Prediction of Binding Affinities for Dna Intercalators by Molecular Dynamics Simulations (original) (raw)
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Computer simulations have been demonstrated to be important for unraveling atomic mechanisms in biological systems. In this study, we show how combining unbiased molecular dynamic simulations with appropriate analysis tools can successfully describe metal-based drug interactions with DNA. To elucidate the noncovalent affinity of cisplatin's family to DNA, we performed extensive all-atom molecular dynamics simulations (3.7 ms total simulation length). The results show that the parent drug, cisplatin, has less affinity to form noncovalent adducts in the major groove than its aquo complexes. Furthermore, the relative position in which the drugs enter the major groove is dependent on the compound's net charge. Based on the simulations, we estimated noncovalent binding free energies through the use of Markov state models. In addition, and to overcome the lack of experimental information, we employed two additional methods: Molecular Mechanics Poisson-Boltzmann Surface Area (MMPB-SA) and steered molecular dynamics with the Jarzynski estimator, with an overall good agreement between the three methods. All complexes show interaction energies below 3 kcal/mol with DNA but the charged hydrolysis products have slightly more favorable binding free energies than the parent drug. Moreover, this study sets the precedent for future unbiased DNA-ligand simulations of more complex binders.
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An increasing number of studies have reported computations of the absolute binding free energy of small ligands to proteins using molecular dynamics (MD) simulations with results that are in good agreement with experiments. This encouraging progress suggests that physics-based approaches hold the promise of making important contributions to the process of drug discovery and optimization in the near future. Two types of approaches are principally used to compute binding free energies with MD simulations. The most widely known are based on alchemical free energy methods, in which the interaction of the ligand with its surrounding are progressively switched off. An alternative method is to use a potential of mean force (PMF), in which the ligand is physically separated from the protein receptor. For both of these computational approaches, restraining potentials affecting the translational, rotational and conformational freedom of the ligand and protein may be activated and released during the simulations to aid convergence and improve the sampling. Such restraining potentials add bias to the simulations, but their effects can be rigorously removed to yield a binding free energy that is properly unbiased with respect to the standard state. A review of recent results is presented. Examples of computations with T4lysozyme mutants, FKBP12, SH2 domain, and cytochrome P450 are discussed and compared. Differences in computational methods are discussed and remaining difficulties and challenges are highlighted.