Anders Wallqvist

Investigation of the temperature dependence of dielectric relaxation in liquid water by THz reflection spectroscopy and molecular dynamics simulation

We report measurements of the real and imaginary part of the dielectric constant of liquid water in the far-infrared region from 0.1 to 2.0 THz in a temperature range from 271.1 to 366.7 K. The data have been obtained with the use of THz time domain reflection spectroscopy, utilizing ultrashort electromagnetic pulses generated from a photoconductive antenna driven by femtosecond laser pulses. A Debye model with an additional relaxation time is used to fit the frequency dependence of the complex dielectric constants. We obtain a fast (fs) and a Debye (ps) relaxation time for the macroscopic polarization. The corresponding time correlation functions have been calculated with molecular dynamics simulations and are compared with experimental relaxation times. The temperature dependence of the Debye relaxation time is analyzed using three models: Transition state theory, a Debye–Stoke–Einstein relation between the viscosity and the Debye time, and a model stating that its temperature dependence can be extrapol...

Classification of scaffold-hopping approaches.

The general goal of drug discovery is to identify novel compounds that are active against a preselected biological target with acceptable pharmacological properties defined by marketed drugs. Scaffold hopping has been widely applied by medicinal chemists to discover equipotent compounds with novel backbones that have improved properties. In this article we classify scaffold hopping into four major categories, namely heterocycle replacements, ring opening or closure, peptidomimetics and topology-based hopping. We review the structural diversity of original and final scaffolds with respect to each category. We discuss the advantages and limitations of small, medium and large-step scaffold hopping. Finally, we summarize software that is frequently used to facilitate different kinds of scaffold-hopping methods.

Distinguishing native conformations of proteins from decoys with an effective free energy estimator based on the OPLS all-atom force field and the surface generalized born solvent model

Protein decoy data sets provide a benchmark for testing scoring functions designed for fold recognition and protein homology modeling problems. It is commonly believed that statistical potentials based on reduced atomic models are better able to discriminate native‐like from misfolded decoys than scoring functions based on more detailed molecular mechanics models. Recent benchmark tests on small data sets, however, suggest otherwise. In this work, we report the results of extensive decoy detection tests using an effective free energy function based on the OPLS all‐atom (OPLS‐AA) force field and the Surface Generalized Born (SGB) model for the solvent electrostatic effects. The OPLS‐AA/SGB effective free energy is used as a scoring function to detect native protein folds among a total of 48,832 decoys for 32 different proteins from Park and Levitts 4‐state‐reduced, Levitts local‐minima, Bakers ROSETTA all‐atom, and Skolnicks decoy sets. Solvent electrostatic effects are included through the Surface Generalized Born (SGB) model. All structures are locally minimized without restraints. From an analysis of the individual energy components of the OPLS‐AA/SGB energy function for the native and the best‐ranked decoy, it is determined that a balance of the terms of the potential is responsible for the minimized energies that most successfully distinguish the native from the misfolded conformations. Different combinations of individual energy terms provide less discrimination than the total energy. The results are consistent with observations that all‐atom molecular potentials coupled with intermediate level solvent dielectric models are competitive with knowledge‐based potentials for decoy detection and protein modeling problems such as fold recognition and homology modeling. Proteins 2002;48:404–422.

Exploring polypharmacology using a ROCS-based target fishing approach.

Polypharmacology has emerged as a new theme in drug discovery. In this paper, we studied polypharmacology using a ligand-based target fishing (LBTF) protocol. To implement the protocol, we first generated a chemogenomic database that links individual protein targets with a specified set of drugs or target representatives. Target profiles were then generated for a given query molecule by computing maximal shape/chemistry overlap between the query molecule and the drug sets assigned to each protein target. The overlap was computed using the program ROCS (Rapid Overlay of Chemical Structures). We validated this approach using the Directory of Useful Decoys (DUD). DUD contains 2950 active compounds, each with 36 property-matched decoys, against 40 protein targets. We chose a set of known drugs to represent each DUD target, and we carried out ligand-based virtual screens using data sets of DUD actives seeded into DUD decoys for each target. We computed Receiver Operator Characteristic (ROC) curves and associated area under the curve (AUC) values. For the majority of targets studied, the AUC values were significantly better than for the case of a random selection of compounds. In a second test, the method successfully identified off-targets for drugs such as rimantadine, propranolol, and domperidone that were consistent with those identified by recent experiments. The results from our ROCS-based target fishing approach are promising and have potential application in drug repurposing for single and multiple targets, identifying targets for orphan compounds, and adverse effect prediction.

