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/.     

A nonparametric empirical Bayes framework for large-scale multiple testing

We propose a flexible and identifiable version of the 2-groups model, motivated by hierarchical Bayes considerations, that features an empirical null and a semiparametric mixture model for the nonnull cases. We use a computationally efficient predictive recursion (PR) marginal likelihood procedure to estimate the model parameters, even the nonparametric mixing distribution. This leads to a nonparametric empirical Bayes testing procedure, which we call PRtest, based on thresholding the estimated local false discovery rates. Simulations and real data examples demonstrate that, compared to existing approaches, PRtest's careful handling of the nonnull density can give a much better fit in the tails of the mixture distribution which, in turn, can lead to more realistic conclusions.     

A nonparametric bootstrap method for testing close linkage vs. pleiotropy of coincident quantitative trait loci.

A novel method using the nonparametric bootstrap is proposed for testing whether a quantitative trait locus (QTL) at one chromosomal position could explain effects on two separate traits. If the single-QTL hypothesis is accepted, pleiotropy could explain the effect on two traits. If it is rejected, then the effects on two traits are due to linked QTLs. The method can be used in conjunction with several QTL mapping methods as long as they provide a straightforward estimate of the number of QTLs detectable from the data set. A selection step was introduced in the bootstrap procedure to reduce the conservativeness of the test of close linkage vs. pleiotropy, so that the erroneous rejection of the null hypothesis of pleiotropy only happens at a frequency equal to the nominal type I error risk specified by the user. The approach was assessed using computer simulations and proved to be relatively unbiased and robust over the range of genetic situations tested. An example of its application on a real data set from a saline stress experiment performed on a recombinant population of wheat (Triticum aestivum L. ) doubled haploid lines is also provided.     

A Bayesian nonparametric method for prediction in EST analysis

Background  

Expressed sequence tags (ESTs) analyses are a fundamental tool for gene identification in organisms. Given a preliminary EST sample from a certain library, several statistical prediction problems arise. In particular, it is of interest to estimate how many new genes can be detected in a future EST sample of given size and also to determine the gene discovery rate: these estimates represent the basis for deciding whether to proceed sequencing the library and, in case of a positive decision, a guideline for selecting the size of the new sample. Such information is also useful for establishing sequencing efficiency in experimental design and for measuring the degree of redundancy of an EST library.     

A simple method for estimating the parameter of substitution rate variation among sites

When the rate variation among sites is described by a gamma distribution, an important problem is how to estimate the shape parameter alpha, which is an index of the degree of among-site rate variation. The parsimony-based methods for estimating alpha are simple but biased, i.e., alpha tends to be overestimated. On the other hand, the likelihood-based methods are asymptotically unbiased but take a huge amount of computational time. In this paper, we have developed a new method to solve this problem: we first estimate the expected number of substitutions at each site, which is corrected for multiple hits, and then estimate the parameter alpha. Our method is computationally as fast as the parsimony method, and the estimation accuracy is much higher than that of parsimony and similar to that of the likelihood method.      

A Bayesian mixture model for across-site heterogeneities in the amino-acid replacement process

Most current models of sequence evolution assume that all sites of a protein evolve under the same substitution process, characterized by a 20 x 20 substitution matrix. Here, we propose to relax this assumption by developing a Bayesian mixture model that allows the amino-acid replacement pattern at different sites of a protein alignment to be described by distinct substitution processes. Our model, named CAT, assumes the existence of distinct processes (or classes) differing by their equilibrium frequencies over the 20 residues. Through the use of a Dirichlet process prior, the total number of classes and their respective amino-acid profiles, as well as the affiliations of each site to a given class, are all free variables of the model. In this way, the CAT model is able to adapt to the complexity actually present in the data, and it yields an estimate of the substitutional heterogeneity through the posterior mean number of classes. We show that a significant level of heterogeneity is present in the substitution patterns of proteins, and that the standard one-matrix model fails to account for this heterogeneity. By evaluating the Bayes factor, we demonstrate that the standard model is outperformed by CAT on all of the data sets which we analyzed. Altogether, these results suggest that the complexity of the pattern of substitution of real sequences is better captured by the CAT model, offering the possibility of studying its impact on phylogenetic reconstruction and its connections with structure-function determinants.     

Diurnal variation in heart rate variability before and after maximal exercise testing

As heart-rate variability (HRV) is under evaluation in clinical applications, the authors sought to better define the interdependent impact of age, maximal exercise, and diurnal variation under physiologic conditions. The authors evaluated the diurnal changes in HRV 24-h pre- and post-maximal aerobic exercise testing to exhaustion in young (19-25 yrs, n?=?12) and middle-aged (40-55 yrs, n?=?12) adults. Subjects wore a portable 5-lead electrocardiogram holter for 48?h (24?h prior to and following a maximal aerobic capacity test). Time-, frequency-, time-frequency-, and scale-invariant-domain measures of HRV were computed from RR-interval data analyzed using a 5-min window size and a 2.5-min step size, resulting in a different set of outputs every 2.5?min. Results were averaged (mean?±?SE) over four prespecified time periods during the morning, afternoon, evening, and night on Day 1 and Day 2. Diurnal changes in HRV in young and middle-aged adults were compared using a two-way, repeated-measures analysis of variance (ANOVA). Young adults demonstrated higher HRV compared to middle-aged adults during periods of wakefulness and sleep prior to maximal exercise stress testing (i.e., high-frequency power during Day 1: young adults: morning 1862?±?496?ms(2), afternoon 1797?±?384?ms(2), evening 1908?±?431?ms(2), and night 3202?±?728?ms(2); middle-aged adults: morning 341?±?53?ms(2), afternoon 405?±?68?ms(2), evening 469?±?80?ms(2), and night 836?±?136?ms(2)) (p < .05). Exercise resulted in reductions in HRV such that multiple measures of HRV were not significantly different between age groups during the afternoon and evening periods. All measures of HRV demonstrated between-group differences overnight on Day 2 (p < .05). Young adults are associated with higher baseline HRV during the daytime. Sleep increases variability equally and proportionally to daytime variability. Given the higher baseline awake HRV and equal rise in HRV during sleep, the change in HRV from sleep to morning with exercise is greater in younger subjects. These physiologic results have clinical significance in understanding the pathophysiology of altered variability in ill patients.     

