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Naphthalenes, a class of persistent organic pollutants, are difficult to remove from environment due to their low water solubility. β-cyclodextrin (β-CD) and its derivatives can encapsulate these compounds and enhance their solubility. For OH-substituted naphthalenes, several inclusion complexes with β-CD and 2,6-di-methyl-β-CD (DMCD) of varying stabilities were reported. However, the role of non-covalent interaction was not defined and the binding mechanisms remain vague. We use a molecular dynamics/quantum mechanics/continuum solvent model to explore the inclusion mechanisms of these systems. Both the measured binding modes and binding trend are well reproduced by our computations. The host-guest non-covalent interaction favors the β-CD complexes, while the hydrophobic interaction favors the DMCD complexes, and the latter plays a more important role in determining the binding trend. A correlation between the polarizability of the inclusion complex as measured by its dipole moment and hydrophobic interaction is also revealed.

Previously, we identified a class of salicylic acid derivatives that display inhibitory activity against the protein tyrosine phosphatase YopH from Yersinia pestis . Because docking study suggested that the large phenyl ring attaching to the salicylic acid core might be exposed to the solvent and might not contribute significantly to binding, we have developed a new class of compounds that no longer contain this phenyl ring. We first devised a synthetic scheme for the compounds and then developed an automated computational screening model surrounding this synthetic scheme to help select a small number of compounds for synthesis and experimental testing. Based on this computational screening model and the analysis of the structure–activity relationship of our previous class of compounds, we have synthesized eight compounds and found five that yield micromolar activity. When applying in a larger scale, the synthetic scheme and the computational screening model developed here should help to identify even more potent inhibitors in the future.

Quite a few reviews on molecular docking have already appeared. This mini-review focuses on methods that incorporate protein flexibility in docking rather than those that treat protein targets as rigid molecules. This is still a challenging problem but there are encouraging recent advances. These methods will be reviewed particularly in light of their applications to protein kinases and phosphatases. In addition to obtaining correct docking pose, recent developments on exploring docking pathways are also highlighted.

Using the docking of p-nitrocatechol sulfate to Yersinia protein tyrosine phosphatase YopH as an example, we showed that an approach based on mining minima followed by cluster and similarity analysis could generate useful insights into docking pathways. Our simulation treated both the ligand and the protein as flexible molecules so that the coupling between their motion could be properly accounted for. Our simulation identified three docking poses; the one with the lowest energy agreed well with experimental structure. The model also predicted the side-chain conformations of the amino acids lying in the binding pocket correctly with the exception of three residues that appeared to be stabilized by two structural water molecules in the crystal structure. The implicit solvent model employed in the simulation could not capture such effects well. We also found four major pathways leading to these docking poses after the ligand entered the mouth of the binding pocket. In addition, the sulfate group of p-nitrocatechol sulfate was found to be important both in binding the ligand to the pocket and in guiding the ligand to dock into the pocket. The coupling of the motion between the protein and the ligand also played an important role in facilitating ligand loading and unloading.

We have successfully coupled the Kohn–Sham with Poisson–Boltzmann equations to predict the solvation free energy, where the Kohn–Sham equations were solved by implementing the flexible pseudo atomic orbitals as in S iesta package. It was found that the calculated solvation free energy is in good agreement with experimental results for small neutral molecules, and its standard error is 1.33 kcal/mol, the correlation coefficient is 0.97. Due to its high efficiency and accuracy, the proposed model can be a promising tool for computing solvation free energies in computer aided drug design in future.

Protein kinases are important targets for designing therapeutic drugs. We describe here a computational approach to extend the usefulness of a single protein-inhibitor structure in aiding the design of protein kinase inhibitors. This approach is based on using sensitivity analysis to identify the most significant functional groups of a lead compound in accounting for binding affinity and on using comparative sequence/structure analysis to examine whether these functional groups would present specificity. A sensitivity analysis study is similar to genetic or chemical modification experiments in which specific features of a lead compound are modified to examine whether they affect properties such as binding affinity. In this study, the binding affinity was estimated by using an implicit-solvent model in which the electrostatic contributions were obtained by solving the Poisson equation, and the hydrophobic effects were accounted for by using surface-area-dependent terms. The comparative sequence/structure analysis involves the study of the amino acid distributions of a large number of protein kinases (384 in this study) near the ligand-binding sites. This analysis provides useful guiding principles for designing specific inhibitors targeted towards a particular kinase. Here, we illustrate the utility of these computational approaches by applying them to identify the determinants of the recognition between the protein kinase A and two of its inhibitors. One inhibitor, balanol, binds to the ATP-binding pocket. The other, protein kinase inhibitor, binds to the substrate-binding site. These analyses have helped to construct pharmacophore models for mining new drug leads from small-molecule libraries and for suggesting how a lead compound or a peptide inhibitor may be modified to generate selective inhibitors.

In order to develop model parameters for simulating the spectroscopic properties of tryptophan and tyrosine in solutions and in proteins, we have carried out Hartree–Fock (HF) and configurational interaction (CI) calculations to study the electronic and spectroscopic properties of these fluorophores. Tryptamine, which is structurally related to tryptophan, has also been studied. The 3-21G basis set was used in most of the calculations and only single excitations were included in the CI calculations. Although calculations at this level of approximations have overestimated the transition energies, they have given transition moments within the range of values obtained from semi-empirical calculations and experimental measurements on indole and several of its derivatives. The calculated transition moments can be used in molecular dynamics simulations to study the fluorescence anisotropy decay (FAD) of these fluorophores. We have also estimated the natural atomic orbital charges of the ground and excited states of tryptamine, tryptophan, and tyrosine. These natural atomic orbital charges can be used in molecular dynamics simulations to study the ground- and excited-state dynamics of these fluorophores. The calculated charge redistribution because of optical excitations of tryptophan at different pHs were consistent with the experimental observations that deprotonated tryptophan had red-shifted absorption and emission spectra. Using AM1 adiabatic potential energy curves and a rotamer model employing an electron-transfer mechanism of fluorescence quenching, we have provided an interpretation of the observed pH-dependent fluorescence decay of tryptophan. The conformational and environmental sensitivity of the electronic and spectroscopic properties of these flurophores has also been examined.