Strategies for the use of computational SAR methods in assessing genotoxicity

The relationship between computational SAR studies and relevant data gathering and generation activities is complex. First, the chemical class to be studied is selected on the basis of information requirements for hazard identification and assessment. Membership in the class is determined by consideration of chemical structure and reactivity. Compilation of the existing bioassay data for this chemical class follows immediately from the specification of the class. Bioassay data, qualitative knowledge of general chemical reactivities in this class, and knowledge concerning potential interactions with biomolecular targets all contribute to the derivation of possible mechanisms for biological activity. Computational studies based on modeling the proposed mechanism of action and/or the existing data base can provide a quantitative basis for the differentiation between chemicals. There is the opportunity for continuing feedback between the quantitative computational studies and the development of a relevant bioassay data base for this chemical class. The qualitative and quantitative information on the potential biological responses obtained will provide a rational basis for extrapolation from the extant data base to the chemicals of interest, and to biological responses significant to the assessment for which complete data are unavailable. Knowledge concerning possible mechanisms of action and preexisting data determine the type of computational study that will be most useful.     

A computer method for the comparison or the quantitative similarity between normal and abnormal collagen or between sequence data and experimental data

In this paper, a method is described to correlate collagen structural data obtained by electron microscopy with chemical sequence data, or to compare two experimental sets of data. In this respect, collagen provides a valuable model system, firstly for studying the chemical basis of ultrastructure and the menchanisms of various treatments on a protein, and secondly for detecting and locating the alterations collagen fibril structure produced by a collagen disorder.     

An automated system designed for large scale NMR data deposition and annotation: application to over 600 assigned chemical shift data entries to the BioMagResBank from the Riken Structural Genomics/Proteomics Initiative internal database

Biomolecular NMR chemical shift data are key information for the functional analysis of biomolecules and the development of new techniques for NMR studies utilizing chemical shift statistical information. Structural genomics projects are major contributors to the accumulation of protein chemical shift information. The management of the large quantities of NMR data generated by each project in a local database and the transfer of the data to the public databases are still formidable tasks because of the complicated nature of NMR data. Here we report an automated and efficient system developed for the deposition and annotation of a large number of data sets including (1)H, (13)C and (15)N resonance assignments used for the structure determination of proteins. We have demonstrated the feasibility of our system by applying it to over 600 entries from the internal database generated by the RIKEN Structural Genomics/Proteomics Initiative (RSGI) to the public database, BioMagResBank (BMRB). We have assessed the quality of the deposited chemical shifts by comparing them with those predicted from the PDB coordinate entry for the corresponding protein. The same comparison for other matched BMRB/PDB entries deposited from 2001-2011 has been carried out and the results suggest that the RSGI entries greatly improved the quality of the BMRB database. Since the entries include chemical shifts acquired under strikingly similar experimental conditions, these NMR data can be expected to be a promising resource to improve current technologies as well as to develop new NMR methods for protein studies.     

Unraveling the structure of the black tea thearubigins

Thearubigins are the most abundant group of phenolic pigments found in black tea accounting for an estimated 60% of the solids in a typical black tea infusion. Fifty years ago the term thearubigins was first introduced and up to now the chemical nature of the thearubigins remains largely unresolved if not mysterious despite many efforts clarifying their structure. This paper summarizes some of our attempts to clarify and elucidate the chemical nature of the thearubigins, presenting for 15 commercially representative teas data obtained using combustion analysis, IR spectroscopy, NMR spectroscopy, Diffusion NMR spectroscopy, UV-vis spectroscopy, Circular Dichroism spectroscopy and atomic force microscopy, MALDI-TOF-MS and ESI-FT-ICR-MS. The thearubigin fractions from these 15 teas are remarkably similar with respect to their spectroscopic fingerprint. The data obtained are consistent with the thearubigins being structures of not more than 2000 Da with more than 5000 individual chemical entities detected that are susceptible to concentration-driven aggregation in aqueous solution, and that retain the chiral properties of the flavanols and theaflavins. By applying petrolomics-style data interpretation strategies and by developing novel data interpretation strategies a structural model for the thearubigins was developed.     

