Molecular dynamics simulations of lipid bilayers.

During the past decade, computer simulations of bilayers have moved from the realm of model systems to realistic systems containing tens of phospholipids along with the requisite number of water molecules hydrating the entire molecular assembly. Concomitant with the ability to model larger and larger systems, simulators have also begun to utilize more accurate numerical tools to ensure that the temperature, pressure, simulation timescales, parameter sets and long-range electrostatic interactions of bilayers are correctly accounted for in a typical molecular dynamics simulation. With these tools in hand, work has already begun to define the structure, function and dynamics of bilayer, bilayer/small molecule and bilayer/protein systems. Thus, we have reached an era in which simulators will tackle more and more detailed issues regarding complex bilayer systems.

New developments in applying quantum mechanics to proteins.

Algorithmic improvements of quantum mechanical methodologies have increased our ability to study the electronic structure of fragments of a biomolecule (e.g. an enzyme active site) or entire biomolecules. Three main strategies have emerged as ways in which quantum mechanics can be applied to biomolecules. The supermolecule approach continues to be utilized, but it is slowly being replaced by the so-called coupled quantum mechanical/molecular mechanical methodologies. An exciting new direction is the continued development and application of linear-scaling quantum mechanical approaches to biomolecular systems.

The Role of Molecular Dynamics Potential of Mean Force Calculations in the Investigation of Enzyme Catalysis.

The potential of mean force simulations, widely applied in Monte Carlo or molecular dynamics simulations, are useful tools to examine the free energy variation as a function of one or more specific reaction coordinate(s) for a given system. Implementation of the potential of mean force in the simulations of biological processes, such as enzyme catalysis, can help overcome the difficulties of sampling specific regions on the energy landscape and provide useful insights to understand the catalytic mechanism. The potential of mean force simulations usually require many, possibly parallelizable, short simulations instead of a few extremely long simulations and, therefore, are fairly manageable for most research facilities. In this chapter, we provide detailed protocols for applying the potential of mean force simulations to investigate enzymatic mechanisms for several different enzyme systems.

Insights into the function of the zinc hydroxide-Thr199-Glu106 hydrogen bonding network in carbonic anhydrases.

The exact functional role of the zinc hydroxide (water)-Thr199-Glu106 hydrogen bond network in the carbonic anhydrases is unknown. However, from the results of molecular dynamics simulations (MD) we are able to better define its function. From computer graphics analysis and MD simulations on the zinc hydroxide form of human carbonic anhydrase II we find that this interaction forces the hydroxide hydrogen atom to be in a "down" position relative to the deep water-binding pocket. From previous work we have found that this pocket is a high-affinity binding site for CO2. We also note that during the timescale of our simulation (126 ps) the hydrogen bonds between the hydroxide hydrogen atom and Thr199 and the one between Thr199 and Glu106 are not fluxional. We propose that the role of the zinc hydroxide (water)-Thr199-Glu106 hydrogen bond network is to lock the hydrogen atom in the down position in order to expose the CO2 molecule bound in the deep water pocket to a lone pair of the hydroxide oxygen atom. This would allow for the rapid reaction of the CO2 molecule around the zinc ion. Furthermore, if the hydroxide hydrogen atom were not locked in the down position the binding of CO2 to the deep water pocket could be interfered with by the unrestrained hydroxide hydrogen atom (e.g. the N-Zn-O-H torsion could undergo rotational transitions that would partially block the deep water pocket). In summary, the roles we ascribe to this hydrogen bonding network are (1) to allow for facile access of CO2 to the deep water pocket and (2) to allow for maximal exposure of a hydroxide oxygen lone pair to the CO2 carbon atom.

One-dimensional molecular representations and similarity calculations: methodology and validation.

Drug discovery research is increasingly dedicated to biological screening on a massive scale, which seems to imply a basic rejection of many computer-assisted techniques originally designed to add rationality to the early stages of discovery. While ever-faster and more clever 3D methodologies continue to be developed and rejected as alternatives to indiscriminant screening, simpler tools based on 2D structure have carved a stable niche in the high-throughput paradigm of drug discovery. Their staying power is due in no small part to simplicity, ease of use, and demonstrated ability to explain structure-activity data. This observation led us to wonder whether an even simpler view of structure might offer an advantage over existing 2D and 3D methods. Accordingly, we introduce 1D representations of chemical structure, which are generated by collapsing a 3D molecular model or a 2D chemical graph onto a single coordinate of atomic positions. Atoms along this coordinate are differentiated according to elemental type, hybridization, and connectivity. By aligning 1D representations to match up identical atom types, a measure of overall structural similarity is afforded. In extensive structure-activity validation tests, 1D similarities consistently outperform both Daylight 2D fingerprints and Cerius(2) pharmacophore fingerprints, suggesting that this new, simple means of representing and comparing structures may offer a significant advantage over existing tried-and-true methods.

Binding preferences of hydroxamate inhibitors of the matrix metalloproteinase human fibroblast collagenase.

