MacKerell lab home page
Kenno Vanommeslaeghe's research home page
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Present Research
Empirical force fields are presently the only computational methods fast enough to routinely perform molecular dynamics simulations of large chemical systems, such as proteins, on relevant time scales. The CHARMM set of force fields is widely used for simulating biomolecular systems, being capable of representing proteins, nucleic acids, lipids and carbohydrates. The CHARMM General Force Field (CGenFF) adds to this a wide range of chemical groups present in biomolecules and drug-like molecules including a large number of heterocyclic scaffolds. CGenFF thus makes it possible to perform "all-CHARM" simulations on drug-target interactions thereby extending the utility of CHARMM force fields to medicinally relevant systems. As a validation, CGenFF was shown to accurately reproduce geometric, vibrational and energetic data, including interactions with water, as well as satisfactorily reproducing the experimental molecular volumes for 111 pure solvents and heats of vaporization of 95 molecules.9
The parametrization philosophy behind the force field focuses on quality at the expense of transferability, with the implementation concentrating on an extensible force field. This is testified by our tutorial that takes the reader through the parametrization of the model systems pyrrolidine and 3-phenoxymethylpyrrolidine.
Following the first release of CGenFF, significant improvements in the force field's coverage of chemical space have been made. In parallel, the CGenFF program was developed. This program performs atom typing and assignment of parameters and charges by analogy in a fully automated fashion. The atom typer is rule-based and programmable, making it easy to implement complex atom typing rules and to update the atom typing scheme as the force field grows. Assignment of bonded parameters is based on substituting atom types in the definition of the desired parameter. A penalty is associated with every substitution and the existing parameter with the lowest total penalty is chosen as an approximation for the desired parameter; the "penalty score" is returned to the user as a measure for the accuracy of the approximation. Charges are assigned using an extended bond-charge increment scheme that is able to capture short- and medium-range inductive and mesomeric effects.10
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Cyberenvironment for MM and SE parameter optimization
The ParamChem project (full title: "Extensible Cyberenvironments for Empirical and Semiempirical Hamiltonian Parameter Optimization and Dissemination") is an NSF sponsored initiative to develop an integrated cyber environment to address the simulation needs of molecular sciences. The proposed infrastructure will provide reference data organizers and generators as well as workflows for automatic parameterization of Molecular Mechanics (MM) Force Fields as well as Semi-Empirical (SE) methods. A comprehensive utility for the optimization and testing of parameters in Force Fields and Semi-Empirical models will be set up, allowing experts in these fields to develop novel models of higher accuracy in shorter time periods. These models can then be made available to the computational chemistry community at large via a parameter database. This will make it easier for computational chemists to find an appropriate model for the system they are studying, and, if necessary, to extend the model to novel functional groups using automated utilities. Currently, we're working on automatic force field parameterization in the context of CGenFF. In the long run, many other Molecular Mechanics as well as Semi-Empirical models will be integrated. From this, a wide range of parameters encompassing biological, organic and inorganic species will be accessible for direct use or further optimization.
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Inhibition of the BCL6/SMRT interaction
This project is a collaborative effort involving the Molecular Biology group of Ari Melnick, the X-ray Crystallography and Structural Biology group of Gil Privé, the Organic Chemistry group of Andy Coop and Alex MacKerell's CADD center. The aim of this collaboration is to develop novel anti-cancer drugs that target the BCL6 oncogenic transcriptional repressor. As part of Alex MacKerell's group, my role consists mainly of assisting in the discovery of new leads by means of in silico screening of libraries of commercially available compounds. In this context, we employ both ligand-based and structure-based drug design strategies. In other words, we identify new leads by their chemical homology to known inhibitors as well as their binding affinity to relevant parts of BCL6, as predicted by docking studies.
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Post-HF and post-DFT evaluation of the dispersion energy
Dispersion interactions play a fundamental role in physics, chemistry and biology, where they appear, for example, in π-π stacking interactions contributing to the structure, catalysis and inhibition, of proteins. Therefore, a theoretical description of these interactions would be desirable. This is not easily accomplished because dispersion interactions can only be described at a level of theory that includes electron correlation. Since current Density Functional Theory (DFT) methods do not correctly reproduce disperion interactions, at least second order Møller-Plesset (MP2) theory must be used. However, systems with a biologically relevant size are currently far beyond the computational reach of this method. Therefore, our goal is to include a "semi-empirical" dispersion correction on top of the DFT energy. This is accomplished by combining a recent approximation scheme by Becke and Johnson with a Hirschfeld-type scheme for partitioning molecular polarizabilities into atomic contributions.7,8
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Past Research
Interplay between stacking and hydrogen bonding in nucleic bases
See reference 6.
Inhibition of Histone Deacetylase (HDAC)
See references 1, 2, 3 and 4.
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Publications & References
Publications
10. K. Vanommeslaeghe et al., in preparation
9. K. Vanommeslaeghe, E. Hatcher, C. Acharya, S. Kundu, S. Zhong, J. Shim, E. Darian, O. Guvench, P. Lopes, I. Vorobyov, A. D. MacKerell Jr., J. Comput. Chem. 2010, 31, 671-690.
8. A. Krishtal, K. Vanommeslaeghe, A. Olasz, T. Veszprémi, C. Van Alsenoy, P. Geerlings, J. Chem. Phys. 2009, 130, 174101.
7. A. Olasz, K. Vanommeslaeghe, A. Krishtal, T. Veszprémi, C. Van Alsenoy, P. Geerlings, J. Chem. Phys. 2007, 127, 224105.
6. K. Vanommeslaeghe, P. Mignon, S. Loverix, D. Tourwé P. Geerlings, J. Chem. Theory Comput. 2006, 2, 1444-1452.
5. K. Van Rompaey, S. Ballet, C. Tömböly, R. De Wachter, K. Vanommeslaeghe, M. Biesemans, R. Willem, D. Tourwé, Eur. J. Org. Chem. 2006, 2899-2911.
4. K. Vanommeslaeghe, S. Loverix, P. Geerlings, D. Tourwé, Bioorg. Med. Chem. 2005, 13, 6070-6082.
3. K. Vanommeslaeghe, F. De Proft, S. Loverix, D. Tourwé, P. Geerlings, Bioorg. Med. Chem. 2005, 13, 3987-3992.
2. K. Vanommeslaeghe, C. Van Alsenoy, F. De Proft, J. C. Martins, D. Tourwé, P. Geerlings, Org. Biomol. Chem. 2003, 1, 2951-2957.
1. K. Vanommeslaeghe, G. Elaut, V. Brecx, P. Papeleu, K. Iterbeke, P. Geerlings, D. Tourwé, V. Rogiers, Bioorg. Med. Chem. Lett. 2003, 13, 1861-1864.
Other references
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Last updated Monday, the 20th of December 2010