Biomedical subjects

Markus A Lill

Publications and source records attributed to Markus A Lill.

15 recordsLinked to original sources

PubMed2007

Simulating alpha/beta selectivity at the human thyroid hormone receptor: consensus scoring using multidimensional QSAR.

We present a consensus-scoring study on the human thyroid hormone receptor alpha and beta using two receptor-modeling concepts (software Quasar and Raptor) that are based on multidimensional QSAR and allow for the explicit simulation of induced fit. The binding mode of 82 agonists and indirect antagonists, spanning an activity range of seven orders of magnitude in K(i), was identified through flexible docking to the respective X-ray crystal structures (Yeti software) and represented by a 4D data set with up to four conformations per compound. The receptor surrogates for the thyroid alpha receptor converged at a cross-validated r(2) of 0.846/0.919 (64 training compounds; for Quasar and Raptor, respectively) and yielded a predictive r(2) of 0.812/0.814 (18 test compounds); the models for the thyroid beta receptor resulted in a cross-validated r(2) of 0.823/0.909 and a predictive r(2) of 0.665/0.796, respectively. Consensus was achieved as, on average, the calculated activities of the training set differ only by a factor of 2.2 in K(i) and those of the test set by a factor of 2.8 when predicted by Quasar and Raptor, respectively.

Binding Sites↗

PubMed2006

Single hepatitis-B virus core capsid binding to individual nuclear pore complexes in Hela cells.

We investigate the interaction of hepatitis B virus capsids lacking a nuclear localization signal with nuclear pore complexes (NPCs) in permeabilized HeLa cells. Confocal and wide-field optical images of the nuclear envelope show well-spaced individual NPCs. Specific interactions of capsids with single NPCs are characterized by extended residence times of capsids in the focal volume which are characterized by fluorescence correlation spectroscopy. In addition, single-capsid-tracking experiments using fast wide-field fluorescence microscopy at 50 frames/s allow us to directly observe specific binding via a dual-color colocalization of capsids and NPCs. We find that binding occurs with high probability on the nuclear-pore ring moiety, at 44 +/- 9 nm radial distance from the central axis.

Capsid↗

PubMed2006

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.

Cytochrome P-450 CYP3A↗

PubMed2006

The challenge of predicting drug toxicity in silico.

Poor pharmacokinetics, side effects and compound toxicity are frequent causes of late-stage failures in drug development. A safe in silico identification of adverse effects triggered by drugs and chemicals would be highly desirable as it not only bears economical potential but also spawns a variety of ecological benefits: sustainable resource management, reduction of animal models and possibly less risky clinical trials. In computer-aided drug discovery, both existing and hypothetical compounds may be studied; the methods are fast, reproducible, and typically based on human bioregulators, making the question of transferability obsolete. In the recent past, our laboratory contributed towards the development of in silico concepts (--> multi-dimensional QSAR) and validated a series of "virtual test kits" based on the oestrogen, androgen, thyroid, and aryl hydrocarbon receptor (endocrine disruption, receptor-mediated toxicity) as well as on the enzyme cytochrome P450 3A4 (metabolic transformations, drug-drug interactions). The test kits are based on the three-dimensional structure of their target protein (i.e. ER(alphabeta), AR, TR(alphabeta), CYP450) or a surrogate thereof (AhR) and were trained using a representative selection of 362 substances. Subsequent evaluation of 107 compounds different therefrom showed that binding affinities are predicted close to experimental uncertainty. These results suggest that our approach is suited for the in silico identification of adverse effects triggered by drugs and chemicals and encouraged us to compile an Internet Database for the virtual screening of drugs and chemicals for toxic effects.

Computational Biology↗

PubMed2005

Impact of induced fit on ligand binding to the androgen receptor: a multidimensional QSAR study to predict endocrine-disrupting effects of environmental chemicals.

We investigated the influence of induced fit of the androgen receptor binding pocket on free energies of ligand binding. On the basis of a novel alignment procedure using flexible docking, molecular dynamics simulations, and linear-interaction energy analysis, we simulated the binding of 119 molecules representing six compound classes. The superposition of the ligand molecules emerging from the combined protocol served as input for Raptor, a receptor-modeling tool based on multidimensional QSAR allowing for ligand-dependent induced fit. Throughout our study, protein flexibility was explicitly accounted for. The model converged at a cross-validated r(2) = 0.858 (88 training compounds) and yielded a predictive r(2) = 0.792 (26 test compounds), thereby predicting the binding affinity of all compounds close to their experimental value. We then challenged the model by testing five molecules not belonging to compound classes used to train the model: the IC(50) values were predicted within a factor of 4.5 compared to the experimental data. The demonstrated predictivity of the model suggests that our approach may well be beneficial for both drug discovery and the screening of environmental chemicals for endocrine-disrupting effects, a problem that has recently become a cause for concern among scientists, environmental advocates, and politicians alike.

