Markus A. Lill

Computer-aided drug design platform using PyMOL

The understanding and optimization of protein-ligand interactions are instrumental to medicinal chemists investigating potential drug candidates. Over the past couple of decades, many powerful standalone tools for computer-aided drug discovery have been developed in academia providing insight into protein-ligand interactions. As programs are developed by various research groups, a consistent user-friendly graphical working environment combining computational techniques such as docking, scoring, molecular dynamics simulations, and free energy calculations is needed. Utilizing PyMOL we have developed such a graphical user interface incorporating individual academic packages designed for protein preparation (AMBER package and Reduce), molecular mechanics applications (AMBER package), and docking and scoring (AutoDock Vina and SLIDE). In addition to amassing several computational tools under one interface, the computational platform also provides a user-friendly combination of different programs. For example, utilizing a molecular dynamics (MD) simulation performed with AMBER as input for ensemble docking with AutoDock Vina. The overarching goal of this work was to provide a computational platform that facilitates medicinal chemists, many who are not experts in computational methodologies, to utilize several common computational techniques germane to drug discovery. Furthermore, our software is open source and is aimed to initiate collaborative efforts among computational researchers to combine other open source computational methods under a single, easily understandable graphical user interface.

Long-chain carboxychromanols, metabolites of vitamin E, are potent inhibitors of cyclooxygenases

Cyclooxygenase (COX-1/COX-2)-catalyzed eicosanoid formation plays a key role in inflammation-associated diseases. Natural forms of vitamin E are recently shown to be metabolized to long-chain carboxychromanols and their sulfated counterparts. Here we find that vitamin E forms differentially inhibit COX-2-catalyzed prostaglandin E2 in IL-1β-stimulated A549 cells without affecting COX-2 expression, showing the relative potency of γ-tocotrienol ≈ δ-tocopherol > γ-tocopherol ≫ α- or β-tocopherol. The cellular inhibition is partially diminished by sesamin, which blocks the metabolism of vitamin E, suggesting that their metabolites may be inhibitory. Consistently, conditioned media enriched with long-chain carboxychromanols, but not their sulfated counterparts or vitamin E, reduce COX-2 activity in COX-preinduced cells with 5 μM arachidonic acid as substrate. Under this condition, 9′- or 13′-carboxychromanol, the vitamin E metabolites that contain a chromanol linked with a 9- or 13-carbon-length carboxylated side chain, inhibits COX-2 with an IC50 of 6 or 4 μM, respectively. But 13′-carboxychromanol inhibits purified COX-1 and COX-2 much more potently than shorter side-chain analogs or vitamin E forms by competitively inhibiting their cyclooxygenase activity with Ki of 3.9 and 10.7 μM, respectively, without affecting the peroxidase activity. Computer simulation consistently indicates that 13′-carboxychromanol binds more strongly than 9′-carboxychromanol to the substrate-binding site of COX-1. Therefore, long-chain carboxychromanols, including 13′-carboxychromanol, are novel cyclooxygenase inhibitors, may serve as anti-inflammation and anticancer agents, and may contribute to the beneficial effects of certain forms of vitamin E.

Proton shuttle in green fluorescent protein studied by dynamic simulations.

As a direct simulation of a multistep proton transfer reaction involving protein residues, the proton relay shuttle between A and I forms of green fluorescent protein (GFP) is simulated in atomic detail by using a special molecular dynamics simulation technique. Electronic excitation of neutral chromophore in wild-type GFP is generally followed by excited-state proton transfer to a nearby glutamic acid residue via a water molecule and a serine residue. Here we show that the second and third transfer steps occur ultrafast on time scales of several tens of femtoseconds. Proton back-shuttle in the ground state is slower and occurs in a different sequence of events. The simulations provide atomic models of various intermediates and yield realistic rate constants for proton transfer events. In particular, we argue that the I form observed spectroscopically under equilibrium conditions may differ from the I form observed as a fast intermediate by an anti to syn rotation of the carboxyl proton of neutral Glu-222.

Challenges Predicting Ligand-Receptor Interactions of Promiscuous Proteins: The Nuclear Receptor PXR

Transcriptional regulation of some genes involved in xenobiotic detoxification and apoptosis is performed via the human pregnane X receptor (PXR) which in turn is activated by structurally diverse agonists including steroid hormones. Activation of PXR has the potential to initiate adverse effects, altering drug pharmacokinetics or perturbing physiological processes. Reliable computational prediction of PXR agonists would be valuable for pharmaceutical and toxicological research. There has been limited success with structure-based modeling approaches to predict human PXR activators. Slightly better success has been achieved with ligand-based modeling methods including quantitative structure-activity relationship (QSAR) analysis, pharmacophore modeling and machine learning. In this study, we present a comprehensive analysis focused on prediction of 115 steroids for ligand binding activity towards human PXR. Six crystal structures were used as templates for docking and ligand-based modeling approaches (two-, three-, four- and five-dimensional analyses). The best success at external prediction was achieved with 5D-QSAR. Bayesian models with FCFP_6 descriptors were validated after leaving a large percentage of the dataset out and using an external test set. Docking of ligands to the PXR structure co-crystallized with hyperforin had the best statistics for this method. Sulfated steroids (which are activators) were consistently predicted as non-activators while, poorly predicted steroids were docked in a reverse mode compared to 5α-androstan-3β-ol. Modeling of human PXR represents a complex challenge by virtue of the large, flexible ligand-binding cavity. This study emphasizes this aspect, illustrating modest success using the largest quantitative data set to date and multiple modeling approaches.

