**7. Interaction of small molecules with enzymes**

The potential of molecular simulations to enhance our understanding of drug behaviour and resistance relies ultimately on their ability to achieve an accurate ranking of drug binding affinities at clinically relevant time scales. Several computational approaches exist to estimate ligand binding affinities and selectivities, with various levels of accuracy and computational expense: free energy perturbation (FEP), thermodynamic integration (TI), lineal response (LR), and molecular mechanics Poisson-Boltzman surface area (MM/PBSA). Identification of conformational preferences and binding site residues, as well as structural and energetic characterisation, is possible using MD simulations [Anzini et al., 2011; Dastidor et al., 2008; Stoika et al., 2008]. It is also possible to estimate conformational energy penalties for adopting the bioactive conformation identified by using a pharmacophore model [Frølund et al., 2005].

198 Bioinformatics

[Bendic & Volanschi, 2006].

the MMFF94 force field have also been performed.

principle proposed by different authors [Veal et al., 1990].

**7. Interaction of small molecules with enzymes** 

the MMX force field and then PMB for gas phase optimisation, followed by re-optimisation in aqueous phase with the PM3 method using the AMSOL package [Silva & Jayasundera, 2002]. Optimisation geometries with AM1 and the use of implicit solvent have been taken into account when considering intercalation versus insertion into the minor or major groove

Calculating the curvature radius of molecular DNA structures has been reported [Slickers et al., 1998] as a new method for understanding the dependence of binding affinity on ligand structure, assuming that strong binders should have a shape complementary to the DNA minor groove. A method for predicting sequence selectivity and minor groove binding, based on MD simulations on DNA sequences with and without the bound ligand, to obtain

Amber force field, developing the necessary parameters, has also been used together with electrostatic potential-derived (ESP) charges and explicit solvent molecules to study bisintercalation into DNA. The targeted molecular dynamics (tMD) approach has been considered for comparing the relative energetic cost involved in creating the intercalation sites and also studying the mechanisms of action [Braña et al., 2004]. It has been found that the electrostatic contribution is a critical characteristic of binding selectivity [Marco et al., 2005]. Reports on duplex and triplex formation of oligonucleotides by stacking aromatic moieties in the major groove, using Amber force field and the GB/SA solvation model in molecular dynamic simulations, can be found in the literature [Andersen et al., 2011]. Studies on docking using GOLD [Kiselev et al., 2010] to optimise the starting structures with

Most of the published molecular modelling studies use two double base pairs or more than eight double base pairs to represent DNA. In our opinion, molecular modelling of DNA intercalation complexes should be done using at least the two base pairs of the intercalation site and an additional base pair at the two strand ends to maintain DNA shape and avoid distortion leading to inaccurate results. That means four base pairs for monointercalation studies and five or six base pairs for bisintercalation ones should be used. Using these DNA models, our studies on the mono and bisintercalation of benzo[*g*]phthalazine derivatives strongly suggest the possibility of bisintercalation and the important role played by an Nmethyl group in stabilising the DNA complex of one of the compounds, throwing some light over the experimental results obtained [Rodríguez-Ciria et al., 2003]. The possibility of bisintercalation for a 1,4-disubstituted piperazine has been studied on duplexes of five and six base pairs, obtaining much better results in the case of five base pairs, in accordance with the theoretical calculations of binding mode not conforming to the neighbouring exclusion

The potential of molecular simulations to enhance our understanding of drug behaviour and resistance relies ultimately on their ability to achieve an accurate ranking of drug binding affinities at clinically relevant time scales. Several computational approaches exist to

an approximate free energy of binding has been proposed [Wang & Laughton, 2010].

A model based on van der Waals intermolecular contribution from Amber and electrostatic interactions derived from the Poisson-Boltzman equation has been used to predict the change in the apparent dissociation constant for a series of six enzyme-substrate complexes during COMBINE analysis [Kmunicek et al., 2001]. In COMBINE analysis, binding energies are calculated for the set of enzyme-substrate complexes using the molecular mechanics force field. The total binding energy, ΔU, may be assumed to be the sum of five terms: the intermolecular interaction energies between the substrate and each enzyme residue, EinterES, the change in the intramolecular energy of the substrate upon binding to the enzyme, ΔES, the change in the intramolecular energy of the enzyme upon binding, ΔEE, the desolvation energy of the substrate, EdesolvS, and the desolvation energy of the enzyme, EdesolvE.

$$
\Delta \mathbf{U} = \mathbf{E}\_{\text{inter}} \mathbf{E}^{\text{S}} + \Delta \mathbf{E}^{\text{S}} + \Delta \mathbf{E}^{\text{E}} + \mathbf{E}\_{\text{desolv}} \mathbf{S} + \mathbf{E}\_{\text{desolv}} \mathbf{E} \tag{3}
$$

When the substrate is a rather small molecule, there is no evidence for large differences in the structure of the enzyme when different substrates are bound and so the second and third are neglected. This method identifies the amino acid residues responsible for modulating enzyme activity [Kmunicek et al., 2005].

Molecular modelling of proteins is sometimes directed towards homology modelling, enabling progress in understanding the mechanisms of action despite the lack of detailed information on the 3D structure of a protein. Molecular dynamic simulations are usually used to test the stability of the complete structure derived from homology modelling [Srinivas et al., 2006].

