Daniel K. Gehlhaar

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

BACKGROUNDnAn 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.nnnRESULTSnWe 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.nnnCONCLUSIONSnWe 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.

Redesigning and characterizing the substrate specificity and activity of Vibrio fluvialis aminotransferase for the synthesis of imagabalin

Several protein engineering approaches were combined to optimize the selectivity and activity of Vibrio fluvialis aminotransferase (Vfat) for the synthesis of (3S,5R)-ethyl 3-amino-5-methyloctanoate; a key intermediate in the synthesis of imagabalin, an advanced candidate for the treatment of generalized anxiety disorder. Starting from wild-type Vfat, which had extremely low activity catalyzing the desired reaction, we engineered an improved enzyme with a 60-fold increase in initial reaction velocity for transamination of (R)-ethyl 5-methyl 3-oxooctanoate to (3S,5R)-ethyl 3-amino-5-methyloctanoate. To achieve this, <450 variants were screened, which allowed accurate assessment of enzyme performance using a low-throughput ultra performance liquid chromatography assay. During the course of this work, crystal structures of Vfat wild type and an improved variant (Vfat variant r414) were solved and they are reported here for the first time. This work also provides insight into the critical residues for substrate specificity for the transamination of (R)-ethyl 5-methyl 3-oxooctanoate and structurally related β-ketoesters.

Evaluation of a published in silico model and construction of a novel bayesian model for predicting phospholipidosis inducing potential

The identification of phospholipidosis (PPL) during preclinical testing in animals is a recognized problem in the pharmaceutical industry. Depending on the intended indication and dosing regimen, PPL can delay or stop development of a compound in the drug discovery process. Therefore, for programs and projects where a PPL finding would have adverse impact on the success of the project, it would be desirable to be able to rapidly identify and screen out those compounds with the potential to induce PPL as early as possible. Currently, electron microscopy is the gold standard method for identifying phospholipidosis, but it is low-throughput and resource-demanding. Therefore, a low-cost, high-throughput screening strategy is required to overcome these limitations and be applicable in the drug discovery cycle. A recent publication by Ploemen et al. (Exp. Toxicol. Pathol. 2004, 55, 347-55) describes a method using the computed physicochemical properties pKa and ClogP as part of a simple calculation to determine a compounds potential to induce PPL. We have evaluated this method using a set of 201 compounds, both public and proprietary, with known in vivo PPL-inducing ability and have found the overall concordance to be 75%. We have proposed simple modifications to the model rules, which improve the models concordance to 80%. Finally, we describe the development of a Bayesian model using the same compound set and found its overall concordance to be 83%.

Molecular replacement by evolutionary search

Stochastic search algorithms can be used to perform rapid six-dimensional molecular-replacement searches. A molecular-replacement procedure has been developed that uses an evolutionary algorithm to simultaneously optimize the orientation and position of a search model in a unit cell. Here, the performance of this algorithm and its dependence on search model quality and choice of target function are examined. Although the evolutionary search procedure is capable of finding solutions with search models that represent only a small fraction of the total scattering matter of the target molecule, the efficiency of the search procedure is highly dependent on the quality of the search model. Polyalanine models frequently provide better search efficiency than all-atom models, even in cases where the side-chain positions are known with high accuracy. Although the success of the search procedure is not highly dependent on the statistic used as the target function, the correlation coefficient between observed and calculated structure-factor amplitudes generally results in better search efficiency than does the R factor. An alternative stochastic search procedure, simulated annealing, provides similar overall performance to evolutionary search. Methods of extending the evolutionary search algorithm to include internal optimization, selection and construction of the search model are now beginning to be investigated.

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.

Monte Carlo simulations of the peptide recognition at the consensus binding site of the constant fragment of human immunoglobulin G: the energy landscape analysis of a hot spot at the intermolecular interface.

