Model-free linkage analysis using likelihoods.

Misspecification of transmission model parameters can produce artifactually negative lod scores at small recombination fractions and in multipoint analysis. To avoid this problem, we have tried to devise a test that aims to detect a genetic effect at a particular locus, rather than attempting to estimate the map position of a locus with specified effect. Maximizing likelihoods over transmission model parameters, as well as linkage parameters, can produce seriously biased parameter estimates and so yield tests that lack power for the detection of linkage. However, constraining the transmission model parameters to produce the correct population prevalence largely avoids this problem. For computational convenience, we recommend that the likelihoods under linkage and non-linkage are independently maximized over a limited set of transmission models, ranging from Mendelian dominant to null effect and from null effect to Mendelian recessive. In order to test for a genetic effect at a given map position, the likelihood under linkage is maximized over admixture, the proportion of families linked. Application to simulated data for a wide range of transmission models in both affected sib pairs and pedigrees demonstrates that the new method is well behaved under the null hypothesis and provides a powerful test for linkage when it is present. This test requires no specification of transmission model parameters, apart from an approximate estimate of the population prevalence. It can be applied equally to sib pairs and pedigrees, and, since it does not diminish the lod score at test positions very close to a marker, it is suitable for application to multipoint data.     

Model-free analysis and permutation tests for allelic associations

In this short report, we address some practical problems in performing likelihood-based allelic association analysis of case-control data. Model-free statistics are proposed and their properties assessed by simulation, and procedures based on permutation tests are described for marker-marker as well as marker-disease associations. A memory-efficient algorithm is developed which enables several highly polymorphic markers to be analysed.     

Model-free functional MRI analysis using topographic independent component analysis

Data-driven fMRI analysis techniques include independent component analysis (ICA) and different types of clustering in the temporal domain. Since each of these methods has its particular strengths, it is natural to look for an approach that unifies Kohonen's self-organizing map and ICA. This is given by the topographic independent component analysis. While achieved by a slight modification of the ICA model, it can be at the same time used to define a topographic order (clusters) between the components, and thus has the usual computational advantages associated with topographic maps. In this contribution, we can show that when applied to fMRI analysis it outperforms FastICA.     

Model-free analysis of binding at lipid membranes employing micro-calorimetric measurements

Based on universal thermodynamic principles (Schwarz in Biophys Chem 86:119–129, 2000) it is shown how measured enthalpy changes can be utilized to determine the relevant binding isotherm as well as the variation of the molar enthalpy change. This is carried out in a novel way involving multiple titration experiments whose evaluation requires no beforehand assumptions or models whatever. An appropriate specific model mechanism may be discussed afterwards and developed in view of the given experimental results. The pertinent procedure is demonstrated using micro-calorimetric data obtained in the case of the local anesthetic dibucaine as it associates with POPC liposomes. Mutual interactions of the bound ligand molecules could be described in terms of repulsive enthalpic and entropic activity coefficients. Apparently these are induced by electrostatic forces and by the finite size of binding sites, respectively.     

Model-free analysis for large proteins at high magnetic field strengths

Protein backbone dynamics is often characterized using model-free analysis of three sets of ¹⁵N relaxation data: longitudinal relaxation rate (R ₁), transverse relaxation rate (R ₂), and ¹⁵N–{H} NOE values. Since the experimental data is limited, a simplified model-free spectral density function is often used that contains one Lorentzian describing overall rotational correlation but not one describing internal motion. The simplified spectral density function may be also used in estimating the overall rotational correlation time, by making the R ₂/R ₁ largely insensitive to internal motions, as well as used as one of the choices in the model selection protocol. However, such approximation may not be valid for analysis of relaxation data of large proteins recorded at high magnetic field strengths since the contribution to longitudinal relaxation from the Lorentzian describing the overall rotational diffusion of the molecule is comparably small relative to that describing internal motion. Here, we quantitatively estimate the errors introduced by the use of the simplified spectral density in model-free analysis for large proteins at high magnetic field strength.     

Model-free model elimination: A new step in the model-free dynamic analysis of NMR relaxation data

Model-free analysis is a technique commonly used within the field of NMR spectroscopy to extract atomic resolution, interpretable dynamic information on multiple timescales from the R ₁, R ₂, and steady state NOE. Model-free approaches employ two disparate areas of data analysis, the discipline of mathematical optimisation, specifically the minimisation of a χ² function, and the statistical field of model selection. By searching through a large number of model-free minimisations, which were setup using synthetic relaxation data whereby the true underlying dynamics is known, certain model-free models have been identified to, at times, fail. This has been characterised as either the internal correlation times, τ _e , τ _f , or τ _s , or the global correlation time parameter, local τ _m , heading towards infinity, the result being that the final parameter values are far from the true values. In a number of cases the minimised χ² value of the failed model is significantly lower than that of all other models and, hence, will be the model which is chosen by model selection techniques. If these models are not removed prior to model selection the final model-free results could be far from the truth. By implementing a series of empirical rules involving inequalities these models can be specifically isolated and removed. Model-free analysis should therefore consist of three distinct steps: model-free minimisation, model-free model elimination, and finally model-free model selection. Failure has also been identified to affect the individual Monte Carlo simulations used within error analysis. Each simulation involves an independent randomised relaxation data set and model-free minimisation, thus simulations suffer from exactly the same types of failure as model-free models. Therefore, to prevent these outliers from causing a significant overestimation of the errors the failed Monte Carlo simulations need to be culled prior to calculating the parameter standard deviations.     