Application of the biotic ligand model to predicting zinc toxicity to rainbow trout,fathead minnow,and Daphnia magna

The Biotic Ligand Model has been previously developed to explain and predict the effects of water chemistry on the toxicity of copper, silver, and cadmium. In this paper, we describe the development and application of a biotic ligand model for zinc (Zn BLM). The data used in the development of the Zn BLM includes acute zinc LC50 data for several aquatic organisms including rainbow trout, fathead minnow, and Daphnia magna. Important chemical effects were observed that influenced the measured zinc toxicity for these organisms including the effects of hardness and pH. A significant amount of the historical toxicity data for zinc includes concentrations that exceeded zinc solubility. These data exhibited very different responses to chemical adjustment than data that were within solubility limits. Toxicity data that were within solubility limits showed evidence of both zinc complexation, and zinc-proton competition and could be well described by a chemical equilibrium approach such as that used by the Zn BLM.     

Stereospecific assignment of the asparagine and glutamine sidechain amide protons in proteins from chemical shift analysis

Side chain amide protons of asparagine and glutamine residues in random-coil peptides are characterized by large chemical shift differences and can be stereospecifically assigned on the basis of their chemical shift values only. The bimodal chemical shift distributions stored in the biological magnetic resonance data bank (BMRB) do not allow such an assignment. However, an analysis of the BMRB shows, that a substantial part of all stored stereospecific assignments is not correct. We show here that in most cases stereospecific assignment can also be done for folded proteins using an unbiased artificial chemical shift data base (UACSB). For a separation of the chemical shifts of the two amide resonance lines with differences ≥0.40 ppm for asparagine and differences ≥0.42 ppm for glutamine, the downfield shifted resonance lines can be assigned to H^δ21 and H^ε21, respectively, at a confidence level >95%. A classifier derived from UASCB can also be used to correct the BMRB data. The program tool AssignmentChecker implemented in AUREMOL calculates the Bayesian probability for a given stereospecific assignment and automatically corrects the assignments for a given list of chemical shifts.     

Value of the tritium test for determining the fat content in the body of rats

An indirect method for estimation of the fat percentage in the animal organism, a tritium test, was studied on laboratory male rats aged 4 and 12 months. Results obtained from the tritium test and direct chemical analysis were compared. With age a mean absolute error of the tritium test increased (from 1 to 8%) as against actual values of the water and fat percentage in the organism obtained by a direct chemical analysis. The data obtained testify to the relative insolvency of the tritium test, as well as the necessity to carry additional investigations in order to obtain adequate data.     

BSD: the Biodegradative Strain Database

The Biodegradative Strain Database (BSD) is a freely-accessible, web-based database providing detailed information on degradative bacteria and the hazardous substances that they degrade, including corresponding literature citations, relevant patents and links to additional web-based biological and chemical data. The BSD (http://bsd.cme.msu.edu) is being developed within the phylogenetic framework of the Ribosomal Database Project II (RDPII: http://rdp.cme.msu.edu/html) to provide a biological complement to the chemical and degradative pathway data of the University of Minnesota Biocatalysis/Biodegradation Database (UM-BBD: http://umbbd.ahc.umn.edu). Data is accessible through a series of strain, chemical and reference lists or by keyword search. The web site also includes on-line data submission and user survey forms to solicit user contributions and suggestions. The current release contains information on over 250 degradative bacterial strains and 150 hazardous substances. The transformation of xenobiotics and other environmentally toxic compounds by microorganisms is central to strategies for biocatalysis and the bioremediation of contaminated environments. However, practical, comprehensive, strain-level information on biocatalytic/biodegradative microbes is not readily available and is often difficult to compile. Similarly, for any given environmental contaminant, there is no single resource that can provide comparative information on the array of identified microbes capable of degrading the chemical. A web site that consolidates and cross-references strain, chemical and reference data related to biocatalysis, biotransformation, biodegradation and bioremediation would be an invaluable tool for academic and industrial researchers and environmental engineers.     

Quantitative use of chemical shifts for the modeling of protein complexes

Despite recent advances in the modeling of protein-protein complexes by docking, additional information is often required to identify the best solutions. For this purpose, NMR data deliver valuable restraints that can be used in the sampling and/or the scoring stage, like in the data-driven docking approach HADDOCK that can make use of NMR chemical shift perturbation (CSP) data to define the binding site on each protein and drive the docking. We show here that a quantitative use of chemical shifts (CS) in the scoring stage can help to resolve ambiguities. A quantitative CS-RMSD score based on (1) H(α) ,(13) C(α) , and (15) N chemical shifts ranks the best solutions always at the top, as demonstrated on a small benchmark of four complexes. It is implemented in a new docking protocol, CS-HADDOCK, which combines CSP data as ambiguous interaction restraints in the sampling stage with the CS-RMSD score in the final scoring stage. This combination of qualitative and quantitative use of chemical shifts increases the reliability of data-driven docking for the structure determination of complexes from limited NMR data.     

