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Kenno Vanommeslaeghe

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MacKerell Lab
Computer-Aided Drug Design Center

University of Maryland, Baltimore

20 Penn Street
HSF-II Rm. 631
Baltimore, MD 21201

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Present Research

The CHARMM General Force Field

Structure and graph of water interactions from 2010 CGenFF paper Empirical force fields20 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 force field is widely used for simulating biomolecular systems, being capable of representing proteins, nucleic acids, lipids and carbohydrates.22 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 tutorials that take the reader through the parametrization process in a step-by-step fashion.
Scheme that was used as graphical abstract for 2012 atom typer paper
Structure and graphs of charge fitting and dihedral scan from 2012 parameter and charge asssignment paper
Following the first release of CGenFF, significant improvements in the force field's coverage of chemical space have been made15,29 and virtual particles were introduced to better capture halogen bonds.31 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 deterministic and based on a programmable decision tree, making it easy to implement complex atom typing rules and to update the atom typing scheme as the force field grows.16 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.17

CGenFF main page


Automatic fitting of Molecular Mechanics parameters

Color graph of how bias affects a typical ill-conditioned pair of dihedrals, from 2015 least-squares parameter fitting paper Automated methods for force field parametrization have attracted renewed interest of the community, but the robustness issues associated with the often ill-conditioned nature of parameter optimization have been vastly underappreciated in the recent literature. We developed a Linear Least Squares (LLS) procedure that is able to simultaneously fit all the bonded parameters in a Class I force field and includes a novel restraining strategy that overcomes robustness issues in the LLS fitting of bonded parameters while minimally impacting the fitted values of well-behaved parameters.26 The same procedure was also used for the fitting of the bond-charge increments in the next release of the CGenFF program, illustrating the method's potential for robustly solving general LLS problems beyond force field parametrization. The fitting part of the methodology was implemented in a C program named "lsfitpar" is available to the community under the Affero GPL. It hoped it will become an important part of the sprawling ecosystem of automatic parametrization interfaces. Future directions include further automation, validation of the methodology for the purpose of charge fitting, testing of its ability to use Monte Carlo conformational sampling data and extending the program's feature set.


Mimetics of secondary structure elements in proteins

Superposition of Bim BH3 helix with its mimic #14 from the 2013 Jung et al paper, with Mcl-1 in the background Helical wheel with side chains interacting with protein My first contact with peptidomimetics mimicking specific secondary structure motifs was in the Dirk Tourwé lab, where this was a major research topic, and where I assisted in conformational studies aimed at determining the β-turn propensity of 4-Amino-1,2,4,5-tetrahydro-2-benzazepin-3-ones and derivatives.5 Several years later, when working in the MacKerell lab, I became involved in Steven Fletcher's research on α-helix and β-sheet mimetics. In this context, I assisted in the design of oligoamide-foldamer-based α-helix mimetics that target the interaction of the BCL-xL oncoprotein with the pro-apoptotic BAK protein,13,18 as well as the design of a 1,2-diphenylacetylene-based scaffold for amphipathic α-helix mimetics with potential applications in binding the Mcl-1 oncoprotein.19 Work on a β-sheet mimetic with therapeutic potential against cancer through a different mechanism is also in progress.


Inhibition of the BCL6/SMRT interaction

Centers of spheres resulting from binding response calculations on BCL6, overlayed with 79-6 at its X-ray position Interaction between BCL6 arginines and 79-6 carboxylates 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.30


Past Research

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.7,8,10,12


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.


Interplay between stacking and hydrogen bonding in nucleic bases

See reference 6.

Inhibition of Histone Deacetylase (HDAC)

See references 1, 2, 3 and 4.


Publications & References

My Google Scholar profile


31. I. S. Gutiérrez, F.-Y. Lin, K. Vanommeslaeghe, J. A. Lemkul, K. A. Armacost, C. L. Brooks III, A. D. MacKerell Jr., Parametrization of halogen bonds in the CHARMM general force field: Improved treatment of ligand-protein interactions, Bioorg. Med. Chem. 2016, 24, 4812-4825. DOI:10.1016/j.bmc.2016.06.034 .

30. M. G. Cardenas, W. Yu, W. Beguelin, M. R. Teater, H. Geng, R. L. Goldstein, E. Oswald, K. Hatzi, S.-N. Yang, J. Cohen, R. Shaknovich, K. Vanommeslaeghe, H. Cheng, D. Liang, H. J. Cho, J. Abbott, W. Tam, W. Du, J. P. Leonard, O. Elemento, L. Cerchietti, T. Cierpicki, F. Xue, A. D. MacKerell Jr., A. M. Melnick, Rationally designed BCL6 inhibitors target activated B cell diffuse large B cell lymphoma, J. Clin. Invest. 2016, 126, 3351-3362. DOI:10.1172/JCI85795 .

29. Y. Xu, K. Vanommeslaeghe, A. Aleksandrov, A. D. MacKerell Jr., L. Nilsson, Additive CHARMM Force Field for Naturally Occurring Modified Ribonucleotides, J. Comput. Chem. 2016, 37, 896-912. DOI:10.1002/jcc.24307 .