Structural investigation of C4b-binding protein by molecular modeling: Localization of putative binding sites

C4b‐binding protein (C4BP) contributes to the regulation of the classical pathway of the complement system and plays an important role in blood coagulation. The main human C4BP isoform is composed of one β‐chain and seven α‐chains essentially built from three and eight complement control protein (CCP) modules, respectively, followed by a nonrepeat carboxy‐terminal region involved in polymerization of the chains. C4BP is known to interact with heparin, C4b, complement factor I, serum amyloid P component, streptococcal Arp and Sir proteins, and factor VIII/VIIIa via its α‐chains and with protein S through its β‐chain. The principal aim of the present study was to localize regions of C4BP involved in the interaction with C4b, Arp, and heparin. For this purpose, a computer model of the 8 CCP modules of C4BP α‐chain was constructed, taking into account data from previous electron microscopy (EM) studies. This structure was investigated in the context of known and/or new experimental data. Analysis of the α‐chain model, together with monoclonal antibody studies and heparin binding experiments, suggests that a patch of positively charged residues, at the interface between the first and second CCP modules, plays an important role in the interaction between C4BP and C4b/Arp/Sir/heparin. Putative binding sites, secondary‐structure prediction for the central core, and an overall reevaluation of the size of the C4BP molecule are also presented. An understanding of these intermolecular interactions should contribute to the rational design of potential therapeutic agents aiming at interfering specifically some of these protein–protein interactions. Proteins 31:391–405, 1998.

Spontaneous Buckling of Lipid Bilayer and Vesicle Budding Induced by Antimicrobial Peptide Magainin 2: A Coarse-Grained Simulation Study

Molecular mechanisms of the action of antimicrobial peptides on bacterial membranes were studied by large scale coarse-grained simulations of magainin 2-dipalmitoylphosphatidylcholine/palmitoyloleoylphosphatidylglycerol (DPPC/POPG) mixed bilayer systems with spatial extents up to 0.1 μm containing up to 1600 peptides. Equilibrium simulations exhibit disordered toroidal pores stabilized by peptides. However, when a layer of peptides is placed near the lipid head groups on one side of the bilayer only, their incorporation leads to a spontaneous buckling of the bilayer. This buckling is followed by the formation of a quasi-spherical vesicular bud connected to the bilayer by a narrow neck. The mean curvature of the budding region is consistent with what is expected based on the dependence of the area per lipid on the peptide-to-lipid ratio in equilibrium simulations. Our simulations suggest that the incorporation of antimicrobial peptides on the exterior surface of a vesicle or a bacterial cell leads to buckling and vesicle budding, presumably accompanied by nucleations of giant transient pores of sizes that are much larger than indicated by equilibrium measurements and simulations.

Docking enzyme-inhibitor complexes using a preference-based free-energy surface

We present a docking scheme that utilizes both a surface complementarity screen as well as an energetic criterion based on surface area burial. Twenty rigid enzyme/inhibitor complexes with known coordinate sets are arbitrarily separated and reassembled to an average all‐atom rms (root mean square) deviation of 1.0 Å from the native complexes. Docking is accomplished by a hierarchical search of geometrically compatible triplets of surface normals on each molecule. A pruned tree of possible bound configurations is built up using successive consideration of larger and larger triplets. The best scoring configurations are then passed through a free‐energy screen where the lowest energy member is selected as the predicted native state. The free energy approximation is derived from observations of surface burial by atom pairs across the interface of known enzyme/inhibitor complexes. The occurrence of specific atom‐atom surface burial, for a set of complexes with well‐defined secondary structure both in the bound and unbound states, is parameterized to mimic the free energy of binding. The docking procedure guides the inhibitor into its native state using orientation and distance‐dependent functions that reproduce the ideal model of free energies with an average rms deviation of 0.9 kcal/mol. For all systems studied, this docking procedure identifies a single, unique minimum energy configuration that is highly compatible with the native state.

Iterative sequence/secondary structure search for protein homologs: comparison with amino acid sequence alignments and application to fold recognition in genome databases

