**5. Selective formation of complexes**

There are several examples of molecular modelling studies on complexes between cyclic receptors and ammonium ions, calixarenes [Choe & Chang, 2002] and crown ethers being the most used. As an example, it is noteworthy to mention the theoretical studies on calix[4]crown-5 and a series of alkyl ammonium ions [Park et al., 2007], having shown that the energy of complex formation depends on the number of amine groups in the alkyl chain as well as on the number of methylene groups between the primary and secondary amine groups, results that agree with experimental measurements. Although the calculations are performed under quite different conditions of vacuum compared with the experimental conditions of the phase system of chloroform-water, the binding properties of calixarenetype compounds towards alkyl ammonium ions have been successfully simulated, providing general and useful explanations for the molecular recognition behaviour.

Using Molecular Modelling to Study Interactions Between Molecules with Biological Activity 197

Docking of a ligand into a receptor may occur via an automated procedure [Subramanian et al., 2000] or manually [Filizola et al., 1999]. In both cases, docking is a combination of two components: a search strategy and a scoring function [Taylor et al., 2002]. The computational method MOLINE (Molecular Interaction Evaluation) was created to study complexes in an unbiased fashion [Alcaro et al., 2000]. It is based on a systematic, automatic and quasi-flexible docking approach that prevents the influence of the chemist`s intuition on generating the configuration. This method has been used with acceptable results in studying

It would be adequate at this point to remember that testing the `drug-receptor complexation' for a receptor model against available experimental data usually involves the use of site-directed mutagenesis experiments. This fact provides information on the amino acids involved in ligand binding and receptor activation. However, it should be noted that the results of mutagenesis studies are not necessarily related to receptor-ligand interactions. In fact, mutations can also alter the 3D structure of a receptor and therefore, modify the binding profile of a ligand by this mechanism. Besides that, efficient binding to a receptor does not guarantee that a ligand will produce a pharmacological action, given that the

Anthracycline antibiotics such as doxorubicin and its analogues have been in common use as anticancer drugs for around half a century. There has been intense interest in the DNAbinding sequence specificity of these compounds in recent years, with the hope of identifying a compound that can modulate gene expression or exhibit reduced toxicity. Cashman and Kellog have studied models of binding for doxorubicin and derivatives [Cashman & Kellog, 2004], looking for sequence specificity and the effects of adding aromatic or aliphatic ring substituents or additional amino or hydroxyl groups. They performed a hydropathic interaction analysis using the HINT program (a Sybyl program module, Tripos Inc.) and four double base pair combinations. Interaction of some intercalators with two double DNA base pairs have also been studied with the density functional based tight binding (DFTB) method [Riahi et al., 2010], despite DFT methods being known to be inherently deficient in calculating stacking interactions, and the Amber force field and then AM1 to dock the intercalator between DNA base pairs [Miri et al., 2004]. Studies on sequence-selectivity of DNA minor groove binding ligands have shown that the most reliable results for AT-rich DNA sequences are obtained when MD simulations are performed in explicit solvent, when the data are processed using the MM-PB/SA approach, and when normal mode analysis is used to estimate configurational entropy changes [Shaikh et al., 2004; Wang & Laughton, 2009]. Use of the GB/SE model with a suitable choice of parameters adequately reproduces the structural and dynamic characteristics in explicitly solvated simulations in approximately a quarter of the computational time, although limitations become apparent when the thermodynamic properties are evaluated [Sands & Laughton, 2004]. Water molecules taking part in the complexation have been studied using

inclusion complexes [Alcaro et al., 2004].

ligand may act as an agonist or antagonist.

**6. Interaction of molecules with DNA** 

Complex formation of compounds containing benzene rings with ammonium cations has also been theoretically studied using many computational techniques, including *ab initio* calculations [Kim et al., 2000]. It has been shown that two types of NH-aromatic π and CHaromatic π interactions, which are important in biological systems, are responsible for binding, and that charged hydrogen bonds versus cation-π interaction is the origin of the high affinity and selectivity of novel receptors for NH4+ over K+ ions [Oh et al., 2000]. Organic molecules complexed with metal cations have also been studied by MM2 molecular modelling [Mishra, 2010]. The search for metal ion selectivity is of interest in the field of biomimetic models of metalloenzymes and molecular modelling helps in the design of new ligands with this purpose [Kaye, 2011].

Molecular modelling has been used to suggest possible contributions of carrier effectivity and selectivity to complex formation in accordance with experimental results [Chipot et al., 1996; Ilioudis et al., 2005]. Our research group has evaluated the possible cation-receptor interactions involved in the complexes with ammonium and metal cations of selective carriers using the Amber force field with appropriate parameters developed by us. The complexation energies obtained are in reasonable agreement with experimental values, taking into account that complexation/decomplexation processes have a great influence on transport rates and are not equally favoured in cyclic and acyclic carriers [Campayo et al., 2004].

Both binding and selectivity in binding can be understood through the combined efforts of several non-covalent interactions, such as hydrogen bonding, electrostatic interactions, hydrophobic interactions, cation-π interactions, π-π stacking interactions and steric complementarity [Späth & König, 2010]. Formation of complexes is also possible in the case of neutral ligands. For example, the interactions between cholesterol and cyclodextrins have been theoretically studied to investigate their 1:1 and 1:2 complexes [Castagne et al., 2010], while the formation of stable complexes between trehalose and benzene compounds have been investigated by the general Amber force field (GAFF) and Gaussian 03 for MP2/6G-31G\*\* calculation of atomic charges [Sakakura et al., 2011].

Docking of a ligand into a receptor may occur via an automated procedure [Subramanian et al., 2000] or manually [Filizola et al., 1999]. In both cases, docking is a combination of two components: a search strategy and a scoring function [Taylor et al., 2002]. The computational method MOLINE (Molecular Interaction Evaluation) was created to study complexes in an unbiased fashion [Alcaro et al., 2000]. It is based on a systematic, automatic and quasi-flexible docking approach that prevents the influence of the chemist`s intuition on generating the configuration. This method has been used with acceptable results in studying inclusion complexes [Alcaro et al., 2004].

It would be adequate at this point to remember that testing the `drug-receptor complexation' for a receptor model against available experimental data usually involves the use of site-directed mutagenesis experiments. This fact provides information on the amino acids involved in ligand binding and receptor activation. However, it should be noted that the results of mutagenesis studies are not necessarily related to receptor-ligand interactions. In fact, mutations can also alter the 3D structure of a receptor and therefore, modify the binding profile of a ligand by this mechanism. Besides that, efficient binding to a receptor does not guarantee that a ligand will produce a pharmacological action, given that the ligand may act as an agonist or antagonist.
