pKa predictions for proteins,RNAs, and DNAs with the Gaussian dielectric function using DelPhi pKa

We developed a Poisson‐Boltzmann based approach to calculate the values of protein ionizable residues (Glu, Asp, His, Lys and Arg), nucleotides of RNA and single stranded DNA. Two novel features were utilized: the dielectric properties of the macromolecules and water phase were modeled via the smooth Gaussian‐based dielectric function in DelPhi and the corresponding electrostatic energies were calculated without defining the molecular surface. We tested the algorithm by calculating values for more than 300 residues from 32 proteins from the PPD dataset and achieved an overall RMSD of 0.77. Particularly, the RMSD of 0.55 was achieved for surface residues, while the RMSD of 1.1 for buried residues. The approach was also found capable of capturing the large shifts of various single point mutations in staphylococcal nuclease (SNase) from ‐cooperative dataset, resulting in an overall RMSD of 1.6 for this set of pKa's. Investigations showed that predictions for most of buried mutant residues of SNase could be improved by using higher dielectric constant values. Furthermore, an option to generate different hydrogen positions also improves predictions for buried carboxyl residues. Finally, the calculations on two RNAs demonstrated the capability of this approach for other types of biomolecules. Proteins 2015; 83:2186–2197. © 2015 Wiley Periodicals, Inc.     

Archaea S‐layer nanotube from a “black smoker” in complex with cyclo‐octasulfur (S8) rings

Elemental sulfur exists primarily as an ring and serves as terminal electron acceptor for a variety of sulfur‐fermenting bacteria. Hyperthermophilic archaea from black smoker vents are an exciting research tool to advance our knowledge of sulfur respiration under extreme conditions. Here, we use a hybrid method approach to demonstrate that the proteinaceous cavities of the S‐layer nanotube of the hyperthermophilic archaeon Staphylothermus marinus act as a storage reservoir for cyclo‐octasulfur . Fully atomistic molecular dynamics (MD) simulations were performed and the method of multiconfigurational thermodynamic integration was employed to compute the absolute free energy for transferring a ring of elemental sulfur from an aqueous bath into the largest hydrophobic cavity of a fragment of archaeal tetrabrachion. Comparisons with earlier MD studies of the free energy of hydration as a function of water occupancy in the same cavity of archaeal tetrabrachion show that the sulfur ring is energetically favored over water.     

Nucleotide‐dependent structural fluctuations and regulation of microtubule‐binding affinity of KIF1A

Molecular motors such as kinesin regulate affinity to a rail protein during the ATP hydrolysis cycle. The regulation mechanism, however, is yet to be determined. To understand this mechanism, we investigated the structural fluctuations of the motor head of the single‐headed kinesin called KIF1A in different nucleotide states using molecular dynamics simulations of a Gō‐like model. We found that the helix at the microtubule (MT) binding site intermittently exhibits a large structural fluctuation when MT is absent. Frequency of this fluctuation changes systematically according to the nucleotide states and correlates strongly with the experimentally observed binding affinity to MT. We also showed that thermal fluctuation enhances the correlation and the interaction with the nucleotide suppresses the fluctuation of the helix . These results suggest that KIF1A regulates affinity to MT by changing the flexibility of the helix during the ATP hydrolysis process: the binding site becomes more flexible in the strong binding state than in the weak binding state. Proteins 2015; 83:809–819. © 2015 Wiley Periodicals, Inc.     

Comparing side chain packing in soluble proteins,protein‐protein interfaces,and transmembrane proteins

We compare side chain prediction and packing of core and non‐core regions of soluble proteins, protein‐protein interfaces, and transmembrane proteins. We first identified or created comparable databases of high‐resolution crystal structures of these 3 protein classes. We show that the solvent‐inaccessible cores of the 3 classes of proteins are equally densely packed. As a result, the side chains of core residues at protein‐protein interfaces and in the membrane‐exposed regions of transmembrane proteins can be predicted by the hard‐sphere plus stereochemical constraint model with the same high prediction accuracies (>90%) as core residues in soluble proteins. We also find that for all 3 classes of proteins, as one moves away from the solvent‐inaccessible core, the packing fraction decreases as the solvent accessibility increases. However, the side chain predictability remains high (80% within ) up to a relative solvent accessibility, , for all 3 protein classes. Our results show that % of the interface regions in protein complexes are “core”, that is, densely packed with side chain conformations that can be accurately predicted using the hard‐sphere model. We propose packing fraction as a metric that can be used to distinguish real protein‐protein interactions from designed, non‐binding, decoys. Our results also show that cores of membrane proteins are the same as cores of soluble proteins. Thus, the computational methods we are developing for the analysis of the effect of hydrophobic core mutations in soluble proteins will be equally applicable to analyses of mutations in membrane proteins.     

