HOME- Bryn Mawr Conference
- Workshops & Training
- 2010 Oxford (Discovery)
- 2010 Oxford (ADMET)
- 2009 Oxford (Discovery)
- Blanchard, H
- Bryant, S
- Coveney, P
- Hardy, B
- Hawkins, P
- Klamt, A
- Knapp, S
- Kranz, M
- Liebeshuetz, J
- Oledzki, P
- Pirok, G
- Wolber, G
- Zamora, I
- Bursary Award
- 2009 Oxford (ADMET)
- 2008 Oxford
- Program
- Exhibition
- Registration
- Jobs
- Contact
- Support
- Schedule
|
|
|
|
|
Poster Session
| eCheminfo Drug Discovery Workshop
Medical Sciences Teaching Center, Oxford University, Oxford, UK
20 - 24 July 2009 |
LIST of POSTERS
1. Dik-Lung Ma (University of Hong Kong), Discovery of Drug-Like Hit of G-quadruplex Binding Ligand by High-Throughput Docking
2. Esther Sala (Rovira & Virgili University), In silico Study of IKKβ Inhibition by Natural Phenolic Compounds
3. Fanny Coppens (Laboratory for Structural & Molecular Microbiology, VIB/VUB), Towards Small Molecule Anti-adhesives as a Means of Novel Virulence-targeted Antimicrobials
4. Horacio Sánchez (Forschungszentrum Karlsruhe), High Throughput in silico Screening against Flexible Protein Receptors
5. Kanin Wichapong (Chulalongkorn University), Search for Novel Inhibitors of Dengue Virus NS2B/NS3 Protease by Combining Virtual Screening and MM-PBSA Binding Free Energy Calculations
6. Laura Guasch (Universitat Rovira i Virgili), Discovery of Natural Product PPARγ Agonists by a Pharmacophore-Based Virtual Screening Workflow
7. Barry Hardy (Douglas Connect), Collaborative Development of Predictive Toxicology Applications using the OpenTox Framework
8. Cynthia Tallant Blanco (Institut de Biologia Molecular de Barcelona), MATRIX METALLOPROTEASES: Fold and Function of their Catalytic Domains
9. Dilip Narayanan (Systems Biology Worldwide), Global and Local Functional Group Signatures of Kinase Inibitors: Implications for Kinase Inhibitor Searching and Design
10. Lari Lehtiö (Åbo Akademi University), From Crystal Structures of PARPs Towards Selective Inhibitors
11. Andrej Perdih (National Institute of Chemistry, Slovenia), Novel Inhibitors of Bacterial MurD and MurE Ligases Discovered by Structure-based Virtual Screening Approach
12. Dan Rathbone (Aston University), Computer Models for the Optimisation of Tissue Transglutaminase Inhibitors
13. Blaz Vehar (National Institute of Chemistry, Slovenia), Virtual Screening for Inhibitors of D-Alanine: D-Alanine Ligase
14. Leyte Winfield (Spelman College), Using Cheminformatics to Design Anti-cancer Drugs
Abstracts
Discovery of Drug-Like Hit of G-quadruplex Binding Ligand by High-Throughput Docking
Dik-Lung Ma,*[a,b] Tat-Shing Lai,[b] Fung-Yi Chan,[b] Wai-Hong Chung,[b] R. Abagyan,[c] Yun-Chung Leung,[b] and Kwok-Yin Wong*[b]
[a] Department of Chemistry and Open Laboratory of Chemical Biology of the Institute of Molecular Technology for Drug Discovery and Synthesis, The University of Hong Kong. [b] Department of Applied Biology and Chemical Technology of Central laboratory of the Institute of Molecular Technology for Drug Discovery and Synthesis, The Hong Kong Polytechnic University, Hong Kong. [c] Department of Molecular Biology, The Scripps Research Institute, La Jolla, CA 92037
There has been considerable interest in the study of G-quadruplex DNA due to its involvement in the regulation of telomerase activities. Human telomeric DNA is composed of a repeated double-stranded [TTAGGG/CCCTAA]n sequence except in the 3'-terminal region, which consists of a single-stranded tandem [TTAGGG] repeated sequence over several hundred bases. In normal somatic cells, approximately 100 bases will be lost during every cell division, and after reaching a critical shortening of the telomere, the cell undergoes apoptosis. In cancer cells, telomeric length is maintained by telomerase, and telomerase activity is expressed in over 90% of tumor cell lines but in relatively few normal cell types. Thus, the inhibition of telomerase activity by ligand-induced stabilization of G-quadruplex has therefore become an attractive strategy for developing new anti-cancer drugs. In order to develop a high-throughput platform for G-quadruplex DNA stabilizing ligands, a computer model has been constructed by using the X-ray crystal structure of the intramolecular human telomeric G-quadruplex DNA. Preliminary results indicated that some of the small molecules found through in silico screening are potential stabilizers for G-quadruplex DNA with telEC50 (effective concentration that inhibited 50% of the telomerase activity vs a drug-free control) in the micro-molar concentration range.