Analytical studies of the ultrastructure and movement of the spermatozoa of freshwater triclads

Fourteen phyllopod (Branchiopoda: Crustacea) species were collected from numerous temporary pools in north-eastern Natal, South Africa. Physical and chemical data for 10 pools, and results for hourly (over 41 hours) and 3-day interval (throughout periods when phyllopods were present) monitoring of 3 study pools are presented. Comparisons were made with other pools with phyllopods in Africa. The data reveal the broad tolerance to variation and extremes in both physical and chemical conditions in African temporary waterbodies. Species diversity appears to be related to pool size and vegetation. Phyllopod distribution does not follow a pattern associated with physical and chemical conditions.     

Patterns of chemical diversity in the Mediterranean sponge Spongia lamella

The intra-specific diversity in secondary metabolites can provide crucial information for understanding species ecology and evolution but has received limited attention in marine chemical ecology. The complex nature of diversity is partially responsible for the lack of studies, which often target a narrow number of major compounds. Here, we investigated the intra-specific chemical diversity of the Mediterranean sponge Spongia lamella. The chemical profiles of seven populations spreading over 1200 km in the Western Mediterranean were obtained by a straightforward SPE-HPLC-DAD-ELSD process whereas the identity of compounds was assessed by comparison between HPLC-MS spectra and literature data. Chemical diversity calculated by richness and Shannon indexes differed significantly between sponge populations but not at a larger regional scale. We used factor analysis, analysis of variance, and regression analysis to examine the chemical variability of this sponge at local and regional scales, to establish general patterns of variation in chemical diversity. The abundance of some metabolites varied significantly between sponge populations. Despite these significant differences between populations, we found a clear pattern of increasing chemical dissimilarity with increasing geographic distance. Additional large spatial scale studies on the chemical diversity of marine organisms will validate the universality or exclusivity of this pattern.     

Building and deploying a cyberinfrastructure for the data-driven design of chemical systems and the exploration of chemical space

Abstract

The use of modern data science has recently emerged as a promising new path to tackling the complex challenges involved in the creation of next-generation chemistry and materials. However, despite the appeal of this potentially transformative development, the chemistry community has yet to incorporate it as a central tool in every-day work. Our research program is designed to enable and advance this emerging research approach. It is centred around the creation of a software ecosystem that brings together physics-based modelling, high-throughput in silico screening and data analytics (i.e. the use of machine learning and informatics for the validation, mining and modelling of chemical data). This cyberinfrastructure is devised to offer a comprehensive set of data science techniques and tools as well as a general-purpose scope to make it as versatile and widely applicable as possible. It also emphasises user-friendliness to make it accessible to the community at large. It thus provides the means for the large-scale exploration of chemical space and for a better understanding of the hidden mechanisms that determine the properties of complex chemical systems. Such insights can dramatically accelerate, streamline and ultimately transform the way chemical research is conducted. Aside from serving as a production-level tool, our cyberinfrastructure is also designed to facilitate and assess methodological innovation. Both the software and method development work are driven by concrete molecular design problems, which also allow us to assess the efficacy of the overall cyberinfrastructure.     

Breaking the hierarchy - a new cluster selection mechanism for hierarchical clustering methods

Background  

Hierarchical clustering methods like Ward's method have been used since decades to understand biological and chemical data sets. In order to get a partition of the data set, it is necessary to choose an optimal level of the hierarchy by a so-called level selection algorithm. In 2005, a new kind of hierarchical clustering method was introduced by Palla et al. that differs in two ways from Ward's method: it can be used on data on which no full similarity matrix is defined and it can produce overlapping clusters, i.e., allow for multiple membership of items in clusters. These features are optimal for biological and chemical data sets but until now no level selection algorithm has been published for this method.     

