Biomedical subjects

Holger Gohlke

Publications and source records attributed to Holger Gohlke.

17 recordsLinked to original sources

PubMed2006

Structure of Aquifex aeolicus argonaute highlights conformational flexibility of the PAZ domain as a potential regulator of RNA-induced silencing complex function.

Gene silencing mediated by RNA interference requires the sequence-specific recognition of target mRNA by the endonuclease Argonaute, the primary enzymatic component of the RNA-induced silencing complex. We report the crystal structure of Aquifex aeolicus Argonaute, refined at 3.2A resolution. Relative to recent Argonaute structures, a 24 degrees reorientation of the PAZ domain in our structure opens a basic cleft between the N-terminal and PAZ domains, exposing the guide strand binding pocket of PAZ. This rearrangement leads to a branched, Y-shaped system of grooves that extends through the molecule and merges in a central channel containing the catalytic residues. A 5.5-ns molecular dynamics simulation of Argonaute shows a strong tendency of the PAZ and N-terminal domains to be mobile. Binding of single-stranded DNA to Argonaute monitored by total internal reflection fluorescence spectroscopy shows biphasic kinetics, also indicative of domain rearrangement upon DNA binding. Conformational rearrangement of the PAZ domain may therefore be critical for the catalytic cycle of Argonaute and the RNA-induced silencing complex.

Bacteria↗

PubMed2006

A natural coarse graining for simulating large biomolecular motion.

Various coarse graining schemes have been proposed to speed up computer simulations of the motion within large biomolecules, which can contain hundreds of thousands of atoms. We point out here that there is a very natural way of doing this, using the rigid regions identified within a biomolecule as the coarse grain elements. Subsequently, computer resources can be concentrated on the flexible connections between the rigid units. Examples of the use of such techniques are given for the protein barnase and the maltodextrin binding protein, using the geometric simulation technique FRODA and the rigidity enhanced elastic network model RCNMA to compute mobilities and atomic displacements.

Bacterial Proteins↗

PubMed2006

Multiscale modeling of macromolecular conformational changes combining concepts from rigidity and elastic network theory.

The development of a two-step approach for multiscale modeling of macromolecular conformational changes is based on recent developments in rigidity and elastic network theory. In the first step, static properties of the macromolecule are determined by decomposing the molecule into rigid clusters by using the graph-theoretical approach FIRST and an all-atom representation of the protein. In this way, rigid clusters are not limited to consist of residues adjacent in sequence or secondary structure elements as in previous studies. Furthermore, flexible links between rigid clusters are identified and can be modeled as such subsequently. In the second step, dynamical properties of the molecule are revealed by the rotations-translations of blocks approach (RTB) using an elastic network model representation of the coarse-grained protein. In this step, only rigid body motions are allowed for rigid clusters, whereas links between them are treated as fully flexible. The approach was tested on a data set of 10 proteins that showed conformational changes on ligand binding. For efficiency, coarse-graining the protein results in a remarkable reduction of memory requirements and computational times by factors of 9 and 27 on average and up to 25 and 125, respectively. For accuracy, directions and magnitudes of motions predicted by our approach agree well with experimentally determined ones, despite embracing in extreme cases >50% of the protein into one rigid cluster. In fact, the results of our method are in general comparable with when no or a uniform coarse-graining is applied; and the results are superior if the movement is dominated by loop or fragment motions. This finding indicates that explicitly distinguishing between flexible and rigid regions is advantageous when using a simplified protein representation in the second step. Finally, motions of atoms in rigid clusters are also well predicted by our approach, which points to the need to consider mobile protein regions in addition to flexible ones when modeling correlated motions.

Elasticity↗

PubMed2006

Targeting protein-protein interactions with small molecules: challenges and perspectives for computational binding epitope detection and ligand finding.

