Model quality assessment using distance constraints from alignments

Given a set of alternative models for a specific protein sequence, the model quality assessment (MQA) problem asks for an assignment of scores to each model in the set. A good MQA program assigns these scores such that they correlate well with real quality of the models, ideally scoring best that model which is closest to the true structure. In this article, we present a new approach for addressing the MQA problem. It is based on distance constraints extracted from alignments to templates of known structure, and is implemented in the Undertaker program for protein structure prediction. One novel feature is that we extract noncontact constraints as well as contact constraints. We describe how the distance constraint extraction is done and we show how they can be used to address the MQA problem. We have compared our method on CASP7 targets and the results show that our method is at least comparable with the best MQA methods that were assessed at CASP7. We also propose a new evaluation measure, Kendall's τ, that is more interpretable than conventional measures used for evaluating MQA methods (Pearson's r and Spearman's ρ). We show clear examples where Kendall's τ agrees much more with our intuition of a correct MQA, and we therefore propose that Kendall's τ be used for future CASP MQA assessments. Proteins 2009. © 2008 Wiley‐Liss, Inc.     

A trait‐based approach to predict population genetic structure in bees

Understanding population genetic structure is key to developing predictions about species susceptibility to environmental change, such as habitat fragmentation and climate change. It has been theorized that life‐history traits may constrain some species in their dispersal and lead to greater signatures of population genetic structure. In this study, we use a quantitative comparative approach to assess if patterns of population genetic structure in bees are driven by three key species‐level life‐history traits: body size, sociality, and diet breadth. Specifically, we reviewed the current literature on bee population genetic structure, as measured by the differentiation indices Nei's G_ST, Hedrick's G′_ST, and Jost's D. We then used phylogenetic generalised linear models to estimate the correlation between the evolution of these traits and patterns of genetic differentiation. Our analyses revealed a negative and significant effect of body size on genetic structure, regardless of differentiation index utilized. For Hedrick's G′_ST and Jost's D, we also found a significant impact of sociality, where social species exhibited lower levels of differentiation than solitary species. We did not find an effect of diet specialization on population genetic structure. Overall, our results suggest that physical dispersal or other functions related to body size are among the most critical for mediating population structure for bees. We further highlight the importance of standardizing population genetic measures to more easily compare studies and to identify the most susceptible species to landscape and climatic changes.     

Toward a detailed understanding of search trajectories in fragment assembly approaches to protein structure prediction

Energy functions, fragment libraries, and search methods constitute three key components of fragment‐assembly methods for protein structure prediction, which are all crucial for their ability to generate high‐accuracy predictions. All of these components are tightly coupled; efficient searching becomes more important as the quality of fragment libraries decreases. Given these relationships, there is currently a poor understanding of the strengths and weaknesses of the sampling approaches currently used in fragment‐assembly techniques. Here, we determine how the performance of search techniques can be assessed in a meaningful manner, given the above problems. We describe a set of techniques that aim to reduce the impact of the energy function, and assess exploration in view of the search space defined by a given fragment library. We illustrate our approach using Rosetta and EdaFold, and show how certain features of these methods encourage or limit conformational exploration. We demonstrate that individual trajectories of Rosetta are susceptible to local minima in the energy landscape, and that this can be linked to non‐uniform sampling across the protein chain. We show that EdaFold's novel approach can help balance broad exploration with locating good low‐energy conformations. This occurs through two mechanisms which cannot be readily differentiated using standard performance measures: exclusion of false minima, followed by an increasingly focused search in low‐energy regions of conformational space. Measures such as ours can be helpful in characterizing new fragment‐based methods in terms of the quality of conformational exploration realized. Proteins 2016; 84:411–426. © 2016 The Authors Proteins: Structure, Function, and Bioinformatics Published by Wiley Periodicals, Inc.     

