Essential concepts for interpreting the dose-response of low-level arsenic exposure in epidemiological studies.

Scientifically robust selections of epidemiological studies and assessments of the dose-response of inorganic arsenic in the low-dose range must consider key issues specific to arsenic in order to reduce risk of bias. The abundance of toxicological, mechanistic, and epidemiological evidence on arsenic enables a nuanced assessment of risk of bias in epidemiological studies of low-level arsenic, as opposed to a generic evaluation based only on standard principles. Important concepts in this context include 1) arsenic metabolism and mode of action for toxicity and carcinogenicity; 2) effects of confounding factors such as diet, health status including nutritional deficiencies, use of tobacco and other substances, and body composition; 3) strengths and limitations of various metrics for assessing relevant exposures consistent with the mode of action; and 4) the potential for bias in the positive direction for the observed dose-response relationship as exposure increases in the low-dose range. As an example, evaluation of a recent dose-response modeling using eight epidemiological studies of inorganic arsenic and bladder cancer demonstrated that the pooled risk estimate was markedly affected by the single study that was ranked as having a high risk of bias, based on the above factors. The other seven studies were also affected by these factors to varying, albeit lesser, degrees that can influence the apparent dose-response in the low-dose range (i.e., drinking water concentration of 65 µg/L or dose of approximately ≤1 µg/kg-day). These issues are relevant considerations for assessing health risks of oral exposures to inorganic arsenic in the U.S. population, and setting evidence-based regulatory limits to protect human health.

Understanding the general packing rearrangements required for successful template based modeling of protein structure from a CASP experiment.

As an alternative to the common template based protein structure prediction methods based on main-chain position, a novel side-chain centric approach has been developed. Together with a Bayesian loop modeling procedure and a combination scoring function, the Stone Soup algorithm was applied to the CASP9 set of template based modeling targets. Although the method did not generate as large of perturbations to the template structures as necessary, the analysis of the results gives unique insights into the differences in packing between the target structures and their templates. Considerable variation in packing is found between target and template structures even when the structures are close, and this variation is found due to 2 and 3 body packing interactions. Outside the inherent restrictions in packing representation of the PDB, the first steps in correctly defining those regions of variable packing have been mapped primarily to local interactions, as the packing at the secondary and tertiary structure are largely conserved. Of the scoring functions used, a loop scoring function based on water structure exhibited some promise for discrimination. These results present a clear structural path for further development of a side-chain centered approach to template based modeling.

Computational analysis of pathogen-borne metallo ß-lactamases reveals discriminating structural features between B1 types.

BACKGROUND: Genes conferring antibiotic resistance to groups of bacterial pathogens are cause for considerable concern, as many once-reliable antibiotics continue to see a reduction in efficacy. The recent discovery of the metallo ß-lactamase blaNDM-1 gene, which appears to grant antibiotic resistance to a variety of Enterobacteriaceae via a mobile plasmid, is one example of this distressing trend. The following work describes a computational analysis of pathogen-borne MBLs that focuses on the structural aspects of characterized proteins. RESULTS: Using both sequence and structural analyses, we examine residues and structural features specific to various pathogen-borne MBL types. This analysis identifies a linker region within MBL-like folds that may act as a discriminating structural feature between these proteins, and specifically resistance-associated acquirable MBLs. Recently released crystal structures of the newly emerged NDM-1 protein were aligned against related MBL structures using a variety of global and local structural alignment methods, and the overall fold conformation is examined for structural conservation. Conservation appears to be present in most areas of the protein, yet is strikingly absent within a linker region, making NDM-1 unique with respect to a linker-based classification scheme. Variability analysis of the NDM-1 crystal structure highlights unique residues in key regions as well as identifying several characteristics shared with other transferable MBLs. CONCLUSIONS: A discriminating linker region identified in MBL proteins is highlighted and examined in the context of NDM-1 and primarily three other MBL types: IMP-1, VIM-2 and ccrA. The presence of an unusual linker region variant and uncommon amino acid composition at specific structurally important sites may help to explain the unusually broad kinetic profile of NDM-1 and may aid in directing research attention to areas of this protein, and possibly other MBLs, that may be targeted for inactivation or attenuation of enzymatic activity.

Near-native protein loop sampling using nonparametric density estimation accommodating sparcity.

Unlike the core structural elements of a protein like regular secondary structure, template based modeling (TBM) has difficulty with loop regions due to their variability in sequence and structure as well as the sparse sampling from a limited number of homologous templates. We present a novel, knowledge-based method for loop sampling that leverages homologous torsion angle information to estimate a continuous joint backbone dihedral angle density at each loop position. The φ,ψ distributions are estimated via a Dirichlet process mixture of hidden Markov models (DPM-HMM). Models are quickly generated based on samples from these distributions and were enriched using an end-to-end distance filter. The performance of the DPM-HMM method was evaluated against a diverse test set in a leave-one-out approach. Candidates as low as 0.45 Å RMSD and with a worst case of 3.66 Å were produced. For the canonical loops like the immunoglobulin complementarity-determining regions (mean RMSD <2.0 Å), the DPM-HMM method performs as well or better than the best templates, demonstrating that our automated method recaptures these canonical loops without inclusion of any IgG specific terms or manual intervention. In cases with poor or few good templates (mean RMSD >7.0 Å), this sampling method produces a population of loop structures to around 3.66 Å for loops up to 17 residues. In a direct test of sampling to the Loopy algorithm, our method demonstrates the ability to sample nearer native structures for both the canonical CDRH1 and non-canonical CDRH3 loops. Lastly, in the realistic test conditions of the CASP9 experiment, successful application of DPM-HMM for 90 loops from 45 TBM targets shows the general applicability of our sampling method in loop modeling problem. These results demonstrate that our DPM-HMM produces an advantage by consistently sampling near native loop structure. The software used in this analysis is available for download at http://www.stat.tamu.edu/~dahl/software/cortorgles/.

