My picture
Kenno Vanommeslaeghe
LinkedIn icon LinkedIn
Google Scholar icon Google Scholar

Analytical Chemistry, Applied Chemometrics
and Molecular Modelling (FABI)

Vrije Universiteit Brussel

Medicine and Pharmacy

Laarbeeklaan 103
B-1090 Brussels


NEWS: I am actively looking for:

For all inquiries, please don't hesitate to contact me using the "Show e-mail" button next to my picture.



General interests and strategy

My general research goal is to develop methods for molecular simulations and apply such methods in drug design projects in collaboration with experimentalists. As such, my research has a fundamental leg consisting of method development and an applied leg wherein I assist my collaborators in answering concrete research questions. To the latter end, a number of collaborations with "neighboring" groups were started, as listed in the section Applied research. This is performed in synergy with fundamental research, as elaborated in the section Fundamental research.


Applied research projects

Modulators for the Cystine/Glutamate Antiporter System xc⁻ (Sxc⁻)

The Cystine/Glutamate Antiporter System xc⁻ (Sxc⁻) belongs to the SLC7 family of plasma membrane transporters and plays an important role in different parts of body, especially in central nervous system. Accordingly, its inhibition has been proposed as therapeutic strategy for several disease states such as cancer-induced bone pain and a number of neurological disorders, some of which are the subject of research led by A. Massie at the NAVI research group. Such research is hindered by a relative scarcity of specific inhibitors for Sxc⁻. Accordingly, the aim of this research project is to design novel selective Sxc⁻ modulators using target-based drug design strategies. A special challenge in this project is that, being a gradient-driven transporter, Sxc⁻ features a low binding affinity for its natural substrates. Consequently, searching for molecules that bind the protein in a similar way as its substrates is not expected to yield strong inhibitors. Instead, we aim to gain detailed knowledge of its transport pathway. This will not only be helpful in guiding our drug design project but also provide fundamental insights into the complex and interesting molecular mechanism of Sxc⁻ as a member of the SLC7 family.

In a first stage of this endeavor, we used a combination of multi-template homology modeling and explicit solvent Molecular Dynamics to obtain four distinct conformations of Sxc⁻.35 A first Virtual Screening round was performed against the inward-open conformation and the resulting 11 compounds were subjected to an in vitro assay in the Neuropharmacology group at the UCLouvain, led by E. Hermans. Follow-up Enhanced Sampling calculations are ongoing using a newly developed method.


Selective glucocorticoid receptor modulators

Numerous biomedical applications have been proposed for the selective modulation of the glucocorticoid receptor, specifically the selective activation or inhibition of its so-called transrepression and transactivation pathways. Through substantial drug design efforts, a number of compounds have been reported that display some degree of selectivity. The present research project aims to zoom in on the different protein-protein interactions that lie at the basis of these pathways, including dimerization. This will allow the identification of strategies to develop highly selective glucocorticoid receptor modulators using target-based drug design methods. In particular, we aim to find: Top

Molecular mechanisms of chiral chromatography

High-Pressure Liquid Chromatography (HPLC) with chiral columns is a rare generally applicable technique for the separation of enantiomers, which is of high and increasing importance in drug discovery, production and analysis. For this reason, it forms a prominent line of research in our FABI research group. Computer models for predicting chiral separations are largely confined to a narrow chemical space defined by their training set.33 In our opinion, the main reasons for this are (1) a relatively limited computational tool chest for characterizing chiral properties of analytes and (2) incomplete insight into the molecular mechanism for the enantioselectivity of common stationary phases such as derivatized amylose and cellulose. The latter point represents a relative lack of fundamental knowledge, which we aim to address by studying the molecular mechanisms of chiral separation on these stationary phases with Molecular Dynamics simulation techniques.39 This study leverages our previously established expertise in force fields for carbohydrates and small organic molecules as well as advanced methods for trajectory analysis. In parallel, we seek to address point (1) above by developing new chiral molecular descriptors that are more strongly anchored in physical properties than the presently available chiral descriptors. A small-scale descriptor development pilot project has been conducted36 and the results of a more elaborate effort will be available soon.38


Fundamental research projects (method development)

Next-generation potential energy functions

Even though a wide variety of Potential Energy Functions (PEF) for molecular mechanics are described in the literature, a large majority of biomolecular simulations are performed using force fields that are based on the relatively crude Class I PEF. Reasons for this are (1) computational cost, (2) the fact that most of the refinements in more sophisticated (in particular class II and III) PEF do not compellingly improve the properties that are commonly studied in life science and (3) the combinatorial explosion of force field parameters associated with extra terms in the PEF, which yields a complex parameter optimization problem with a shortage of relevant target data. Yet, specific shortcomings of the Class I additive PEF have become increasingly obvious in the last decades, prompting the development of potential energy terms for polarization. While still accounting for a minority of biomolecular simulations, the resulting polarizable force fields have enjoyed a steady increase in popularity. In line with points (1) and (3) above, this can for a large part be attributed to the ever increasing availability of computer power and efficient computational methods, which have not only proven instrumental in the parametrization of polarizable force fields, but also mitigate the computational cost of their application. Further along this line of thinking, it is our opinion that polarization is merely the tip of an iceberg of PEF improvements that address real shortcomings in the field and have come within reach through new insights, increased computational power and improved methods. Therefore, we are planning to critically explore the ways current force fields can be improved, with a pragmatic focus on Concerning the last point, it may seem counterintuitive that a more sophisticated force field would be easier to parametrize. However, a large part of the difficulty in the development of present-generation force fields is the careful balancing of errors induced by the PEF's shortcomings, so that properties of interest are accurately reproduced. This specific effort is expected to decrease when using a PEF that has less fundamental shortcoming in the first place. Accordingly, our next-generation potential energy functions are anticipated to facilitate the creation of a standardized and highly automated fitting procedure for "non-standard" chemical entities (which is in high demand).


