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09.30 - 10.00 |
Registration
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| 10.00 - 10.05 |
Introduction (David Manallack)
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| 10.05 - 10.10 | Introduction to the RSC MMG (Darren Flower) | |
| 10.10 - 10.30 | Stuart Rison | The evolution and structural anatomy of small molecule metabolism pathways in Escherichia coli |
| 10.30 - 10.50 | Kelly Paine | BACBIX |
| 10.50 - 11.10 | Paul Watson | Knowledge based functional group similarity |
| 11.10 - 11.30 | Miklos Vargyas | Three-dimensional flexible database searching using interval analysis |
| 11.30 - 11.50 | Anselm Horn | Vancomycin dimer formation and binding to a model peptide - A theoretical study |
| 11.50 - 12.10 | Yana Dobrogorskaya | Quantum chemical studies of reactions of the 1,1'-azobiscarbamide with the zinc finger domains in the HIV-1 nucleocapsid protein (NCp7) |
| 12.10 - 13.30 |
Lunch
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| 13.30 - 13.50 | Szabolcs Csepregi | The SPA docking method |
| 13.50 - 14.10 | Krisztina Boda | Generating synthetically accessible ligands by de novo design |
| 14.10 - 14.30 | Christina Grindon | Porting the AMBER forcefield to LAMMPS - massively parallel molecular dynamics simulations of DNA |
| 14.30 - 14.50 | M.B. Ulmschneider | Computer simulations of the 7 TM a-helices of Bacteriorhodopsin |
| 14.50 - 14.20 |
Coffee
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| 15.20 - 15.40 | Andrew Worgan | STOP! You're killing me! - (Mechanistic QSAR investigations of a new acute environmental toxicological assay) |
| 15.40 - 16.00 | Maria Hinaje | The mode of action of destruxins, and their structure/activity relationship (SARs) |
| 16.00 - 16.15 | Presentation Skills (David Manallack) | |
| 16.15 - 16.30 | Mystery Talk (Mystery Speaker) | |
| 16.30 - 16.40 |
Judges deliberations
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| 16.40 - 17.00 |
Prizes
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| 17.00 onwards |
Networking down the pub
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We assigned putative structural assignments to over three-quarters of the gene products in E. coli SMM pathways using a number of strategies (including BLAST, PSI-BLAST and SAM-T99 HMM searches).
From these assignments we clarified the domain structure and, evolutionary relationships between SMM genes. In turn, we determined the extent to which domains are duplicated within and across pathways and have combined in enzymes. We also investigated which functional features are conserved in families of homologues.
We consider the evolution of enzymes and pathways in the light of this new information.
Our focus at the Edward Jenner Institute is on bacterial virulence factors as potential vaccine targets. Areas of homology between related proteins can indicate a shared function, and fingerprinting methodologies like PRINTS [2] can elucidate protein families based on key conserved sequence areas, or "motifs". Used in conjunction with known structures, this approach to classifying proteins is both straightforward and simple. Weak homologues can be grouped with relative ease, and there is a considerable reduction in background noise. So far, approx. 50 entries have been submitted to the PRINTS database, with more expected. In addition, an in-house collaboration analysing Pseudomonas proteins has produced at least two potential proteins for further in vitro work.
The title also lends itself to a planned online database of virulence factors. BACBIX will be a web-based collection of protein entries and in silico tools dealing specifically with microbial proteins of pathogenic importance. Another collaboration with Dr. T. Attwood's group at Manchester will generate an entry method designed to create PRINTS fingerprints automatically. Speculative modelling of bacterial porins [3] essential for membrane transport is also envisaged.
To summarise, there is a need for novel in silico tools to analyse the wealth of microbial genome sequence available at present. Quick identification of potential vaccine/drug targets through the use of bacterial bioinformatics (BacBix) coupled to more traditional methods should prove successful. We have identified novel targets, and are in the process of carrying out further work on these moieties.
