The 2nd International Conference on Drug Discovery & Therapy: Dubai, February 1 - 4, 2010


Invited Speaker

"In Silico" Drug Design Based on the Modified Semi-Empirical Quantum Chemical PM6 Method
Jindrich Fanfrlik, Agnieszka K. Bronowska, Jan Rezac, and Pavel Hobza
Czech Republic


Ligand-protein docking is broadly used to predict the binding modes and binding affinities of novel drug candidates and as such plays an increasingly important role in rational drug discovery. Protein docking produces a pose generation, i.e. a list of various positions where the ligand can be bound. In the second step of the docking procedure, the binding free energies are estimated by using scoring functions. The design of the scoring functions represents without a doubt a critical step in the whole process, where a deep understanding of noncovalent interactions (1) plays a key role. Without knowledge of this function, which should not only differentiate between binders and non-binders but also rank the binders, successful "in silico" drug design would not be possible. Most of the scoring functions are based on empirical functions, also called molecular mechanics (MM). The use of empirical functions is fully understandable, because the protein targets are extended systems with several thousands of atoms. On the other hand, the limitations of these functions are also well known; among them, the inability to describe quantum effects becomes the most serious. Because of the size of the systems being considered, the nonempirical ab initio quantum mechanical (QM) approach is not feasible, and also the use of the QM/MM approach is limited. A compromise would be the use of semiempirical QM (SQM) techniques. Considering the complexity of protein-ligand binding, it is understandable why these methods have been used only rarely up to now. The way toward their use has been paved only recently when the new and very efficient SQM method appeared (PM6, Ref. 2) and when the first studies improving its efficiency by adding the correction terms for dispersion and H-bonding energies were published (3). The second generation of these corrections, very recently introduced in our laboratory (4), improves the H-bonding considerably, and the PM6-DH2 method provides excellent results (with an almost chemical accuracy) for different types of noncovalent interactions. This is no doubt the prerequisite for any further theoretical activity in the field. In addition, due to the introduction of the linear scaling procedure and of the reaction field (2), the technique can be used for extended systems having several thousand atoms in a water environment, which allows us to consider the whole protein.

Analyzing the binding process, we realize that the second critical step after binding is ligand relaxation and desolvation. The solvation energy was evaluated using the advanced techniques of a self-consistent reaction field (5), which is known to provide reliable energies for not only neutral but also charged systems.

The total binding free energy of a protein-ligand complex is calculated in the present scoring function as a sum of the binding free energy of the complex and the relaxation and desolvation free energy of its subsystems. All of these terms have been shown to be important, and none of them can be neglected.

Our docking scheme was tested on the HIV-1 protease and bovine carbonic anhydrase II (6). The results were compared to the structural crystallographic data and the experimental binding data (ITC, SPR). The PM6-DH2 scoring improved the docking results dramatically and reproduced the experimental results correctly. Not only were the ten binders and ten non-binders in our training-ligand sets correctly assigned, but also the proper order of all the binders was achieved.

The PM6-DH2 scoring thus provides a novel, valuable and very promising tool for rational drug discovery and de novo design.

1. P. Hobza, K. Müller-Dethlefs, Noncovalent Interactions. The Royal Society of Chemistry, Cambridge, 2010.
2. J. J. P. Stewart, J. Mol. Model, 2007, 13 (12), 1173-1213.
3. J. Rezac, J. Fanfrlik, D. Salahub and P. Hobza, J.Chem.Theory Comput., 2009, 5, 1749-1760.
4. M. Korth, M. Pitonak, J. Rezac and P.Hobza, J.Chem. Theory Comput., accepted.
5. Gaussian09.
6. Jindrich Fanfrlik, Agnieszka K. Bronowska, Jan Rezac, Jiong Ran and Pavel Hobza, submitted.
















 

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