A promising way to interfere with biological processes is through the control of protein-protein interactions by means of small molecules that modulate the formation of protein-protein complexes. Although the feasibility of this approach has been demonstrated in principle by recent results, many of the small-molecule modulators known to date have not been found by rational design approaches. In large part this is due to the challenges that one faces in dealing with protein binding epitopes compared to, e.g., enzyme binding pockets. Recent advances in the understanding of the energetics and dynamics of protein binding interfaces and methodological developments in the field of structure-based drug design methods may open up a way to apply rational design approaches also for finding protein-protein interaction modulators. These advances and developments include (I) computational approaches to dissect binding interfaces in terms of energetic contributions of single residues (to identify "hot spot" residues), (II) prediction of potential binding sites from unbound protein structures, (III) recognition of allosteric binding sites as alternatives to directly targeting interfaces, (IV) docking approaches that consider protein flexibility and improved descriptions of the solvent influence on electrostatic interactions, and (V) data-driven docking approaches. Here, we will summarize these developments with a particular emphasis on their applicability to screen for or design small-molecule modulators of protein-protein interactions.

Binding Sites↗

PubMed2005

DrugScore(CSD)-knowledge-based scoring function derived from small molecule crystal data with superior recognition rate of near-native ligand poses and better affinity prediction.

Following the formalism used for the development of the knowledge-based scoring function DrugScore, new distance-dependent pair potentials are obtained from nonbonded interactions in small organic molecule crystal packings. Compared to potentials derived from protein-ligand complexes, the better resolved small molecule structures provide relevant contact data in a more balanced distribution of atom types and produce potentials of superior statistical significance and more detailed shape. Applied to recognizing binding geometries of ligands docked into proteins, this new scoring function (DrugScore(CSD)) ranks the crystal structures of 100 protein-ligand complexes best among up to 100 generated decoy geometries in 77% of all cases. Accepting root-mean-square deviations (rmsd) of up to 2 angstroms from the native pose as well-docked solutions, a correct binding mode is found in 87% of the cases. This translates into an improvement of the new scoring function of 57% with respect to the retrieval of the crystal structure and 20% with respect to the identification of a well-docked ligand pose compared to the original Protein Data Bank-based DrugScore. In the analysis of decoy geometries of cross-docking studies, DrugScore(CSD) shows equivalent or increased performance compared to the original PDB-based DrugScore. Furthermore, DrugScore(CSD) predicts binding affinities convincingly. Reducing the set of docking solutions to examples that deviate increasingly from the native pose results in a loss of performance of DrugScore(CSD). This indicates that a necessary prerequisite to successfully resolving the scoring problem with a more discriminative scoring function is the generation of highly accurate ligand poses, which approximate the native pose to below 1 angstroms rmsd, in a docking run.

Crystallography, X-Ray↗

PubMed2005

Improving binding mode predictions by docking into protein-specifically adapted potential fields.

The development of a protein-specifically adapted objective function for docking is described. Structural and energetic information about known protein-ligand complexes is exploited to tailor knowledge-based potentials using a "reverse", protein-based CoMFA-type (=AFMoC) approach. That way, effects due to protein flexibility and information about multiple solvation schemes can be implicitly incorporated. Compared to the application of AFMoC for binding affinity predictions, a Shannon entropy based column filtering of the descriptor matrix and the capping of adapted repulsive potentials within the binding site have turned out to be crucial for the success of this method. The new developed approach (AFMoC(obj)) was validated on a data set of 66 HIV-1 protease inhibitors, for which experimental structural information was available. Convincingly, for ligands with up to 20 rotatable bonds, in more than 75% of all cases a binding mode below 2 A rmsd has been identified on the first scoring rank when AFMoC(obj)-based potentials were used as the objective function in AutoDock. With respect to nonadapted DrugScore or AutoDock fields, the binding mode prediction accuracy was significantly improved by 14%. Noteworthy, very similar results were obtained for training and test set compounds, demonstrating the strength and robustness of this method. Implications of our findings for binding affinity predictions and its usage in virtual screening are further discussed.

Binding Sites↗

PubMed2005

The Amber biomolecular simulation programs.

We describe the development, current features, and some directions for future development of the Amber package of computer programs. This package evolved from a program that was constructed in the late 1970s to do Assisted Model Building with Energy Refinement, and now contains a group of programs embodying a number of powerful tools of modern computational chemistry, focused on molecular dynamics and free energy calculations of proteins, nucleic acids, and carbohydrates.

Algorithms↗

PubMed2004

Change in protein flexibility upon complex formation: analysis of Ras-Raf using molecular dynamics and a molecular framework approach.