References
1. Hueck, C.J (1998) Microbiol Mol Biol Rev 329-433
2. Atwood, T.K., Croning, M.D.R., Flower, D.R., Lewis, A.P., Mabey, J.E., Scordis,
P., Selley, J. and Wright W. (2000) Nucleic Acids Res 28:225-227.
3. Koebnik, R., Locher, K.P. and Van Gelder, P. (2000) Mol Microbiol 37:239-253.
Protein-ligand interactions are fundamental for any sort of biological activity. Therefore calculation of the similarity between groups based upon non-bonded contacts is a useful measure of the ability of one functional group to act as a biosteric replacement for another.
IsoStar is the definitive database of experimental and theoretical information on non-bonded interactions. The experimental information contained within the database is derived from the Cambridge Structural Database (CSD) and the Protein Data Bank (PDB). Information is presented in the form of scatterplots that show the spatial distribution of non-bonded contacts between two chemical groupings.
Using the IsoStar scatterplots, the three-dimensional similarity of the functional groups (central groups) contained within the database are calculated based upon the spatial distribution and density of the contact groups. Scatterplots can be converted to propensity maps for specific probe atoms in a contact group. The propensity maps are compared by superimposing the central groups in a chemically relevant fashion. The similarity is calculated based upon the degree of overlap of the propensity maps. This procedure has been carried out for all the central groups in IsoStar.
The results of these similarity calculations are validated using the Bioster database by comparing the similarities for known biosteric functional groups with those for random pairs of functional groups. The results show a marked difference in the similarities of the random pairs versus the known biosteric pairs, indicating that the IsoStar propensity maps are a good descriptor of three-dimensional similarity.
A method that uses an exhaustive analysis of the conformational space of a ligand molecule, and is capable to delivering a guarantee of this type will be presented. This method, based on interval analysis (an evolving field of mathematics) uses a continuous representation and search technique without the need of particular discrete sample points. As a result, failure to find a given pharmacophoric pattern in a ligand means, that no such conformation does exists. On the other hand, if there are suitable conformations, then all of them are found.
The effectiveness of this novel method will be demonstrated using three and four point pharmacophores.
Multiple drug resistance of Gram-positive bacteria is a serious problem in current medicinal treatment of infections. Thus, vancomycin glycopeptide antibiotics have gained in clinical importance. Experimental results lead to the assumption that the dimerisation of vancomycin (V1:V2) is responsible for enhanced activity due to a stronger binding of the terminal -L-Lys-D-Ala-D-Ala sequence of the precursor peptide of the growing bacterial cell wall.[1,2]
Because the exact nature of the binding mechanism is not known - electronic polarisation is thought to play a key role - MO calculations using the semiempirical program package VAMP[3] were carried out in order to study the influence of the dimerisation upon peptide binding.
Our calculations show that the dimerisation induces considerable polarisation in the peptide binding area of the free monomeric unit by influencing one specific interaction site. To investigate the binding mechanism in detail a model peptide (Ac-D-Ala-D-Ala; AcAA) was chosen and docked to vancomycin.
Furthermore, a discussion of solvation effects upon of vancomycin monomer (V2), dimer (V1:V2) and dimer peptide complex (V1:V2:AcAA) will complement the analysis of the gas phase electrostatics.
Vancomycin monomer and model peptide Ac-D-Ala-A-Ala
References
[1] Mackay, J.P.; Gerhard, U.; Beauregard, D.A.; Maplestone, R.A.; Williams,
D.H. J.Am.Chem.Soc. 1994, 116, 4573-4580
[2] Mackay, J.P.; Gerhard, U.; Beauregard, D.A.; Westwell, M.S.; Searle, M.S.;
Williams, D.H. J.Am.Chem.Soc. 1994, 116, 4581-4590 and references therein.
[3] Clark, T.; Alex, A.; Beck, B.; Chandrasekhar, J.; Gedeck, P.; Horn, A.;
Hutter, M.; Martin, B.; Rauhut, G.; Sauer, S.; Schindler, T.; Steinke, T. VAMP
7.5, Erlangen, 2000.
