A novel selective 11beta-hydroxysteroid dehydrogenase type 1 inhibitor prevents human adipogenesis.

Glucocorticoid excess increases fat mass, preferentially within omental depots; yet circulating cortisol concentrations are normal in most patients with metabolic syndrome (MS). At a pre-receptor level, 11beta-hydroxysteroid dehydrogenase type 1 (11beta-HSD1) activates cortisol from cortisone locally within adipose tissue, and inhibition of 11beta-HSD1 in liver and adipose tissue has been proposed as a novel therapy to treat MS by reducing hepatic glucose output and adiposity. Using a transformed human subcutaneous preadipocyte cell line (Chub-S7) and human primary preadipocytes, we have defined the role of glucocorticoids and 11beta-HSD1 in regulating adipose tissue differentiation. Human cells were differentiated with 1.0 microM cortisol (F), or cortisone (E) with or without 100 nM of a highly selective 11beta-HSD1 inhibitor PF-877423. 11beta-HSD1 mRNA expression increased across adipocyte differentiation (P<0.001, n=4), which was paralleled by an increase in 11beta-HSD1 oxo-reductase activity (from nil on day 0 to 5.9+/-1.9 pmol/mg per h on day 16, P<0.01, n=7). Cortisone enhanced adipocyte differentiation; fatty acid-binding protein 4 expression increased 312-fold (P<0.001) and glycerol-3-phosphate dehydrogenase 47-fold (P<0.001) versus controls. This was abolished by co-incubation with PF-877423. In addition, cellular lipid content decreased significantly. These findings were confirmed in the primary cultures of human subcutaneous preadipocytes. The increase in 11beta-HSD1 mRNA expression and activity is essential for the induction of human adipogenesis. Blocking adipogenesis with a novel and specific 11beta-HSD1 inhibitor may represent a novel approach to treat obesity in patients with MS.

Hierarchy of simulation models in predicting molecular recognition mechanisms from the binding energy landscapes: structural analysis of the peptide complexes with SH2 domains.

Computer simulations using the simplified energy function and simulated tempering dynamics have accurately determined the native structure of the pYVPML, SVLpYTAVQPNE, and SPGEpYVNIEF peptides in the complexes with SH2 domains. Structural and equilibrium aspects of the peptide binding with SH2 domains have been studied by generating temperature-dependent binding free energy landscapes. Once some native peptide-SH2 domain contacts are constrained, the underlying binding free energy profile has the funnel-like shape that leads to a rapid and consistent acquisition of the native structure. The dominant native topology of the peptide-SH2 domain complexes represents an extended peptide conformation with strong specific interactions in the phosphotyrosine pocket and hydrophobic interactions of the peptide residues C-terminal to the pTyr group. The topological features of the peptide-protein interface are primarily determined by the thermodynamically stable phosphotyrosyl group. A diversity of structurally different binding orientations has been observed for the amino-terminal residues to the phosphotyrosine. The dominant native topology for the peptide residues carboxy-terminal to the phosphotyrosine is tolerant to flexibility in this region of the peptide-SH2 domain interface observed in equilibrium simulations. The energy landscape analysis has revealed a broad, entropically favorable topology of the native binding mode for the bound peptides, which is robust to structural perturbations. This could provide an additional positive mechanism underlying tolerance of the SH2 domains to hydrophobic conservative substitutions in the peptide specificity region.

Deciphering common failures in molecular docking of ligand-protein complexes.

Common failures in predicting crystal structures of ligand-protein complexes are investigated for three ligand-protein systems by a combined thermodynamic and kinetic analysis of the binding energy landscapes. Misdocked predictions in ligand-protein docking are classified as 'soft' and 'hard' failures. While a soft failure arises when the search algorithm is unable to find the global energy minimum corresponding to the crystal structure, a hard failure results from a flaw of the energy function to qualify the crystal structure as the predicted lowest energy conformation in docking simulations. We find that neither the determination of a single structure with the lowest energy nor finding the most common binding mode is sufficient to predict crystal structures of the complexes, which belong to the category of hard failures. In a proposed hierarchical approach, structural similarity clustering of the conformations, generated from equilibrium simulations with the simplified energy function, is followed by energy refinement with the AMBER force field. This protocol, that involves a hierarchy of energy functions, resolves some common failures in ligand-protein docking and detects crystallographic binding modes that were not found during docking simulations.