Changes in flexibility upon protein-protein complex formation of H-Ras and the Ras-binding domain of C-Raf1 have been investigated using the molecular framework approach FIRST (Floppy Inclusion and Rigid Substructure Topology) and molecular dynamics simulations (MD) of in total approximately 35 ns length. In a computational time of about one second, FIRST identifies flexible and rigid regions in a single, static three-dimensional molecular framework, whose vertices represent protein atoms and whose edges represent covalent and non-covalent (hydrogen bond and hydrophobic) constraints and fixed bond angles within the protein. The two methods show a very good agreement with respect to the identification of changes in flexibility in both binding partners on a local scale. This implies that flexibility can be successfully predicted by identifying which bonds limit motion within a molecule and how they are coupled. In particular, as identified by MD, the beta-sheet in Raf shows considerably more pronounced orientational correlations in the bound state compared to the unbound state. Similarly, FIRST assigns the beta-sheet to the largest rigid cluster of the complex. Interestingly, FIRST allows us to identify that interactions across the interface (but not conformational changes upon complex formation) result in the observed rigidification. Since regions of the beta-sheet of Raf that do not interact directly with Ras become rigidified, this also demonstrates the long-range aspect to rigidity percolation. Possible implications of the change of flexibility of the Ras-binding domain of Raf on the activation of Raf upon complex formation are discussed. Finally, the sensitivity of FIRST results with respect to the representation of non-covalent interactions used as constraints is probed.

Algorithms↗

PubMed2004

Assessing scoring functions for protein-ligand interactions.

An assessment of nine scoring functions commonly applied in docking using a set of 189 protein-ligand complexes is presented. The scoring functions include the CHARMm potential, the scoring function DrugScore, the scoring function used in AutoDock, the three scoring functions implemented in DOCK, as well as three scoring functions implemented in the CScore module in SYBYL (PMF, Gold, ChemScore). We evaluated the abilities of these scoring functions to recognize near-native configurations among a set of decoys and to rank binding affinities. Binding site decoys were generated by molecular dynamics with restraints. To investigate whether the scoring functions can also be applied for binding site detection, decoys on the protein surface were generated. The influence of the assignment of protonation states was probed by either assigning "standard" protonation states to binding site residues or adjusting protonation states according to experimental evidence. The role of solvation models in conjunction with CHARMm was explored in detail. These include a distance-dependent dielectric function, a generalized Born model, and the Poisson equation. We evaluated the effect of using a rigid receptor on the outcome of docking by generating all-pairs decoys ("cross-decoys") for six trypsin and seven HIV-1 protease complexes. The scoring functions perform well to discriminate near-native from misdocked conformations, with CHARMm, DOCK-energy, DrugScore, ChemScore, and AutoDock yielding recognition rates of around 80%. Significant degradation in performance is observed in going from decoy to cross-decoy recognition for CHARMm in the case of HIV-1 protease, whereas DrugScore and ChemScore, as well as CHARMm in the case of trypsin, show only small deterioration. In contrast, the prediction of binding affinities remains problematic for all of the scoring functions. ChemScore gives the highest correlation value with R(2) = 0.51 for the set of 189 complexes and R(2) = 0.43 for the set of 116 complexes that does not contain any of the complexes used to calibrate this scoring function. Neither a more accurate treatment of solvation nor a more sophisticated charge model for zinc improves the quality of the results. Improved modeling of the protonation states, however, leads to a better prediction of binding affinities in the case of the generalized Born and the Poisson continuum models used in conjunction with the CHARMm force field.

Binding Sites↗

PubMed2004

Converging free energy estimates: MM-PB(GB)SA studies on the protein-protein complex Ras-Raf.