Benzhydryl Compounds↗

PubMed2005

In silico prediction of harmful effects triggered by drugs and chemicals.

While the computer-assisted discovery and optimization of drug candidates based on the known three-dimensional structure of the macromolecular target (structure-based design) or a binding-site surrogate (receptor modeling) is doubtless one of the more potent approaches in rational drug design, the simulation and quantification of side effects triggered by drugs and chemicals are still in their infancy. Major obstacles include the often not available 3D structure of the molecular target, the low specificity of the involved bioregulators and the identification of the controlling metabolic pathways. In the recent past, our laboratory has explored concepts allowing to simulate receptor-mediated toxic phenomena by developing algorithms, allowing to construct realistic 3D binding-site surrogates of receptors known or assumed triggering adverse effects and validating them against large batches of molecular data. The underlying technology (software Quasar and Raptor, respectively) specifically allows for induced fit, solvation phenomena and entropic effects. It has been applied to various systems both of pharmacological and toxicological interest including the neurokinin-1, chemokine-3, bradykinin B(2), steroid, 5 HT(2A), aryl hydrocarbon, estrogen and androgen receptor, respectively. In this account, we describe the design of a virtual laboratory allowing for a reliable estimation of harmful effects triggered by drugs, chemicals and their metabolites in silico. In the recent past, the Biographics Laboratory 3R has compiled a 3D database including the surrogates of three major receptor systems known to mediate adverse effects (the aryl hydrocarbon, the estrogen and the androgen receptor, respectively) and validated them against a total of 345 compounds (drugs, chemicals, toxins) using multidimensional QSAR technologies. Within this pilot project, we could demonstrate that our virtual laboratory is able to both recognize toxic compounds substantially different from those used in the training set as well as to classify harmless compounds as being nontoxic. This suggests that our approach may be used for the prediction of adverse effects of drug molecules and chemicals. It is the aim to provide cost-covering access to this technology--particularly to universities, hospitals and regulatory bodies--as it bears a significant potential to recognize hazardous compounds early in the development process and hence improve resource and waste management as well as reduce animal testing. The Biographics Laboratory 3R is a non-profit-oriented organization aimed at reducing animal experimentation in the biomedical sciences by computational approaches (cf. http://www.biograf.ch).

Computer Simulation↗

PubMed2005

Kinetics of the initial steps of G protein-coupled receptor-mediated cellular signaling revealed by single-molecule imaging.

We report on an in vivo single-molecule study of the signaling kinetics of G protein-coupled receptors (GPCR) performed using the neurokinin 1 receptor (NK1R) as a representative member. The NK1R signaling cascade is triggered by the specific binding of a fluorescently labeled agonist, substance P (SP). The diffusion of single receptor-ligand complexes in plasma membrane of living HEK 293 cells is imaged using fast single-molecule wide-field fluorescence microscopy at 100 ms time resolution. Diffusion trajectories are obtained which show intra- and intertrace heterogeneity in the diffusion mode. To investigate universal patterns in the diffusion trajectories we take the ligand-binding event as the common starting point. This synchronization allows us to observe changes in the character of the ligand-receptor-complex diffusion. Specifically, we find that the diffusion of ligand-receptor complexes is slowed down significantly and becomes more constrained as a function of time during the first 1000 ms. The decelerated and more constrained diffusion is attributed to an increasing interaction of the GPCR with cellular structures after the ligand-receptor complex is formed.

Cell Line↗

PubMed2005

Combining protein modeling and 6D-QSAR. Simulating the binding of structurally diverse ligands to the estrogen receptor.

We present a concept for the in silico simulation of adverse effects triggered by drugs and chemicals. The underlying philosophy combines flexible docking (software Yeti) for the identification of the binding mode(s) and 6D-QSAR (software Quasar) for their quantification. The results obtained for 106 diverse molecules binding to the estrogen receptor (q2 = 0.903; p2 = 0.885) suggest that our approach is suitable for the identification of an endocrine-disrupting potential associated with drugs and chemicals.

Binding Sites↗

PubMed2005

Novel ligands for the chemokine receptor-3 (CCR3): a receptor-modeling study based on 5D-QSAR.

We recently reported the development of a receptor-modeling concept based on 5D-QSAR (quantitative structure-activity relationships) and which explicitly allows for the simulation of induced fit. In this account, we report its utilization toward the design of novel compounds able to inhibit the chemokine receptor-3 (CCR3). The study was based on a total of 141 compounds, representing four different substance classes. Using the Quasar software, we built two receptor surrogates that yielded a cross-validated r(2) value of 0.950/0.861 and a predictive r(2) of 0.879/0.798, respectively. The model was then employed to predict the activity of 58 hypothetical compounds featuring two variation patterns: lipophilic substitutions and amphiphilic H-bond acceptors. Eleven of the proposed ligands show a calculated binding affinity lower than any compound within the training set; the most potent candidate molecule is expected to bind at an IC(50) of 0.3 nM.

