Metal ions play significant roles in biological systems. Accurate molecular dynamics (MD) simulations on these systems require a validated set of parameters. Although there are more detailed ways to model metal ions, the nonbonded model, which employs a 12-6 Lennard-Jones (LJ) term plus an electrostatic potential is still widely used in MD simulations today due to its simple form. However, LJ parameters have limited transferability due to different combining rules, various water models and diverse simulation methods. Recently, simulations employing a Particle Mesh Ewald (PME) treatment for long-range electrostatics have become more and more popular owing to their speed and accuracy. In the present work we have systematically designed LJ parameters for 24 +2 metal (M(II)) cations to reproduce different experimental properties appropriate for the Lorentz-Berthelot combining rules and PME simulations. We began by testing the transferability of currently available M(II) ion LJ parameters. The results showed that there are differences between simulations employing Ewald summation with other simulation methods and that it was necessary to design new parameters specific for PME based simulations. Employing the thermodynamic integration (TI) method and performing periodic boundary MD simulations employing PME, allowed for the systematic investigation of the LJ parameter space. Hydration free energies (HFEs), the ion-oxygen distance in the first solvation shell (IOD) and coordination numbers (CNs) were obtained for various combinations of the parameters of the LJ potential for four widely used water models (TIP3P, SPC/E, TIP4P and TIP4PEW). Results showed that the three simulated properties were highly correlated. Meanwhile, M(II) ions with the same parameters in different water models produce remarkably different HFEs but similar structural properties. It is difficult to reproduce various experimental values simultaneously because the nonbonded model underestimates the interaction between the metal ions and water molecules at short range. Moreover, the extent of underestimation increases successively for the TIP3P, SPC/E, TIP4PEW and TIP4P water models. Nonetheless, we fitted a curve to describe the relationship between ε (the well depth) and radius (Rmin/2) from experimental data on noble gases to facilitate the generation of the best possible compromise models. Hence, by targeting different experimental values, we developed three sets of parameters for M(II) cations for three different water models (TIP3P, SPC/E and TIP4PEW). These parameters we feel represent the best possible compromise that can be achieved using the nonbonded model for the ions in combination with simple water models. From a computational uncertainty analysis we estimate that the uncertainty in our computed HFEs is on the order of ±1kcal/mol. Further improvements will require more advanced non-bonded models likely with inclusion of polarization.

A largely unsolved problem in computational biochemistry is the accurate prediction of binding affinities of small ligands to protein receptors. We present a detailed analysis of the systematic and random errors present in computational methods through the use of error probability density functions, specifically for computed interaction energies between chemical fragments comprising a protein-ligand complex. An HIV-II protease crystal structure with a bound ligand (indinavir) was chosen as a model protein-ligand complex. The complex was decomposed into twenty-one (21) interacting fragment pairs, which were studied using a number of computational methods. The chemically accurate complete basis set coupled cluster theory (CCSD(T)/CBS) interaction energies were used as reference values to generate our error estimates. In our analysis we observed significant systematic and random errors in most methods, which was surprising especially for parameterized classical and semiempirical quantum mechanical calculations. After propagating these fragment-based error estimates over the entire protein-ligand complex, our total error estimates for many methods are large compared to the experimentally determined free energy of binding. Thus, we conclude that statistical error analysis is a necessary addition to any scoring function attempting to produce reliable binding affinity predictions.

Sterically demanding and conformationally stable N,N'-ditertiaryalkyl-N,N'-diphenyl acyclic diaminocarbenes (ADCs) were developed. Bulky ADC-Au catalysts not only showed competitive reactivities in hydroamination and enyne cyclization but also demonstrated unique ligand properties different from bulky N-heterocyclic carbene (NHC) counterparts.

The routine prediction of three-dimensional protein structure from sequence remains a challenge in computational biochemistry. It has been intuited that calculated energies from physics-based scoring functions are able to distinguish native from nonnative folds based on previous performance with small proteins and that conformational sampling is the fundamental bottleneck to successful folding. We demonstrate that as protein size increases, errors in the computed energies become a significant problem. We show, by using error probability density functions, that physics-based scores contain significant systematic and random errors relative to accurate reference energies. These errors propagate throughout an entire protein and distort its energy landscape to such an extent that modern scoring functions should have little chance of success in finding the free energy minima of large proteins. Nonetheless, by understanding errors in physics-based score functions, they can be reduced in a post-hoc manner, improving accuracy in energy computation and fold discrimination.

Substrate ingress and product egress from the active site of urease is tightly controlled by an active site flap. Molecular dynamics simulations of urease reveal a previously unobserved, wide-open flap state that, unlike the well-characterized closed and open states, allows ready access to the metal cluster in the active site. This state is easily reached, via low free energy barriers, from the closed and open states. Additionally, we find that even when the flap is closed, a region of the binding pocket is solvent exposed leading to the hypothesis that it may act as a substrate/product reservoir. The newly identified wide-open state offers further opportunities for small molecule drug discovery by defining a more extensive active site pocket than has been previously described.

We have used atomistic molecular dynamics simulations to study the molecular-scale structure of poly(L-lysine) dendrimers homogeneously functionalized with naphthalene disulfonate caps from the first generation to the sixth generation. These dendrimers behave as typical dendrimers in poor solvent. As the generation number increases, there is a change from small molecule behavior to more polymer-like behavior. The first-and secondgeneration dendrimers, behaving as small molecules, are flexible, aspherical, and exposed to the environment, and their caps cluster together. Third-and fourth-generation dendrimers exhibit a transition toward polymeric behavior. The fifth-and sixth-generation dendrimers are large, essentially spherical globules, with a dense core, an irregular and highly grooved surface, and caps which are evenly distributed. Cap-cap interaction in all generations is favorable and is characterized by face-to-face naphthalene stacking.

We have used molecular dynamics simulations to study the structures of a range of heterogeneously functionalized ("variegated") dendrimers and the distributions in space of their caps. By a virtual capping approach, we compare an asymmetric dendrimer (dendritic poly(L-lysine), PLL) and a symmetric polyamide (SPAM) dendrimer. SPAM is larger and more spherical than PLL, and its caps are more evenly distributed throughout the molecule. Protonation of an amine-capped dendrimer causes substantial swelling. The distribution of caps relative to each other is strongly affected by conformational change. Simulation of a SPAM framework with chemically distinct caps shows that whole-dendrimer properties are largely unaffected by the topological arrangement of the caps. Using both modeling approaches, we found that differentiation at the dendrimer's core produces strongly dipolar dendrimers, while differentiation between branches in the outermost generation produces largely nonpolar dendrimers, with a gradual transition between these extremes. The effect of variegation topology on spatial distribution of caps is modified by the interactions between the caps (particularly electrostatic interactions) and with other species in the environment.

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