Random Partition Distribution Indexed by Pairwise Information.

We propose a random partition distribution indexed by pairwise similarity information such that partitions compatible with the similarities are given more probability. The use of pairwise similarities, in the form of distances, is common in some clustering algorithms (e.g., hierarchical clustering), but we show how to use this type of information to define a prior partition distribution for flexible Bayesian modeling. A defining feature of the distribution is that it allocates probability among partitions within a given number of subsets, but it does not shift probability among sets of partitions with different numbers of subsets. Our distribution places more probability on partitions that group similar items yet keeps the total probability of partitions with a given number of subsets constant. The distribution of the number of subsets (and its moments) is available in closed-form and is not a function of the similarities. Our formulation has an explicit probability mass function (with a tractable normalizing constant) so the full suite of MCMC methods may be used for posterior inference. We compare our distribution with several existing partition distributions, showing that our formulation has attractive properties. We provide three demonstrations to highlight the features and relative performance of our distribution.

KScons: a Bayesian approach for protein residue contact prediction using the knob-socket model of protein tertiary structure.

MOTIVATION: By simplifying the many-bodied complexity of residue packing into patterns of simple pairwise secondary structure interactions between a single knob residue with a three-residue socket, the knob-socket construct allows a more direct incorporation of structural information into the prediction of residue contacts. By modeling the preferences between the amino acid composition of a socket and knob, we undertake an investigation of the knob-socket construct's ability to improve the prediction of residue contacts. The statistical model considers three priors and two posterior estimations to better understand how the input data affects predictions. This produces six implementations of KScons that are tested on three sets: PSICOV, CASP10 and CASP11. We compare against the current leading contact prediction methods. RESULTS: The results demonstrate the usefulness as well as the limits of knob-socket based structural modeling of protein contacts. The construct is able to extract good predictions from known structural homologs, while its performance degrades when no homologs exist. Among our six implementations, KScons MST-MP (which uses the multiple structure alignment prior and marginal posterior incorporating structural homolog information) performs the best in all three prediction sets. An analysis of recall and precision finds that KScons MST-MP improves accuracy not only by improving identification of true positives, but also by decreasing the number of false positives. Over the CASP10 and CASP11 sets, KScons MST-MP performs better than the leading methods using only evolutionary coupling data, but not quite as well as the supervised learning methods of MetaPSICOV and CoinDCA-NN that incorporate a large set of structural features. CONTACT: qiwei.li@rice.eduSupplementary information: Supplementary data are available at Bioinformatics online.

Bayesian model of protein primary sequence for secondary structure prediction.

Determining the primary structure (i.e., amino acid sequence) of a protein has become cheaper, faster, and more accurate. Higher order protein structure provides insight into a protein's function in the cell. Understanding a protein's secondary structure is a first step towards this goal. Therefore, a number of computational prediction methods have been developed to predict secondary structure from just the primary amino acid sequence. The most successful methods use machine learning approaches that are quite accurate, but do not directly incorporate structural information. As a step towards improving secondary structure reduction given the primary structure, we propose a Bayesian model based on the knob-socket model of protein packing in secondary structure. The method considers the packing influence of residues on the secondary structure determination, including those packed close in space but distant in sequence. By performing an assessment of our method on 2 test sets we show how incorporation of multiple sequence alignment data, similarly to PSIPRED, provides balance and improves the accuracy of the predictions. Software implementing the methods is provided as a web application and a stand-alone implementation.

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.

A metagenomic study of diet-dependent interaction between gut microbiota and host in infants reveals differences in immune response.

BACKGROUND: Gut microbiota and the host exist in a mutualistic relationship, with the functional composition of the microbiota strongly affecting the health and well-being of the host. Thus, it is important to develop a synthetic approach to study the host transcriptome and the microbiome simultaneously. Early microbial colonization in infants is critically important for directing neonatal intestinal and immune development, and is especially attractive for studying the development of human-commensal interactions. Here we report the results from a simultaneous study of the gut microbiome and host epithelial transcriptome of three-month-old exclusively breast- and formula-fed infants. RESULTS: Variation in both host mRNA expression and the microbiome phylogenetic and functional profiles was observed between breast- and formula-fed infants. To examine the interdependent relationship between host epithelial cell gene expression and bacterial metagenomic-based profiles, the host transcriptome and functionally profiled microbiome data were subjected to novel multivariate statistical analyses. Gut microbiota metagenome virulence characteristics concurrently varied with immunity-related gene expression in epithelial cells between the formula-fed and the breast-fed infants. CONCLUSIONS: Our data provide insight into the integrated responses of the host transcriptome and microbiome to dietary substrates in the early neonatal period. We demonstrate that differences in diet can affect, via gut colonization, host expression of genes associated with the innate immune system. Furthermore, the methodology presented in this study can be adapted to assess other host-commensal and host-pathogen interactions using genomic and transcriptomic data, providing a synthetic genomics-based picture of host-commensal relationships.