28. C. Domene, C. Jorgensen, K. Vanommeslaeghe, C. J. Schofield, A. D. MacKerell Jr., Quantifying the binding interaction between the hypoxia-inducible transcription factor and the von Hippel Lindau suppressor, J. Chem. Theory Comput. 2015, 11, 3946-3954. DOI:10.1021/acs.jctc.5b00411 .

27. C. Jorgensen, L. Darre, K. Vanommeslaeghe, K. Omoto, D. Pryde, C. Domene, In-silico identification of PAP-1 binding sites in the Kv1.2 potassium channel, Mol. Pharmaceutics 2015, 12, 1299-1307. DOI:10.1021/acs.molpharmaceut.5b00023 .

26. K. Vanommeslaeghe, M. Yang, A. D. MacKerell Jr., Robustness in the fitting of Molecular Mechanics parameters, J. Comput. Chem. 2015, 36, 1083-1101. DOI:10.1002/jcc.23897 .

25. S. Jo, X. Cheng, S. M. Islam, L. Huang, H. Rui, A. Zhu, H. S. Lee, Y. Qi, W. Han, K. Vanommeslaeghe, A. D. MacKerell Jr., Benoît Roux, W. Im, CHARMM-GUI PDB Manipulator for Advanced Modeling and Simulations of Proteins Containing Nonstandard Residues, Adv. Protein Chem. Struct. Biol. 2014, 96, 235-265. DOI:10.1016/bs.apcsb.2014.06.002 .

24. N. R. Kern, H. S. Lee, E. L. Wu, S. Park, K. Vanommeslaeghe, A. D. MacKerell Jr., J. B. Klauda, S. Jo, W. Im, Lipid-Linked Oligosaccharides in Membranes Sample Conformations that Facilitate Binding to Oligosaccharyltransferase, Biophys. J. 2014, 107, 1885-1895. DOI:10.1016/j.bpj.2014.09.007 .

23. S. S. Mallajosyula, K. Vanommeslaeghe, A. D. MacKerell Jr., Perturbation of Long-Range Water Dynamics as the Mechanism for the Antifreeze Activity of Antifreeze Glycoprotein, J. Phys. Chem. B 2014, 118, 11696-11706. DOI:10.1021/jp508128d .

22. K. Vanommeslaeghe, A. D. MacKerell Jr., CHARMM additive and polarizable force fields for biophysics and computer-aided drug design, Biochim. Biophys. Acta 2015, 1850, 861-871. DOI:10.1016/j.bbagen.2014.08.004 .

21. P. Kumar, S. A. Bojarowski, K. N. Jarzembska, S. Domagała, K. Vanommeslaeghe, A. D. MacKerell Jr., P. M. Dominiak, A Comparative Study of Transferable Aspherical Pseudoatom Databank and Classical Force Fields for Predicting Electrostatic Interactions in Molecular Dimers, J. Chem. Theory Comput. 2014, 10, 1652-1664. DOI:10.1021/ct4011129 .

20. K. Vanommeslaeghe, O. Guvench, A. D. MacKerell Jr., Molecular Mechanics, Curr. Pharm. Des. 2014, 20, 3281-3292. DOI:10.2174/13816128113199990600 .

19. K.-Y. Jung, K. Vanommeslaeghe, M. E. Lanning, J. L. Yap, C. Gordon, P. T. Wilder, A. D. MacKerell Jr., S. Fletcher, Amphipathic α-helix mimetics based on a 1,2-diphenylacetylene scaffold, Org. Lett. 2013, 15, 3234-3237. DOI:10.1021/ol401197n .

18. X. Cao, J. L. Yap, M. K. Newell-Rogers, C. Peddaboina, W. Jiang, H. T. Papaconstantinou, D. Jupitor, A. Rai, K.-Y. Jung, R. P. Tubin, W. Yu, K. Vanommeslaeghe, P. T. Wilder, A. D. MacKerell Jr., S. Fletcher, R. W. Smythe, The novel BH3 alpha-helix mimetic JY-1-106 induces apoptosis in a subset of cancer cells (lung cancer, colon cancer and mesothelioma) by disrupting Bcl-xL and Mcl-1 protein-protein interactions with Bak, Mol. Cancer 2013, 12:42. DOI:10.1186/1476-4598-12-42 .

17. K. Vanommeslaeghe, E. P. Raman, A. D. MacKerell Jr., Automation of the CHARMM General Force Field (CGenFF) II: Assignment of bonded parameters and partial atomic charges, J. Chem. Inf. Model. 2012, 52, 3155-3168. DOI:10.1021/ci3003649 .

16. K. Vanommeslaeghe, A. D. MacKerell Jr., Automation of the CHARMM General Force Field (CGenFF) I: bond perception and atom typing, J. Chem. Inf. Model. 2012, 52, 3144-3154. DOI:10.1021/ci300363c .

15. W. Yu, X. He, K. Vanommeslaeghe, A. D. MacKerell Jr., Extension of the CHARMM General Force Field to Sulfonyl-Containing Compounds and Its Utility in Biomolecular Simulations, J. Comput. Chem. 2012, 33, 2451-2468. DOI:10.1002/jcc.23067 .