The assessment of water quality in the Artois-Picardie water basin (France) by the use of diatom indices

The performance of six diatom indices to evaluate water quality has been studied in the Artois-Picardie water basin. Results show that all of them satisfactorily assess organic pollution which is the main phenomenon responsible for the degradation of water quality throughout the year. However, only the Specific Pollution Sensitivity Index (SPI), the Generic Diatom Index (GDI) and the Commission of Economical Community Index (CEC) show significant correlations with the ionic strength (expressed by chlorides, sulphates and conductivity) and eutrophication (expressed by chlorophyll and nitrates). Diatom indices do not integrate chemical parameters in the same way. Best correlations are obtained through simultaneous or average chemical analyses whereas for other parameters, best correlations are noted with chemical analyses carried out just before diatom sampling. Tests performed on different chemical data sets show that it is possible to make a realistic estimation of water quality during the summer in the Artois-Picardie water basin by using SPI in September.     

Chemical compound navigator: a web-based chem-BLAST, chemical taxonomy-based search engine for browsing compounds

A novel technique to annotate, query, and analyze chemical compounds has been developed and is illustrated by using the inhibitor data on HIV protease-inhibitor complexes. In this method, all chemical compounds are annotated in terms of standard chemical structural fragments. These standard fragments are defined by using criteria, such as chemical classification; structural, chemical, or functional groups; and commercial, scientific or common names or synonyms. These fragments are then organized into a data tree based on their chemical substructures. Search engines have been developed to use this data tree to enable query on inhibitors of HIV protease (http://xpdb.nist.gov/hivsdb/hivsdb.html). These search engines use a new novel technique, Chemical Block Layered Alignment of Substructure Technique (Chem-BLAST) to search on the fragments of an inhibitor to look for its chemical structural neighbors. This novel technique to annotate and query compounds lays the foundation for the use of the Semantic Web concept on chemical compounds to allow end users to group, sort, and search structural neighbors accurately and efficiently. During annotation, it enables the attachment of "meaning" (i.e., semantics) to data in a manner that far exceeds the current practice of associating "metadata" with data by creating a knowledge base (or ontology) associated with compounds. Intended users of the technique are the research community and pharmaceutical industry, for which it will provide a new tool to better identify novel chemical structural neighbors to aid drug discovery.     

Determination of the genotoxic status of a chemical

Before a non-genotoxic mechanism of action is proposed for a rodent carcinogen, or predicted for an untested chemical, adequate knowledge of the genotoxic status of the chemical must exist. The current absence of absolute agreement on the set of assays to use when evaluating genotoxicity suggests that a practical approach should be adopted, as outlined, for example, by Arni et al. (1988). In that approach, the chemical is evaluated for gene mutagenicity to Salmonella, for its ability to induce chromosomal aberrations in cultured mammalian cells, and if appropriate, gene mutations in cultured mammalian cells. Consideration of the chemical structure and the expected metabolism of the test chemical also contribute to the classification of an agent as a putative non-genotoxin. Judgement is required when interpreting isolated positive test responses generated in peripheral short-term tests. When attempting to study the mode of action of an established rodent carcinogen that is thought to operate by a non-genotoxic mechanism of action, genotoxicity data generated in vivo, if possible in the target tissue, are of great value. Compounds defined as non-genotoxic by these methods then become candidates for the study of those non-genotoxic toxicities that may predict or explain non-genotoxic rodent carcinogenesis.     

PPM: a side-chain and backbone chemical shift predictor for the assessment of protein conformational ensembles

The combination of the wide availability of protein backbone and side-chain NMR chemical shifts with advances in understanding of their relationship to protein structure makes these parameters useful for the assessment of structural-dynamic protein models. A new chemical shift predictor (PPM) is introduced, which is solely based on physical?Cchemical contributions to the chemical shifts for both the protein backbone and methyl-bearing amino-acid side chains. To explicitly account for the effects of protein dynamics on chemical shifts, PPM was directly refined against 100?ns long molecular dynamics (MD) simulations of 35 proteins with known experimental NMR chemical shifts. It is found that the prediction of methyl-proton chemical shifts by PPM from MD ensembles is improved over other methods, while backbone C??, C??, C??, N, and H^N chemical shifts are predicted at an accuracy comparable to the latest generation of chemical shift prediction programs. PPM is particularly suitable for the rapid evaluation of large protein conformational ensembles on their consistency with experimental NMR data and the possible improvement of protein force fields from chemical shifts.     