Benzene Derivatives↗

PubMed2005

Virtual test kits for predicting harmful effects triggered by drugs and chemicals mediated by specific proteins.

Poor pharmacokinetics and toxicity are not only frequent causes of late-stage failures in drug development but also a source for unnecessary animal tests. In drug discovery and for the assessment of the toxic potential of chemicals, in silico techniques are nowadays considered as valuable alternatives to in vivo approaches. Based on a receptor-modelling concept developed at our laboratory (multidimensional QSAR), we have developed and validated virtual test kits for the estrogen, androgen and aryl hydrocarbon receptor (endocrine disruption), for cytochrome P450 3A4 (metabolic transformations) and most recently for the thyroid receptor. These surrogates have been tested against a total of 430 compounds and are able to predict the binding affinity close to the experimental uncertainty. These results suggest that our approach is suited for the in silico identification of adverse effects triggered by drugs and chemicals. Consequently, we are prepared to offer a free testing to selected academic institutions and non-profit oriented organisations.

Cytochrome P-450 CYP3A↗

PubMed2004

Raptor: combining dual-shell representation, induced-fit simulation, and hydrophobicity scoring in receptor modeling: application toward the simulation of structurally diverse ligand sets.

We present a novel receptor-modeling approach (software Raptor) based on multidimensional quantitative structure-activity relationships (QSARs). To accurately predict relative free energies of ligand binding, it is of utmost importance to simulate induced fit. In Raptor, we explicitly and anisotropically allow for this phenomenon by a dual-shell representation of the receptor surrogate. In our concept, induced fit is not limited to steric aspects but includes the variation of the physicochemical fields along with it. The underlying scoring function for evaluating ligand-receptor interactions includes directional terms for hydrogen bonding and hydrophobicity and thereby treats solvation effects implicitly. This makes the approach independent from a partial-charge model and, as a consequence, allows one to smoothly model ligand molecules binding to the receptor with different net charges. We have applied the new concept toward the estimation of ligand-binding energies associated with the chemokine receptor-3 (50 ligands: r(2) = 0.965; p(2) = 0.932), the bradykinin B(2) receptor (52 ligands: r(2) = 0.949; p(2) = 0.859), and the estrogen receptor (116 ligands: r(2) = 0.908; p(2) = 0.907), respectively.

Binding Sites↗

PubMed2004

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.

Computer Simulation↗

PubMed2003

Internet laboratory for predicting harmful effects triggered by drugs and chemicals--a progress report.

The main objective of our institution is to establish a virtual laboratory on the Internet to allow for a reliable in silico estimation of harmful effects triggered by drugs, chemicals and their metabolites. In the past two years, we have compiled a pilot system including the 3D models of five receptors known to mediate adverse effects (the Ah, 5HT(2A), cannabinoid, GABA(A), and estrogen receptor, respectively) and tested them against 280 compounds (drugs, chemicals, toxins). Within this set-up we could demonstrate that our concept is able to both recognise toxic compounds substantially different from those used in the training set as well as to classify harmless compounds clearly as being non-toxic at low-level doses. This suggests that our approach can be used for the prediction of adverse effects of drug molecules and chemicals. It is the aim to provide free access to this 3D data base, particularly to universities, hospitals and regulatory bodies as it bears a significant potential to recognise hazardous compounds early in the development process and withdraw them from the evaluation pipeline. Hence, for substances recognised as hazardous in silico, subsequent toxicity tests involving animal models become obsolete.

Animal Testing Alternatives↗

PubMed2002

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.

Computer Simulation↗

PubMedDate not supplied

Combining 4D pharmacophore generation and multidimensional QSAR: modeling ligand binding to the bradykinin B2 receptor.

We recently reported the development of two receptor-modeling concepts (software Quasar and Raptor) based on multidimensional quantitative structure-activity relationships (QSAR) and allowing for the explicit simulation of induced fit. As the identification of the bioactive configuration of ligand molecules in such studies is all but unambiguous, each compound may be represented by an ensemble of different conformations, orientations, stereoisomers, and protonation states, leading to a 4D data set. In this account, we present a novel technology (software Symposar) allowed to automatically generate a 4D pharmacophore as input for multidimensional QSAR. Symposar aligns ligands utilizing fuzzylike 2D-subfeature mapping and, subsequently, a Monte Carlo search on a 3D similarity grid. The two-step concept (4D pharmacophore generation and quantification of ligand binding by multidimensional QSAR) was applied to 186 compounds binding to the bradykinin B2 receptor. The prediction of their binding affinity by means of the Quasar and Raptor technologies allowed for consensus scoring and generated topologically and quantitatively consistent receptor models. These converged at a cross-validated r2 of 0.752 and 0.815 and yielded a predictive r2 of 0.784 and 0.853 for a test set (for Quasar and Raptor, respectively).

Binding Sites↗