Characterizing the regularity of tetrahedral packing motifs in protein tertiary structure.

MOTIVATION: While protein secondary structure is well understood, representing the repetitive nature of tertiary packing in proteins remains difficult. We have developed a construct called the relative packing group (RPG) that applies the clique concept from graph theory as a natural basis for defining the packing motifs in proteins. An RPG is defined as a clique of residues, where every member contacts all others as determined by the Delaunay tessellation. Geometrically similar RPGs define a regular element of tertiary structure or tertiary motif (TerMo). This intuitive construct provides a simple approach to characterize general repetitive elements of tertiary structure. RESULTS: A dataset of over 4 million tetrahedral RPGs was clustered using different criteria to characterize the various aspects of regular tertiary structure in TerMos. Grouping this data within the SCOP classification levels of Family, Superfamily, Fold, Class and PDB showed that similar packing is shared across different folds. Classification of RPGs based on residue sequence locality reveals topological preferences according to protein sizes and secondary structure. We find that larger proteins favor RPGs with three local residues packed against a non-local residue. Classifying by secondary structure, helices prefer mostly local residues, sheets favor at least two local residues, while turns and coil populate with more local residues. To depict these TerMos, we have developed 2 complementary and intuitive representations: (i) Dirichlet process mixture density estimation of the torsion angle distributions and (ii) kernel density estimation of the Cartesian coordinate distribution. The TerMo library and representations software are available upon request.

A DIRICHLET PROCESS MIXTURE OF HIDDEN MARKOV MODELS FOR PROTEIN STRUCTURE PREDICTION.

By providing new insights into the distribution of a protein's torsion angles, recent statistical models for this data have pointed the way to more efficient methods for protein structure prediction. Most current approaches have concentrated on bivariate models at a single sequence position. There is, however, considerable value in simultaneously modeling angle pairs at multiple sequence positions in a protein. One area of application for such models is in structure prediction for the highly variable loop and turn regions. Such modeling is difficult due to the fact that the number of known protein structures available to estimate these torsion angle distributions is typically small. Furthermore, the data is "sparse" in that not all proteins have angle pairs at each sequence position. We propose a new semiparametric model for the joint distributions of angle pairs at multiple sequence positions. Our model accommodates sparse data by leveraging known information about the behavior of protein secondary structure. We demonstrate our technique by predicting the torsion angles in a loop from the globin fold family. Our results show that a template-based approach can now be successfully extended to modeling the notoriously difficult loop and turn regions.

Efficient experimental design for binary matched pairs data.

The analysis of data from matched pairs binary experiments, often performed with McNemar's test, presents a unique experimental design challenge in dealing with the effect of the discordance probability, p. Most approaches for determining size and power use point estimates or maximization, but this fails to account for the considerable variability across values of the nuisance parameter that occur for all common tests. We recommend viewing the size and power functions across the full range of possible discordance probability values, which gives a complete picture of the behavior of a test for any given sample size. This method also allows us to compare the behavior of different hypothesis tests. We present exact power and size functions for several tests, including the original McNemar's test and its most common variants, and compare their properties. This analysis reveals that, in general, McNemar's test comes closest to the nominal size and has the highest power. We also demonstrate our technique using the transmission/disequilibrium test (TDT) to check for linkage between schizophrenia and a locus related to the D(3) dopamine receptor, and on a hypnosis pain management data set.

Density Estimation for Protein Conformation Angles Using a Bivariate von Mises Distribution and Bayesian Nonparametrics.

Interest in predicting protein backbone conformational angles has prompted the development of modeling and inference procedures for bivariate angular distributions. We present a Bayesian approach to density estimation for bivariate angular data that uses a Dirichlet process mixture model and a bivariate von Mises distribution. We derive the necessary full conditional distributions to fit the model, as well as the details for sampling from the posterior predictive distribution. We show how our density estimation method makes it possible to improve current approaches for protein structure prediction by comparing the performance of the so-called "whole" and "half" position distributions. Current methods in the field are based on whole position distributions, as density estimation for the half positions requires techniques, such as ours, that can provide good estimates for small datasets. With our method we are able to demonstrate that half position data provides a better approximation for the distribution of conformational angles at a given sequence position, therefore providing increased efficiency and accuracy in structure prediction.

Truncation of the correlation consistent basis sets: an effective approach to the reduction of computational cost?

The systematic reduction of commonly used basis sets as a means to reduce computational cost is examined for a small test set of molecules, which includes H(2), CH(4), NH(3), H(2)O, HF, and HCN. Coupled cluster with single, double, and quasiperturbative triple excitations calculations were performed using both the correlation consistent basis sets, and a set of systematically reduced basis sets to examine both the impact of the reduction upon the accuracy of the structures and energies, and the computational cost savings achieved. The effect of several truncation scenarios upon basis set convergence is also examined. Overall, for the systems studied, a reduction can occur which preserves the well-established systematic convergence behavior of the correlation consistent basis sets.