Towards understanding the mechanisms of molecular recognition by computer simulations of ligand-protein interactions.

The thermodynamic and kinetic aspects of molecular recognition for the methotrexate (MTX)-dihydrofolate reductase (DHFR) ligand-protein system are investigated by the binding energy landscape approach. The impact of 'hot' and 'cold' errors in ligand mutations on the thermodynamic stability of the native MTX-DHFR complex is analyzed, and relationships between the molecular recognition mechanism and the degree of ligand optimization are discussed. The nature and relative stability of intermediates and thermodynamic phases on the ligand-protein association pathway are studied, providing new insights into connections between protein folding and molecular recognition mechanisms, and cooperativity of ligand-protein binding. The results of kinetic docking simulations are rationalized based on the thermodynamic properties determined from equilibrium simulations and the shape of the underlying binding energy landscape. We show how evolutionary ligand selection for a receptor active site can produce well-optimized ligand-protein systems such as MTX-DHFR complex with the thermodynamically stable native structure and a direct transition mechanism of binding from unbound conformations to the unique native structure.

Thermodynamics and kinetics of ligand-protein binding studied with the weighted histogram analysis method and simulated annealing.

The thermodynamics of ligand-protein molecular recognition is investigated by the energy landscape approach for two systems: methotrexate(MTX)--dihydrofolate reductase(DHFR) and biotin-streptavidin. The temperature-dependent binding free energy profile is determined using the weighted histogram analysis method. Two different force fields are employed in this study: a simplified model of ligand-protein interactions and the AMBER force field with a soft core smoothing component, used to soften the repulsive part of the potential. The results of multiple docking simulations are rationalized from the shape of the binding free energy profile that characterizes the thermodynamics of the binding process.

Structure-based design, synthesis, and biological evaluation of irreversible human rhinovirus 3C protease inhibitors. 4. Incorporation of P1 lactam moieties as L-glutamine replacements.

The structure-based design, chemical synthesis, and biological evaluation of various human rhinovirus (HRV) 3C protease (3CP) inhibitors which incorporate P1 lactam moieties in lieu of an L-glutamine residue are described. These compounds are comprised of a tripeptidyl or peptidomimetic binding determinant and an ethyl propenoate Michael acceptor moiety which forms an irreversible covalent adduct with the active site cysteine residue of the 3C enzyme. The P1-lactam-containing inhibitors display significantly increased 3CP inhibition activity along with improved antirhinoviral properties relative to corresponding L-glutamine-derived molecules. In addition, several lactam-containing compounds exhibit excellent selectivity for HRV 3CP over several other serine and cysteine proteases and are not appreciably degraded by a variety of biological agents. One of the most potent inhibitors (AG7088, mean antirhinoviral EC90 approximately 0.10 microM, n = 46 serotypes) is shown to warrant additional preclinical development to explore its potential for use as an antirhinoviral agent.

Molecular anchors with large stability gaps ensure linear binding free energy relationships for hydrophobic substituents.

Ligand-protein docking simulations are employed to analyze the binding energy landscape of the pipecolinyl fragment that serves as a recognition core of the FK506 ligand in binding with the FKBP12 protein. This fragment acts as a molecular anchor that specifically binds within the protein active site in a unique binding mode, in harmony with the structure of the FK506-FKBP12 complex. Molecular anchors are characterized by a large stability gap, defined to be the free energy of a ligand bound in the native binding mode relative to the free energy of alternative binding modes. For ligands that share a common anchor fragment, a linear binding free energy relationship may be expected for hydrophobic substituents provided they do not abrogate the anchor binding mode. Changes in solvent-accessible surface area for these peripheral groups are used to rationalize the relative binding affinities of a series of FKBP12-ligand complexes which share the pipecolinyl anchor fragment. A series of benzene derivatives that bind to a mutant form of T4 lysozyme is also analyzed, and implications for structure-based drug design are discussed.

