Search

A profound understanding of the physicochemical properties of organic crystals is crucial for topics from material science to drug discovery. Using molecular dynamics (MD) simulations with a sufficiently accurate force field, microscopic insight into structure and dynamics can be obtained of materials, including liquids and biomolecules. They are a valuable complement to experimental investigations that are used routinely in drug design, but not very often for studies of organic crystals. Indeed, the often delicate interactions in organic crystals act as a sensitive probe to investigate the accuracy of force fields. Here, we study the structural, dynamic, and thermodynamic properties of 30 organic crystals using the popular general AMBER force field (GAFF). In particular, we investigate both solid-solid and solid-liquid phase transitions. Melting points were determined using extensive solid-liquid coexistence simulations. For many compounds, we detect a phase transition from an ordered to a plastic crystal in the simulations. Based on the translational and rotational dynamics of the compounds, we can rationalize the properties of the plastic crystal phase. MD simulations can therefore help to answer the important question of whether or not organic crystals have a plastic crystal phase, and if so, what are the underlying factors in the molecular structure determining that.

The human genome codes only a few thousand druggable proteins, mainly receptors and enzymes. While this pool of available drug targets is limited, there is an untapped potential for discovering new drug-binding mechanisms and modes. For example, enzymes with long binding cavities offer numerous prerequisite binding sites that may be visited by an inhibitor during migration from a bulk solution to the destination site. Drug design can use these prerequisite sites as new structural targets. However, identifying these ephemeral sites is challenging. Here, we introduce a new method called NetBinder for the systematic identification and classification of prerequisite binding sites at atomic resolution. NetBinder is based on atomistic simulations of the full inhibitor binding process and provides a networking framework on which to select the most important binding modes and uncover the entire binding mechanism, including previously undiscovered events. NetBinder was validated by a study of the binding mechanism of blebbistatin (a potent inhibitor) to myosin 2 (a promising target for cancer chemotherapy). Myosin 2 is a good test enzyme because, like other potential targets, it has a long internal binding cavity that provides blebbistatin with numerous potential prerequisite binding sites. The mechanism proposed by NetBinder of myosin 2 structural changes during blebbistatin binding shows excellent agreement with experimentally determined binding sites and structural changes. While NetBinder was tested on myosin 2, it may easily be adopted to other proteins with long internal cavities, such as G-protein-coupled receptors or ion channels, the most popular current drug targets. NetBinder provides a new paradigm for drug design by a network-based elucidation of binding mechanisms at an atomic resolution.

Equilibrium simulations of molecular systems allow to extract many physicochemical properties. Given an "accurate enough" model, a "large enough" simulation system and "long enough" simulations, such calculations should yield accurate predictions of properties that can be tested by experimental measurements. Non-equilibrium simulations can be used as a tool to obtain specific properties like viscosity or conductivity, but they have the drawback that in general only one property per simulation is produced. In addition, a range of methods is available for computing free energy differences. We here review the state of the art of using classical simulation models for generating quantitative predictions. Popular force fields have significant predictive power already but there is room for improvement. Bonded force potentials may need to be replaced by more accurate ones to better reproduce vibrational frequencies. Simplification of non-bonded force terms, such as cut-offs for electrostatic or dispersion interactions, should be avoided. Routine usage of force field methods will therefore require some tuning of parameters. Despite the extensive toolbox that is available for producing quantitative results, the computational cost of explicit solvent simulation is significant and therefore, approximate methods like implicit solvent models remain popular and are still being developed. Based on fundamental arguments as well as on examples of solvation free energies, host-guest complexation and non-covalent association of molecules in solution, we conclude that implicit solvents as well as algorithmic simplifications are most useful when validation using experimental data or rigorous theoretical treatments is possible.

The world desperately needs new technologies and solutions for gas capture and separation. To make this possible, molecular modeling is applied here to investigate the structural, thermodynamic, and dynamical properties of a model for the poly(urethane urea) (PUU) oligomer model to selectively capture CO2 in the presence of CH4. In this work, we applied a well-known approach to derive atomic partial charges for atoms in a polymer chain based on self-consistent sampling using quantum chemistry and stochastic dynamics. The interactions of the gases with the PUU model were studied in a pure gas based system as well as in a gas mixture. A detailed structure characterization revealed high interaction of CO2 molecules with the hard segments of the PUU. Therefore, the structural and energy properties explain the reasons for the greater CO2 sorption than CH4. We find that the CO2 sorption is higher than the CH4 with a selectivity of 7.5 at 298 K for the gas mixture. We characterized the Gibbs dividing surface for each system, and the CO2 is confined for a long time at the gas-oligomer model interface. The simulated oligomer model showed performance above the 2008 Robeson's upper bound and may be a potential material for CO2/CH4 separation. Further computational and experimental studies are needed to evaluate the material.

Force fields for the study of biomolecules have been developed in a predominantly organic manner by regular updates over half a century. Together with better algorithms and advances in computer technology, force fields have improved to yield more robust predictions. However, there are also indications to suggest that intramolecular energy functions have not become better and that there still is room for improvement. In this review, systematic efforts toward development of novel force fields from scratch are described. This includes an estimate of the complexity of the problem and the prerequisites in the form of data and algorithms. It is suggested that in order to make progress, an effort is needed to standardize reference data and force field validation benchmarks.

Nuclear magnetic resonance spectroscopy is used routinely for studying the three-dimensional structures and dynamics of proteins. Structure determination is usually done by adding restraints based upon NMR data to a classical energy function and performing restrained molecular simulations. Here we report on the implementation of a script to extract NMR restraints from a NMR-STAR file and export it to the GROMACS software. With this package, it is possible to model distance restraints, dihedral restraints, and orientation restraints. The output from the script is validated by performing simulations with and without restraints, including the ab initio refinement of one peptide.