Estimating protein-protein interaction energies is a very challenging task for current simulation protocols. Here, absolute binding free energies are reported for the complex H-Ras/C-Raf1 using the MM-PB(GB)SA approach, testing the internal consistency and model dependence of the results. Averaging gas-phase energies (MM), solvation free energies as determined by Generalized Born models (GB/SA), and entropic contributions calculated by normal mode analysis for snapshots obtained from 10 ns explicit-solvent molecular dynamics in general results in an overestimation of the binding affinity when a solvent-accessible surface area-dependent model is used to estimate the nonpolar solvation contribution. Applying the sum of a cavity solvation free energy and explicitly modeled solute-solvent van der Waals interaction energies instead provides less negative estimates for the nonpolar solvation contribution. When the polar contribution to the solvation free energy is determined by solving the Poisson-Boltzmann equation (PB) instead, the calculated binding affinity strongly depends on the atomic radii set chosen. For three GB models investigated, different absolute deviations from PB energies were found for the unbound proteins and the complex. As an alternative to normal-mode calculations, quasiharmonic analyses have been performed to estimate entropic contributions due to changes of solute flexibility upon binding. However, such entropy estimates do not converge after 10 ns of simulation time, indicating that sampling issues may limit the applicability of this approach. Finally, binding free energies estimated from snapshots of the unbound proteins extracted from the complex trajectory result in an underestimate of binding affinity. This points to the need to exercise caution in applying the computationally cheaper "one-trajectory-alternative" to systems where there may be significant changes in flexibility and structure due to binding. The best estimate for the binding free energy of Ras-Raf obtained in this study of -8.3 kcal mol(-1) is in good agreement with the experimental result of -9.6 kcal mol(-1), however, further probing the transferability of the applied protocol that led to this result is necessary.

Calorimetry↗

PubMed2003

Ligand-supported homology modelling of protein binding-sites using knowledge-based potentials.

A new approach, MOBILE, is presented that models protein binding-sites including bound ligand molecules as restraints. Initially generated, homology models of the target protein are refined iteratively by including information about bioactive ligands as spatial restraints and optimising the mutual interactions between the ligands and the binding-sites. Thus optimised models can be used for structure-based drug design and virtual screening. In a first step, ligands are docked into an averaged ensemble of crude homology models of the target protein. In the next step, improved homology models are generated, considering explicitly the previously placed ligands by defining restraints between protein and ligand atoms. These restraints are expressed in terms of knowledge-based distance-dependent pair potentials, which were compiled from crystallographically determined protein-ligand complexes. Subsequently, the most favourable models are selected by ranking the interactions between the ligands and the generated pockets using these potentials. Final models are obtained by selecting the best-ranked side-chain conformers from various models, followed by an energy optimisation of the entire complex using a common force-field. Application of the knowledge-based pair potentials proved efficient to restrain the homology modelling process and to score and optimise the modelled protein-ligand complexes. For a test set of 46 protein-ligand complexes, taken from the Protein Data Bank (PDB), the success rate of producing near-native binding-site geometries (rmsd<2.0A) with MODELLER is 70% when the ligand restrains the homology modelling process in its native orientation. Scoring these complexes with the knowledge-based potentials, in 66% of the cases a pose with rmsd <2.0A is found on rank 1. Finally, MOBILE has been applied to two case studies modelling factor Xa based on trypsin and aldose reductase based on aldehyde reductase.

Binding Sites↗

PubMed2003

Insights into protein-protein binding by binding free energy calculation and free energy decomposition for the Ras-Raf and Ras-RalGDS complexes.

Absolute binding free energy calculations and free energy decompositions are presented for the protein-protein complexes H-Ras/C-Raf1 and H-Ras/RalGDS. Ras is a central switch in the regulation of cell proliferation and differentiation. In our study, we investigate the capability of the molecular mechanics (MM)-generalized Born surface area (GBSA) approach to estimate absolute binding free energies for the protein-protein complexes. Averaging gas-phase energies, solvation free energies, and entropic contributions over snapshots extracted from trajectories of the unbound proteins and the complexes, calculated binding free energies (Ras-Raf: -15.0(+/-6.3)kcal mol(-1); Ras-RalGDS: -19.5(+/-5.9)kcal mol(-1)) are in fair agreement with experimentally determined values (-9.6 kcal mol(-1); -8.4 kcal mol(-1)), if appropriate ionic strength is taken into account. Structural determinants of the binding affinity of Ras-Raf and Ras-RalGDS are identified by means of free energy decomposition. For the first time, computationally inexpensive generalized Born (GB) calculations are applied in this context to partition solvation free energies along with gas-phase energies between residues of both binding partners. For selected residues, in addition, entropic contributions are estimated by classical statistical mechanics. Comparison of the decomposition results with experimentally determined binding free energy differences for alanine mutants of interface residues yielded correlations with r(2)=0.55 and 0.46 for Ras-Raf and Ras-RalGDS, respectively. Extension of the decomposition reveals residues as far apart as 25A from the binding epitope that can contribute significantly to binding free energy. These "hotspots" are found to show large atomic fluctuations in the unbound proteins, indicating that they reside in structurally less stable regions. Furthermore, hotspot residues experience a significantly larger-than-average decrease in local fluctuations upon complex formation. Finally, by calculating a pair-wise decomposition of interactions, interaction pathways originating in the binding epitope of Raf are found that protrude through the protein structure towards the loop L1. This explains the finding of a conformational change in this region upon complex formation with Ras, and it may trigger a larger structural change in Raf, which is considered to be necessary for activation of the effector by Ras.