Molecular docking examples can be used to compare relative stabilities of the complexes, but not calculate binding affinities, since changes in entropy and solvation effects are not taken into account [Pastorin et al., 2006; Tschammer et al., 2011]. In any case, docking calculations are common studies on novel drugs, Autodock being one of the most used docking programs [see for example Venskutonyte et al., 2011]. Docking programs treat enzymes and substrates as rigid entities, but flexible docking is also possible, if several different protein conformations extracted from molecular dynamic simulations are used [Roumen et al., 2010].

In our laboratory, molecular modelling has been tentatively used to study the trypanosomicidal activity of some phthalazine derivatives. Results obtained with Amber force field implemented in HyperChem 8.0 plus our own necessary parameters, and with AutoDock 4.2 using the PDB structure for *T. cruzi* Fe-SOD enzyme, were in accordance with experimental data, helping to explain the experimental results obtained. However, if there is no PDB structure for the desired enzyme and only a model of the active site, as for *Leishmania* Fe-SOD enzyme, results obtained with our calculations do not agree with the experimental ones when compared to the *T. cruzi* ones. This indicates that the interaction with the external part of the enzyme plays an important role as it might collaborate in, or make access to the active site difficult, since the enzyme shape and conformation plays a crucial role in its activity [Sanchez-Moreno et al., 2011; Yunta, unpublished results].

Using Molecular Modelling to Study Interactions Between Molecules with Biological Activity 201

parameter transferability. *J. Phys. Chem. A,* Vol. 114, No. 44, (November 2010), pp.

Alcaro, S., Battaglia, D. & Ortuso, F. (2004). Molecular modeling of β-cyclodextrin inclusions complexes with pharmaceutical compounds. *Arkivoc*, Vol. 2004, No. 5, (February 2004),

Alcaro, S., Gasparrini, F. Incani, O., Mecucci, S., Misiti, D., Pierini, M. & Villani, C. (2000). A 'quasi-flexible' automatic docking processing for sudying stereoselective recognition mechanisms. Part 1. Protocol validation. *J. Comput. Chem.,* Vol. 21, No. 7, (May 2000),

Ali, Sk.M., Mainly, D.K., De, S. & Shenoi, M.R.K. (2008). Ligands for selective metal ion extraction: a molecular modeling approach. *Desalination*, Vol. 232, No. 1-3, (November

Alkorta, I., Blanco, F., Deyà, P.M., Elguero, J., Estarellas, C, Frontera, A. & Quiñonero, D. (2010). Cooperativity in multiple unusual weak bonds. *Theor. Chem. Acc.,* Vol. 126, No.

Allinger, N.L. (1976). Calculation of molecular structure and energy by force field methods, In: *Advances in Physical Organic Chemistry,* Vol. 13. Gold, V & Bethell, D. (Eds.), pp. 1-82,

Allinger, N.L., Chen, K. & Lii, J.H. (1996). An improved force field (MM4) for saturated hydrocarbons. *J. Comput. Chem.,* Vol. 17, No. 5-6, (April 1996), pp. 642-668. ISSN: 1096-

Allinger, N.L. & Durkin, K.A. (2000). Van der Waals effects between hydrogen and first row atoms in molecular mechanics (MM3/MM4). *J. Comput. Chem.*, Vol. 21, No. 14

Allinger, N.L. & Yan, Q.L. (1993). Molecular mechanics (MM3) - calculations of vinyl ethers, and related compounds. *J. Am. Chem. Soc.,* Vol. 115, No. , ( 1993), pp. 11918-11925. ISSN:

Andersen, N.K., Døssing, H., Jensen, F., Vester, B. & Nielsen, P. (2011). Duplex and triplex formation of mixed pyrimidine oligonucleotides with stacking of phenyl-triazole moieties in the major groove. *J. Org. Chem.,* Vol. 76, No. 15, (August 2011), pp. 6177-

Anzini, M., Valenti, S., Braile, C., Cappelli, A., Vomero, S., Alcaro, S., Ortuso, F., Marinelli, L., Limongelli, V., Novellino, E., Betti, L., Giannaccini, G., Lucacchini, A., Daniele, S., Martini, C., Ghelardini, C., Mannelli, L.D.C., Giorgi, G., Mascia, M.P. & Biggio, G. (2011). New insight into the central benzodiazepine receptor-ligand interactions: design, synthesis, biological evaluation, and molecular modeling of 3-substituted 6-phenyl-4*H*imidazo[1,5-*a*][1,4]benzodiazepines and related compounds. *J. Med. Chem.,* Vol. 54, No.

Aqvist, J., Medina, C. & Samuelson, J.E. (1994). A new method for predicting binding affinity in computer aided drug design. *Protein Eng.* Vol. 7, No. 3, (March 1994), pp. 385-

Baker, N.A. (2005). Improving implicit solvent simulations: a poisson-centric view. *Curr.* 

*Opin. Struct. Biol.,* Vol. 15, No. 2 (April 2005), pp. 137-143. ISSN: 0959-440X

11964-11970. ISSN: 1089-5639

pp. 107-117. ISSN: 1424-6376

pp. 515-530. ISSN: 1096-987X

987X

0002-7863

6187. ISSN: 0022-3263

391. ISSN: 0269-2139

2008), pp. 181-190. ISSN: 0011-9164

1-2, (May 2010), pp.1-14. ISSN: 1432-2234

Elsevier, ISBN: 978-0120335138, Amsterdam

(November 2000), pp. 1229-1242. ISSN: 1096-987X

16, (August 2011), pp. 5694-5711. ISSN: 0022-2623