Mean field analysis of FKBP12 complexes with FK506 and rapamycin: implications for a role of crystallographic water molecules in molecular recognition and specificity.

Mean field analysis of FKBP12 complexes with FK506 and rapamycin has been performed by using structures obtained from molecular docking simulations on a simple, yet robust molecular recognition energy landscape. When crystallographic water molecules are included in the simulations as an extension of the FKBP12 protein surface, there is an appreciable stability gap between the energy of the native FKBP12-FK506 complex and energies of conformations with the "native-like" binding mode. By contrast, the energy spectrum of the FKBP12-rapamycin complex is dense regardless of the presence of the water molecules. The stability gap in the FKBP12-FK506 system is determined by two critical water molecules from the effector region that participate in a network of specific hydrogen bond interactions. This interaction pattern protects the integrity and precision of the composite ligand-protein effector surface in the binary FKBP12-FK506 complex and is preserved in the crystal structure of the FKBP12-FK506-calcineurin ternary complex. These features of the binding energy landscapes provide useful insights into specific and nonspecific aspects of FK506 and rapamycin recognition.

Structural consensus in ligand-protein docking identifies recognition peptide motifs that bind streptavidin.

Computational structure prediction of streptavidin-peptide complexes for known recognition sequences and a number of random di-, tri-, and tetrapeptides has been conducted, and mechanisms of peptide recognition with streptavidin have been investigated by a new computational protocol. The structural consensus criterion, which is computed from multiple docking simulations and measures the accessibility of the dominant binding mode, identifies recognition motifs from a set of random peptide sequences, whereas energetic analysis is less discriminatory. The predicted conformations of recognition tripeptide and tetrapeptide sequences are also in structural harmony and composed of peptide fragments that are individually unfrustrated in their bound conformation, resulting in a minimally frustrated energy landscape for recognition peptides.

Unraveling principles of lead discovery: from unfrustrated energy landscapes to novel molecular anchors.

The search for novel leads is a critical step in the drug discovery process. Computational approaches to identify new lead molecules have focused on discovering complete ligands by evaluating the binding affinity of a large number of candidates, a task of considerable complexity. A new computational method is introduced in this work based on the premise that the primary molecular recognition event in the protein binding site may be accomplished by small core fragments that serve as molecular anchors, providing a structurally stable platform that can be subsequently tailored into complete ligands. To fulfill its role, we show that an effective molecular anchor must meet both the thermodynamic requirement of relative energetic stability of a single binding mode and its consistent kinetic accessibility, which may be measured by the structural consensus of multiple docking simulations. From a large number of candidates, this technique is able to identify known core fragments responsible for primary recognition by the FK506 binding protein (FKBP-12), along with a diverse repertoire of novel molecular cores. By contrast, absolute energetic criteria for selecting molecular anchors are found to be promiscuous. A relationship between a minimum frustration principle of binding energy landscapes and receptor-specific molecular anchors in their role as "recognition nuclei" is established, thereby unraveling a mechanism of lead discovery and providing a practical route to receptor-biased computational combinatorial chemistry.

Exploring the energy landscapes of molecular recognition by a genetic algorithm: analysis of the requirements for robust docking of HIV-1 protease and FKBP-12 complexes.