Dynamic water networks in cytochrome C oxidase from Paracoccus denitrificans investigated by molecular dynamics simulations.

We present a molecular dynamics study of cytochrome c oxidase from Paracoccus denitrificans in the fully oxidized state, embedded in a fully hydrated dimyristoylphosphatidylcholine lipid bilayer membrane. Parallel simulations with different levels of protein hydration, 1.125 ns each in length, were carried out under conditions of constant temperature and pressure using three-dimensional periodic boundary conditions and full electrostatics to investigate the distribution and dynamics of water molecules and their corresponding hydrogen-bonded networks inside cytochrome c oxidase. The majority of the water molecules had residence times shorter than 100 ps, but a few water molecules are fixed inside the protein for up to 1.125 ns. The hydrogen-bonded network in cytochrome c oxidase is not uniformly distributed, and the degree of water arrangement is variable. The average number of solvent sites in the proton-conducting K- and D-pathways was determined. In contrast to single water files in narrow geometries we observe significant diffusion of individual water molecules along these pathways. The highly fluctuating hydrogen-bonded networks, combined with the significant diffusion of individual water molecules, provide a basis for the transfer of protons in cytochrome c oxidase, therefore leading to a better understanding of the mechanism of proton pumping.

Efficient incorporation of protein flexibility and dynamics into molecular docking simulations

Flexibility and dynamics are protein characteristics that are essential for the process of molecular recognition. Conformational changes in the protein that are coupled to ligand binding are described by the biophysical models of induced fit and conformational selection. Different concepts that incorporate protein flexibility into protein-ligand docking within the context of these two models are reviewed. Several computational studies that discuss the validity and possible limitations of such approaches will be presented. Finally, different approaches that incorporate protein dynamics, e.g., configurational entropy, and solvation effects into docking will be highlighted.

Molecular dynamics simulation of proton transport with quantum mechanically derived proton hopping rates (Q-HOP MD)

A very efficient scheme is presented to simulate proton transport by classical molecular dynamics simulation coupled with quantum mechanically derived proton hopping. Simulated proton transfer rates and proton diffusion constants for an excess proton in a box of water molecules are in good agreement with experimental data and with previous simulations that employed empirical valence bond (EVB) theory. For the first time, the proton occupancy of an aspartic acid residue in water was computed directly by MD simulations. Locally enhanced sampling or multi copy techniques were used to facilitate proton release in simulations of an imidazole ring in a solvent box. Summarizing, a quasiclassical description of proton transfer dynamics has been able to capture important kinetic and thermodynamic features of these systems at less than 50% computational overhead compared to standard molecular dynamics simulations. The method can be easily generalized to simulate the protonation equilibria of a large number of titra...

Integrating structure-based and ligand-based approaches for computational drug design

Methods utilized in computer-aided drug design can be classified into two major categories: structure based and ligand based, using information on the structure of the protein or on the biological and physicochemical properties of bound ligands, respectively. In recent years there has been a trend towards integrating these two methods in order to enhance the reliability and efficiency of computer-aided drug-design approaches by combining information from both the ligand and the protein. This trend resulted in a variety of methods that include: pseudoreceptor methods, pharmacophore methods, fingerprint methods and approaches integrating docking with similarity-based methods. In this article, we will describe the concepts behind each method and selected applications.

Metrics for measuring distances in configuration spaces

In order to characterize molecular structures we introduce configurational fingerprint vectors which are counterparts of quantities used experimentally to identify structures. The Euclidean distance between the configurational fingerprint vectors satisfies the properties of a metric and can therefore safely be used to measure dissimilarities between configurations in the high dimensional configuration space. In particular we show that these metrics are a perfect and computationally cheap replacement for the root-mean-square distance (RMSD) when one has to decide whether two noise contaminated configurations are identical or not. We introduce a Monte Carlo approach to obtain the global minimum of the RMSD between configurations, which is obtained from a global minimization over all translations, rotations, and permutations of atomic indices.

Prediction of Small‐Molecule Binding to Cytochrome P450 3A4: Flexible Docking Combined with Multidimensional QSAR

The inhibition of cytochrome P450 3A4 (CYP3A4) by small molecules is a major mechanism associated with undesired drug–drug interactions, which are responsible for a substantial number of late‐stage failures in the pharmaceutical drug‐development process. For a quantitative prediction of associated pharmacokinetic parameters, a computational model was developed that allows prediction of the inhibitory potential of 48 structurally diverse molecules. Based on the experimental structure of CYP3A4, possible binding modes were first sampled by using automated docking (Yeti software) taking protein flexibility into account. The results are consistent with both X‐ray crystallographic data and data from metabolic studies. Next, an ensemble of energetically favorable orientations was composed into a 4D dataset for use as input for a multidimensional QSAR technique (Raptor software). A dual‐shell binding‐site model that allows an explicit induced fit was then generated by using hydrophobicity scoring and hydrogen‐bond propensity. The simulation reached a cross‐validated r2 value of 0.825 and a predictive r2 value of 0.659. On average, the predicted binding affinity of the training ligands deviates by a factor of 2.7 from the experiment; those of the test set deviate by a factor of 3.8 in Ki.