In silico Study of IKKβ Inhibition by Natural Phenolic Compounds
Sala E (a), Iwaszkiewicz J (b), Zoete V (b), Grosdidier A (b), Guasch L (a), Garcia-Vallve S (a), Michielin O (b) and Pujadas G (a)
a Nutrigenomics Research Group. Rovira & Virgili University. Campus Sescelades. C/ Marcel•lí Domingo s/n, 43007 Tarragona, Catalonia, Spain.
b Swiss Institute of Bioinformatics (SIB), Molecular Modeling Group, Quartier Sorges, Bâtiment Génopode, CH-1015 Lausanne, Switzerland.
The NF-κβ pathway plays an important role in regulating the expression of the cellular genes that control the immune and inflammatory response. IKKβ is a serine-threonine protein kinase that belongs to the IKK complex and is critically involved in activating the transcription factor NF-κβ in response to various inflammatory stimuli, hence the interest in synthesizing molecules that can inhibit IKKβ. The research interest of this study lies in the relationship between natural compounds and health, and particularly in how the phenolic compounds from grape seed extract can prevent cardiovascular diseases. Our hypothesis is that phenolic compounds can inhibit the IKKβ kinase because previous in vitro studies by our group have shown that some of these compounds can inhibit NF-κβ translocation to the nucleus. The same studies suggest that regulating upstream proteins such as IKK may inhibit degradation of Ikβ (regulatory protein). The aim of the present work is to use a docking-based virtual screening approach to identify the inhibitors of IKKβ in a database of natural phenolic compounds. Since no experimental structure of IKKβ has been deposited in the public Protein Data Bank, the homology model of the IKKβ was built using Modeller 9v5 software. The molecular docking studies were performed using EADock v2.0 and GLIDE v5.0. The results of this investigation are expected to elucidate the molecular background of natural phenolic compounds effect on human health.
Keywords: prediction, homology model, docking, IKKβ, natural compounds.
Towards Small Molecule Anti-adhesives as a Means of Novel Virulence-targeted Antimicrobials
Fanny Coppens, Alvin Lo, Adinda Wellens, Julie Bouckaert & Han Remaut
VIB/VUB, Laboratory for Structural & Molecular Microbiology, Building E, 4th floor, Pleinlaan 2, 1050 Brussels, Belgium.
Most present-day antibiotics used to treat bacterial infections have a bactericidal or bacteriostatic mode of action. This results in a selective pressure that enhances the occurrence and horizontal spread of antibiotic resistance. This is particularly problematic for hospital or community-acquired infections, where intensive antibiotic use has led to an increasing prevalence of multi-resistant strains. The negative impact that these usually broad-spectrum antibiotics have on the microflora of the host is an additional matter of concern, increasing, for example, vulnerability for opportunistic pathogens such as Clostridium difficile.
An alternative approach to the use of broad-spectrum bactericidal or bacteriostatic antibiotics is the development of drug-like molecules targeted against specific bacterial virulence factors. Such anti-virulence drugs aim to inhibit bacterial pathogenesis and/or persistence, without harming the host microflora, both reducing the selective pressure leading to the occurrence of antibiotic resistance.
