However, this leaves the paradox of how to describe both scaffold similarity and dissimilarity simultaneously. In this paper, the current statuses of scaffolds and scaffold hopping are reviewed based on published examples of scaffold hopping from the literature. An investigation of the degree to which it is possible to formulate a more rigorous definition of scaffolds and hopping in the context of molecular topologies is considered. These techniques are adapted from chemoinformatics to be applied in the design of new medicinal compounds.

Chemistry, Pharmaceutical↗
PubMed2005

A chemoinformatics analysis of hit lists obtained from high-throughput affinity-selection screening.

The high-throughput affinity-selection screening platform SpeedScreen was recently reported by the Novartis Institutes for BioMedical Research as a homogeneous, label-free screening technology with mass-spectrometry readout. SpeedScreen relies on the screening of compound mixtures with various target proteins and uses fast size-exclusion chromatography to separate target-bound from unbound substances. After disintegration of the target-binder complex, the binder molecules are identified by their molecular masses using liquid chromatography/mass spectrometry. The authors report an analysis of the molecular properties of hits obtained with SpeedScreen on 26 targets screened within the past few years at Novartis using this technology. Affinity-based SpeedScreen is a robust high-throughput screening technology that does not accumulate frequent hitters or potential covalent binders. The hits are representative of the most commonly identified scaffold classes observed for known drugs. Validated SpeedScreen hits tend to be enriched on more lipophilic and larger-molecular-weight compounds compared to the whole library. The potential for a reduced SpeedScreen screening set to be used in case only limited protein quantities are available is evaluated. Such a reduced compound set should also maximize the coverage of the high-performing regions of the chemical property and class spaces; chemoinformatics methods including genetic algorithms and divisive K-means clustering are used for this aim.

Chromatography, High Pressure Liquid↗
PubMed2005

Enhancing the effectiveness of similarity-based virtual screening using nearest-neighbor information.

We test the hypothesis that fusing the outputs of similarity searches based on a single bioactive reference structure and on its nearest neighbors (of unknown activity) is more effective (in terms of numbers of high-ranked active structures) than a similarity search involving just the reference structure. This turbo similarity searching approach provides a simple way to enhance the effectiveness of simulated virtual screening searches of the MDL Drug Data Report database.

Computing Methodologies↗
PubMed2005

Key aspects of the Novartis compound collection enhancement project for the compilation of a comprehensive chemogenomics drug discovery screening collection.

The NIBR (Novartis Institutes for BioMedical Research) compound collection enrichment and enhancement project integrates corporate internal combinatorial compound synthesis and external compound acquisition activities in order to build up a comprehensive screening collection for a modern drug discovery organization. The main purpose of the screening collection is to supply the Novartis drug discovery pipeline with hit-to-lead compounds for today's and the future's portfolio of drug discovery programs, and to provide tool compounds for the chemogenomics investigation of novel biological pathways and circuits. As such, it integrates designed focused and diversity-based compound sets from the synthetic and natural paradigms able to cope with druggable and currently deemed undruggable targets and molecular interaction modes. Herein, we will summarize together with new trends published in the literature, scientific challenges faced and key approaches taken at NIBR to match the chemical and biological spaces.

Animals↗
PubMed2005

Library design for fragment based screening.

According to Hann's model of molecular complexity an increased probability of detection binding to a target protein can be expected when small, low complex molecular fragments are screened with high sensitivity instead of full-sized ligands with lower sensitivity. Analysis of the HTS summary data of Novartis and comparison with NMR screening results obtained on generic fragment libraries indicate this expectation to be true with hitrates of 0.001% - 0.151% observed in the identification of ligands with an IC(50) threshold in the micromolar range in an HTS setup and hitrates above or equal to 3% observed in NMR screening of fragments with an affinity threshold in the millimolar range. It is however necessary to keep in mind that the sets of target studied were not identical for both method and the experience in NMR screening is too limited for a final conclusion. The term hitrate as used here reflects only the success rate in the observation of ligand binding event. It must not be confused with the overall success rate of fragment and high throughput screening in the lead finding process, which can be entirely different, since the steps required to follow-up a ligand binding event to a lead are different for both methods. A survey of fragment-based lead discovery case studies given in the literature shows that in approximately half of the cases the initial hit fragment was discovered by screening a generic library, whereas in the other cases some knowledge about an initial ligands or the protein binding site has been used, whereas systematic virtual screening of fragment databases has been only rarely reported. As comparatively high hitrates were obtained, further consideration to optimize the generic fragment screening library were directed to the chemical tractability of the fragment. As several functional groups preferred by chemists for modification and linking of the fragments are also preferentially involved in interactions between the fragments and the target protein, a set of screening fragments was derived from chemical building blocks by masking its linker group by a chemical transformation which can be later on used in the chemical follow-up of the fragment hit. For example primary amines can be masked as acetamides. If the screening fragment is active the related building block can then be used for synthesis of a follow-up library.