An empirical scoring function and a flexible molecular docking method based on it are being developed at the University of Leeds.
The scoring function contains elements describing Van der Waals, hydrogen bonding, metal ion bonding, hydrophobic, rotatable bond entropy and dihedral strain energy terms. The coefficients of different terms are obtained by regression analysis based on a training set of 50 protein-ligand complexes from The Protein Data Bank. The scoring function also comprises a novel building of a hydrogen and metal bonding framework within the receptor-ligand complex by rotating the terminal rotatable bonds to achieve optimal hydrogen and metal bonding geometries.
The docking method is based on a novel simulated annealing minimization algorithm called Systematic Population Annealing (SPA) which has been developed in our laboratory and applied to the scoring function above. During the optimization the ligand is treated flexible by rotating around internal single bonds and the receptor is kept rigid, apart from the terminal bonds above.
The presentation will provide an overview of the scoring and docking methods of SPA, will also show some results and compare it to other docking methods.
One of the deficiencies of De Novo molecular structure design programs is that after a time and memory consuming structure generation process, many of the solutions produced may not be synthetically accessible.
The approach used in Synthetic Sprout (SYNSPROUT), a new variant of SPROUT, is to build synthetic constraints into the structure generation process by starting with a library of readily available starting materials, which are used in the initial docking and building up process. This process only permits joins which correspond exactly to chemical reactions from a user created knowledge base.
The current version of the program works well with medium sized databases of starting materials. For large databases such as ACD, the combinatorial nature of the structure generation process means that even the recently developed parallel version would be too slow and work in hand is geared to overcoming this problem.
The presentation will provide an overview of the main concepts and problems together with examples of the system in action.
LAMMPS (Large Atomic/Molecular Massively Parallel Simulator) [3,4] is a parallel MD code with accurate treatment of long-range electrostatic interactions, and the potential to generate simulations up to the 100ns timescale with current supercomputers. We have successfully ported the AMBER forcefield for DNA to LAMMPS and performed initial bench marking studies.
Short (10ps) simulations indicate that the code scales linearly with increasing number of processors (tested to 64), in contrast to AMBER which performs much more poorly in parallel situations.
Static energy analysis of conformations of the DNA dodecamer CTTTTGCAAAAG shows excellent agreement between AMBER and LAMMPS. MD simulations (up to 3ns) show stable dynamics and RMS atomic fluctuations in line with previous AMBER results on the same sequence. However, on closer inspection of the trajectory data there are some inconsistancies between AMBER and LAMMPS. The LAMMPS simulations show lower entropies than AMBER and localised motions are found from Principal Component Analysis (PCA). We attribute this to an over-tight temperature scaling parameter. More simulations are being carried out using different temperature scaling parameters to find an optimal value which gives results more comparable to AMBER. Comparable entropies and PCA results will confirm how well the overall dynamics of the DNA is preserved between the two simulation methods.
References
[1] Sherer EC, Harris SA, Soliva R, Orozco M, Laughton CA, J.Am.Chem.Soc, 1999,
121, p5981-5991.
[2] Cubero E, Sherer EC, Luque FJ, Orozco M, Laughton CA, J.Am.Chem.Soc, 1999,
121, p8653-8654.
[3] Plimpton S.J, Hendrickson B.A, J.Comp.Chem, 1996, 17, p326-337.
[4] Plimpton S.J, Pollock R, Stevens M, Proc of the 8th SIAM Conference on Parallel
Processing for Scientific Computing, Minneapolis,MN. March 1997.
Approximately 90 compounds representing several mechanisms of toxicological action have been tested using this assay and the data analysed using mechanistic Quantitative Structure-Activity Relationships (QSARs).
QSAR analysis has shown that, in general, the 15 minute acute algal assay under development produces results which may be quantified using traditional physico-chemical parameters such as Log KOW and ELUMO and produces results similar to those generated by other toxicological methodologies.
It has also been shown that each mechanism of action produces QSAR equations
using between 10 and 20 compounds which are comparable to other QSAR studies
using many more compounds.