Structure refinement of protein model decoys requires accurate side‐chain placement

In this study, the application of temperature‐based replica‐exchange (T‐ReX) simulations for structure refinement of decoys taken from the I‐TASSER dataset was examined. A set of eight nonredundant proteins was investigated using self‐guided Langevin dynamics (SGLD) with a generalized Born implicit solvent model to sample conformational space. For two of the protein test cases, a comparison of the SGLD/T‐ReX method with that of a hybrid explicit/implicit solvent molecular dynamics T‐ReX simulation model is provided. Additionally, the effect of side‐chain placement among the starting decoy structures, using alternative rotamer conformations taken from the SCWRL4 modeling program, was investigated. The simulation results showed that, despite having near‐native backbone conformations among the starting decoys, the determinant of their refinement is side‐chain packing to a level that satisfies a minimum threshold of native contacts to allow efficient excursions toward the downhill refinement regime on the energy landscape. By repacking using SCWRL4 and by applying the RWplus statistical potential for structure identification, the SGLD/T‐ReX simulations achieved refinement to an average of 38% increase in the number of native contacts relative to the original I‐TASSER decoy sets and a 25% reduction in values of C_α root‐mean‐square deviation. The hybrid model succeeded in obtaining a sharper funnel to low‐energy states for a modeled target than the implicit solvent SGLD model; yet, structure identification remained roughly the same. Without meeting a threshold of near‐native packing of side chains, the T‐ReX simulations degrade the accuracy of the decoys, and subsequently, refinement becomes tantamount to the protein folding problem. Proteins 2013. 2012 Published by Wiley Periodicals, Inc.     

Paramagnetic relaxation enhancement‐assisted structural characterization of a partially disordered conformation of ubiquitin

Nuclear magnetic resonance (NMR) is a powerful tool to study three‐dimensional structures as well as protein conformational fluctuations in solution, but it is compromised by increases in peak widths and missing signals. We previously reported that ubiquitin has two folded conformations, N₁ and N₂ and plus another folded conformation, I, in which some amide group signals of residues 33–41 almost disappeared above 3 kbar at pH 4.5 and 273 K. Thus, well‐converged structural models could not be obtained for this region owing to the absence of distance restraints. Here, we reexamine the problem using the ubiquitin Q41N variant as a model for this locally disordered conformation, I. We demonstrate that the variant shows pressure‐induced loss of backbone amide group signals at residues 28, 33, 36, and 39–41 like the wild‐type, with a similar but smaller effect on CαH and CβH signals. In order to characterize this I structure, we measured paramagnetic relaxation enhancement (PRE) under high pressure to obtain distance restraints, and calculated the structure assisted by Bayesian inference. We conclude that the more disordered I conformation observed at pH 4.0, 278 K, and 2.5 kbar largely retained the N₂ conformation, although the amide groups at residues 33–41 have more heterogeneous conformations and more contact with water, which differ from the N₁ and N₂ states. The PRE‐assisted strategy has the potential to improve structural characterization of proteins that lack NMR signals, especially for relatively more open and hydrated protein conformations.     

Loop prediction for a GPCR homology model: Algorithms and results

We present loop structure prediction results of the intracellular and extracellular loops of four G‐protein‐coupled receptors (GPCRs): bovine rhodopsin (bRh), the turkey β1‐adrenergic (β1Ar), the human β2‐adrenergic (β2Ar) and the human A2a adenosine receptor (A2Ar) in perturbed environments. We used the protein local optimization program, which builds thousands of loop candidates by sampling rotamer states of the loops' constituent amino acids. The candidate loops are discriminated between with our physics‐based, all‐atom energy function, which is based on the OPLS force field with implicit solvent and several correction terms. For relevant cases, explicit membrane molecules are included to simulate the effect of the membrane on loop structure. We also discuss a new sampling algorithm that divides phase space into different regions, allowing more thorough sampling of long loops that greatly improves results. In the first half of the paper, loop prediction is done with the GPCRs' transmembrane domains fixed in their crystallographic positions, while the loops are built one‐by‐one. Side chains near the loops are also in non‐native conformations. The second half describes a full homology model of β2Ar using β1Ar as a template. No information about the crystal structure of β2Ar was used to build this homology model. We are able to capture the architecture of short loops and the very long second extracellular loop, which is key for ligand binding. We believe this the first successful example of an RMSD validated, physics‐based loop prediction in the context of a GPCR homology model. Proteins 2013. © 2012 Wiley Periodicals, Inc.     

