Prediction of binding affinities for DNA intercalators by molecular dynamics simulations

The computer modelling and simulation methods are widely used in rational drug design to obtain information necessary for understanding interactions between a ligand (drug molecule) and its cellular macromolecular target on molecular level. The determination of free energies of binding for ligand-target complexes is one of the crucial points in those studies. In recent years several methods have been proposed to solve this problem. The majority of them use molecular dynamics (MD) simulations. Two, most popular methods: (i) a free energy perturbation method (FEP), and (ii) a linear response (LR) method, are shortly presented in this paper together with their limitations and advantages. In this work I presented the first attempt to use LR approach to 10 anti-tumour agents able to intercalate into DNA. The LR relationship obtained in the present study indicated that in the system studied the electrostatic term has no influence on the free energy of binding. The relationship is now successfully used in our research group in further molecular modelling studies concerning DNA intercalators with similar structure

Molecular modelling in the rational design of some anti-tumor and antifungal agents

In this paper we present our approaches and results concerning application of molecular modelling techniques in the design of new chemotherapeutic agents for the control of eukariotic systems, comprising compounds for the treatment of systemic fungal infections and tumor deseases. In the case of anti-tumor agents we focused our attention on molecular properties of natural and synthetic anthraquinones. In the area of antifungal compounds we adopted two approaches. In one of them we examine molecular nature of undesirable properties of polyene macrolide antifungal antibiotic - amphotericin B using molecular modelling techniques. Another approach was aimed at the development of selective inactivator of glucosamine synthase, a novel target for antifungal compounds. In this problem we have used computational chemistry methods to identify structural features responsible for the selective inactivation of the target enzyme