In silico elucidation of the molecular mechanism defining the adverse effect of selective estrogen receptor modulators

Early identification of adverse effect of preclinical and commercial drugs is crucial in developing highly efficient therapeutics, since unexpected adverse drug effects account for one-third of all drug failures in drug development. To correlate protein–drug interactions at the molecule level with their clinical outcomes at the organism level, we have developed an integrated approach to studying protein–ligand interactions on a structural proteome-wide scale by combining protein functional site similarity search, small molecule screening, and protein–ligand binding affinity profile analysis. By applying this methodology, we have elucidated a possible molecular mechanism for the previously observed, but molecularly uncharacterized, side effect of selective estrogen receptor modulators (SERMs). The side effect involves the inhibition of the Sacroplasmic Reticulum Ca2+ ion channel ATPase protein (SERCA) transmembrane domain. The prediction provides molecular insight into reducing the adverse effect of SERMs and is supported by clinical and in vitro observations. The strategy used in this case study is being applied to discover off-targets for other commercially available pharmaceuticals. The process can be included in a drug discovery pipeline in an effort to optimize drug leads and reduce unwanted side effects.     

Medicinal mushrooms and cancer therapy: translating a traditional practice into Western medicine

Modern medical practice relies heavily on the use of highly purified pharmaceutical compounds whose purity can be easily assessed and whose pharmaceutical activity and toxicity show clear structure-function relationships. In contrast, many herbal medicines contain mixtures of natural compounds that have not undergone detailed chemical analyses and whose mechanism of action is not known. Traditional folk medicine and ethno-pharmacology coupled to bioprospecting have been an important source of many anticancer agents as well as other medicines. With the current decline in the number of new molecular entities from the pharmaceutical industry, novel anticancer agents are being sought from traditional medicine. As the example of medicinal mushrooms demonstrates, however, translating traditional Eastern practices into acceptable evidence-based Western therapies is difficult. Different manufacturing standards, criteria of purity, and under-powered clinical trials make assessment of efficacy and toxicity by Western standards of clinical evidence difficult. Purified bioactive compounds derived from medicinal mushrooms are a potentially important new source of anticancer agents; their assimilation into Western drug discovery programs and clinical trials also provides a framework for the study and use of other traditional medicines.     

MSClust: a tool for unsupervised mass spectra extraction of chromatography-mass spectrometry ion-wise aligned data

Mass peak alignment (ion-wise alignment) has recently become a popular method for unsupervised data analysis in untargeted metabolic profiling. Here we present MSClust-a software tool for analysis GC-MS and LC-MS datasets derived from untargeted profiling. MSClust performs data reduction using unsupervised clustering and extraction of putative metabolite mass spectra from ion-wise chromatographic alignment data. The algorithm is based on the subtractive fuzzy clustering method that allows unsupervised determination of a number of metabolites in a data set and can deal with uncertain memberships of mass peaks in overlapping mass spectra. This approach is based purely on the actual information present in the data and does not require any prior metabolite knowledge. MSClust can be applied for both GC-MS and LC-MS alignment data sets. ELECTRONIC SUPPLEMENTARY MATERIAL: The online version of this article (doi:10.1007/s11306-011-0368-2) contains supplementary material, which is available to authorized users.     

Integrated metabonomic analysis of bromobenzene-induced hepatotoxicity: novel induction of 5-oxoprolinosis

We present here a definitive metabonomic analysis in order to detect novel biomarker and metabolite information, implicating specific putative protein targets in the toxicological mechanism of bromobenzene-induced centrilobular hepatic necrosis. Male Han-Wistar rats were dosed with bromobenzene (1.5 g/kg, n = 25) and blood plasma, urine and liver samples were collected for NMR and magic angle spinning (MAS) NMR spectroscopy at various time-points postdose, with histopathology and clinical pathology performed in parallel. Liver samples were analyzed by 600 MHz 1H MAS NMR techniques and the resultant spectra were correlated to sequential 1H NMR measurements in urine and blood plasma using pattern recognition methods. 1D 1H NMR spectra were data-reduced and analyzed using principal components analysis (PCA) to show the time-dependent biochemical variations induced by bromobenzene toxicity. In addition to a holistic view of the effect of hepatic toxicity on the metabolome, a number of putative protein targets of bromobenzene and its metabolites were identified including those enzymes of the glutathione cycle, exemplified by the presence of a novel biomarker, 5-oxoproline, in liver tissue, blood plasma, and urine. As such, this work establishes the importance of metabonomics technology in resolving the mechanistic complexity of drug toxicity as well as the benefits of frontloading this approach in drug safety evaluation and biomarker discovery.     