尼泊尔生态系统质量评估与时空格局变化——基于参照条件的评估方法

良好的生态系统质量是维持人类社会供给需求和可持续发展目标实现的重要保障。针对尼泊尔自然地理环境复杂多样,区域间气候差异明显的特点,结合基于参照条件的评估方法可以得到生态系统质量的相对水平值,其结果能够反映出不同的变化信息。植被是区域生态系统质量变化的重要指示器,利用尼泊尔五大地理区以及四种主要植被生态系统类型划分出20个生态评估区,从表征植被生态系统的水平结构、生产功能和垂直结构3个方面计算生态参数相对密度指标（RVI）,结合主成分分析法构建植被生态系统质量指数（VEQI）,并以其国家自然保护区为参照,构建基于参照条件的生态系统质量评估模型,计算了尼泊尔2016和2020年基于参照条件的植被生态系统质量指数（VEQI''）并分析其生态系统质量的时空格局变化。结果表明：（1）2016至2020年,尼泊尔生态系统质量现实值VEQI的平均值增加了3.49%,总体上,在参照生态系统质量（VEQI_ref）提高（约1.41%）的背景下,生态系统质量相对水平值VEQI''增加了1.42%;（2）对于尼泊尔地区,评估区89%分位数的VEQI与其对应的国家自然保护区的参照值具有很强的相关性,总体差异较小,可以代替作为参照值;（3）从空间格局变化趋势来看,尼泊尔生态系统质量变好、基本稳定和变差的面积分别占植被生态系统总面积的74.16%、14.25%和11.59%。与数量不足、较难收集利用的野外观测台站数据相比,国家自然保护区更接近理想参照生态系统的假设,通过有限的自然保护区确定生态评估区的参照值,实现生态系统质量的快速评估,其结果具有更好的时空可比性,可以为区域生态质量变化评估及量化分析等方面提供参考。     

From signal perception to signal transduction: ligand‐induced dimeric switch of DctB sensory domain in solution

Sinorhizobium meliloti DctB is a typical transmembrane sensory histidine kinase, which senses C₄‐dicarboxylic acids (DCA) and regulates the expression of DctA, the DCA transporter. We previously reported the crystal structures of its periplasmic sensory domain (DctBp) in apo and succinate‐bound states, and these structures showed dramatic conformational changes at dimeric level. Here we show a ligand‐induced dimeric switch in solution and a strong correlation between DctBp's dimerization states and the in vivo activities of DctB. Using site‐directed mutagenesis, we identify important determinants for signal perception and transduction. Specifically, we show that the ligand‐binding pocket is essential for DCA‐induced ‘on’ activity of DctB. Mutations at different sections of DctBp's dimerization interface can lock full‐length DctB at either ‘on’ or ‘off’ state, independent of ligand binding. Taken together, these results suggest that DctBp's signal perception and transduction occur through a ‘ligand‐induced dimeric switch’, in which the changes in the dimeric conformations upon ligand binding are responsible for the signal transduction in DctB.     

Using collections of structural models to predict changes of binding affinity caused by mutations in protein–protein interactions

Protein–protein interactions (PPIs) in all the molecular aspects that take place both inside and outside cells. However, determining experimentally the structure and affinity of PPIs is expensive and time consuming. Therefore, the development of computational tools, as a complement to experimental methods, is fundamental. Here, we present a computational suite: MODPIN, to model and predict the changes of binding affinity of PPIs. In this approach we use homology modeling to derive the structures of PPIs and score them using state‐of‐the‐art scoring functions. We explore the conformational space of PPIs by generating not a single structural model but a collection of structural models with different conformations based on several templates. We apply the approach to predict the changes in free energy upon mutations and splicing variants of large datasets of PPIs to statistically quantify the quality and accuracy of the predictions. As an example, we use MODPIN to study the effect of mutations in the interaction between colicin endonuclease 9 and colicin endonuclease 2 immune protein from Escherichia coli. Finally, we have compared our results with other state‐of‐art methods.     