Here we present the development of anti-adhesive compounds, small drug-like molecules that target bacterial cell adhesion, a process important both in the onset and persistence of bacterial infections. Two model systems are presented: (a) blood group antigen binding adhesins in the human gastric pathogen Helicobacter pylori and (b) fimbrial adhesins in uropathogenic Escherichia coli (UPEC).
|
|
High Throughput in silico Screening against Flexible Protein Receptors
Horacio Sánchez, Bernhard Fischer, Daria Kokh, Holger Merlitz, Wolfgang Wenzel
Forschungszentrum Karlsruhe, Germany
Based on the stochastic tunneling method (STUN) [1] we have developed FlexScreen [2], a novel strategy for high-throughput in-silico screening of large ligand databases. Each ligand of the database is docked against the receptor using an all-atom representation of both ligand and receptor. The ligands with the best evaluated affinity are selected as lead candidates for drug development. Using the thymidine kinase inhibitors as a prototypical example we documented [3] the shortcomings of rigid receptor screens in a realistic system. We demonstrate a gain in both overall binding energy and overall rank of the known substrates when two screens with a rigid and flexible (up to 15 sidechain dihedral angles) receptor are compared. We note that the STUN suffers only a comparatively small loss of efficiency when an increasing number of receptor degrees of freedom is considered. FlexScreen thus offers a viable compromise [4] between docking flexibility and computational efficiency to perform fully automated database screens on hundreds of thousands of ligands. We also investigate enrichment rates [5] of rigid, soft and flexible receptor models [6] for 12 diverse receptors using libraries containing up to 13000 molecules. A flexible sidechain model with flexible dihedral angles for up to 12 aminoacids increased both binding propensity and enrichment rates: EF_1 values increased by 35% on average with respect to rigid-docking (3-8 flexible sidechains). This methodology will be soon available for the Cell processor and Pipeline Pilot.
Bibliography
1. Wenzel, W. and K. Hamacher, Stochastic tunneling approach for global minimization of complex potential energy landscapes. Physical Review Letters, 1999. 82(15): p. 3003-3007.
2. Merlitz, H., B. Burghardt, and W. Wenzel, Application of the stochastic tunneling method to high throughput database screening. Chemical Physics Letters, 2003. 370(1-2): p. 68-73.
3. Merlitz, H., B. Burghardt, and W. Wenzel, Impact of receptor conformation on in silico screening performance. Chemical Physics Letters, 2004. 390(4-6): p. 500-505.
4. Fischer, B., et al., Accuracy of binding mode prediction with a cascadic stochastic tunneling method. Proteins-Structure Function and Bioinformatics, 2007. 68(1): p. 195-204.
5. Kokh, D.B. and W.G. Wenzel, Flexible side chain models improve enrichment rates in in silico screening. Journal of Medicinal Chemistry, 2008. 51(19): p. 5919-5931.
6. Fischer, B., K. Fukuzawa, and W. Wenzel, Receptor-specific scoring functions derived from quantum chemical models improve affinity estimates for in-silico drug discovery. Proteins-Structure Function and Bioinformatics, 2008. 70(4): p. 1264-1273.
Search for Novel Inhibitors of Dengue Virus NS2B/NS3 Protease by Combining Virtual Screening and MM-PBSA Binding Free Energy Calculations
(b)
Kanin Wichapong (a,b), Somsak Pianwanit (a), Wolfgang Sippl (b), and Sirirat Kokpol (a)
aDepartment of Chemistry, Faculty of Science, Chulalongkorn University, Bangkok, 10330, Thailand
bDepartment of Pharmaceutical Chemistry, Martin-Luther-University of Halle-Wittenberg, 06120, Halle (Saale), Germany
Dengue Virus (DV) infection is a severe public-health problem in tropical and subtropical regions. A replication cycle of DV requires the NS3 protease with a central 40 amino acid hydrophilic domain of NS2B acting as an essential cofactor for cleaving polyprotein precursors. Therefore, NS2B/NS3 protease is one of the promising targets for drug development against DV infection. A structure-based pharmacophore model derived directly from docking poses was generated. A second dynamic pharmacophore model was generated using a series of representative frames from a protease-inhibitor molecular dynamics simulation. Both pharmacophore models were applied to search for potential hits in multi-conformational databases, including NCI, ChemBridge, Maybridge and ZINC (drug-like subset). Hits derived from the pharmacophore search were then subjected to molecular docking and ranked by docking score. Subsequently, the 200 top-ranked compounds were energy minimized within the binding pocket of the enzyme. These hits were visually inspected for protease-interaction and selected for calculating the binding free energy using the MM-PBSA approach. The derived binding free energy of hits compounds were compared with known inhibitors and novel potent inhibitors were proposed for biological testing.
