Automatic particle pickup method using a neural network has high accuracy by applying an initial weight derived from eigenimages: a new reference free method for single-particle analysis

The single-particle analysis is a structure-determining method for electron microscope (EM) images which does not require crystal. In this method, the projections are picked up and averaged by the images of similar Euler angles to improve the signal to noise ratio, and then create a 3-D reconstruction. The selection of a large number of particles from the cryo-EM micrographs is a pre-requisite for obtaining a high resolution. To pickup a low-contrast cryo-EM protein image, we have recently found that a three-layer pyramidal-type neural network is successful in detecting such a faint image, which had been difficult to detect by other methods. The connection weights between the input and hidden layers, which work as a matching filter, have revealed that they reflect characters of the particle projections in the training data. The images stored in terms of the connection weights were complex, more similar to the eigenimages which are created by the principal component analysis of the learning images rather than to the averages of the particle projections. When we set the initial learning weights according to the eigenimages in advance, the learning period was able to be shortened to less than half the time of the NN whose initial weights had been set randomly. Further, the pickup accuracy increased from 90 to 98%, and a combination of the matching filters were found to work as an integrated matching filter there. The integrated filters were amazingly similar to averaged projections and can be used directly as references for further two-dimensional averaging. Therefore, this research also presents a brand-new reference-free method for single-particle analysis.     

Multivariate segregation analysis for quantitative traits in line crosses

Segregation analysis is a method of detecting major genes for quantitative traits without using marker information. It serves as an important tool in helping investigators to plan further studies such as quantitative trait loci mapping or more sophisticated genomic analyses. However, current methods of segregation analysis for a single trait typically have low statistical power. We propose a multivariate segregation analysis (MSA) that takes advantage of the correlation structure of multiple quantitative traits to detect major genes. This method not only increases the statistical power, but allows dissection of the genetic architecture underlying the trait complex. In MSA the observed phenotypes of multiple correlated traits are fitted to a multivariate Gaussian mixture model. Model parameters are estimated under the maximum likelihood framework via the expectation-maximization algorithm. The presence of major genes is tested using likelihood ratio test statistics. Pleiotropy is distinguished from close linkage by comparing three possible models using the Bayesian information criterion. Two simulation experiments were performed based on the F(2) mating design. In the first, the statistical properties of MSA under varying heritabilities and sample sizes were investigated and the results compared with those obtained from single-trait analysis. In the second simulation the efficacy of MSA in separating pleiotropy from close linkage was demonstrated. Finally, the new method was applied to real data and detected a major gene responsible for both plant height and tiller number in rice.     

A regionalizable statistical model of intersecting regions in protein-ligand binding cavities

Finding elements of proteins that influence ligand binding specificity is an essential aspect of research in many fields. To assist in this effort, this paper presents two statistical models, based on the same theoretical foundation, for evaluating structural similarity among binding cavities. The first model specializes in the "unified" comparison of whole cavities, enabling the selection of cavities that are too dissimilar to have similar binding specificity. The second model enables a "regionalized" comparison of cavities within a user-defined region, enabling the selection of cavities that are too dissimilar to bind the same molecular fragments in the given region. We applied these models to analyze the ligand binding cavities of the serine protease and enolase superfamilies. Next, we observed that our unified model correctly separated sets of cavities with identical binding preferences from other sets with varying binding preferences, and that our regionalized model correctly distinguished cavity regions that are too dissimilar to bind similar molecular fragments in the user-defined region. These observations point to applications of statistical modeling that can be used to examine and, more importantly, identify influential structural similarities within binding site structure in order to better detect influences on protein-ligand binding specificity.     

Detecting susceptibility genes in case-control studies using set association

Complex diseases are generally caused by intricate interactions of multiple genes and environmental factors. Most available linkage and association methods are developed to identify individual susceptibility genes assuming a simple disease model blind to any possible gene - gene and gene - environmental interactions. We used a set association method that uses single-nucleotide polymorphism markers to locate genetic variation responsible for complex diseases in which multiple genes are involved. Here we extended the set association method from bi-allelic to multiallelic markers. In addition, we studied the type I error rates and power for both approaches using simulations based on the coalescent process. Both bi-allelic set association (BSA) and multiallelic set association (MSA) tests have the correct type I error rates. In addition, BSA and MSA can have more power than individual marker analysis when multiple genes are involved in a complex disease. We applied the MSA approach to the simulated data sets from Genetic Analysis Workshop 13. High cholesterol level was used as the definitive phenotype for a disease. MSA failed to detect markers with significant linkage disequilibrium with genes responsible for cholesterol level. This is due to the wide spacing between the markers and the lack of association between the marker loci and the simulated phenotype.     