Investigating the linkage between disease‐causing amino acid variants and their effect on protein stability and binding

Single amino acid variations (SAV) occurring in human population result in natural differences between individuals or cause diseases. It is well understood that the molecular effect of SAV can be manifested as changes of the wild type characteristics of the corresponding protein, among which are the protein stability and protein interactions. Typically the effect of SAV on protein stability and interactions was assessed via the changes of the wild type folding and binding free energies. However, in terms of SAV affecting protein functionally and disease susceptibility, one wants to know to what extend the wild type function is perturbed by the SAV. Here it is demonstrated that relative, rather than the absolute, change of the folding and binding free energy serves as a good indicator for SAV association with disease. Using HumVar as a source for disease‐causing SAV and experimentally determined free energy changes from ProTherm and SKEMPI databases, correlation coefficients (CC) between the disease index and relative folding and binding probability indexes, respectively, was achieved. The obtained CCs demonstrated the applicability of the proposed approach and it served as good indicator for SAV association with disease. Proteins 2016; 84:232–239. © 2015 Wiley Periodicals, Inc.     

Thermodynamics of Aβ16–21 dissociation from a fibril: Enthalpy,entropy, and volumetric properties

Here, we provide insights into the thermodynamic properties of A dissociation from an amyloid fibril using all‐atom molecular dynamics simulations in explicit water. An umbrella sampling protocol is used to compute potentials of mean force (PMF) as a function of the distance ξ between centers‐of‐mass of the A peptide and the preformed fibril at nine temperatures. Changes in the enthalpy and the entropic energy are determined from the temperature dependence of these PMF(s) and the average volume of the simulation box is computed as a function of ξ. We find that the PMF at 310 K is dominated by enthalpy while the entropic energy does not change significantly during dissociation. The volume of the system decreases during dissociation. Moreover, the magnitude of this volume change also decreases with increasing temperature. By defining dock and lock states using the solvent accessible surface area (SASA), we find that the behavior of the electrostatic energy is different in these two states. It increases (unfavorable) and decreases (favorable) during dissociation in lock and dock states, respectively, while the energy due to Lennard‐Jones interactions increases continuously in these states. Our simulations also highlight the importance of hydrophobic interactions in accounting for the stability of A . Proteins 2015; 83:1963–1972. © 2015 Wiley Periodicals, Inc.     

Exploration of conformational changes in lactose permease upon sugar binding and proton transfer through coarse‐grained simulations

Escherichia coli lactose permease (LacY) actively transports lactose and other galactosides across cell membranes through lactose/H⁺ symport process. Lactose/H⁺ symport is a highly complex process that involves sugar translocation, H⁺ transfer, and large‐scale protein conformational changes. The complete picture of lactose/H⁺ symport is largely unclear due to the complexity and multiscale nature of the process. In this work, we develop the force field for sugar molecules compatible with PACE, a hybrid and coarse‐grained force field that couples the united‐atom protein models with the coarse‐grained MARTINI water/lipid. After validation, we implement the new force field to investigate the binding of a ‐d ‐galactopyranosyl‐1‐thio‐ ‐d ‐galactopyranoside (TDG) molecule to a wild‐type LacY. Results show that the local interactions between TDG and LacY at the binding pocket are consistent with the X‐ray experiment. Transitions from inward‐facing to outward‐facing conformations upon TDG binding and protonation of Glu269 have been achieved from ～5.5 µs simulations. Both the opening of the periplasmic side and closure of the cytoplasmic side of LacY are consistent with double electron–electron resonance and thiol cross‐linking experiments. Our analysis suggests that the conformational changes of LacY are a cumulative consequence of interdomain H‐bonds breaking at the periplasmic side, interdomain salt‐bridge formation at the cytoplasmic side, and the TDG orientational changes during the transition.     