Model-free linkage analysis with covariates confirms linkage of prostate cancer to chromosomes 1 and 4

As with many complex genetic diseases, genome scans for prostate cancer have given conflicting results, often failing to provide replication of previous findings. One factor contributing to the lack of consistency across studies is locus heterogeneity, which can weaken or even eliminate evidence for linkage that is present only in a subset of families. Currently, most analyses either fail to account for locus heterogeneity or attempt to account for it only by partitioning data sets into smaller and smaller portions. In the present study, we model locus heterogeneity among affected sib pairs with prostate cancer by including covariates in the linkage analysis that serve as surrogate measures of between-family linkage differences. The model is a modification of the Olson conditional logistic model for affected relative pairs. By including Gleason score, age at onset, male-to-male transmission, and/or number of affected first-degree family members as covariates, we detected linkage near three locations that were previously identified by linkage (1q24-25 [HPC1; LOD score 3.25, P=.00012], 1q42.2-43 [PCAP; LOD score 2.84, P=.0030], and 4q [LOD score 2.80, P=.00038]), near the androgen-receptor locus on Xq12-13 (AR; LOD score 3.06, P=.00053), and at five new locations (LOD score > 2.5). Without covariates, only a few weak-to-moderate linkage signals were found, none of which replicate findings of previous genome scans. We conclude that covariate-based linkage analysis greatly improves the likelihood that linked regions will be found by incorporation of information about heterogeneity within the sample.     

Iterative cluster analysis of protein interaction data

MOTIVATION: Generation of fast tools of hierarchical clustering to be applied when distances among elements of a set are constrained, causing frequent distance ties, as happens in protein interaction data. RESULTS: We present in this work the program UVCLUSTER, that iteratively explores distance datasets using hierarchical clustering. Once the user selects a group of proteins, UVCLUSTER converts the set of primary distances among them (i.e. the minimum number of steps, or interactions, required to connect two proteins) into secondary distances that measure the strength of the connection between each pair of proteins when the interactions for all the proteins in the group are considered. We show that this novel strategy has advantages over conventional clustering methods to explore protein-protein interaction data. UVCLUSTER easily incorporates the information of the largest available interaction datasets to generate comprehensive primary distance tables. The versatility, simplicity of use and high speed of UVCLUSTER on standard personal computers suggest that it can be a benchmark analytical tool for interactome data analysis. AVAILABILITY: The program is available upon request from the authors, free for academic users. Additional information available at http://www.uv.es/genomica/UVCLUSTER.     

Optimized electrostatic surfaces parallel increased thermostability: a structural bioinformatic analysis

It has been known for some time that thermophilic proteins generally have increased numbers of non-covalent interactions (salt bridges, hydrogen bonds, etc.) compared with their mesophilic orthologs. Recently, anecdotal structural comparisons suggest that non-specific acid-base ion pairs on the protein surface can be an evolutionary efficient mechanism to increase thermostability. In this comprehensive structural analysis, we confirm this to be the case. Comparison of 127 orthologous mesophilic- thermophilic protein groups indicates a clear preference for stabilizing acid-base pairs on the surface of thermophilic proteins. Compared with positions in the core, stabilizing surface mutations are less likely to disrupt the tertiary structure, and thus more likely to be evolutionarily selected. Therefore, we believe that our results, in addition to being theoretically interesting, will facilitate identification of charge-altering mutations likely to increase the stability of a particular protein structure.     

Optimized conditions for rapd analysis inPinus radiata

Summary Pinus radiata is the most important softwood plantation species in Australia and New Zealand. The improtance of this species in forestry has led to an increasing demand to improve the efficiency of selection time of the production population, which currently takes 13 yr by traditional methods. With the application of molecular biology techniques such as random amplified polymorphic DNA (RAPD) the selection period can be reduced to 6 yr. In this study, the conditions for RAPD were optimized and the feasibility of this marker system was investigated with different families ofPinus radiata from Tasmania and South Australia. Best concentrations of Taq-polymerase (1 U), magnesium chloride (2 mM), and template DNA (20 ng) were selected to test different polymerase chain reaction (PCR) thermocycler profiles. Devey's et al. (1996) program was the most effective for production of clear RAPD bands. Best conditions were investigated to screen 10–12 bp arbitrary Breasatec and Operon primers. Both types were found useful at detecting genetic variation between families. Seventy percent and thirty percent of the selected Bresatec and Operon primers, respectively, produced polymorphic bands.     

Iterative stepwise discriminant analysis: a meta-algorithm for detecting quantitative sequence motifs.