14. E. P. Raman, K. Vanommeslaeghe, A. D. MacKerell Jr., Site-Specific Fragment Identification Guided by Single-Step Free Energy Perturbation Calculations, J. Chem. Theory Comput. 2012, 8, 3513-3525. DOI:10.1021/ct300088r .

13. J. L. Yap, X. B. Cao, K. Vanommeslaeghe, K. Y. Jung, C. Peddaboina, P. T. Wilder, A. Nan, A. D. MacKerell Jr., W. R. Smythe, S. Fletcher, Relaxation of the rigid backbone of an oligoamide-foldamer-based α-helix mimetic: identification of potent Bcl-xL inhibitors, Org. Biomol. Chem. 2012, 10, 2928-2933. DOI:10.1039/c2ob07125h .

12. A. Krishtal, D. Geldof, K. Vanommeslaeghe, C. Van Alsenoy, P. Geerlings, Evaluating London Dispersion Interactions in DFT: A Nonlocal Anisotropic Buckingham-Hirshfeld Model, J. Chem. Theory Comput. 2012, 8, 125-134. DOI:10.1021/ct200718y .

11. O. Guvench, S. S. Mallajosyula, E. P. Raman, E. Hatcher, K. Vanommeslaeghe, T. J. Foster, F. W. Jamison, A. D. MacKerell Jr., CHARMM Additive All-Atom Force Field for Carbohydrate Derivatives and Its Utility in Polysaccharide and Carbohydrate-Protein Modeling, J. Chem. Theory Comput. 2011, 7, 3162-3180. DOI:10.1021/ct200328p .

10. A. Krishtal, K. Vanommeslaeghe, D. Geldof, C. Van Alsenoy, P. Geerlings, Importance of anisotropy in the evaluation of dispersion interactions, Phys. Rev. A 2011, 83:024501. DOI:10.1103/PhysRevA.83.024501 .

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., CHARMM General Force Field (CGenFF): A force field for drug-like molecules compatible with the CHARMM all-atom additive biological force fields, J. Comput. Chem. 2010, 31, 671-690. DOI:10.1002/jcc.21367 .

8. A. Krishtal, K. Vanommeslaeghe, A. Olasz, T. Veszprémi, C. Van Alsenoy, P. Geerlings, Accurate interaction energies at DFT level by means of an efficient dispersion correction, J. Chem. Phys. 2009, 130:174101. DOI:10.1063/1.3126248 .

7. A. Olasz, K. Vanommeslaeghe, A. Krishtal, T. Veszprémi, C. Van Alsenoy, P. Geerlings, The use of atomic intrinsic polarizabilities in the evaluation of the dispersion energy, J. Chem. Phys. 2007, 127:224105. DOI:10.1063/1.2805391 .

6. K. Vanommeslaeghe, P. Mignon, S. Loverix, D. Tourwé P. Geerlings, Influence of stacking on the hydrogen bond donating capacity of nucleic bases, J. Chem. Theory Comput. 2006, 2, 1444-1452. DOI:10.1021/ct600150n .

5. K. Van Rompaey, S. Ballet, C. Tömböly, R. De Wachter, K. Vanommeslaeghe, M. Biesemans, R. Willem, D. Tourwé, Synthesis and evaluation of the β-turn properties of 4-amino-1,2,4,5-tetrahydro-2-benzazepin-3-ones and of their spirocyclic derivative, Eur. J. Org. Chem. 2006, 2899-2911. DOI:10.1002/ejoc.200500996 .

4. K. Vanommeslaeghe, S. Loverix, P. Geerlings, D. Tourwé, DFT-based Ranking of Zinc-chelating Groups in Histone Deacetylase Inhibitors, Bioorg. Med. Chem. 2005, 13, 6070-6082. DOI:10.1016/j.bmc.2005.06.009 .

3. K. Vanommeslaeghe, F. De Proft, S. Loverix, D. Tourwé, P. Geerlings, Theoretical study revealing the functioning of a novel combination of catalytic motives in Histone Deacetylase, Bioorg. Med. Chem. 2005, 13, 3987-3992. DOI:10.1016/j.bmc.2005.04.001 .

2. K. Vanommeslaeghe, C. Van Alsenoy, F. De Proft, J. C. Martins, D. Tourwé, P. Geerlings, Ab Initio study of the binding of Trichostatin A (TSA) in the active site of Histone Deacetylase Like Protein (HDLP), Org. Biomol. Chem. 2003, 1, 2951-2957. DOI:10.1039/b304707e .

1. K. Vanommeslaeghe, G. Elaut, V. Brecx, P. Papeleu, K. Iterbeke, P. Geerlings, D. Tourwé, V. Rogiers, Amide analogues of TSA: synthesis, binding mode analysis and HDAC inhibition, Bioorg. Med. Chem. Lett. 2003, 13, 1861-1864. DOI:10.1016/S0960-894X(03)00284-1 .

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Last updated Tuesday, the 28th of February 2017