Inferring a weighted elastic network from partial unfolding with coarse‐grained simulations

A number of studies have demonstrated that simple elastic network models can reproduce experimental B‐factors, providing insights into the structure–function properties of proteins. Here, we report a study on how to improve an elastic network model and explore its performance by predicting the experimental B‐factors. Elastic network models are built on the experimental coordinates, and they only take the pairs of atoms within a given cutoff distance r_c into account. These models describe the interactions by elastic springs with the same force constant. We have developed a method based on numerical simulations with a simple coarse‐grained force field, to attribute weights to these spring constants. This method considers the time that two atoms remain connected in the network during partial unfolding, establishing a means of measuring the strength of each link. We examined two different coarse‐grained force fields and explored the computation of these weights by unfolding the native structures. Proteins 2014; 82:119–129. © 2013 Wiley Periodicals, Inc.     

Antibody side chain conformations are position‐dependent

Side chain prediction is an integral component of computational antibody design and structure prediction. Current antibody modelling tools use backbone‐dependent rotamer libraries with conformations taken from general proteins. Here we present our antibody‐specific rotamer library, where rotamers are binned according to their immunogenetics (IMGT) position, rather than their local backbone geometry. We find that for some amino acid types at certain positions, only a restricted number of side chain conformations are ever observed. Using this information, we are able to reduce the breadth of the rotamer sampling space. Based on our rotamer library, we built a side chain predictor, position‐dependent antibody rotamer swapper (PEARS). On a blind test set of 95 antibody model structures, PEARS had the highest average χ₁ and accuracy (78.7% and 64.8%) compared to three leading backbone‐dependent side chain predictors. Our use of IMGT position, rather than backbone ϕ/ψ, meant that PEARS was more robust to errors in the backbone of the model structure. PEARS also achieved the lowest number of side chain–side chain clashes. PEARS is freely available as a web application at http://opig.stats.ox.ac.uk/webapps/pears .     

Roles of conserved tryptophans in trimerization of HIV‐1 membrane‐proximal external regions: Implications for virucidal design via alchemical free‐energy molecular simulations

The Dual‐Action Virolytic Entry Inhibitors, or “DAVEI's,” are a class of recombinant fusions of a lectin, a linker polypeptide, and a 15‐residue fragment from the membrane‐proximal external region (MPER) of HIV‐1 gp41. DAVEI's trigger rupture of HIV‐1 virions, and the interaction site between DAVEI MPER and HIV‐1 lies in the gp41 component of the envelope glycoprotein Env. Here, we explore the hypothesis that DAVEI MPER engages Env gp41 in a mode structurally similar to a crystallographic MPER trimer. We used alchemical free‐energy perturbation to assess the thermodynamic roles of each of the four conserved tryptophan residues on each protomer of MPER₃. We found that a W666A mutation had a large positive for all three protomers, while W672A had a large positive for only two of the three protomers, with the other tryptophans remaining unimportant contributors to MPER₃ stability. The protomer for which W672 is not important is unique in the placement of its W666 sidechain between the other two protomers. We show that the unique orientation of this W666 sidechain azimuthally rotates its protomer away from the orientation it would have if the trimer were symmetric, resulting in the diminished interaction of this W672 with the rest of MPER₃. Our findings are consistent with our previous experimental study of W‐to‐A mutants of DAVEI. This suggests that DAVEI MPER may engage HIV‐1 Env to form a mixed trimer state in which one DAVEI MPER forms a trimer by displacing a more weakly interacting protomer of the endogenous Env MPER trimer.     

Different fates of deposited and in a temperate forest in northeast China: a 15N tracer study

Increasing atmospheric reactive nitrogen (N) deposition due to human activities could change N cycling in terrestrial ecosystems. However, the differences between the fates of deposited and are still not fully understood. Here, we investigated the fates of deposited and , respectively, via the application of ¹⁵NH₄NO₃ and NH₄¹⁵NO₃ in a temperate forest ecosystem. Results showed that at 410 days after tracer application, most was immobilized in litter layer (50 ± 2%), while a considerable amount of penetrated into 0–5 cm mineral soil (42 ± 2%), indicating that litter layer and 0–5 cm mineral soil were the major N sinks of and , respectively. Broad‐leaved trees assimilated more ¹⁵N under NH₄¹⁵NO₃ treatment compared to under ¹⁵NH₄NO₃ treatment, indicating their preference for –N. At 410 days after tracer application, 16 ± 4% added ¹⁵N was found in aboveground biomass under treatment, which was twice more than that under treatment (6 ± 1%). At the same time, approximately 80% added ¹⁵N was recovered in soil and plants under both treatments, which suggested that this forest had high potential for retention of deposited N. These results provided evidence that there were great differences between the fates of deposited and , which could help us better understand the mechanisms and capability of forest ecosystems as a sink of reactive nitrogen.     