MOTIVATION Sequence alignment techniques have been developed into extremely powerful tools for identifying the folding families and function of proteins in newly sequenced genomes. For a sufficiently low sequence identity it is necessary to incorporate additional structural information to positively detect homologous proteins. We have carried out an extensive analysis of the effectiveness of incorporating secondary structure information directly into the alignments for fold recognition and identification of distant protein homologs. A secondary structure similarity matrix based on a database of three-dimensionally aligned proteins was first constructed. An iterative application of dynamic programming was used which incorporates linear combinations of amino acid and secondary structure sequence similarity scores. Initially, only primary sequence information is used. Subsequently contributions from secondary structure are phased in and new homologous proteins are positively identified if their scores are consistent with the predetermined error rate. RESULTS We used the SCOP40 database, where only PDB sequences that have 40% homology or less are included, to calibrate homology detection by the combined amino acid and secondary structure sequence alignments. Combining predicted secondary structure with sequence information results in a 8-15% increase in homology detection within SCOP40 relative to the pairwise alignments using only amino acid sequence data at an error rate of 0.01 errors per query; a 35% increase is observed when the actual secondary structure sequences are used. Incorporating predicted secondary structure information in the analysis of six small genomes yields an improvement in the homology detection of approximately 20% over SSEARCH pairwise alignments, but no improvement in the total number of homologs detected over PSI-BLAST, at an error rate of 0.01 errors per query. However, because the pairwise alignments based on combinations of amino acid and secondary structure similarity are different from those produced by PSI-BLAST and the error rates can be calibrated, it is possible to combine the results of both searches. An additional 25% relative improvement in the number of genes identified at an error rate of 0.01 is observed when the data is pooled in this way. Similarly for the SCOP40 dataset, PSI-BLAST detected 15% of all possible homologs, whereas the pooled results increased the total number of homologs detected to 19%. These results are compared with recent reports of homology detection using sequence profiling methods. AVAILABILITY Secondary structure alignment homepage at http://lutece.rutgers.edu/ssas CONTACT anders@rutchem.rutgers.edu; ronlevy@lutece.rutgers.edu SUPPLEMENTARY INFORMATION Genome sequence/structure alignment results at http://lutece.rutgers.edu/ss_fold_predictions.

Linking tumor cell cytotoxicity to mechanism of drug action: an integrated analysis of gene expression, small-molecule screening and structural databases.

An integrated, bioinformatic analysis of three databases comprising tumor‐cell‐based small molecule screening data, gene expression measurements, and PDB (Protein Data Bank) ligand–target structures has been developed for probing mechanism of drug action (MOA). Clustering analysis of GI50 profiles for the NCIs database of compounds screened across a panel of tumor cells (NCI60) was used to select a subset of unique cytotoxic responses for about 4000 small molecules. Drug–gene–PDB relationships for this test set were examined by correlative analysis of cytotoxic response and differential gene expression profiles within the NCI60 and structural comparisons with known ligand–target crystallographic complexes. A survey of molecular features within these compounds finds thirteen conserved Compound Classes, each class exhibiting chemical features important for interactions with a variety of biological targets. Protein targets for an additional twelve Compound Classes could be directly assigned using drug‐protein interactions observed in the crystallographic database. Results from the analysis of constitutive gene expressions established a clear connection between chemo‐resistance and overexpression of gene families associated with the extracellular matrix, cytoskeletal organization, and xenobiotic metabolism. Conversely, chemo‐sensitivity implicated overexpression of gene families involved in homeostatic functions of nucleic acid repair, aryl hydrocarbon metabolism, heat shock response, proteasome degradation and apoptosis. Correlations between chemo‐responsiveness and differential gene expressions identified chemotypes with nonselective (i.e., many) molecular targets from those likely to have selective (i.e., few) molecular targets. Applications of data mining strategies that jointly utilize tumor cell screening, genomic, and structural data are presented for hypotheses generation and identifying novel anticancer candidates. Proteins 2005. Published 2005 Wiley‐Liss, Inc.

Modeling Phenotypic Metabolic Adaptations of Mycobacterium tuberculosis H37Rv under Hypoxia

The ability to adapt to different conditions is key for Mycobacterium tuberculosis, the causative agent of tuberculosis (TB), to successfully infect human hosts. Adaptations allow the organism to evade the host immune responses during acute infections and persist for an extended period of time during the latent infectious stage. In latently infected individuals, estimated to include one-third of the human population, the organism exists in a variety of metabolic states, which impedes the development of a simple strategy for controlling or eradicating this disease. Direct knowledge of the metabolic states of M. tuberculosis in patients would aid in the management of the disease as well as in forming the basis for developing new drugs and designing more efficacious drug cocktails. Here, we propose an in silico approach to create state-specific models based on readily available gene expression data. The coupling of differential gene expression data with a metabolic network model allowed us to characterize the metabolic adaptations of M. tuberculosis H37Rv to hypoxia. Given the microarray data for the alterations in gene expression, our model predicted reduced oxygen uptake, ATP production changes, and a global change from an oxidative to a reductive tricarboxylic acid (TCA) program. Alterations in the biomass composition indicated an increase in the cell wall metabolites required for cell-wall growth, as well as heightened accumulation of triacylglycerol in preparation for a low-nutrient, low metabolic activity life style. In contrast, the gene expression program in the deletion mutant of dosR, which encodes the immediate hypoxic response regulator, failed to adapt to low-oxygen stress. Our predictions were compatible with recent experimental observations of M. tuberculosis activity under hypoxic and anaerobic conditions. Importantly, alterations in the flow and accumulation of a particular metabolite were not necessarily directly linked to differential gene expression of the enzymes catalyzing the related metabolic reactions.