Discovery of Natural Product PPARγ Agonists by a Pharmacophore-Based Virtual Screening Workflow
Laura Guasch (1), Patrick Markt (2), Gudrun Spitzer (3), Markus Mühlbacher (3), Esther Sala (1), Montserrat Vaqué (1), Gerard Pujadas (1), Gerhard Wolber (2), Klaus Liedl (3) and Santi Garcia-Vallvé (1)
1 Grup de Recerca en Nutrigenòmica, Departament de Bioquímica i Biotecnologia, Universitat Rovira i Virgili, Tarragona, Spain
2 Department of Pharmaceutical Chemistry, Institute of Pharmacy and Center for Molecular Biosciences Innsbruck (CMBI), University of Innsbruck, Austria
3 Department of Theoretical Chemistry, Institute of General, Inorganic, and Theoretical Chemistry, University of Innsbruck, Austria
Peroxisome proliferator-activated receptors (PPARs), members of the superfamily of nuclear receptors, are transcription factors that control the expression of genes involved in fatty acid metabolism. They function as cellular lipid sensors that activate transcription in response to the binding of ligands, generally fatty acids and their eicosanoids metabolites. One important class of synthetic agonist of PPAR-gamma is the thiazolidinediones (TZDs). The ligand binding pocket is quite large and binds several types of ligands. The most essential feature of total agonists is the hydrogen-bonds network involving the carboxylate group of the ligands with Ser289, His323, His 449 and Tyr473 of PPAR-gamma. The rest of the ligand structure is basically hydrophobic. The partial agonists adopt a distinct binding mode and have no H-bonding interactions with PPARg. The 51 crystal structures of PPAR-gamma avaliable give molecular insights for the improved PPARg potency and selectivity. Recently, it has been shown that different molecules from natural extracts from various sources can act as PPARs agonists. Therefore, natural extracts may contain a large number of potential PPAR-agonists yet to be discovered and that would have an important value for the development of new drugs for the treatment of the several diseases. The aim of this study is to identify novel PPAR-gamma agonists from natural compounds, using the nearly 90,000 natural molecules available in the ZINC database to predict their power as PPARs agonists. We applied a virtual screening workflow based on a combination of pharmacophore modeling with 3D shape and electrostatic similarity screening techniques to discover novel scaffolds for PPAR-gamma ligands.
|
|
Collaborative Development of Predictive Toxicology Applications using the OpenTox Framework
(a)
B. Hardy* (a), N. Douglas (a), C. Helma (b), M. Rautenberg (b), N. Jeliazkova (c), V. Jeliazkov (c), L. Boyanov (c), C. Jiang (c), M. Martinov (c), R. Benigni (d), O. Tcheremenskaia (d), S. Kramer (e), T. Girschick (e), F. Buchwald (e), J. Wicker (e), A. Karwath (f), M. Gütlein (f), A. Maunz (f), H. Sarimveis (g), G. Melagraki (g), A. Afantitis (g), P. Sopasakis (g), D. Gallagher (h), V. Poroikov (i), D. Filimonov (i), A. Zakharov (i), A. Lagunin (i), T. Gloriozova (i), S. Novikov (i), N. Skvortsova (i), S. Chawla (j), S. Bowlus (j), I. Ghosh (k), S. Ray (k), G. Singhai (k), O. Prakash (k), S. Escher (l), S. Weiss (l)
a. Douglas Connect, b. In Silico Toxicology, c. Ideaconsult, d. Istituto Superiore di Sanita', e. Technical University of Munich, f. Albert Ludwigs University Freiburg, g. National Technical University of Athens, h. David Gallagher, i. Institute of Biomedical Chemistry of the Russian Academy of Medical Sciences, j. Seascape Learning, k. Jawaharlal Nehru University, l. Fraunhofer Institute for Toxicology & Experimental Medicine
The EC-funded FP7 project “OpenTox” ( www.opentox.org ) is developing an Open Source-based predictive toxicology framework that provides a unified access to toxicological data and (Quantitative) Structure-Activity Relationship i.e., (Q)SAR models. OpenTox provides tools for the integration of data, for the generation and validation of (Q)SAR models for toxic effects, libraries for the development and integration of (Q)SAR algorithms, and scientifically sound validation routines. OpenTox will support the development of applications for non-computational specialists in addition to interfaces for risk assessors, toxicological experts and model and algorithm developers.