speed‐ne: Software to simulate and estimate genetic effective population size (Ne) from linkage disequilibrium observed in single samples

The genetic effective population size, N_e, can be estimated from the average gametic disequilibrium () between pairs of loci, but such estimates require evaluation of assumptions and currently have few methods to estimate confidence intervals. speed‐ne is a suite of matlab computer code functions to estimate from with a graphical user interface and a rich set of outputs that aid in understanding data patterns and comparing multiple estimators. speed‐ne includes functions to either generate or input simulated genotype data to facilitate comparative studies of estimators under various population genetic scenarios. speed‐ne was validated with data simulated under both time‐forward and time‐backward coalescent models of genetic drift. Three classes of estimators were compared with simulated data to examine several general questions: what are the impacts of microsatellite null alleles on , how should missing data be treated, and does disequilibrium contributed by reduced recombination among some loci in a sample impact . Estimators differed greatly in precision in the scenarios examined, and a widely employed estimator exhibited the largest variances among replicate data sets. speed‐ne implements several jackknife approaches to estimate confidence intervals, and simulated data showed that jackknifing over loci and jackknifing over individuals provided ~95% confidence interval coverage for some estimators and should be useful for empirical studies. speed‐ne provides an open‐source extensible tool for estimation of from empirical genotype data and to conduct simulations of both microsatellite and single nucleotide polymorphism (SNP) data types to develop expectations and to compare estimators.     

Effects of chronic elevated nitrate concentrations on the structure and function of river biofilms

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Summary goodness‐of‐fit statistics for binary generalized linear models with noncanonical link functions

Generalized linear models (GLM) with a canonical logit link function are the primary modeling technique used to relate a binary outcome to predictor variables. However, noncanonical links can offer more flexibility, producing convenient analytical quantities (e.g., probit GLMs in toxicology) and desired measures of effect (e.g., relative risk from log GLMs). Many summary goodness‐of‐fit (GOF) statistics exist for logistic GLM. Their properties make the development of GOF statistics relatively straightforward, but it can be more difficult under noncanonical links. Although GOF tests for logistic GLM with continuous covariates (GLMCC) have been applied to GLMCCs with log links, we know of no GOF tests in the literature specifically developed for GLMCCs that can be applied regardless of link function chosen. We generalize the Tsiatis GOF statistic originally developed for logistic GLMCCs, (), so that it can be applied under any link function. Further, we show that the algebraically related Hosmer–Lemeshow () and Pigeon–Heyse (J²) statistics can be applied directly. In a simulation study, , , and J² were used to evaluate the fit of probit, log–log, complementary log–log, and log models, all calculated with a common grouping method. The statistic consistently maintained Type I error rates, while those of and J² were often lower than expected if terms with little influence were included. Generally, the statistics had similar power to detect an incorrect model. An exception occurred when a log GLMCC was incorrectly fit to data generated from a logistic GLMCC. In this case, had more power than or J².     

Purging putative siblings from population genetic data sets: a cautionary view

Interest has surged recently in removing siblings from population genetic data sets before conducting downstream analyses. However, even if the pedigree is inferred correctly, this has the potential to do more harm than good. We used computer simulations and empirical samples of coho salmon to evaluate strategies for adjusting samples to account for family structure. We compared performance in full samples and sibling‐reduced samples of estimators of allele frequency (), population differentiation () and effective population size (). Results: (i) unless simulated samples included large family groups together with a component of unrelated individuals, removing siblings generally reduced precision of and ; (ii) based on the linkage disequilibrium method was largely unbiased using full random samples but became increasingly upwardly biased under aggressive purging of siblings. Under nonrandom sampling (some families over‐represented), using full samples was downwardly biased; removing just the right ‘Goldilocks’ fraction of siblings could produce an unbiased estimate, but this sweet spot varied widely among scenarios; (iii) weighting individuals based on the inferred pedigree (to produce a best linear unbiased estimator, BLUE) maximized precision of when the inferred pedigree was correct but performed poorly when the pedigree was wrong; (iv) a variant of sibling removal that leaves intact small sibling groups appears to be more robust to errors in inferences about family structure. Our results illustrate the complex challenges posed by presence of family structure, suggest that no single optimal solution exists and argue for caution in adjusting population genetic data sets for the presence of putative siblings without fully understanding the consequences.     