A nonparametric method for fitting a single exponential to biological data.

A nonparametric (“median”) method for fitting a single exponential to biological data is described. Its performance compared with that of least-squares alternatives has been assessed by analyzing simulated experimental data. The method appears to be reliable and relatively efficient.     

A model method for testing cosmic radiation

The experiments showed that after gamma irradiation as well as after irradiation by secondary cosmic rays (hard component)Equisetum arvense spores produce in the first developmental phases prothalia at a changed rate,i.e. in favour of the females. This effect being specific for radiation cannot be produced by heat, pressure or changes of electric and magnetic field (i.e. factors appearing in higher sea levels with a stronger intensity of cosmic rays). It will be possible to apply the sensitivity of the mentioned method for investigating the problems of testing cosmic ray variations. The possibility of its application in spontaneous variability research is not less important.     

蜜蜂褪黑素的测定方法

在脊椎动物中,褪黑素(5-甲氧基-N-乙酰色胺,MLT)是一种由松果体分泌的具有典型光周期信号作用的神经内分泌激素。近年在昆虫头部也发现了这种化学物质。该文简述了褪黑素常见测定方法,详细介绍了褪黑素的免疫测定方法RIA(radionimmunoassay),并使用该方法初步测定了中华蜜蜂ApisceranaFabricius工蜂头部褪黑素的含量,结果显示褪黑素含量与工蜂的社会分工相关。     

A new method for testing gaussian processes

A method of testing gaussian processes, based on the characteristic function, is presented. An evaluation of possible gaussian inclusions in some stochastic processes is also proposed.     

Conservation of the C-type lectin fold for accommodating massive sequence variation in archaeal diversity-generating retroelements

Background

Diversity-generating retroelements (DGRs) provide organisms with a unique means for adaptation to a dynamic environment through massive protein sequence variation. The potential scope of this variation exceeds that of the vertebrate adaptive immune system. DGRs were known to exist only in viruses and bacteria until their recent discovery in archaea belonging to the ‘microbial dark matter’, specifically in organisms closely related to Nanoarchaeota. However, Nanoarchaeota DGR variable proteins were unassignable to known protein folds and apparently unrelated to characterized DGR variable proteins.

Results

To address the issue of how Nanoarchaeota DGR variable proteins accommodate massive sequence variation, we determined the 2.52 Å resolution limit crystal structure of one such protein, AvpA, which revealed a C-type lectin (CLec)-fold that organizes a putative ligand-binding site that is capable of accommodating 10¹³ sequences. This fold is surprisingly reminiscent of the CLec-folds of viral and bacterial DGR variable protein, but differs sufficiently to define a new CLec-fold subclass, which is consistent with early divergence between bacterial and archaeal DGRs. The structure also enabled identification of a group of AvpA-like proteins in multiple putative DGRs from uncultivated archaea. These variable proteins may aid Nanoarchaeota and these uncultivated archaea in symbiotic relationships.

Conclusions

Our results have uncovered the widespread conservation of the CLec-fold in viruses, bacteria, and archaea for accommodating massive sequence variation. In addition, to our knowledge, this is the first report of an archaeal CLec-fold protein.

     

For or against: the importance of variation in growth rate for testing the EICA hypothesis

The evolution of increased competitive ability (EICA) hypothesis proposes that invasive species evolve decreased defense and increased competitive ability following natural enemy release. Previous studies have found evidence both for and against EICA. The resource-enemy release hypothesis (R-ERH) suggests that fast-growing species may experience stronger enemy release than slow-growing species. On the basis of R-ERH, the prediction of EICA will be held true for slow-growing genotypes, i.e., the slow-growing genotypes from the introduced range will be less resistant to herbivory and grow faster than those from the home range; while the EICA will not be held for fast-growing genotypes, i.e., there will be no significant differences in growth and defense traits between the introduced and native fast-growing genotypes. We tested these predictions preliminarily using five populations of the invasive plant Alternanthera philoxeroides. This species has two varieties in its home range, which showed a distinct growth-defense strategy: the northern A. p. var. acutifolia (Apa) had higher growth rate but lower resistance, while the southern A. p. var. obtusifolia (Apo) had lower growth rate but higher resistance level. Our results suggest that the EICA hypothesis is consistent with the slow-growing Apo, but not with the fast-growing Apa. We suggest that evolutionary changes in growth or resistance following enemy release are influenced by variation in growth rate within an invasive alien plant. These findings have important implications for the EICA hypothesis, and may partially explain why previous studies have found evidence both for and against EICA.     

A novel method for estimating substitution rate variation among sites in a large dataset of homologous DNA sequences

We present here a novel method to estimate the site-specific relative variability in large sets of homologous sequences. It is based on the simple idea that the more closely related are the compared sequences, the higher the probability of observing nucleotide changes at rapidly evolving sites. A simulation study has been carried out to support the reliability of the method, which has been applied also to analyzing the site variability of all available human sequences corresponding to the two hypervariable regions of the mitochondrial D-loop.