OpenTox is relevant for the implementation of REACH as it allows risk assessors to access experimental data, (Q)SAR models and toxicological information from a unified interface that adheres to European and international regulatory requirements including OECD Guidelines for validation and reporting. The OpenTox framework is being populated initially with data and models for chronic, genotoxic and carcinogenic effects. These are the endpoints where computational methods promise the greatest potential reduction in animal testing required under REACH. Initial research has defined the essential components of the framework architecture, approach to data access, schema and management, use of controlled vocabularies and ontologies, web service and communications protocols, and selection and integration of algorithms for predictive modelling. The initial results of this research and next steps will be discussed.
OpenTox has been initiated as a collaborative project involving a combination of 11 different enterprise, university and government research groups to design and build the initial framework. Additionally numerous organizations with industry, regulatory or expert interests are being included from the start in providing guidance and direction. The goal is to expand OpenTox as a community project enabling additional expert and user participants to be involved in developments in as timely a manner as possible. To this end, our agreed upon intention is to carry out developments in an open and transparent manner from the early days of the project, and to open up discussions and development to the global community at large, who may either participate in developments or provide user perspectives. Cooperation on data standards, data integration, ontologies, integration of algorithm predictions from different methods, and testing and validation all have significant collaboration opportunities and benefits for the community. Additionally, practices for building effective collaborations from the OpenTox community approach will be discussed.
About OpenTox
OpenTox - An Open Source Predictive Toxicology Framework, www.opentox.org, is funded under the EU Seventh Framework Program: HEALTH-2007-1.3-3 Promotion, development, validation, acceptance and implementation of QSARs (Quantitative Structure-Activity Relationships) for toxicology, Project Reference Number Health-F5-2008-200787 (2008-2011).
Advisory Board
European Centre for the Validation of Alternative Methods, European Chemicals Bureau, U.S Environmental Protection Agency, U.S. Food & Drug Administration, Nestle, Roche, AstraZeneca, LHASA, Leadscope, University of North Carolina, EC Environment Directorate General, Organisation for Economic Co-operation & Development, CADASTER and Bayer Healthcare
*Contact Address: Dr. Barry Hardy, OpenTox Project Coordinator and Director, Community of Practice & Research Activities, Douglas Connect GmbH, Baermeggenweg 14, 4314 Zeiningen, Switzerland. Email: barry.hardy –(at)- douglasconnect.com
MATRIX METALLOPROTEASES: Fold and function of their catalytic domains
Cynthia Tallant, Aniebrys Marrero, F. Xavier Gomis-Rüth
Protelysis Lab, Molecular Biology Institute of Barcelona – CSIC, Barcelona Science Park, Helix Building, c/Baldiri Reixach, 15 -21, E-08028 Barcelona, Spain.
The matrix metalloproteinases (MMPs) are zinc dependent endopeptidases known for their ability to cleave one or several extracellular matrix (ECM) constituents, as well as non-matrix proteins. They are widely involved in metabolism regulation through both extensive protein degradation and selective peptide-bond hydrolysis. If MMPs are not subjected at any control, they become destructive, which lead to pathologies such as arthritis, inflammation and cancer. The main therapeutic strategy to combat the deregulation of MMPs is the design of drugs to target their catalytic domains, for which purpose detailed structural knowledge is essential. MMPs belong to the “metzincin” clan of metalloproteases, they are characterized for having an extended zinc-binding motif, HEXXHXXGXXH, which contains three zinc-binding histidines and a glutamate (acts as the general base). Additionally, a conserved methionine lying within a “Met-turn” provides a hydrophobic base for the zinc-binding site. Most MMPs are secreted as inactive zymogens with an N-terminal ca. 80-residue pro-domain, which folds into a three-helix globular domain and inhibits the catalytic zinc through a “cysteine-switch”. Removal of the pro-domain enables access of a catalytic solvent molecule and substrate molecules to the active-site cleft, which harbors a hydrophobic S1’ –pocket as main determinant of specificity. Together with the catalytic zinc ion, this pocket has been targeted since the onset of drug development against MMPs. However, the inability of first- and second-generation inhibitors to distinguish between different MMPs led to failures in clinical trials. More recent approaches have produced highly specific inhibitors to tackle selected MMPs, thus anticipating the development of more successful drugs in the near future.