An algorithm is presented for detecting a quantitative pattern in peptide fragments that bind class II major histocompatibility complex (MHC) molecules. It is referred to as a meta-algorithm because it requires successive applications of Stepwise Discriminate Analysis (SDA). On every iteration the best subsequence candidates are selected from sequences known to bind class II MHC molecules. When SDA compares probable binding subsequences with subsequences known not to bind class II MHC molecules, a quantitative model emerges that is capable of classifying subsequences as binding or non-binding. In an iterative manner, the resultant model is utilized as a criterion for selecting probable binding subsequence candidates. The procedure is repeated until models converge. In the illustrated examples, the final models correctly classify over 95% of the peptides in a database of peptides whose binding affinity for HLA-DR1 is known. The final model can then be used to predict the binding affinity of peptides that have not yet been laboratory tested.     

Optimized design and data analysis of tag-based cytosine methylation assays

Using the type III restriction-modification enzyme EcoP15I, we isolated sequences flanking sites digested by the methylation-sensitive HpaII enzyme or its methylation-insensitive MspI isoschizomer for massively parallel sequencing. A novel data transformation allows us to normalise HpaII by MspI counts, resulting in more accurate quantification of methylation at >1.8 million loci in the human genome. This HELP-tagging assay is not sensitive to sequence polymorphism or base composition and allows exploration of both CG-rich and depleted genomic contexts.     

Model-free methods of analyzing domain motions in proteins from simulation: A comparison of normal mode analysis and molecular dynamics simulation of lysozyme

Model-free methods are introduced to determine quantities pertaining to protein domain motions from normal mode analyses and molecular dynamics simulations. For the normal mode analysis, the methods are based on the assumption that in low frequency modes, domain motions can be well approximated by modes of motion external to the domains. To analyze the molecular dynamics trajectory, a principal component analysis tailored specifically to analyze interdomain motions is applied. A method based on the curl of the atomic displacements is described, which yields a sharp discrimination of domains, and which defines a unique interdomain screw-axis. Hinge axes are defined and classified as twist or closure axes depending on their direction. The methods have been tested on lysozyme. A remarkable correspondence was found between the first normal mode axis and the first principal mode axis, with both axes passing within 3 Å of the alpha-carbon atoms of residues 2, 39, and 56 of human lysozyme, and near the interdomain helix. The axes of the first modes are overwhelmingly closure axes. A lesser degree of correspondence is found for the second modes, but in both cases they are more twist axes than closure axes. Both analyses reveal that the interdomain connections allow only these two degrees of freedom, one more than provided by a pure mechanical hinge. Proteins 27:425–437, 1997. © 1997 Wiley-Liss, Inc.     

Optimized production and analysis of the staphylococcal multidrug efflux protein QacA

The plasmid-encoded QacA multidrug transport protein confers high-level resistance to a range of commonly used antimicrobials and is carried by widespread clinical strains of the human pathogen Staphylococcus aureus making it a potential target for future drug therapies. In order to obtain a sufficient yield of QacA protein for structural and biophysical studies, an optimized strategy for QacA overexpression was developed. QacA expression, directed from several vector systems in Escherichia coli, was tested under various growth and induction conditions and a synthetic qacA gene, codon-optimized for expression in E. coli was developed. Despite the extreme hydrophobicity and potential toxicity of the QacA secondary transport protein, a strategy based on the pBAD expression system, yielding up to four milligrams of approximately 95% pure QacA protein per litre of liquid culture, was devised. Purified QacA protein was examined using circular dichroism spectroscopy and displayed a secondary structure akin to that predicted from in silico analyses. Additionally, detergent solubilized QacA protein was shown to bind its fluorescent substrate rhodamine 6G with micro-molar affinity using a fluorescence polarization-based binding assay, similar to other multidrug transport proteins. To check the applicability of the expression/purification system described for QacA to other staphylococcal secondary transporters, the gene encoding the TetA(K) tetracycline efflux protein, which was previously recalcitrant to overexpression, was incorporated into the pBAD-based system and shown to be readily produced at easily detectable levels. Therefore, this expression system could be of general use for the production of secondary transport proteins in E. coli.     

Iterative linear integral isoconversional method: theory and application

In this work, the theory of the iterative linear integral isoconversional method was illustrated in detail. This method allows the dependence of the activation energy (E_α) on the conversion degree to be accurately determined in a short time. Moreover, the method can yield the term [A_αf(α)] (A_α: the frequency factor at conversion α, f(α): the reaction model). The obtained E_α and [A_αf(α)] values can be used to reconstruct the kinetic conversion data at experimental and extrapolated conditions. The suggested method was applied to the experimental data of combustion of biomass fast pyrolysis char, and the corresponding kinetic parameters were obtained.     

Optimized analysis of intracellular adenosine and guanosine phosphates in Escherichia coli.

To investigate the intracellular concentrations of adenosine phosphates in Escherichia coli, especially during bioreactor cultivations, a method that enables reproducible determination of adenosine phosphates in culture solutions containing at least 0.25 g dry cell weight/L has been developed. The detection limits of AMP, ADP, and ATP were found to be as low as 1 pmol. The method involves fast sampling, instantaneous inactivation of cell metabolism, extraction of nucleotides, and quantitative analysis by ion-pair reversed-phase HPLC.