Combinatorial Chemistry Techniques↗
PubMed2004

Comparison of topological descriptors for similarity-based virtual screening using multiple bioactive reference structures.

This paper reports a detailed comparison of a range of different types of 2D fingerprints when used for similarity-based virtual screening with multiple reference structures. Experiments with the MDL Drug Data Report database demonstrate the effectiveness of fingerprints that encode circular substructure descriptors generated using the Morgan algorithm. These fingerprints are notably more effective than fingerprints based on a fragment dictionary, on hashing and on topological pharmacophores. The combination of these fingerprints with data fusion based on similarity scores provides both an effective and an efficient approach to virtual screening in lead-discovery programmes.

Algorithms↗
PubMed2003

Chemogenomics knowledge-based strategies in drug discovery.

In the postgenomic age of drug discovery, targets can no longer be viewed as singular objects having no relationship to one another. All targets are now visible and the systematic exploration of selected target families appears to be a promising way to speed up and further industrialize target-based drug discovery. Chemogenomics refers to such systematic exploration of target families and aims to identify all possible ligands of all target families. Because biology works by applying prior knowledge to an unknown entity, chemogenomics approaches are expected to be especially effective within the previously well-explored target families, for which, in addition to the protein sequence and structure information, considerable knowledge of pharmacologically active structural classes and structure-activity relationships exists. For the new target families, chemical knowledge will have to be generated and beyond biological target validation, the emphasis is on chemistry to provide the molecules with which their novel biology and pharmacology can be studied. Using examples from the previously most successfully explored target families, the GPCR family in particular, we summarize herein our current chemogenomics knowledge-based strategies for drug discovery, which are founded on the high integration of chem and bioinformatics, thereby providing a molecular informatics frame for the exploration of the new target families.

Computational Biology↗
PubMed2003

Design of small molecule libraries for NMR screening and other applications in drug discovery.

There are conceptual differences between high-throughput screening (HTS) and fragment-based screening by NMR. The number of compounds in libraries for NMR screening may be significantly smaller than those used for HTS. Because one relies on a small library its design is significantly important and is the object of this article. A short introduction on fragment-based NMR screening approaches will be provided. Although there are currently very few reports describing the design of libraries of small molecules for NMR screening, aspects of the question of how to compile diverse collections of small molecular fragments useful for drug design were previously addressed for the purposes of combinatorial library design and de novo drug design. As these disciplines are highly interrelated and are applied in an interconnected manner with NMR screening within the drug discovery process, a review of combinatorial library design and especially the building block or fragment selection strategies applied for combinatorial library design and de novo design is well suited to reveal fundamental strategies and potential techniques for the design of NMR screening libraries. This section will be rounded off by a report on hands-on-experience with the design of the Novartis second-site NMR screening library and practical considerations for the design of compound mixtures. Rather than providing an exact protocol general guidelines will be indicated.

Chemistry, Pharmaceutical↗
PubMed2002

Biphenyls as potential mimetics of protein alpha-helix.

Based on theoretical arguments, 2,6,3',5'-substituted biphenyl analogues are proposed as protein alpha-helix mimetics superimposing the side chains of the residues i, i+1, i+3 and i+4. Knowing that many protein-protein interactions of therapeutical relevance involve alpha-helix contacts, the communication outlines how this novel category of scaffolds might potentially open access to such targets.

Animals↗
PubMedDate not supplied

An ontology for pharmaceutical ligands and its application for in silico screening and library design.

Annotation efforts in biosciences have focused in past years mainly on the annotation of genomic sequences. Only very limited effort has been put into annotation schemes for pharmaceutical ligands. Here we propose annotation schemes for the ligands of four major target classes, enzymes, G protein-coupled receptors (GPCRs), nuclear receptors (NRs), and ligand-gated ion channels (LGICs), and outline their usage for in silico screening and combinatorial library design. The proposed schemes cover ligand functionality and hierarchical levels of target classification. The classification schemes are based on those established by the EC, GPCRDB, NuclearDB, and LGICDB. The ligands of the MDL Drug Data Report (MDDR) database serve as a reference data set of known pharmacologically active compounds. All ligands were annotated according to the schemes when attribution was possible based on the activity classification provided by the reference database. The purpose of the ligand-target classification schemes is to allow annotation-based searching of the ligand database. In addition, the biological sequence information of the target is directly linkable to the ligand, hereby allowing sequence similarity-based identification of ligands of next homologous receptors. Ligands of specified levels can easily be retrieved to serve as comprehensive reference sets for cheminformatics-based similarity searches and for design of target class focused compound libraries. Retrospective in silico screening experiments within the MDDR01.1 database, searching for structures binding to dopamine D2, all dopamine receptors and all amine-binding class A GPCRs using known dopamine D2 binding compounds as a reference set, have shown that such reference sets are in particular useful for the identification of ligands binding to receptors closely related to the reference system. The potential for ligand identification drops with increasing phylogenetic distance. The analysis of the focus of a tertiary amine based combinatorial library compared to known amine binding class A GPCRs, peptide binding class A GPCRs, and LGIC ligands constitutes a second application scenario which illustrates how the focus of a combinatorial library can be treated quantitatively. The provided annotation schemes, which bridge chem- and bioinformatics by linking ligands to sequences, are expected to be of key utility for further systematic chemogenomics exploration of previously well explored target families.