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.

The contributions of molecular framework to IMS collision cross-sections of gas-phase peptide ions.

Molecular dynamics (MD) is an essential tool for correlating collision cross-section data determined by ion mobility spectrometry (IMS) with candidate (calculated) structures. Conventional methods used for ion structure determination rely on comparing the measured cross-sections with the calculated collision cross-section for the lowest energy structure(s) taken from a large pool of candidate structures generated through multiple tiers of simulated annealing. We are developing methods to evaluate candidate structures from an ensemble of many conformations rather than the lowest energy structure. Here, we describe computational simulations and clustering methods to assign backbone conformations for singly-protonated ions of the model peptide (NH(2)-Met-Ile-Phe-Ala-Gly-Ile-Lys-COOH) formed by both MALDI and ESI, and compare the structures of MIFAGIK derivatives to test the 'sensitivity' of the cluster analysis method. Cluster analysis suggests that [MIFAGIK + H](+) ions formed by MALDI have a predominantly turn structure even though the low-energy ions prefer partial helical conformers. Although the ions formed by ESI have collision cross-sections that are different from those formed by MALDI, the results of cluster analysis indicate that the ions backbone structures are similar. Chemical modifications (N-acetyl, methylester as well as addition of Boc or Fmoc groups) to MIFAGIK alter the distribution of various conformers; the most dramatic changes are observed for the [M + Na](+) ion, which show a strong preference for random coil conformers owing to the strong solvation by the backbone amide groups.

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.

Spiked Dirichlet Process Prior for Bayesian Multiple Hypothesis Testing in Random Effects Models.

We propose a Bayesian method for multiple hypothesis testing in random effects models that uses Dirichlet process (DP) priors for a nonparametric treatment of the random effects distribution. We consider a general model formulation which accommodates a variety of multiple treatment conditions. A key feature of our method is the use of a product of spiked distributions, i.e., mixtures of a point-mass and continuous distributions, as the centering distribution for the DP prior. Adopting these spiked centering priors readily accommodates sharp null hypotheses and allows for the estimation of the posterior probabilities of such hypotheses. Dirichlet process mixture models naturally borrow information across objects through model-based clustering while inference on single hypotheses averages over clustering uncertainty. We demonstrate via a simulation study that our method yields increased sensitivity in multiple hypothesis testing and produces a lower proportion of false discoveries than other competitive methods. While our modeling framework is general, here we present an application in the context of gene expression from microarray experiments. In our application, the modeling framework allows simultaneous inference on the parameters governing differential expression and inference on the clustering of genes. We use experimental data on the transcriptional response to oxidative stress in mouse heart muscle and compare the results from our procedure with existing nonparametric Bayesian methods that provide only a ranking of the genes by their evidence for differential expression.

Assessing side-chain perturbations of the protein backbone: a knowledge-based classification of residue Ramachandran space.

Grouping the 20 residues is a classic strategy to discover ordered patterns and insights about the fundamental nature of proteins, their structure, and how they fold. Usually, this categorization is based on the biophysical and/or structural properties of a residue's side-chain group. We extend this approach to understand the effects of side chains on backbone conformation and to perform a knowledge-based classification of amino acids by comparing their backbone phi, psi distributions in different types of secondary structure. At this finer, more specific resolution, torsion angle data are often sparse and discontinuous (especially for nonhelical classes) even though a comprehensive set of protein structures is used. To ensure the precision of Ramachandran plot comparisons, we applied a rigorous Bayesian density estimation method that produces continuous estimates of the backbone phi, psi distributions. Based on this statistical modeling, a robust hierarchical clustering was performed using a divergence score to measure the similarity between plots. There were seven general groups based on the clusters from the complete Ramachandran data: nonpolar/beta-branched (Ile and Val), AsX (Asn and Asp), long (Met, Gln, Arg, Glu, Lys, and Leu), aromatic (Phe, Tyr, His, and Cys), small (Ala and Ser), bulky (Thr and Trp), and, lastly, the singletons of Gly and Pro. At the level of secondary structure (helix, sheet, turn, and coil), these groups remain somewhat consistent, although there are a few significant variations. Besides the expected uniqueness of the Gly and Pro distributions, the nonpolar/beta-branched and AsX clusters were very consistent across all types of secondary structure. Effectively, this consistency across the secondary structure classes implies that side-chain steric effects strongly influence a residue's backbone torsion angle conformation. These results help to explain the plasticity of amino acid substitutions on protein structure and should help in protein design and structure evaluation.

Genome-wide expression profiling reveals EBV-associated inhibition of MHC class I expression in nasopharyngeal carcinoma.