A Stronger Necessary Condition for the Multistationarity of Chemical Reaction Networks

Biochemical reaction networks grow bigger and bigger, fed by the high-throughput data provided by biologists and bred in open repositories of models allowing merging and evolution. Nevertheless, since the available data is still very far from permitting the identification of the increasing number of kinetic parameters of such models, the necessity of structural analyses for describing the dynamics of chemical networks appears stronger every day. Using the structural information, notably from the stoichiometric matrix, of a biochemical reaction system, we state a more strict version of the famous Thomas’ necessary condition for multistationarity. In particular, the obvious cases where Thomas’ condition was trivially satisfied, mutual inhibition due to a multimolecular reaction and mutual activation due to a reversible reaction, can now easily be ruled out. This more strict condition shall not be seen as some version of Thomas’ circuit functionality for the continuous case but rather as related and complementary to the whole domain of the structural analysis of (bio)chemical reaction systems, as pioneered by the chemical reaction network theory.     

Actinomycin D-deoxynucleotide complexes as models for the actinomycin D-DNA complex. The use of nuclear magnetic resonance to determine the stoichiometry and the geometry of the complexes.

The use of proton and carbon-13 magnetic resonance spectroscopy for the determination of the geometry and the stoichiometry of the actinomycin D-deoxyguanosine 5'-monophosphate complex is outlined. The dimerization of actinomycin D has been reexamined by recording the proton magnetic resonance spectrum of actinomycin D to much lower concentrations through the use of Fourier transform nuclear magnetic resonance techniques. The effect of the actinomycin D dimerization on the observed chemical shifts that results from the additon of nucleotides to an actinomycin D solution is directly demonstrated by comparing the actinomycin D-nucleotide titrations at both low (approximately 0.3 mM) and high (approximately 12 mM) concentrations of actinomycin D. In the presence of excess nucleotide the chemical shifts of the actinomycin D groups were essentially the same for both the low and high concentration titrations. The complexes of actinomycin D with pdG-dC, dG-dC, deoxyguanosine 3'-monophosphate, G-C, C-G, dIMP(5'), 2, 6-diaminopurine deoxyribose, and other nucleotides were also investigated by proton magnetic resonance and visible spectral titrations. These data were interpreted in terms of the molecular geometry of the complexes and in terms of the effect of the structure of the nucleotide base on the relative binding affinity of the nucleotides for the two nucleotide binding sites of actinomycin D. The carbon-13 chemical shifts of dGMP(5') were measured as a function of concentration over the concentration range of 0.5-0.025 M. The infinite dilution carbon-13 chemical shifts were graphically estimated from the dilution curves. These values were used to calculate the changes in the chemical shifts of the dGMP carbons that result from the formation of an actinomycin D-(dGMP)2 complex. It was not possible to interpret these carbon-13 chemical shift changes in terms of only ring current effects, which thus rules out the use of carbon-13 spectroscopy in the determination of the geometries of the actinomycin D complexes with the mono- and dinucleotides. The induced chemical shifts in the proton spectra may be used in the determination of the geometries of the complexes. A consideration of these data for the above nucleotide series shows that the predominant complex formed is one in which the guanine rings in the two nucleotide binding sites of actinomycin D are oriented in a manner very similar to that observed in the cocrystalline complex of actinomycin D with deoxyguanosine.     

A procedure to validate and correct the 13C chemical shift calibration of RNA datasets

Chemical shifts reflect the structural environment of a certain nucleus and can be used to extract structural and dynamic information. Proper calibration is indispensable to extract such information from chemical shifts. Whereas a variety of procedures exist to verify the chemical shift calibration for proteins, no such procedure is available for RNAs to date. We present here a procedure to analyze and correct the calibration of ¹³C NMR data of RNAs. Our procedure uses five ¹³C chemical shifts as a reference, each of them found in a narrow shift range in most datasets deposited in the Biological Magnetic Resonance Bank. In 49 datasets we could evaluate the ¹³C calibration and detect errors or inconsistencies in RNA ¹³C chemical shifts based on these chemical shift reference values. More than half of the datasets (27 out of those 49) were found to be improperly referenced or contained inconsistencies. This large inconsistency rate possibly explains that no clear structure–¹³C chemical shift relationship has emerged for RNA so far. We were able to recalibrate or correct 17 datasets resulting in 39 usable ¹³C datasets. 6 new datasets from our lab were used to verify our method increasing the database to 45 usable datasets. We can now search for structure–chemical shift relationships with this improved list of ¹³C chemical shift data. This is demonstrated by a clear relationship between ribose ¹³C shifts and the sugar pucker, which can be used to predict a C2′- or C3′-endo conformation of the ribose with high accuracy. The improved quality of the chemical shift data allows statistical analysis with the potential to facilitate assignment procedures, and the extraction of restraints for structure calculations of RNA.