Reference-free alignment and sorting of single-molecule force spectroscopy data

Single-molecule force spectroscopy has become a versatile tool for investigating the (un)folding of proteins and other polymeric molecules. Like other single-molecule techniques, single-molecule force spectroscopy requires recording and analysis of large data sets to extract statistically meaningful conclusions. Here, we present a data analysis tool that provides efficient filtering of heterogeneous data sets, brings spectra into register based on a reference-free alignment algorithm, and determines automatically the location of unfolding barriers. Furthermore, it groups spectra according to the number of unfolding events, subclassifies the spectra using cross correlation-based sorting, and extracts unfolding pathways by principal component analysis and clustering methods to extracted peak positions. Our approach has been tested on a data set obtained through mechanical unfolding of bacteriorhodopsin (bR), which contained a significant number of spectra that did not show the well-known bR fingerprint. In addition, we have tested the performance of the data analysis tool on unfolding data of the soluble multidomain (Ig27)(8) protein.     

Systematic identification of proteins that elicit drug side effects

Side effect similarities of drugs have recently been employed to predict new drug targets, and networks of side effects and targets have been used to better understand the mechanism of action of drugs. Here, we report a large‐scale analysis to systematically predict and characterize proteins that cause drug side effects. We integrated phenotypic data obtained during clinical trials with known drug–target relations to identify overrepresented protein–side effect combinations. Using independent data, we confirm that most of these overrepresentations point to proteins which, when perturbed, cause side effects. Of 1428 side effects studied, 732 were predicted to be predominantly caused by individual proteins, at least 137 of them backed by existing pharmacological or phenotypic data. We prove this concept in vivo by confirming our prediction that activation of the serotonin 7 receptor (HTR7) is responsible for hyperesthesia in mice, which, in turn, can be prevented by a drug that selectively inhibits HTR7. Taken together, we show that a large fraction of complex drug side effects are mediated by individual proteins and create a reference for such relations.     

Drug-specific T cells derived from patients with drug-induced allergic hepatitis.

Drug-induced allergic hepatitis is a tissue-specific inflammatory disease caused by hypersensitivity to a particular drug. Although the frequency of drug-induced allergic hepatitis appears to increase in proportion to the medicine, the mechanism by which tissue specificity is determined is still to be elucidated. In this study, we established CD4+ T cell clones specific for particular drugs from patients with drug-induced allergic hepatitis accompanied with mild blood eosinophilia and analyzed the possible role of liver protein as a directing factor of liver-specific inflammatory reactions. All CD4+ T cell clones obtained from two patients with this disease proliferated in response to a combination of the particular drug plus liver specific protein (LSP), which consists of over 30 proteins. Some T cell clones were responsive to an antigenic conformation consisting of the 200-kDa glycoprotein (partly purified LSP), a component of LSP, plus the causal drug. In contrast, all CD4+ T cell clones from a patient with simple drug-induced eosinophilia responded to the causal drug in the absence of LSP and partly purified LSP. These data suggested that LSP or partly purified LSP of the appropriate Ag is the target that leads to liver-specific inflammation in drug-induced allergic hepatitis. Furthermore, T cell lines derived from patients with drug-induced allergic hepatitis and simple drug-induced eosinophilia produced large amounts of IL-5 after the appropriate antigenic stimulation, whereas CD4+ T cell clones from donors with a normal amount of peripheral blood eosinophils secreted a much less IL-5. Taken together, these results indicate that overproduction of IL-5 by the allergen-sensitized T cells may result in blood eosinophilia.     

Formononetin,J1 and J2 have different effects on endothelial cells via EWSAT1‐TRAF6 and its downstream pathway

Formononetin is a natural isoflavone compound found mainly in Chinese herbal medicines such as astragalus and red clover. It is considered to be a typical phytooestrogen. In our previous experiments, it was found that formononetin has a two‐way regulatory effect on endothelial cells (ECs): low concentrations promote the proliferation of ECs and high concentrations have an inhibitory effect. To find a specific mechanism of action and provide a better clinical effect, we performed a structural transformation of formononetin and selected better medicinal properties for formononetin modifier J1 and J2 from a variety of modified constructs. The MTT assay measured the effects of drugs on human umbilical vein endothelial cell (HUVEC) activity. Scratch and transwell experiments validated the effects of the drugs on HUVEC migration and invasion. An in vivo assessment effect of the drugs on ovariectomized rats. Long‐chain non‐coding RNA for EWSAT1, which is abnormally highly expressed in HUVEC, was screened by gene chip, and the effect of the drug on its expression was detected by PCR after the drug was applied. The downstream factors and their pathways were analysed, and the changes in the protein levels after drug treatment were evaluated by Western blot. In conclusion, the mechanism of action of formononetin, J1 and J2 on ECs may be through EWSAT1‐TRAF6 and its downstream pathways.     