To identify the molecular mechanisms by which EBV-associated epithelial cancers are maintained, we measured the expression of essentially all human genes and all latent EBV genes in a collection of 31 laser-captured, microdissected nasopharyngeal carcinoma (NPC) tissue samples and 10 normal nasopharyngeal tissues. Global gene expression profiles clearly distinguished tumors from normal healthy epithelium. Expression levels of six viral genes (EBNA1, EBNA2, EBNA3A, EBNA3B, LMP1, and LMP2A) were correlated among themselves and strongly inversely correlated with the expression of a large subset of host genes. Among the human genes whose inhibition was most strongly correlated with increased EBV gene expression were multiple MHC class I HLA genes involved in regulating immune response via antigen presentation. The association between EBV gene expression and inhibition of MHC class I HLA expression implies that antigen display is either directly inhibited by EBV, facilitating immune evasion by tumor cells, and/or that tumor cells with inhibited presentation are selected for their ability to sustain higher levels of EBV to take maximum advantage of EBV oncogene-mediated tumor-promoting actions. Our data clearly reflect such tumor promotion, showing that deregulation of key proteins involved in apoptosis (BCL2-related protein A1 and Fas apoptotic inhibitory molecule), cell cycle checkpoints (AKIP, SCYL1, and NIN), and metastasis (matrix metalloproteinase 1) is closely correlated with the levels of EBV gene expression in NPC.

Accommodative ciliary body and lens function in rhesus monkeys, I: normal lens, zonule and ciliary process configuration in the iridectomized eye.

PURPOSE: The underlying causes of presbyopia, and the functional relationship between the ciliary muscle and lens during aging are unclear. In the current study, these relationships were studied in rhesus monkeys, whose accommodative apparatus and age-related loss of accommodation are similar to those in humans. METHODS: Centripetal ciliary body and lens equator movements were measured during accommodation in 28 eyes of 21 rhesus monkeys (ages, 5.7-26 years) by goniovideography. Ultrasound biomicroscopy was performed in 21 eyes of 17 monkeys. Narrowing of the angle between the anterior aspect of the ciliary body and the inner aspect of the cornea was used as a surrogate indicator of forward ciliary body movement during accommodation. RESULTS: Average centripetal ciliary body movement in older eyes (age > or =17 years, n = 16) was approximately 20% (0.09 mm) less than in young eyes (age, 6-10 years, n = 6), but not enough to explain the 60% (0.21 mm) loss in centripetal lens movement nor the 76% (10.2 D) loss in accommodative amplitude. Average forward ciliary body movement was 67% (49 degrees ) less in older (n = 11) versus young (n = 6) eyes. Maximum accommodative amplitude correlated significantly with the amplitude of centripetal lens movement (0.02 +/- 0.003 mm/D; n = 28; P < 0.001) and with forward ciliary body movement (3.34 +/- 0.54 deg/D; n = 21; P = 0.01). CONCLUSIONS: Decreased lens movement with age could be in part secondary to extralenticular age-related changes, such as loss of ciliary body forward movement. Ciliary body centripetal movement may not be the limiting component in accommodation in the older eye.

Vitreous glutamate concentration and axon loss in monkeys with experimental glaucoma.

OBJECTIVE: To evaluate vitreous glutamate concentration and axon loss in monkeys with experimental glaucoma. METHODS: We induced unilateral chronic glaucoma by means of laser trabecular destruction in 14 rhesus and 6 cynomolgus monkeys. Intraocular pressure (IOP) was monitored weekly. We assessed optic nerve damage clinically and photographically. Vitreous, sampled immediately before enucleation, was analyzed for glutamate concentration by means of high-performance liquid chromatography. We quantified percentage of axon loss after histopathologic sectioning of the optic nerve, compared median glutamate concentration ratios, and assessed correlation of glutamate concentration, axon count, IOP, cup-disc ratio, duration of IOP elevation, and age. RESULTS: Median vitreous glutamate concentration in glaucomatous eyes was 7.0 micromol/L (range, 3.0-88.6 micromol/L) vs 6.7 micromol/L (range, 2.8-87.4 micromol/L) in control eyes. The ratio (glaucomatous to control eyes) was 1.08. We found no significant correlation between vitreous glutamate concentration ratio and any of the other variables. The IOP, disc cupping, and axon loss were correlated. CONCLUSIONS: We found no difference between vitreous glutamate concentration in glaucomatous and contralateral control monkey eyes when the entire data set was examined and no evidence of correlation between vitreous glutamate concentration and axon loss. CLINICAL RELEVANCE: Vitreous concentration of the excitotoxic amino acid glutamate, thought to be associated with retinal ganglion cell death in glaucoma, was not altered in this study.