This review will discuss the general architecture of MMP catalytic domains and its implication in function, zymogenic activation and drug design.
Global and Local Functional Group Signatures of Kinase Inibitors: Implications for Kinase Inhibitor Searching and Design
Dilip Narayanan (Systems Biology Worldwide)
Abnormal phosphorylation of cellular proteins by kinases leads to dysfunctional signalling pathways and thus to diseases like cancer. After G-protein coupled Receptors, Kinases are one of the most important drug target families in drug discovery. Approaches like kinomics attempt to understand the selectivity and poly pharmacology of kinase inhibitors in terms of genomics. Such chemogenomic approaches towards kinases can also be coded as algorithms in chemoinformatics.
OntomineTM, finds Global and Local positive constraints (conserved Organic functional group counts) and negative constraints (absence of organic functional groups) in biologically or therapeutically related set of small molecules e.g. kinase inhibitors.
The AMBIT bioasassay data (1) can be used to find functional groups patterns or constraints representative of particular kinases in the dataset. For the first part of our study we left out 10% of the small molecule ligands in the bioassay data and compared the predictions with known protein kinase inhibition patterns in the AMBIT assay. The prediction results were comparable to the known inhibition profile.
In the second study the positive and negative constraints were used to search an SDF database to characterize and find novel kinase inhibitors. The results show the predicted kinase inhibitors and their selectivity profile.
Our results demonstate that the in silico assay can capture the key reasons for specific kinase interactions. Thus, this in silico signature can be used to design or search for novel kinase inhibitors. It can be further extended by incorporating position specific information on functional groups.
(1) Karaman, M.W. et al. A quantitative analysis of kinase inhibitor selectivity. Nat. Biotechnol. 26, 127-132.
|
|
From Crystal Structures of PARPs Towards Selective Inhibitors
Lari Lehtiö
Åbo Akademi University, Department of Biochemistry and Pharmacy, Turku, Finland
Several new crystal structures of the catalytic domains of human poly(ADP-ribose) polymerases (PARPs) have been recently solved by the Structural Genomics Consortium in Stockholm (1) . This includes structures of PARP-3 (2), Tankyrase-1 (3), PARP-10, PARP-12, PARP-14 and PARP-15 of the enzyme family with 17 members. Although the enzymes vary a lot in their overall architecture, the NAD+ binding sites are structurally conserved. Several enzymes of the PARP-family have been recently characterized and it has become evident that the PARP inhibitors display comparable potencies towards many enzymes. That is the case also with the human PARP-3, a poorly characterized member of the PARP-family. The interactions of inhibitors binding to nicotinamide binding pocket are very similar, as demonstrated by PARP-3 complex structures. As PARP-1 inhibitors are in clinical trials it is important to get information about the inhibitor selectivity in order to avoid off-target effects. There is also increasing interest in utilizing other PARPs as drug targets. A research group has been established in Åbo Akademi with a focus on developing selective inhibitors using structure-based-drug design, x-ray crystallography and high throughput screening methods.
References
1. http://sgc.ki.se/
2. Lehtiö, L. et al. (2009) J. Med. Chem. 52:3108-11.
3. Lehtiö, L. et al. (2008) J. Mol. Biol. 379:136-45.
Novel Inhibitors of Bacterial MurD and MurE Ligases Discovered by Structure-based Virtual Screening Approach
(a)
Andrej Perdih (a), Andreja Kovač (b), Gerhard Wolber (c,d), Didier Blanot (e), Stanislav Gobec (b), Tom Solmajer (a)
a National Institute of Chemistry, Hajdrihova 19, 1001 Ljubljana, Slovenia
b Faculty of Pharmacy, University of Ljubljana, Aškerčeva 7, 1000 Ljubljana, Slovenia
c Inte:Ligand GmbH, Mariahilferstrasse 74B/11, 1070 Vienna, Austria
d Institute of Pharmacy and Center for Molecular Biosciences Innsbruck (CMBI), University of Innsbruck, Innrain 52c, 6020 Innsbruck, Austria
e Enveloppes Bactériennes et Antibiotiques, IBBMC, UMR 8619 CNRS, Univ Paris-Sud, 91405 Orsay, France
The growing occurrence of bacterial resistance to most of the available antibiotics has underlined an urgent need for the discovery of novel efficacious antibacterial agents. The biosynthesis of the bacterial peptidoglycan represents a collection of highly selective unexploited targets for novel antibacterial drug design.