Combinatorial Chemistry Techniques↗
PubMedDate not supplied

Similarity metrics for ligands reflecting the similarity of the target proteins.

In this study we evaluate how far the scope of similarity searching can be extended to identify not only ligands binding to the same target as the reference ligand(s) but also ligands of other homologous targets without initially known ligands. This "homology-based similarity searching" requires molecular representations reflecting the ability of a molecule to interact with target proteins. The Similog keys, which are introduced here as a new molecular representation, were designed to fulfill such requirements. They are based only on the molecular constitution and are counts of atom triplets. Each triplet is characterized by the graph distances and the types of its atoms. The atom-typing scheme classifies each atom by its function as H-bond donor or acceptor and by its electronegativity and bulkiness. In this study the Similog keys are investigated in retrospective in silico screening experiments and compared with other conformation independent molecular representations. Studied were molecules of the MDDR database for which the activity data was augmented by standardized target classification information from public protein classification databases. The MDDR molecule set was split randomly into two halves. The first half formed the candidate set. Ligands of four targets (dopamine D2 receptor, opioid delta-receptor, factor Xa serine protease, and progesterone receptor) were taken from the second half to form the respective reference sets. Different similarity calculation methods are used to rank the molecules of the candidate set by their similarity to each of the four reference sets. The accumulated counts of molecules binding to the reference target and groups of targets with decreasing homology to it were examined as a function of the similarity rank for each reference set and similarity method. In summary, similarity searching based on Unity 2D-fingerprints or Similog keys are found to be equally effective in the identification of molecules binding to the same target as the reference set. However, the application of the Similog keys is more effective in comparison with the other investigated methods in the identification of ligands binding to any target belonging to the same family as the reference target. We attribute this superiority to the fact that the Similog keys provide a generalization of the chemical elements and that the keys are counted instead of merely noting their presence or absence in a binary form. The second most effective molecular representation are the occurrence counts of the public ISIS key fragments, which like the Similog method, incorporates key counting as well as a generalization of the chemical elements. The results obtained suggest that ligands for a new target can be identified by the following three-step procedure: 1. Select at least one target with known ligands which is homologous to the new target. 2. Combine the known ligands of the selected target(s) to a reference set. 3. Search candidate ligands for the new targets by their similarity to the reference set using the Similog method. This clearly enlarges the scope of similarity searching from the classical application for a single target to the identification of candidate ligands for whole target families and is expected to be of key utility for further systematic chemogenomics exploration of previously well explored target families.

Algorithms↗
PubMedDate not supplied

Comparison of fingerprint-based methods for virtual screening using multiple bioactive reference structures.

Fingerprint-based similarity searching is widely used for virtual screening when only a single bioactive reference structure is available. This paper reviews three distinct ways of carrying out such searches when multiple bioactive reference structures are available: merging the individual fingerprints into a single combined fingerprint; applying data fusion to the similarity rankings resulting from individual similarity searches; and approximations to substructural analysis. Extended searches on the MDL Drug Data Report database suggest that fusing similarity scores is the most effective general approach, with the best individual results coming from the binary kernel discrimination technique.

Molecular Structure↗
PubMedDate not supplied

Sequential superparamagnetic clustering for unbiased classification of high-dimensional chemical data.

For the clustering of chemical structures that are described by the Similog, ISIS count, and ISIS binary fingerprints, we propose a sequential superparamagnetic clustering approach. To appropriately handle nonbinary feature keys, we introduce an extension of the binary Tanimoto similarity measure. In our applications, data sets composed of structures from seven chemically distinct compound classes are evaluated and correctly clustered. The comparison, with results from leading methods, indicates the superiority of our sequential superparamagnetic clustering approach.

Journal Article↗
PubMedDate not supplied

New methods for ligand-based virtual screening: use of data fusion and machine learning to enhance the effectiveness of similarity searching.

Similarity searching using a single bioactive reference structure is a well-established technique for accessing chemical structure databases. This paper describes two extensions of the basic approach. First, we discuss the use of group fusion to combine the results of similarity searches when multiple reference structures are available. We demonstrate that this technique is notably more effective than conventional similarity searching in scaffold-hopping searches for structurally diverse sets of active molecules; conversely, the technique will do little to improve the search performance if the actives are structurally homogeneous. Second, we make the assumption that the nearest neighbors resulting from a similarity search, using a single bioactive reference structure, are also active and use this assumption to implement approximate forms of group fusion, substructural analysis, and binary kernel discrimination. This approach, called turbo similarity searching, is notably more effective than conventional similarity searching.

Artificial Intelligence↗