COBALT: constraint-based alignment tool for multiple protein sequences

MOTIVATION: A tool that simultaneously aligns multiple protein sequences, automatically utilizes information about protein domains, and has a good compromise between speed and accuracy will have practical advantages over current tools. RESULTS: We describe COBALT, a constraint based alignment tool that implements a general framework for multiple alignment of protein sequences. COBALT finds a collection of pairwise constraints derived from database searches, sequence similarity and user input, combines these pairwise constraints, and then incorporates them into a progressive multiple alignment. We show that using constraints derived from the conserved domain database (CDD) and PROSITE protein-motif database improves COBALT's alignment quality. We also show that COBALT has reasonable runtime performance and alignment accuracy comparable to or exceeding that of other tools for a broad range of problems. AVAILABILITY: COBALT is included in the NCBI C++ toolkit. A Linux executable for COBALT, and CDD and PROSITE data used is available at: ftp://ftp.ncbi.nlm.nih.gov/pub/agarwala/cobalt     

Comparative Proteomics Among Cytochrome P450 Family 1 for Differential Substrate Specificity

Apart from playing key roles in drug metabolism and adverse drug–drug interactions, CYPs are potential drug targets to treat a variety of diseases. The intervention of over expression of P450 1A1 (CYP1A1) in tumor cells is identified as a novel strategy for anticancer therapy. We investigated three isoforms of CYP1 family (CYP1A1, CYP1A2, and CYP1B1) for their substrate specificity. The understanding of macromolecular features that govern substrate specificity is required to understand the interplay between the protein function and dynamics. This can help in design of new antitumor molecule specifically metabolized by CYP1A1 to mediate their antitumor activity. In the present study, we carried out the comparative protein structure analysis of the three isoforms. Sequence alignment, root mean square deviation (RMSD) analysis, B-factor analysis was performed to give a better understanding of the macromolecular features involved in substrate specificity and to understand the interplay between protein dynamics and functions which will have important implications on rational design of anticancer drugs. We identified the differences in amino acid residues among the three isoforms of CYP1 family, which may account for differential substrate specificity. Six putative substrate recognition sequences are characterized along with the regions they form in the protein structure. Further the RMSD and B-factor analysis provides the information about the identified residues having the maximum RMSD and B-factor deviations.     

Pharmacogenetics and drug development: the path to safer and more effective drugs

Pharmacogenetics provides opportunities for informed decision-making along the pharmaceutical pipeline. There is a growing literature of retrospective studies of marketed medicines that describe efficacy or safety on the basis of patient genotypes. These studies emphasize the potential prospective use of genome information to enhance success in finding new medicines. An example of a prospective efficacy pharmacogenetic Phase-IIA proof-of-concept study is described. Inserting a rapidly performed efficacy pharmacogenetic step after initial clinical data are obtained can provide confidence for a commitment to full drug development. The rapid identification of adverse events during and after drug development using genomic mapping tools is also reviewed.     

Improving the alignment quality of consistency based aligners with an evaluation function using synonymous protein words

Most sequence alignment tools can successfully align protein sequences with higher levels of sequence identity. The accuracy of corresponding structure alignment, however, decreases rapidly when considering distantly related sequences (<20% identity). In this range of identity, alignments optimized so as to maximize sequence similarity are often inaccurate from a structural point of view. Over the last two decades, most multiple protein aligners have been optimized for their capacity to reproduce structure-based alignments while using sequence information. Methods currently available differ essentially in the similarity measurement between aligned residues using substitution matrices, Fourier transform, sophisticated profile-profile functions, or consistency-based approaches, more recently.In this paper, we present a flexible similarity measure for residue pairs to improve the quality of protein sequence alignment. Our approach, called SymAlign, relies on the identification of conserved words found across a sizeable fraction of the considered dataset, and supported by evolutionary analysis. These words are then used to define a position specific substitution matrix that better reflects the biological significance of local similarity. The experiment results show that the SymAlign scoring scheme can be incorporated within T-Coffee to improve sequence alignment accuracy. We also demonstrate that SymAlign is less sensitive to the presence of structurally non-similar proteins. In the analysis of the relationship between sequence identity and structure similarity, SymAlign can better differentiate structurally similar proteins from non- similar proteins. We show that protein sequence alignments can be significantly improved using a similarity estimation based on weighted n-grams. In our analysis of the alignments thus produced, sequence conservation becomes a better indicator of structural similarity. SymAlign also provides alignment visualization that can display sub-optimal alignments on dot-matrices. The visualization makes it easy to identify well-supported alternative alignments that may not have been identified by dynamic programming. SymAlign is available at http://bio-cluster.iis.sinica.edu.tw/SymAlign/.     