In the peptidoglycan biosynthetic pathway MurD (UDP-N-acetylmuramoyl-l-alanine:d-glutamate ligase), a three domain bacterial protein, catalyses a highly specific incorporation of the d-glutamate to the cytoplasmic intermediate UDP-N-acetyl-muramoyl-l-alanine (UMA) utilizing ATP hydrolysis to ADP and Pi. The presence of “open” conformations of the MurD enzyme suggests that binding of the ligands UMA and ATP is accompanied by the closure of the C-terminal domain to the catalytically active “closed form” making it a highly complex dynamic target (1). In addition, its proposed sequential enzymatic mechanism was corroborated by structural, biochemical and recently computational studies (2). Structural studies of the N-sulfonyl-glutamic acid inhibitors of MurD enabled the possibility of examining the binding modes of this class of compounds and binding free energy calculations provided valuable information to the lead optimization phase of the drug discovery cycle (3).
Based on the available MurD crystal structures co-crystallised with N-sulfonyl glutamic acid inhibitors, a virtual screening campaign was performed, combining three-dimensional structure-based pharmacophores and molecular docking calculations. A novel class of glutamic acid surrogates—benzene 1,3-dicarboxylic acid derivatives was identified and found to possess dual MurD and MurE inhibitory activity (4).
References:
1. Perdih, A.; Kotnik, M.; Hodoscek, M.; Solmajer, T. Proteins: Structure, Func. Bioinf.,
2007, 68, 243.
2. Perdih, A.; Hodoscek, M.; Solmajer, T. Proteins: Structure, Func. Bioinf., 2009, 74, 744.
3. Perdih, A.; Bren, U.; Solmajer, T. J. Mol. Model., 2009, 15, 983.
4. Perdih, A.; Kovač, A.; Wolber, G.; Blanot, D.; Gobec, S.; Solmajer, T. Biorg. Med. Chem. Lett., 2009, 19, 2668.
Computer Models for the Optimisation of Tissue Transglutaminase Inhibitors
Dan Rathbone, Alexandre Mongeot, Russell Collighan and Martin Griffin
School of Life & Health Sciences, Aston University, Birmingham, UK
As part of a drug design and development programme funded by the Marie Curie Research and Training Network we needed to optimise an in silico model for tissue transglutaminase 2 (TG2). We have prepared and tested several dipeptide analogue suicide inhibitors of TG2 and used the biological test data to guide the optimisation of the active site model via docking and molecular dynamics studies. An initial TG2 protein model was constructed from the published coordinates of a proposed active from of TG2 containing a covalently-bound pentapeptide inhibitor (PDB ID 2Q3Z.pdb). Missing residues were added and the peptidick inhibitor was deleted. A key assumption was that for a compound to be active as a suicide inhibitor, it must be able to dock such that the key electrophilic carbon of its warhead resides close to the S- of CYS277 in the active site. Docked TG2-ligand complexes of various active compounds, where the warhead-CYS277 S- distance (d) was less than 4 Å, were subjected to molecular dynamics simulations (Amber 9; explicit water, 300K, constant pressure) to allow further refinement of the model. The most successful model produced, in terms of docking good inhibitors and discriminating against poor inhibitors, was used for all further TG2 protein-ligand docking studies. A set of irreversible inhibitors covering an IC50 range from 4 to 1000 µM was docked into the refined TG2 protein model (CAChe WorkSystem Pro, Fujitsu Ltd; both ligand and active site side-chains flexible). Two sets of criteria were used to predict the potency of the docked compounds and thus to assess the TG2 model. The compounds were evaluated as follows: Predicted to be good irreversible inhibitors if the docking score was less than -600 Kcal/mol and d was less than 4.2 Å; Predicted to be poor irreversible inhibitors if the docking score was greater than -500 Kcal/mol and d was greater than 5.1 Å or the molecule docked with the warhead pointing away from CYS277. The model correctly assessed the extremes of potency, i.e. most of the potent (9/11; IC50 < 10 µM) and the weak (8/9; IC50 > 20 µM) inhibitors. There remains, however, a hinterland of compounds lying between IC50 10 – 20 µM where the model was not predictive. Our previously published SAR data for these compounds suggested that there might be two different binding modes to the enzyme. (Griffin et al, Bioorganic & Medicinal Chemistry Letters, 2008, 18, 5559–5562) A corresponding pattern of binding is seen in the docking studies presented here. The model provides a reasonable discrimination between potent TG2 inhibitors and inactive compounds and this computer model is being used to design further inhibitors of the enzyme.