In this paper we report molecular dynamics (MD) and free energy perturbation (FEP) studies carried out on enzyme-inhibitor (two hydroxamates that only differ by a carbon-carbon double bond) complexes of human fibroblast collagenase to obtain insights into the structural and energetic preferences of these inhibitors. We have developed a bonded model for the catalytic and structural zinc centers (Hoops, S. C.; et al. J. Am. Chem. Soc. 1991, 113, 8262-8270) where the electrostatic representation for this model was derived using a novel quantum-mechanical/molecular-mechanical (QM/MM) minimization procedure followed by electrostatic potential fitting. The resulting bonded model for the zinc ions was then used to generate MD trajectories for structural analysis and FEP studies. This model has satisfactorily reproduced the structural features of the active site, and furthermore, the FEP simulations gave relative free energies of binding in good agreement with experimental results. MD simulations in conjunction with the FEP are able to provide a structural explanation regarding why one hydroxamate inhibitor is favored over the other, and we are also able to make predictions about changes in the inhibitor that would enhance protein-inhibitor interactions.

Prediction of drug absorption using multivariate statistics.

Literature data on compounds both well- and poorly-absorbed in humans were used to build a statistical pattern recognition model of passive intestinal absorption. Robust outlier detection was utilized to analyze the well-absorbed compounds, some of which were intermingled with the poorly-absorbed compounds in the model space. Outliers were identified as being actively transported. The descriptors chosen for inclusion in the model were PSA and AlogP98, based on consideration of the physical processes involved in membrane permeability and the interrelationships and redundancies between available descriptors. These descriptors are quite straightforward for a medicinal chemist to interpret, enhancing the utility of the model. Molecular weight, while often used in passive absorption models, was shown to be superfluous, as it is already a component of both PSA and AlogP98. Extensive validation of the model on hundreds of known orally delivered drugs, "drug-like" molecules, and Pharmacopeia, Inc. compounds, which had been assayed for Caco-2 cell permeability, demonstrated a good rate of successful predictions (74-92%, depending on the dataset and exact criterion used).

Molecular dynamics study of the IIA binding site in human serum albumin: influence of the protonation state of Lys195 and Lys199.

The IIA binding site of human serum albumin (HSA) preferentially binds hydrophobic organic anions of medium size (e.g., aspirin, benzylpenicillin, warfarin, etc.) and bilirubin. This binding ability is particularly important for the distribution, metabolism, and efficacy of drugs. In addition, HSA can also covalently link to different IIA substrates owing to the presence of a highly reactive residue, Lys199, which is strategically located in the IIA site. Herein, we present results of three restrained molecular dynamics (MD) simulations of the IIA binding site on the HSA protein. From these simulations, we have determined the influence that the ionization state of the key residue, Lys199, and the nearby Lys195 has on the structure and dynamics of the IIA binding site. When Lys199 is neutral the computed average distances for the most significant interresidue contacts are in good agreement with those estimated from the X-ray coordinates. The analysis of the solvent structure and dynamics indicates that the basic form of Lys199 is likely connected to the acid form of Lys195 through a network of H-bonding water molecules with a donor --> acceptor character. The presence of these water bridges can be important for stabilizing the configuration of the IIA binding site and/or promoting a potential Lys195 --> Lys199 proton-transfer process. These results suggest that both lysine residues located in the IIA binding site of HSA, Lys195 and Lys199, could play a combined and comparable chemical role. Our simulations also give insight into the binding of bilirubin to HSA.

Application of the free energy perturbation method to human carbonic anhydrase II inhibitors.

An analysis of the free energy perturbation (FEP) method is presented that attempts to evaluate the efficacy of the FEP method in the drug discovery process. To accomplish this we have evaluated whether the FEP technique can accurately predict energetic and structural quantities relating to the inhibition of human carbonic anhydrase II (HCAII) by sulfonamides. Three well-characterized (both structurally and energetically) sulfonamide inhibitors of HCAII were examined in this study, 1a, 1b, and 1c. Results from FEP simulations on these compounds indicate that the FEP method can predict energetic trends reasonably well; however, the FEP method was less successful in reproducing detailed structural data. In particular, an expected movement of His-64 when inhibitor 1c was bound did not occur. We conclude that the FEP method can be used to determine relative free energies of binding but cannot be relied upon to reproduce subtle geometric changes.

GB/SA water model for the Merck molecular force field (MMFF).

A revised generalized Born/surface area (GB/SA) continuum solvation model has been developed for water that is compatible with the Merck molecular force field (MMFF). This model gives free energies of aqueous solvation that are comparable in accuracy to the original water model when the OPLS* force field is employed. The average unsigned error in aqueous deltaGsol using the new water model and MMFF is 0.62 kcal/mol for a training set of 82 solutes compared to 1.24 kcal/mol for the original GB/SA water model and MMFF. The average unsigned errors for 47 neutral solutes outside the training set and 10 ions are 0.96 and 2.32 kcal/mol, respectively. By comparison, the average errors for the test set and ions using the original GB/SA water model are 1.76 and 5.32 kcal/mol. This revised parameter set provides a more accurate representation of aqueous solvation for use with MMFF.