OXBench: A benchmark for evaluation of protein multiple sequence alignment accuracy

Background  

The alignment of two or more protein sequences provides a powerful guide in the prediction of the protein structure and in identifying key functional residues, however, the utility of any prediction is completely dependent on the accuracy of the alignment. In this paper we describe a suite of reference alignments derived from the comparison of protein three-dimensional structures together with evaluation measures and software that allow automatically generated alignments to be benchmarked. We test the OXBench benchmark suite on alignments generated by the AMPS multiple alignment method, then apply the suite to compare eight different multiple alignment algorithms. The benchmark shows the current state-of-the art for alignment accuracy and provides a baseline against which new alignment algorithms may be judged.     

Structure and Dynamics of the Membrane-Bound Form of Pf1 Coat Protein: Implications of Structural Rearrangement for Virus Assembly

The three-dimensional structure of the membrane-bound form of the major coat protein of Pf1 bacteriophage was determined in phospholipid bilayers using orientation restraints derived from both solid-state and solution NMR experiments. In contrast to previous structures determined solely in detergent micelles, the structure in bilayers contains information about the spatial arrangement of the protein within the membrane, and thus provides insights to the bacteriophage assembly process from membrane-inserted to bacteriophage-associated protein. Comparisons between the membrane-bound form of the coat protein and the previously determined structural form found in filamentous bacteriophage particles demonstrate that it undergoes a significant structural rearrangement during the membrane-mediated virus assembly process. The rotation of the transmembrane helix (Q16-A46) around its long axis changes dramatically (by 160°) to obtain the proper alignment for packing in the virus particles. Furthermore, the N-terminal amphipathic helix (V2-G17) tilts away from the membrane surface and becomes parallel with the transmembrane helix to form one nearly continuous long helix. The spectra obtained in glass-aligned planar lipid bilayers, magnetically aligned lipid bilayers (bicelles), and isotropic lipid bicelles reflect the effects of backbone motions and enable the backbone dynamics of the N-terminal helix to be characterized. Only resonances from the mobile N-terminal helix and the C-terminus (A46) are observed in the solution NMR spectra of the protein in isotropic q > 1 bicelles, whereas only resonances from the immobile transmembrane helix are observed in the solid-state ¹H/¹⁵N-separated local field spectra in magnetically aligned bicelles. The N-terminal helix and the hinge that connects it to the transmembrane helix are significantly more dynamic than the rest of the protein, thus facilitating structural rearrangement during bacteriophage assembly.     

The use of chemical design tools to transform proteomics data into drug candidates

The Human Genome Project has fueled the massive information-driven growth of genomics and proteomics and promises to deliver new insights into biology and medicine. Since proteins represent the majority of drug targets, these molecules are the focus of activity in pharmaceutical and biotechnology organizations. In this article, we describe the processes by which computational drug design may be used to exploit protein structural information to create virtual small molecules that may become novel medicines. Experimental protein structure determination, site exploration, and virtual screening provide a foundation for small molecule generation in silico, thus creating the bridge between proteomics and drug discovery.     

Molecular modeling of protein function regions

Experimental protein structures often provide extensive insight into the mode and specificity of small molecule binding, and this information is useful for understanding protein function and for the design of drugs. We have performed an analysis of the reliability with which ligand-binding information can be deduced from computer model structures, as opposed to experimentally derived ones. Models produced as part of the CASP experiments are used. The accuracy of contacts between protein model atoms and experimentally determined ligand atom positions is the main criterion. Only comparative models are included (i.e., models based on a sequence relationship between the protein of interest and a known structure). We find that, as expected, contact errors increase with decreasing sequence identity used as a basis for modeling. Analysis of the causes of errors shows that sequence alignment errors between model and experimental template have the most deleterious effect. In general, good, but not perfect, insight into ligand binding can be obtained from models based on a sequence relationship, providing there are no alignment errors in the model. The results support a structural genomics strategy based on experimental sampling of structure space so that all protein domains can be modeled on the basis of 30% or higher sequence identity.     