|
|
Virtual Screening for Inhibitors of D-Alanine: D-Alanine Ligase
B. Vehar, J. Konc, N. Carl, D. Janezic
National Institute of Chemistry, Hajdrihova 19, SI-1001 Ljubljana, Slovenia
The appearance of drug-resistant bacteria is a strong motive for the development of new antimicrobial agents. A well validated target for antibacterial therapy is the system of enzymes responsible for the construction of peptidoglycan, an essential component of the bacterial cell wall which provides the structural integrity necessary for bacterial cells to resist internal osmotic pressure. The terminal dipeptide, D-Ala-D-Ala, of the peptidoglycan precursor UDPMurNAc-pentapetide is a crucial building block involved in peptidoglycan cross-linking. It is synthesized in the bacterial cytoplasm by the enzyme D-alanine:D-alanine ligase (Ddl).
In Escherichia coli and Salmonella typhimurium, two Ddl isoforms, DdlA and DdlB exist with a similar kinetic characteristics and substrate specificity. Since both show similar susceptibility to known inhibitors and crystal structures only of the wild-type E. coli DdlB and its Y216F mutant derivate have been reported, we focused our attention on the DdlB isoform.
Structure-based virtual screening of the NCI Diversity Set of almost 2000 compounds was performed with a DdlB isoform from Escherichia coli using the computational tool AutoDock 4.0. AutoDock has a free-energy scoring function based on a linear regression analysis of a set of diverse protein-ligand complexes with known inhibition constants. This scoring function uses the AMBER force field to estimate the free energy of binding of a ligand to its target. A hybrid global-local evolutionary algorithm is used to search the phase space of the ligand-macromolecule system.
In a preliminary docking study of DdlB with Mg2+ ions present in the active site we observed that the steric obstructions imposed by these ions prevent most of the compounds from the NCI Diversity Set from successfully fitting into the binding cavity, so it was elected to remove them from the binding cavity prior to docking. The NCI Diversity Set was thus docked to the DdlB active site from which all heteroatoms had been removed. Docking results were obtained as a ranked list of structures based on their mean estimated binding free energies. The rank of each compound was determined by the calculated average binding free energy of the most populated cluster.
The 130 best ranked compounds from this screen were tested in a biochemical assay for their inhibition of E. coli DdlB. Three compounds were identified that inhibit the enzyme with Ki values in micromolar range. To confirm the potential of these compounds for further development we evaluated their in vitro antimicrobial activities. Hit compounds NSC86005 and NSC176327 are ATP competitive inhibitors with Ki values in low micromolar range and the compound NSC130813 inhibits the enzyme in a noncompetitive manner. NSC130813 and NSC176327 possess antibacterial activities against Gram-positive and Gram-negative bacteria and are therefore promising starting points for further optimization.
Using Cheminformatics to Design Anti-cancer Drugs
Leyte Winfield, PhD, Assistant Professor of Organic Chemistry, Spelman College, 350 Spelman Lane, SW Box 231, Atlanta, GA 30314, USA
With cancer-related fatalities being the second leading cause of death in the US, understanding the activity of effective chemotherapeutic agents is critical to addressing prostate and other cancers. Novel benzimidazole compounds have been designed with the goal of understanding the structure-activity relationship of potential anti-cancer agents. The compounds displayed anti-proliferative activity, and several cheminformatic methods have been utilized to explain their plausible mechanism of action. The QSAR and docking models will be presented.
|
|
|
|
|
|
|
|