Sucrose in aqueous solution revisited, Part 2: adaptively biased molecular dynamics simulations and computational analysis of NMR relaxation

We report 100 ns molecular dynamics simulations, at various temperatures, of sucrose in water (with concentrations of sucrose ranging from 0.02 to 4M), and in a 7:3 water‐DMSO mixture. Convergence of the resulting conformational ensembles was checked using adaptive‐biased simulations along the glycosidic Φ and ψ torsion angles. NMR relaxation parameters, including longitudinal (R₁) and transverse (R₂) relaxation rates, nuclear Overhauser enhancements (NOE), and generalized order parameter (S²) were computed from the resulting time‐correlation functions. The amplitude and time scales of molecular motions change with temperature and concentration in ways that track closely with experimental results, and are consistent with a model in which sucrose conformational fluctuations are limited (with 80–90% of the conformations having ??ψ values within 20° of an average conformation), but with some important differences in conformation between pure water and DMSO‐water mixtures. © 2011 Wiley Periodicals, Inc. Biopolymers 97: 289–302, 2012.     

Choosing and using diversity indices: insights for ecological applications from the German Biodiversity Exploratories

Biodiversity, a multidimensional property of natural systems, is difficult to quantify partly because of the multitude of indices proposed for this purpose. Indices aim to describe general properties of communities that allow us to compare different regions, taxa, and trophic levels. Therefore, they are of fundamental importance for environmental monitoring and conservation, although there is no consensus about which indices are more appropriate and informative. We tested several common diversity indices in a range of simple to complex statistical analyses in order to determine whether some were better suited for certain analyses than others. We used data collected around the focal plant Plantago lanceolata on 60 temperate grassland plots embedded in an agricultural landscape to explore relationships between the common diversity indices of species richness (S), Shannon's diversity (H'), Simpson's diversity (D₁), Simpson's dominance (D₂), Simpson's evenness (E), and Berger–Parker dominance (BP). We calculated each of these indices for herbaceous plants, arbuscular mycorrhizal fungi, aboveground arthropods, belowground insect larvae, and P. lanceolata molecular and chemical diversity. Including these trait‐based measures of diversity allowed us to test whether or not they behaved similarly to the better studied species diversity. We used path analysis to determine whether compound indices detected more relationships between diversities of different organisms and traits than more basic indices. In the path models, more paths were significant when using H', even though all models except that with E were equally reliable. This demonstrates that while common diversity indices may appear interchangeable in simple analyses, when considering complex interactions, the choice of index can profoundly alter the interpretation of results. Data mining in order to identify the index producing the most significant results should be avoided, but simultaneously considering analyses using multiple indices can provide greater insight into the interactions in a system.     

A Beta-mixture model for assessing genetic population structure

Summary An important fraction of recently generated molecular data is dominant markers. They contain substantial information about genetic variation but dominance makes it impossible to apply standard techniques to calculate measures of genetic differentiation, such as F‐statistics. In this article, we propose a new Bayesian beta‐mixture model that more accurately describes the genetic structure from dominant markers and estimates multiple F_ST s from the sample. The model also has important application for codominant markers and single‐nucleotide polymorphism (SNP) data. The number of F_ST is assumed unknown beforehand and follows a random distribution. The reversible jump algorithm is used to estimate the unknown number of multiple F_ST s. We evaluate the performance of three split proposals and the overall performance of the proposed model based on simulated dominant marker data. The model could reliably identify and estimate a spectrum of degrees of genetic differentiation present in multiple loci. The estimates of F_ST s also incorporate uncertainty about the magnitude of within‐population inbreeding coefficient. We illustrate the method with two examples, one using dominant marker data from a rare orchid and the other using codominant marker data from human populations.     

Structural basis of integrin transmembrane activation

Integrins are cell adhesion receptors that transmit bidirectional signals across plasma membrane and are crucial for many biological functions. Recent structural studies of integrin transmembrane (TM) and cytoplasmic domains have shed light on their conformational changes during integrin activation. A structure of the resting state was solved based on Rosetta computational modeling and experimental data using intact integrins on mammalian cell surface. In this structure, the α_IIb GXXXG motif and their β₃ counterparts of the TM domains associate with ridge‐in‐groove packing, and the α_IIb GFFKR motif and the β₃ Lys‐716 in the cytoplasmic segments play a critical role in the α/β association. Comparing this structure with the NMR structures of the monomeric α_IIb and β₃ (represented as active conformations), the α subunit helix remains similar after dissociation whereas β subunit helix is tilted by embedding additional 5–6 residues into the lipid bilayer. These conformational changes are critical for integrin activation and signaling across the plasma membrane. We thus propose a new model of integrin TM activation in which the recent NMR structure of the α_IIbβ₃ TM/cytoplasmic complex represents an intermediate or transient state, and the electrostatic interaction in the cytoplasmic region is important for priming the initial α/β association, but not absolutely necessary for the resting state. J. Cell. Biochem. 109: 447–452, 2010. © 2009 Wiley‐Liss, Inc.     