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.


Improved potentials for nonbonded interaction

In agreement with the research goals outlined above, we set out to map the present field in nonbonded potentials. During this search, an opportunity presented itself to contribute to the state of the art by developing our own potential for nonbonded interactions. Its first iteration, which is the subject of a recently submitted manuscript,37 yields a near-quantitative agreement with high-level QM interaction energies of noble gas dimers and has interesting conceptual implications. However, the model needs to be extended to make it applicable to molecular systems. This will be the subject of future work.


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 tutorialsExternal link icon 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 programExternal link icon 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

My CGenFF page (mainly links to current and archived CGenFF resources)


Efficiently driving conformational changes in biomolecules

In its most basic form, Adaptive Biasing Force (ABF) enhances conformational sampling along a small number of predefined (transition) coordinates while determining the corresponding Potential of Mean Force. Unfortunately, the convergence rate of basic ABF on biomolecules is not always optimal. Multiple strategies and ABF variants have been developed to combat this problem. However, their effective use largely negates the upfront simplicity of the ABF method, requiring pre-existing knowledge of the system as well as some fundamental insight in free energy methods. In the present project, we demonstrated how a previously underappreciated hysteresis mechanism causes basic ABF simulations to fail catastrophically on two real-life biomolecular test cases. We subsequently developed a new ABF variant that effectively addresses this issue without introducing additional parameters or complexity, thereby retaining the simplicity and ease-of-use of basic ABF. We anticipate that the resulting method will be appealing for use by inexperienced operators as well as for the purpose of automating certain types of free energy calculations. A manuscript about this work is in preparation (which is why so few details are given on this website 😜).


Past Research

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 Andrew 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 consisted mainly of assisting in the discovery of new leads by means of in silico screening of libraries of commercially available compounds. In this context, I employed both ligand-based and structure-based drug design strategies. In other words, I identified 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


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


39. F. Ameli, R. Van de Velde, Y. Vander Heyden, D. Mangelings, K. Vanommeslaeghe, manuscript in preparation.

38. J. Peeters, P. De Gauquier, F. Ameli, Y. Vander Heyden, D. Mangelings, K. Vanommeslaeghe, manuscript in preparation.

37. J. Peeters, K. Vanommeslaeghe, A simple model for the Pauli Repulsion with possible utility in QM, MM and Chemical Education, submitted. DOI: 10.26434/chemrxiv-2024-jchct .

36. P. De Gauquier, J. Peeters, K. Vanommeslaeghe, Y. Vander Heyden, D. Mangelings, Modelling the enantiorecognition of structurally diverse pharmaceuticals on O-substituted polysaccharide-based stationary phases, Talanta 2023, 259, 124497. DOI: 10.1016/j.talanta.2023.124497 .

35. T. D. Hang, H. M. Hung, P. Beckers, N. Desmet, M. Lamrani, A. Massie, E. Hermans, K. Vanommeslaeghe, Structural investigation of human cystine/glutamate antiporter System xc⁻ (Sxc⁻) using homology modeling and molecular dynamics, Front. Mol. Biosci. 2022, 126:1064199. DOI: 10.3389/fmolb.2022.1064199 .

34. C. Jeong, R. Franklin, K. J. Edler, K. Vanommeslaeghe, S. Krueger, J. E. Curtis, Styrene-Maleic Acid Copolymer Nanodiscs to Determine the Shape of Membrane Proteins, J. Phys. Chem. B 2022, 126, 1034-1044. DOI: 10.1021/acs.jpcb.1c05050 .

33. P. De Gauquier, K. Vanommeslaeghe, Y. Vander Heyden, D. Mangelings, Modelling approaches for chiral chromatography on polysaccharide-based and macrocyclic antibiotic chiral selectors: A review, Anal. Chim. Acta 2022, 1198, 338861. DOI: 10.1016/j.aca.2021.338861 .

32. L. A. Burns, J. C. Faver, Z. Zheng, M. S. Marshall, D. G. A. Smith, K. Vanommeslaeghe, A. D. MacKerell Jr., K. M. Merz, and C. D. Sherrill, The BioFragment Database (BFDb): An open-data platform for computational chemistry analysis of noncovalent interactions, J. Chem. Phys. 2017, 147:161727. DOI: 10.1063/1.5001028 .

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 .


Last updated Friday, the 5th of April 2024