Statistical method for detecting structural change in the growth process

Summary .   Due to competition among individual trees and other exogenous factors that change the growth environment, each tree grows following its own growth trend with some structural changes in growth over time. In the present article, a new method is proposed to detect a structural change in the growth process. We formulate the method as a simple statistical test for signal detection without constructing any specific model for the structural change. To evaluate the p -value of the test, the tube method is developed because the regular distribution theory is insufficient. Using two sets of tree diameter growth data sampled from planted forest stands of Cryptomeria japonica in Japan, we conduct an analysis of identifying the effect of thinning on the growth process as a structural change. Our results demonstrate that the proposed method is useful to identify the structural change caused by thinning. We also provide the properties of the method in terms of the size and power of the test.     

Detection of allosteric signal transmission by information-theoretic analysis of protein dynamics

Allostery offers a highly specific way to modulate protein function. Therefore, understanding this mechanism is of increasing interest for protein science and drug discovery. However, allosteric signal transmission is difficult to detect experimentally and to model because it is often mediated by local structural changes propagating along multiple pathways. To address this, we developed a method to identify communication pathways by an information-theoretical analysis of molecular dynamics simulations. Signal propagation was described as information exchange through a network of correlated local motions, modeled as transitions between canonical states of protein fragments. The method was used to describe allostery in two-component regulatory systems. In particular, the transmission from the allosteric site to the signaling surface of the receiver domain NtrC was shown to be mediated by a layer of hub residues. The location of hubs preferentially connected to the allosteric site was found in close agreement with key residues experimentally identified as involved in the signal transmission. The comparison with the networks of the homologues CheY and FixJ highlighted similarities in their dynamics. In particular, we showed that a preorganized network of fragment connections between the allosteric and functional sites exists already in the inactive state of all three proteins.     

Automatic CTF correction for single particles based upon multivariate statistical analysis of individual power spectra

Three-dimensional electron cryomicroscopy of randomly oriented single particles is a method that is suitable for the determination of three-dimensional structures of macromolecular complexes at molecular resolution. However, the electron-microscopical projection images are modulated by a contrast transfer function (CTF) that prevents the calculation of three-dimensional reconstructions of biological complexes at high resolution from uncorrected images. We describe here an automated method for the accurate determination and correction of the CTF parameters defocus, twofold astigmatism and amplitude-contrast proportion from single-particle images. At the same time, the method allows the frequency-dependent signal decrease (B factor) and the non-convoluted background signal to be estimated. The method involves the classification of the power spectra of single-particle images into groups with similar CTF parameters; this is done by multivariate statistical analysis (MSA) and hierarchically ascending classification (HAC). Averaging over several power spectra generates class averages with enhanced signal-to-noise ratios. The correct CTF parameters can be deduced from these class averages by applying an iterative correlation procedure with theoretical CTF functions; they are then used to correct the raw images. Furthermore, the method enables the tilt axis of the sample holder to be determined and allows the elimination of individual poor-quality images that show high drift or charging effects.     

Statistical method for surface pattern-matching between dissimilar molecules: electrostatic potentials and accessible surfaces

This paper outlines a statistical method for patternmatching between surfaces and is applicable to structural and energetic patterns found on molecular surfaces. Correlation coefficients generated for the pattern match are scale invariant. Regression analysis applied to the patterns reveals the scaling and displacement relationships. The method for measuring the similarities between molecular surfaces of two dissimilar molecules held infixed orientations is given explicitly. Implicit in this procedure is a method for studying the inverse phenomenon, namely complementarity between surface parameters at a binding site and its ligand. The method has been used to assess surface differences in structural similarities generated by computer fitting and by visual comparison. Various pitfalls likely to be encountered in evaluating molecular structural similarities are noted.     