The Dual Arrhenius and Michaelis–Menten kinetics model for decomposition of soil organic matter at hourly to seasonal time scales

Decomposition of soil carbon stocks is one of the largest potential biotic feedbacks to climate change. Models of decomposition of soil organic matter and of soil respiration rely on empirical functions that relate variation in temperature and soil water content to rates of microbial metabolism using soil‐C substrates. Here, we describe a unifying modeling framework to combine the effects of temperature, soil water content, and soluble substrate supply on decomposition of soluble soil‐C substrates using simple functions based on process concepts. The model's backbone is the Michaelis–Menten equation, which describes the relationship between reaction velocity and soluble organic‐C and O₂ substrate concentrations at an enzyme's reactive site, which are determined by diffusivity functions based on soil water content. Temperature sensitivity is simulated by allowing the maximum velocity of the reaction (V_max) to vary according to Arrhenius function. The Dual Arrhenius and Michaelis–Menten kinetics (DAMM) model core was able to predict effectively observations from of laboratory enzyme assays of β‐glucosidase and phenol‐oxidase across a range of substrate concentrations and incubation temperatures. The model also functioned as well or better than purely empirical models for simulating hourly and seasonal soil respiration data from a trenched plot in a deciduous forest at the Harvard Forest, in northeastern United States. The DAMM model demonstrates that enzymatic processes can be intrinsically temperature sensitive, but environmental constrains of substrate supply under soil moisture extremes can prevent that response to temperature from being observed. We discuss how DAMM could serve as a core module that is informed by other modules regarding microbial dynamics and supply of soluble‐C substrates from plant inputs and from desorption of physically stabilized soil‐C pools. Most importantly, it presents a way forward from purely empirical representation of temperature and moisture responses and integrates temperature‐sensitive enzymatic processes with constraints of substrate supply.     

DockRank: Ranking docked conformations using partner‐specific sequence homology‐based protein interface prediction

Selecting near‐native conformations from the immense number of conformations generated by docking programs remains a major challenge in molecular docking. We introduce DockRank, a novel approach to scoring docked conformations based on the degree to which the interface residues of the docked conformation match a set of predicted interface residues. DockRank uses interface residues predicted by partner‐specific sequence homology‐based protein–protein interface predictor (PS‐HomPPI), which predicts the interface residues of a query protein with a specific interaction partner. We compared the performance of DockRank with several state‐of‐the‐art docking scoring functions using Success Rate (the percentage of cases that have at least one near‐native conformation among the top m conformations) and Hit Rate (the percentage of near‐native conformations that are included among the top m conformations). In cases where it is possible to obtain partner‐specific (PS) interface predictions from PS‐HomPPI, DockRank consistently outperforms both (i) ZRank and IRAD, two state‐of‐the‐art energy‐based scoring functions (improving Success Rate by up to 4‐fold); and (ii) Variants of DockRank that use predicted interface residues obtained from several protein interface predictors that do not take into account the binding partner in making interface predictions (improving success rate by up to 39‐fold). The latter result underscores the importance of using partner‐specific interface residues in scoring docked conformations. We show that DockRank, when used to re‐rank the conformations returned by ClusPro, improves upon the original ClusPro rankings in terms of both Success Rate and Hit Rate. DockRank is available as a server at http://einstein.cs.iastate.edu/DockRank/ . Proteins 2014; 82:250–267. © 2013 Wiley Periodicals, Inc.     