Biophysical Phenomena↗

PubMed2003

3D QSAR analyses-guided rational design of novel ligands for the (alpha4)2(beta2)3 nicotinic acetylcholine receptor.

Three-dimensional quantitative structure-activity relationship methods, the comparative molecular field analysis (CoMFA) and the comparative molecular similarity indices analysis (CoMSIA), were applied using a training set of 45 ligands of the (alpha4)2(beta2)3 nicotinic acetylcholine receptor (nAChR). All compounds are related to (-)-epibatidine, (-)-cytisine, (+)-anatoxin-a, and (-)-ferruginine, and additionally, novel diazabicyclo[4.2.1]nonane- and quinuclidin-2-ene-based structures were included. Their biological data have been determined by utilizing the same experimental protocol. Statistically reliable models of good predictive power (CoMFA r2 = 0.928, q2 = 0.692, no. of components = 3; CoMSIA r2 = 0.899, q2 = 0.701, no. of components = 3) were achieved. The results obtained were graphically interpreted in terms of field contribution maps. Hence, physicochemical determinants of binding, such as steric and electrostatic and, for the first time, hydrophobic, hydrogen bond donor, and hydrogen bond acceptor properties, were mapped back onto the molecular structures of a set of nAChR modulators. In particular, changes in the binding affinity of the modulators as a result of modifications in the aromatic ring systems could be rationalized by the steric, electrostatic, hydrophobic, and hydrogen bond acceptor properties. These results were used to guide the rational design of new nAChR ligands such as 48-52 and 54, which were subsequently synthesized for the first time and tested. Key steps of our synthetic approaches were successfully applied Stille and Suzuki cross-coupling reactions. Predictive r2 values of 0.614 and 0.660 for CoMFA and CoMSIA, respectively, obtained for 22 in part previously unknown ligands for the (alpha4)2(beta2)3 subtype, demonstrate the high quality of the 3D QSAR models.

Alkaloids↗

PubMed2002

DrugScore meets CoMFA: adaptation of fields for molecular comparison (AFMoC) or how to tailor knowledge-based pair-potentials to a particular protein.

The development of a new tailor-made scoring function to predict binding affinities of protein-ligand complexes is described. Knowledge-based pair-potentials are specifically adapted to a particular protein by considering additional ligand-based information. The formalism applied to derive the new function is similar to the well-known CoMFA approach, however, the fields used in the approach originate from the protein environment (and not from the aligned ligands as in CoMFA, thus, a "reverse" CoMFA (= AFMoC) named Adaptation of Fields for Molecular Comparison is performed). A regular-spaced grid is placed into the binding site and knowledge-based pair-potentials between protein atoms and ligand atom probes are mapped onto the grid intersections resulting in "potential fields". By multiplying distance-dependent atom-type properties of actual ligands docked into the binding site with the neighboring grid values, "interaction fields" are produced from the original "potential fields". In a PLS analysis, these atom-type specific interaction fields are correlated to the actual binding affinities of the embedded ligands, resulting in individual weighting factors for each field value. As in CoMFA, the results of the analysis can be interpreted in graphical terms by contribution maps, and binding affinities of novel ligands are predicted by applying the derived 3D QSAR equation. The scope of the new method is demonstrated using thermolysin and glycogen phosphorylase b as test examples. Impressive improvements of the predictive power for affinity prediction can be achieved compared to the application of the original knowledge-based potentials by considering a sample set of only 15 known training ligands. Thus, with growing information about the drug target studied, the new method allows one to move gradually from generally valid to protein-specifically adapted pair-potentials, depending on the amount of training information available and its degree of structural diversity. In addition, convincing predictive power is also achieved for ligand poses generated by automatic docking tools.

Databases, Factual↗

PubMed2002

Approaches to the description and prediction of the binding affinity of small-molecule ligands to macromolecular receptors.