Investigation by fluorescence spectroscopy, resonance rayleigh scattering and zeta potential approaches of the separate and simultaneous binding effect of Paclitaxel and estradiol with human serum albumin

Separate and simultaneous binding effects of paclitaxel (a drug with anti-tumor activity) and estradiol (used for treating multiple maladies) with human serum albumin (HSA) were investigated by fluorescence quenching, UV absorption, circular dichroism, zeta potential and molecular dynamic techniques. An extensive fluorescence quenching was observed during the reaction of drugs and HSA and was rationalized in terms of a static quenching mechanism. The molecular distances between the donor (HSA) and acceptors (paclitaxel or estradiol) in binary and ternary systems were estimated according to F?rster's theory of dipole-dipole non-radiation energy transfer. The features of drug-induced structural disturbances of HSA have been studied in detail by synchronous fluorescence and circular dichroism (CD) analysis. The resonance Rayleigh scattering (RRS) intensities were proportional to the paclitaxel and estradiol concentrations in the range of respectively (0-8)×10(-6) and (0-1)×10(-4) mM in binary systems. The critical induced aggregation concentrations (C(CIAC)) of paclitaxel and estradiol for binary and ternary systems were determined by nonlinear relationships between the enhancement of the RRS intensities and the drug concentrations. A comparison between binary and ternary systems for two drugs allowed us to estimate the effect of a drug on the initial formation aggregation of the second drug. The zeta potential results were used to verify the existence of complexation and confirmed the C(CIAC) values obtained by the RRS technique. This phenomenon was supported by a progressive rise of the protein charge to a reversal point as a consequence of drug binding. The quantitative analysis data of circular dichroism (CD) spectra demonstrated that the binding of paclitaxel and/or estradiol to HSA induced conformational changes in HSA. Moreover, the α-helix content in HSA greatly decreased in the presence of paclitaxel as opposed when estradiol was present. Protein-ligand docking suggested that estradiol bound to residues situated in subdomain IIA of HSA. On the other hand, in the ternary system, the presence of the first drug decreased the binding affinity of the second drug to HSA. Therefore binding effects of paclitaxel and estradiol with HSA alone have different behavior than simultaneous interaction.     

The HSSP database of protein structure-sequence alignments and family profiles.

HSSP (http: //www.sander.embl-ebi.ac.uk/hssp/) is a derived database merging structure (3-D) and sequence (1-D) information. For each protein of known 3D structure from the Protein Data Bank (PDB), we provide a multiple sequence alignment of putative homologues and a sequence profile characteristic of the protein family, centered on the known structure. The list of homologues is the result of an iterative database search in SWISS-PROT using a position-weighted dynamic programming method for sequence profile alignment (MaxHom). The database is updated frequently. The listed putative homologues are very likely to have the same 3D structure as the PDB protein to which they have been aligned. As a result, the database not only provides aligned sequence families, but also implies secondary and tertiary structures covering 33% of all sequences in SWISS-PROT.     

Sensitive profiling of chemically diverse bioactive lipids

Here, we present an improved method for sensitive profiling of lipids in a single high-performance liquid chromatography-electrospray ionization-quadrupole time of flight mass spectrometry experiment. The approach consists of i) sensitive isocratic elution, which takes advantage of C18 column material that is resistant to increased pH values induced by piperidine, ii) chemometric alignment of mass spectra followed by differential analysis of ion intensities, and iii) semiquantitative analysis of extracted ion chromatograms of interest. A key advantage of this method is its wide applicability to extracts that harbor lipids of considerable chemical complexity. The method allows qualitative and semiquantitative analysis of fatty acyls, glycerophospholipids (such as glycerophosphatidylinositols, glycerophosphatidylserines, and glycerophosphatidylcholines in brain extracts), phosphatidylinositol mannosides, acylated glycerophospholipids, sphingolipids (including ceramides and gangliosides in brain extracts), and, for the first time with ESI, prenols and mycolic acids (MAs). MAs are targets in antimycobacterial therapy, and they play an important immunomodulatory role during host-pathogen interactions. We compared high-resolution mass spectra of MAs derived from Mycobacterium bovis Bacille Camette-Guérin during entry into nonreplicative conditions induced by oxygen deprivation (hypoxic dormancy). Although the overall composition is not drastically altered, there are pronounced differences in individual MAs. alpha-MAs accumulate during entry into dormancy, whereas a subpopulation of keto-MAs is almost entirely eliminated. This effect is reversed upon resuscitation of dormant mycobacteria. These results provide detailed chemical information with relevance to drug development and immunobiology of mycobacteria.