Binding conformations and QSAR of CA-4 analogs as tubulin inhibitors

A theoretical study on the binding conformations and the quantitative structure–activity relationship (QSAR) of combretastatin A4 (CA-4) analogs as inhibitors toward tubulin has been carried out using docking analysis and comparative molecular field analysis (CoMFA). The appropriate binding orientations and conformations of these compounds interacting with tubulin were revealed by the docking study; and a 3D-QSAR model showing significant statistical quality and satisfactory predictive ability was established, in which the correlation coefficient (R²) and cross-validation coefficient (q²) were 0.955 and 0.66, respectively. The same model was further applied to predict the pIC₅₀ values for 16 congeneric compounds as external test set, and the predictive correlation coefficient R²_pred reached 0.883. Other tests on additional validations further confirmed the satisfactory predictive power of the model. In this work, it was very interesting to find that the 3D topology structure of the active site of tubulin from the docking analysis was in good agreement with the 3D-QSAR model from CoMFA for this series of compounds. Some key structural factors of the compounds responsible for cytotoxicity were reasonably presented. These theoretical results can offer useful references for understanding the action mechanism and directing the molecular design of this kind of inhibitor with improved activity.     

Ab initio construction of protein tertiary structures using a hierarchical approach

We present a hierarchical method to predict protein tertiary structure models from sequence. We start with complete enumeration of conformations using a simple tetrahedral lattice model. We then build conformations with increasing detail, and at each step select a subset of conformations using empirical energy functions with increasing complexity. After enumeration on lattice, we select a subset of low energy conformations using a statistical residue-residue contact energy function, and generate all-atom models using predicted secondary structure. A combined knowledge-based atomic level energy function is then used to select subsets of the all-atom models. The final predictions are generated using a consensus distance geometry procedure. We test the feasibility of the procedure on a set of 12 small proteins covering a wide range of protein topologies. A rigorous double-blind test of our method was made under the auspices of the CASP3 experiment, where we did ab initio structure predictions for 12 proteins using this approach. The performance of our methodology at CASP3 is reasonably good and completely consistent with our initial tests.     

Comparison of thermoluminescence characteristics in γ‐ray and C5+ ion beam‐irradiated LiCaAlF6:Ce phosphor

We compare the thermoluminescence (TL) behavior of Ce³⁺ ion‐activated LiCaAlF₆ exposed to γ‐rays and a carbon ion beam. The reported phosphor is synthesized using an in‐house precipitation method with varying concentrations of activator ion and is characterized by X‐ray diffraction (XRD) and TL. Rietveld refinement is performed to study the structural statistics. The TL glow curve consists of a prominent glow peak at 232°C with three shoulders at 115, 159 and 333°C when exposed to γ‐rays from a ⁶⁰Co source. When exposed to a C⁵⁺ ion beam, the TL glow curve consists of five peaks with peak temperatures near 156, 221, 250, 287 and 330°C, and is found to vary slightly with changing fluence. Glow curve convolution deconvolution (GCCD) functions are applied to the TL curves for complete analysis of the glow curve structure and TL traps. The order of kinetics (b), activation energy (E) and frequency factor are determined using Chen's peak shape method and theoretical curves are drawn using GCCD functions. A track interaction model (TIM) is used to explain the sublinearity/saturation at higher fluences. Ion beam parameters are analyzed using Monte‐Carlo simulation‐based SRIM‐2013 code. Copyright © 2016 John Wiley & Sons, Ltd.     

Exploring the free energy landscape of a model β‐hairpin peptide and its isoform

Secondary structural transitions from α‐helix to β‐sheet conformations are observed in several misfolding diseases including Alzheimer's and Parkinson's. Determining factors contributing favorably to the formation of each of these secondary structures is therefore essential to better understand these disease states. β‐hairpin peptides form basic components of anti‐parallel β‐sheets and are suitable model systems for characterizing the fundamental forces stabilizing β‐sheets in fibrillar structures. In this study, we explore the free energy landscape of the model β‐hairpin peptide GB1 and its E2 isoform that preferentially adopts α‐helical conformations at ambient conditions. Umbrella sampling simulations using all‐atom models and explicit solvent are performed over a large range of end‐to‐end distances. Our results show the strong preference of GB1 and the E2 isoform for β‐hairpin and α‐helical conformations, respectively, consistent with previous studies. We show that the unfolded states of GB1 are largely populated by misfolded β‐hairpin structures which differ from each other in the position of the β‐turn. We discuss the energetic factors contributing favorably to the formation of α‐helix and β‐hairpin conformations in these peptides and highlight the energetic role of hydrogen bonds and non‐bonded interactions. Proteins 2014; 82:2394–2402. © 2014 Wiley Periodicals, Inc.