Sensitivity analysis of effective population size to demographic parameters in house sparrow populations

The ratio between the effective and the census population size, , is an important measure of the long‐term viability and sustainability of a population. Understanding which demographic processes that affect most will improve our understanding of how genetic drift and the probability of fixation of alleles is affected by demography. This knowledge may also be of vital importance in management of endangered populations and species. Here, we use data from 13 natural populations of house sparrow (Passer domesticus) in Norway to calculate the demographic parameters that determine . Using the global variance‐based Sobol’ method for the sensitivity analyses, we found that was most sensitive to demographic variance, especially among older individuals. Furthermore, the individual reproductive values (that determine the demographic variance) were most sensitive to variation in fecundity. Our results draw attention to the applicability of sensitivity analyses in population management and conservation. For population management aiming to reduce the loss of genetic variation, a sensitivity analysis may indicate the demographic parameters towards which resources should be focused. The result of such an analysis may depend on the life history and mating system of the population or species under consideration, because the vital rates and sex–age classes that is most sensitive to may change accordingly.     

Deep soil carbon dynamics are driven more by soil type than by climate: a worldwide meta‐analysis of radiocarbon profiles

The response of soil carbon dynamics to climate and land‐use change will affect both the future climate and the quality of ecosystems. Deep soil carbon (>20 cm) is the primary component of the soil carbon pool, but the dynamics of deep soil carbon remain poorly understood. Therefore, radiocarbon activity (C), which is a function of the age of carbon, may help to understand the rates of soil carbon biodegradation and stabilization. We analyzed the published C contents in 122 profiles of mineral soil that were well distributed in most of the large world biomes, except for the boreal zone. With a multivariate extension of a linear mixed‐effects model whose inference was based on the parallel combination of two algorithms, the expectation–maximization (EM) and the Metropolis–Hasting algorithms, we expressed soil C profiles as a four‐parameter function of depth. The four‐parameter model produced insightful predictions of soil C as dependent on depth, soil type, climate, vegetation, land‐use and date of sampling (). Further analysis with the model showed that the age of topsoil carbon was primarily affected by climate and cultivation. By contrast, the age of deep soil carbon was affected more by soil taxa than by climate and thus illustrated the strong dependence of soil carbon dynamics on other pedologic traits such as clay content and mineralogy.     

Estimators for QST and coalescence times

Comparisons of to can provide insights into the evolutionary processes that lead to differentiation, or lack thereof, among the phenotypes of different groups (e.g., populations, species), and these comparisons have been performed on a variety of taxa, including humans. Here, I show that for neutrally evolving (i.e., by genetic drift, mutation, and gene flow alone) quantitative characters, the two commonly used estimators have somewhat different interpretations in terms of coalescence times, particularly when the number of groups that have been sampled is small. A similar situation occurs for estimators. Consequently, when observations come from only a small number of groups, which is not an unusual situation, it is important to match estimators appropriately when comparing to .     

High‐throughput identification of protein mutant stability computed from a double mutant fitness landscape

The effect of a mutation on protein stability is traditionally measured by genetic construction, expression, purification, and physical analysis using low‐throughput methods. This process is tedious and limits the number of mutants able to be examined in a single study. In contrast, functional fitness effects can be measured in a high‐throughput manner by various deep mutational scanning tools. Using protein GB 1, we have recently demonstrated the feasibility of estimating the mutational stability effect ( G) of single‐substitution based on the functional fitness profile of all double‐substitutions. The principle is to identify genetic backgrounds that have an exhausted stability margin. The functional effect of an additional substitution on these genetic backgrounds can then be used to compute the mutational G based on the biophysical relationship between functional fitness and thermodynamic stability. However, to identify such genetic backgrounds, the approach described in our previous study required a benchmark dataset, which is a set of known mutational G. In this study, a benchmark‐independent approach is developed. The genetic backgrounds of interest are identified using k‐means clustering with the integration of structural information. We further demonstrated that a reasonable approximation of G can also be obtained without taking structural information into account. In summary, this study describes a novel method for computing G from double‐substitution functional fitness profiles alone, without relying on any known mutational G as a benchmark.