Structure-Based Statistical Mechanical Model Accounts for the Causality and Energetics of Allosteric Communication

Allostery is one of the pervasive mechanisms through which proteins in living systems carry out enzymatic activity, cell signaling, and metabolism control. Effective modeling of the protein function regulation requires a synthesis of the thermodynamic and structural views of allostery. We present here a structure-based statistical mechanical model of allostery, allowing one to observe causality of communication between regulatory and functional sites, and to estimate per residue free energy changes. Based on the consideration of ligand free and ligand bound systems in the context of a harmonic model, corresponding sets of characteristic normal modes are obtained and used as inputs for an allosteric potential. This potential quantifies the mean work exerted on a residue due to the local motion of its neighbors. Subsequently, in a statistical mechanical framework the entropic contribution to allosteric free energy of a residue is directly calculated from the comparison of conformational ensembles in the ligand free and ligand bound systems. As a result, this method provides a systematic approach for analyzing the energetics of allosteric communication based on a single structure. The feasibility of the approach was tested on a variety of allosteric proteins, heterogeneous in terms of size, topology and degree of oligomerization. The allosteric free energy calculations show the diversity of ways and complexity of scenarios existing in the phenomenology of allosteric causality and communication. The presented model is a step forward in developing the computational techniques aimed at detecting allosteric sites and obtaining the discriminative power between agonistic and antagonistic effectors, which are among the major goals in allosteric drug design.     

电子束辐照对透明质酸功能及结构特性的影响

为了探讨电子束辐照对透明质酸功能及结构特性的影响,选择5、10、20、40、80 、100和150 kGy的电子束辐照固体透明质酸,测定透明质酸辐照前后分子量、特性粘度 、pH值、抗氧化性、紫外光谱、红外光谱、电镜图片的变化.结果表明,辐照降低透明 质酸的分子量、特性粘度、pH值;透明质酸在辐照前后的吸收特征峰没有显著的改变, 吸收强度增强;透明质酸形状随着辐照剂量的升高,由块状逐渐变成颗粒状;透明质酸 对DPPH·自由基的清除作用和还原力随着辐照剂量的增大逐渐增强.电子束辐照对透明 质酸分子结构和功能有一定的影响,但对其一级结构没有影响.     

Topology representing network enables highly accurate classification of protein images taken by cryo electron-microscope without masking

In single-particle analysis, a three-dimensional (3-D) structure of a protein is constructed using electron microscopy (EM). As these images are very noisy in general, the primary process of this 3-D reconstruction is the classification of images according to their Euler angles, the images in each classified group then being averaged to reduce the noise level. In our newly developed strategy of classification, we introduce a topology representing network (TRN) method. It is a modified method of a growing neural gas network (GNG). In this system, a network structure is automatically determined in response to the images input through a growing process. After learning without a masking procedure, the GNG creates clear averages of the inputs as unit coordinates in multi-dimensional space, which are then utilized for classification. In the process, connections are automatically created between highly related units and their positions are shifted where the inputs are distributed in multi-dimensional space. Consequently, several separated groups of connected units are formed. Although the interrelationship of units in this space are not easily understood, we succeeded in solving this problem by converting the unit positions into two-dimensional (2-D) space, and by further optimizing the unit positions with the simulated annealing (SA) method. In the optimized 2-D map, visualization of the connections of units provided rich information about clustering. As demonstrated here, this method is clearly superior to both the multi-variate statistical analysis (MSA) and the self-organizing map (SOM) as a classification method and provides a first reliable classification method which can be used without masking for very noisy images.     

On the accuracy of inferring energetic coupling between distant sites in protein families from evolutionary imprints: Illustrations using lattice model

It is suspected that correlated motions among a subset of spatially separated residues drive conformational dynamics not only in multidomain but also in single domain proteins. Sequence and structure‐based methods have been proposed to determine covariation between two sites on a protein. The statistical coupling analysis (SCA) that compares the changes in probability at two sites in a multiple sequence alignment (MSA) and a subset of the MSA has been used to infer the network of residues that encodes allosteric signals in protein families. The structural perturbation method (SPM), that probes the response of a local perturbation at all other sites, has been used to probe the allostery wiring diagram in biological machines and enzymes. To assess the efficacy of the SCA, we used an exactly soluble two dimensional lattice model and performed double‐mutant cycle (DMC) calculations to predict the extent of physical coupling between two sites. The predictions of the SCA and the DMC results show that only residues that are in contact in the native state are accurately identified. In addition, covariations among strongly interacting residues are most easily identified by the SCA. These conclusions are consistent with the DMC experiments on the PDZ family. Good correlation between the SCA and the DMC is only obtained by performing multiple experiments that vary the nature of amino acids at a given site. In contrast, the energetic coupling found in experiments for the PDZ domain are recovered using the SPM. We also predict, using the SPM, several residues that are coupled energetically. Proteins 2009. © 2009 Wiley‐Liss, Inc.     