Monte Carlo simulations of molecular recognition at the consensus binding site of the constant fragment (Fc) of human immunoglobulin G (Ig) protein have been performed to analyze structural and thermodynamic aspects of binding for the 13‐residue cyclic peptide DCAWHLGELVWCT. The energy landscape analysis of a hot spot at the intermolecular interface using alanine scanning and equilibrium‐simulated tempering dynamics with the simplified, knowledge‐based energy function has enabled the role of the protein hot spot residues in providing the thermodynamic stability of the native structure to be determined. We have found that hydrophobic interactions between the peptide and the Met‐252, Ile‐253, His‐433, and His‐435 protein residues are critical to guarantee the thermodynamic stability of the crystallographic binding mode of the complex. Binding free energy calculations, using a molecular mechanics force field and a solvation energy model, combined with alanine scanning have been conducted to determine the energetic contribution of the protein hot spot residues in binding affinity. The conserved Asn‐434, Ser‐254, and Tyr‐436 protein residues contribute significantly to the binding affinity of the peptide–protein complex, serving as an energetic hot spot at the intermolecular interface. The results suggest that evolutionary conserved hot spot protein residues at the intermolecular interface may be partitioned in fulfilling thermodynamic stability of the native binding mode and contributing to the binding affinity of the complex. Proteins 2002;48:539–557.

Computational detection of the binding‐site hot spot at the remodeled human growth hormone–receptor interface

A hierarchical computational approach is used to identify the engineered binding‐site cavity at the remodeled intermolecular interface between the mutants of human growth hormone (hGH) and the extracellular domain of its receptor (hGHbp). Multiple docking simulations are conducted with the remodeled hGH–hGHbp complex for a panel of potent benzimidazole‐containing inhibitors that can restore the binding affinity of the wild‐type complex, and for a set of known nonactive small molecules that contain different heterocyclic motifs. Structural clustering of ligand‐bound conformations and binding free‐energy calculations, using the AMBER force field and a continuum solvation model, can rapidly locate and screen numerous ligand‐binding modes on the protein surface and detect the binding‐site hot spot at the intermolecular interface. Structural orientation of the benzimidazole motif in the binding‐site cavity closely mimics the position of the hot spot residue W104 in the crystal structure of the wild‐type complex, which is recognized as an important structural requirement for restoring binding affinity. Despite numerous pockets on the protein surface of the mutant hGH–hGHbp complex, the binding‐site cavity presents the energetically favorable hot spot for the benzimidazole‐containing inhibitors, whereas for a set of nonactive molecules, the lowest energy ligand conformations do not necessarily bind in the engineered cavity. The results reveal a dominant role of the intermolecular van der Waals interactions in providing favorable ligand–protein energetics in the redesigned interface, in agreement with the experimental and computational alanine scanning of the hGH–hGHbp complex. Proteins 2003.

Parallel simulated tempering dynamics of ligand-protein binding with ensembles of protein conformations

Abstract Simulated tempering dynamics with the simplified energy model and the ensemble of protein conformations have been performed for the SB203386 inhibitor binding with HIV-1 protease. Equilibrium simulations with multiple protein conformations implicitly incorporate protein flexibility and rank HIV-1 protease conformations according to the average ligand–protein interaction energies. Subsequent energy refinement with a molecular mechanics force field accurately quantifies the energetics of the low-energy ligand binding modes. The results suggest that the mobility of the SB203386 inhibitor is effectively restricted to two symmetry-related binding modes and this may prevent the inhibitor from adapting to distorted binding sites in mutant conformations.

Navigating ligand–protein binding free energy landscapes: universality and diversity of protein folding and molecular recognition mechanisms

Abstract Thermodynamic and kinetic aspects of ligand–protein binding are studied for the methotrexate–dihydrofolate reductase system from the binding free energy profile constructed as a function of the order parameter. Thermodynamic stability of the native complex and a cooperative transition to the unique native structure suggest the nucleation kinetic mechanism at the equilibrium transition temperature. Structural properties of the transition state ensemble and the ensemble of nucleation conformations are determined by kinetic simulations of the transmission coefficient and ligand–protein association pathways. Structural analysis of the transition states and the nucleation conformations reconciles different views on the nucleation mechanism in protein folding.

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.