The influence of a xenobiotic compound on an organism is usually summarized by the expression biological activity. If a controlled, therapeutically relevant, and regulatory action is observed the compound has potential as a drug, otherwise its toxicity on the biological system is of interest. However, what do we understand by the biological activity? In principle, the overall effect on an organism has to be considered. However, because of the complexity of the interrelated processes involved, as a simplification primarily the "main action" on the organism is taken into consideration. On the molecular level, biological activity corresponds to the binding of a (low-molecular weight) compound to a macromolecular receptor, usually a protein. Enzymatic reactions or signal-transduction cascades are thereby influenced with respect to their function for the organism. We regard this binding as a process under equilibrium conditions; thus, binding can be described as an association or dissociation process. Accordingly, biological activity is expressed as the affinity of both partners for each other, as a thermodynamic equilibrium quantity. How well do we understand these terms and how well are they theoretically predictable today? The holy grail of rational drug design is the prediction of the biological activity of a compound. The processes involving ligand binding are extremely complicated, both ligand and protein are flexible molecules, and the energy inventory between the bound and unbound states must be considered in aqueous solution. How sophisticated and reliable are our experimental approaches to obtaining the necessary insight? The present review summarizes our current understanding of the binding affinity of a small-molecule ligand to a protein. Both theoretical and empirical approaches for predicting binding affinity, starting from the three-dimensional structure of a protein-ligand complex, will be described and compared. Experimental methods, primarily microcalorimetry, will be discussed. As a perspective, our own knowledge-based approach towards affinity prediction and experimental data on factorizing binding contributions to protein-ligand binding will be presented.

Animals↗

PubMed2002

Docking into knowledge-based potential fields: a comparative evaluation of DrugScore.

A new application of DrugScore is reported in which the knowledge-based pair potentials serve as objective function in docking optimizations. The Lamarckian genetic algorithm of AutoDock is used to search for favorable ligand binding modes guided by DrugScore grids as representations of the protein binding site. The approach is found to be successful in many cases where DrugScore-based re-ranking of already docked ligand conformations does not yield satisfactory results. Compared to the AutoDock scoring function, DrugScore yields slightly superior results in flexible docking.

Algorithms↗

PubMed2002

Synthesis and nicotinic binding studies on enantiopure diazine analogues of the novel (2-chloro-5-pyridyl)-9-azabicyclo[4.2.1]non-2-ene UB-165.

As part of our program aimed at optimizing therapeutic effects over toxic effects (as observed in the naturally occurring nicotinic acetylcholine receptor modulators (-)-nicotine, (-)-epibatidine, (-)-ferruginine, and (+)-anatoxin-a), we investigated the bioisosteric potential of diazines in the field of (+)-anatoxin-a-type structures. In the series of diazine analogues of deschloro-UB-165 (DUB-165, 6), bioisosteric replacement of the 3-pyridyl pharmacophoric element by a 4-pyridazinyl, 5-pyrimidinyl, or 2-pyrazinyl moiety resulted in novel nAChR ligands 7, 8, and 9. A palladium-catalyzed Suzuki cross-coupling of the 3-diethylboranylpyridine (14) and a Stille cross-coupling of the corresponding tributylstannyl diazines 15-17 with the vinyl triflate 13 of the N-protected 9-azabicyclo[4.2.1]nonan-2-one 12 constitute the key steps in the syntheses of these enantiopure anatoxinoids 6-9. Studies of the in vitro affinity for (alpha4)(2)(beta2)(3), alpha3(beta)4, and alpha7 nAChR subtypes by radioligand binding assays demonstrated that the diazine analogues 7-9 can be considered as pharmacologically attractive bioisosteres of DUB-165 (6) but with different effects on the binding affinity with regard to the diazine moiety. The pyrimidine-containing bioisostere 8 turned out to be the most active diazine analogue, which interacts potently (K(i) = 0.14 nM) with the (alpha4)(2)(beta2)(3) subtype and differentiates significantly among the nAChR subtypes investigated. The nitrogens in this anatoxinoid 8 show by far the most negative atomic charges (calculated using the AM1 Hamiltonian). This qualitatively correlates with the highest binding affinity observed for 8 for all subtypes under consideration.

Adrenal Glands↗