A new approach to the detection and statistical classification of Ca2+ sparks

The availability of high-speed, two-dimensional (2-D) confocal microscopes and the expanding armamentarium of fluorescent probes presents unprecedented opportunities and new challenges for studying the spatial and temporal dynamics of cellular processes. The need to remove subjectivity from the detection process, the difficulty of the human eye to detect subtle changes in fluorescence in these 2-D images, and the large volume of data produced by these confocal microscopes call for the need to develop algorithms to automatically mark the changes in fluorescence. These fluorescence signal changes are often subtle, so the statistical estimate of the likelihood that the detected signal is not noise is an integral part of the detection algorithm. This statistical estimation is fundamental to our new approach to detection; in earlier Ca(2+) spark detectors, this statistical assessment was incidental to detection. Importantly, the use of the statistical properties of the signal local to the spark, instead of over the whole image, reduces the false positive and false negative rates. We developed an automatic spark detection algorithm based on these principles and used it to detect sparks on an inhomogeneous background of transverse tubule-labeled rat ventricular cells. Because of the large region of the cell surveyed by the confocal microscope, we can detect a large enough number of sparks to measure the dynamic changes in spark frequency in individual cells. We also found, in contrast to earlier results, that cardiac sparks are spatially symmetric. This new approach puts the detection of fluorescent signals on a firm statistical foundation.     

Learning generative models for protein fold families

We introduce a new approach to learning statistical models from multiple sequence alignments (MSA) of proteins. Our method, called GREMLIN (Generative REgularized ModeLs of proteINs), learns an undirected probabilistic graphical model of the amino acid composition within the MSA. The resulting model encodes both the position-specific conservation statistics and the correlated mutation statistics between sequential and long-range pairs of residues. Existing techniques for learning graphical models from MSA either make strong, and often inappropriate assumptions about the conditional independencies within the MSA (e.g., Hidden Markov Models), or else use suboptimal algorithms to learn the parameters of the model. In contrast, GREMLIN makes no a priori assumptions about the conditional independencies within the MSA. We formulate and solve a convex optimization problem, thus guaranteeing that we find a globally optimal model at convergence. The resulting model is also generative, allowing for the design of new protein sequences that have the same statistical properties as those in the MSA. We perform a detailed analysis of covariation statistics on the extensively studied WW and PDZ domains and show that our method out-performs an existing algorithm for learning undirected probabilistic graphical models from MSA. We then apply our approach to 71 additional families from the PFAM database and demonstrate that the resulting models significantly out-perform Hidden Markov Models in terms of predictive accuracy.     

Accurate reconstruction of insertion-deletion histories by statistical phylogenetics

The Multiple Sequence Alignment (MSA) is a computational abstraction that represents a partial summary either of indel history, or of structural similarity. Taking the former view (indel history), it is possible to use formal automata theory to generalize the phylogenetic likelihood framework for finite substitution models (Dayhoff's probability matrices and Felsenstein's pruning algorithm) to arbitrary-length sequences. In this paper, we report results of a simulation-based benchmark of several methods for reconstruction of indel history. The methods tested include a relatively new algorithm for statistical marginalization of MSAs that sums over a stochastically-sampled ensemble of the most probable evolutionary histories. For mammalian evolutionary parameters on several different trees, the single most likely history sampled by our algorithm appears less biased than histories reconstructed by other MSA methods. The algorithm can also be used for alignment-free inference, where the MSA is explicitly summed out of the analysis. As an illustration of our method, we discuss reconstruction of the evolutionary histories of human protein-coding genes.     

Characterization of global molecular shape transitions between “open” and “closed” protein conformations

Induced-fit configurational transitions in proteins can take many forms. In cases, we find small “closures” of a loop onto the substrate. In other cases, the structural changes triggered by a ligand involve large rearrangements that affect entire domains. The nature of these transitions is normally assessed by a visual analysis or in terms of simple local geometrical parameters, such as interresidue distances, backbone dihedral angles, and relative displacements between domains. This approach is limited and rather undiscriminating. In this work, we apply recently introduced ideas from macromolecular shape analysis to characterize the global shape changes accompanying “open closed” transitions in proteins. Here, we monitor two distinct properties simultaneously: molecular size and self-entanglements. The method is applied to some proteins exhibiting pairs of structurally different conformations (adenylate kinase, hexokinase, citrate synthase, alcohol dehydrogenase, triosephosphate isomerase, thioredoxin, and aspartate amino-transferase). The conformational change associated with these proteins is classified according to an order parameter that considers various molecular shape features. The results allow one to recognize, in a nonvisual fashion, the likely occurrence of local or global structural rearrangements. In addition, the technique provides an insight into folding features that may remain invariant during the configurational transitions. © 1996 John Wiley & Sons, Inc.