Energy landscapes of molecular recognition are explored by performing "semi-rigid" docking of FK-506 and rapamycin with the Fukisawa binding protein (FKBP-12), and flexible docking simulations of the Ro-31-8959 and AG-1284 inhibitors with HIV-1 protease by a genetic algorithm. The requirements of a molecular recognition model to meet thermodynamic and kinetic criteria of ligand-protein docking simultaneously are investigated using a family of simple molecular recognition energy functions. The critical factor that determines the success rate in predicting the structure of ligand-protein complexes is found to be the roughness of the binding energy landscape, in accordance with a minimal frustration principle. The results suggest that further progress in structure prediction of ligand-protein complexes can be achieved by designing molecular recognition energy functions that generate binding landscapes with reduced frustration.

A mean field model of ligand-protein interactions: implications for the structural assessment of human immunodeficiency virus type 1 protease complexes and receptor-specific binding.

We propose a general mean field model of ligand-protein interactions to determine the thermodynamic equilibrium of a system at finite temperature. The method is employed in structural assessments of two human immuno-deficiency virus type 1 protease complexes where the gross effects of protein flexibility are incorporated by utilizing a data base of crystal structures. Analysis of the energy spectra for these complexes has revealed that structural and thermo-dynamic aspects of molecular recognition can be rationalized on the basis of the extent of frustration in the binding energy landscape. In particular, the relationship between receptor-specific binding of these ligands to human immunodeficiency virus type 1 protease and a minimal frustration principle is analyzed.

Visualization of fast energy flow and solvent caging in unimolecular dynamics.

Energy flow in solution between physically or chemically evolving solute molecules and the surrounding solvent significantly affects the nature of chemical dynamics in liquids. It determines the extent to which the statistical theory of reaction rates is valid; the transfer of energy between solute and solvent influences the ease with which the transition state evolves into the products--the process central to transition-state theory. But analysing the energy flow in liquid-phase dynamics is difficult because these systems are so complex, and the degrees of freedom are consequently so numerous. Here we present a way to address this challenge. We introduce an approach for visualizing the energy flow directly, and apply it to the isomerization of cyclohexane (between boat and chair conformations) in liquid carbon disulphide, a process for which detailed information about the molecular motions is available from molecular dynamics simulations. Our method reveals in pictorial form the formation and relaxation of a solvent cage, and shows that the relaxation has a strong effect on energy flow to and from the transition state on sub-picosecond timescales. We anticipate that this visualization approach will be generally useful for elucidating dynamical molecular processes in solution.

Molecular recognition of the inhibitor AG-1343 by HIV-1 protease: conformationally flexible docking by evolutionary programming.

BACKGROUND: An important prerequisite for computational structure-based drug design is prediction of the structures of ligand-protein complexes that have not yet been experimentally determined by X-ray crystallography or NMR. For this task, docking of rigid ligands is inadequate because it assumes knowledge of the conformation of the bound ligand. Docking of flexible ligands would be desirable, but requires one to search an enormous conformational space. We set out to develop a strategy for flexible docking by combining a simple model of ligand-protein interactions for molecular recognition with an evolutionary programming search technique. RESULTS: We have developed an intermolecular energy function that incorporates steric and hydrogen-bonding terms. The parameters in this function were obtained by docking in three different protein systems. The effectiveness of this method was demonstrated by conformationally flexible docking of the inhibitor AG-1343, a potential new drug against AIDS, into HIV-1 protease. For this molecule, which has nine rotatable bonds, the crystal structure was reproduced within 1.5 A root-mean-square deviation 34 times in 100 simulations, each requiring eight minutes on a Silicon Graphics R4400 workstation. The energy function correctly evaluates the crystal structure as the global energy minimum. CONCLUSIONS: We believe that a solution of the docking problem may be achieved by matching a simple model of molecular recognition with an efficient search procedure. The necessary ingredients of a molecular recognition model include only steric and hydrogen-bond interaction terms. Although these terms are not necessarily sufficient to predict binding affinity, they describe ligand-protein interactions faithfully enough to enable a docking program to predict the structure of the bound ligand. This docking strategy thus provides an important tool for the interdisciplinary field of rational drug design.