Evaluating mixture models for building RNA knowledge-based potentials

Ribonucleic acid (RNA) molecules play important roles in a variety of biological processes. To properly function, RNA molecules usually have to fold to specific structures, and therefore understanding RNA structure is vital in comprehending how RNA functions. One approach to understanding and predicting biomolecular structure is to use knowledge-based potentials built from experimentally determined structures. These types of potentials have been shown to be effective for predicting both protein and RNA structures, but their utility is limited by their significantly rugged nature. This ruggedness (and hence the potential's usefulness) depends heavily on the choice of bin width to sort structural information (e.g. distances) but the appropriate bin width is not known a priori. To circumvent the binning problem, we compared knowledge-based potentials built from inter-atomic distances in RNA structures using different mixture models (Kernel Density Estimation, Expectation Minimization and Dirichlet Process). We show that the smooth knowledge-based potential built from Dirichlet process is successful in selecting native-like RNA models from different sets of structural decoys with comparable efficacy to a potential developed by spline-fitting - a commonly taken approach - to binned distance histograms. The less rugged nature of our potential suggests its applicability in diverse types of structural modeling.     

The fidelity of the translation of the genetic code.

Aminoacyl-tRNA synthetases play a central role in maintaining accuracy during the translation of the genetic code. To achieve this challenging task they have to discriminate against amino acids that are very closely related not only in structure but also in chemical nature. A 'double-sieve' editing model was proposed in the late seventies to explain how two closely related amino acids may be discriminated. However, a clear understanding of this mechanism required structural information on synthetases that are faced with such a problem of amino acid discrimination. The first structural basis for the editing model came recently from the crystal structure of isoleucyl-tRNA synthetase, a class I synthetase, which has to discriminate against valine. The structure showed the presence of two catalytic sites in the same enzyme, one for activation, a coarse sieve which binds both isoleucine and valine, and another for editing, a fine sieve which binds only valine and rejects isoleucine. Another structure of the enzyme in complex with tRNA showed that the tRNA is responsible for the translocation of the misactivated amino-acid substrate from the catalytic site to the editing site. These studies were mainly focused on class I synthetases and the situation was not clear about how class II enzymes discriminate against similar amino acids. The recent structural and enzymatic studies on threonyl-tRNA synthetase, a class II enzyme, reveal how this challenging task is achieved by using a unique zinc ion in the active site as well as by employing a separate domain for specific editing activity. These studies led us to propose a model which emphasizes the mirror symmetrical approach of the two classes of enzymes and highlights that tRNA is the key player in the evolution of these class of enzymes.     

结构稳定性: 概念、方法和应用

群落内物种间相互作用的结构是高度组织化的。群落结构对多物种共存的影响机制是群落生态学的核心科学问题之一。目前生态学界在这一问题上存在多种不同的观点。一个可能的原因是, 由于环境因子的复杂性, 大部分研究忽略了环境因子对群落结构和物种共存的重要影响。在这一背景下, 近期发展起来的结构稳定性理论系统地联系了群落结构、环境因子和物种共存, 并在此基础上建立了一个和经验数据紧密结合的理论框架。本文首先简要回顾了当前关于群落结构研究的争鸣, 然后介绍了结构稳定性的理论框架和计算方法, 最后详细介绍了结构稳定性理论在不同生态群落和不同生态学问题中的应用。在全球气候变化的背景下, 结构稳定性理论提供了一种新的视角来理解群落层面的生物多样性维持机制。     

Identify catalytic triads of serine hydrolases by support vector machines

The core of an enzyme molecule is its active site from the viewpoints of both academic research and industrial application. To reveal the structural and functional mechanism of an enzyme, one needs to know its active site; to conduct structure-based drug design by regulating the function of an enzyme, one needs to know the active site and its microenvironment as well. Given the atomic coordinates of an enzyme molecule, how can we predict its active site? To tackle such a problem, a distance group approach was proposed and the support vector machine algorithm applied to predict the catalytic triad of serine hydrolase family. The success rate by jackknife test for the 139 serine hydrolases was 85%, implying that the method is quite promising and may become a useful tool in structural bioinformatics.     

Exploring structure space

The genome projects are changing biology by providing the genetic blueprints of entire organisms. The blueprints are tantalizing but we cannot deduce everything we need to know from them, including the structures and detailed functions of proteins. In this paper we describe an approach for obtaining structural information about proteins on a genomic scale. We describe how structural and functional information might eventually be put together to form a basis for describing life at many levels. We then describe how structural information fits into this picture and classes of proteins for which structural information would be useful in a genomic context. We conclude with a proposal for an initiative to determine protein structures on a very large scale.This revised version was published online in October 2005 with corrections to the Cover Date.     

Low-resolution docking: Prediction of complexes for underdetermined structures

One of the most fundamental questions concerning ligand-receptor interaction is whether such a process of intermolecular association is generally determined by local structural elements of the participating molecules, or whether there are also large-scale motifs in molecule structures that facilitate complex formation. From the point of view of practical docking computations, the elaborate character of local structural details in ligand-receptor interaction creates a large number of false-positive matches, which interfere with determination of the best fit. Another significant obstacle in protein docking is the problem of structural data inaccuracy (poor structure resolution, conformational changes upon complex formation, etc.). Our study [Vakser (1995) Protein Eng., 8, 371–377], based on ultralow (∼7 Å resolution) representation of molecular structures, allowes to average all high-resolution structural details, and still predict most of the structural features of the ligand-receptor complex. The approach dramatically improves the signal-to-noise ratio in determination of the best fit, and moves the structure inaccuracy tolerance to the range of the macrostructure. In the present paper, we describe a further validation of the main principles of this approach and a detailed analysis of the low-resolution docking results. This includes clustering of ligand positions around the receptor molecule and cross-validation of ligands and receptors from different complexes. We also discuss the important implications of the approach to the multiple-minima problem and a possible role of different structural elements in the recognition mechanism. © 1996 John Wiley & Sons, Inc.     

SEGA: semiglobal graph alignment for structure-based protein comparison

Comparative analysis is a topic of utmost importance in structural bioinformatics. Recently, a structural counterpart to sequence alignment, called multiple graph alignment, was introduced as a tool for the comparison of protein structures in general and protein binding sites in particular. Using approximate graph matching techniques, this method enables the identification of approximately conserved patterns in functionally related structures. In this paper, we introduce a new method for computing graph alignments motivated by two problems of the original approach, a conceptual and a computational one. First, the existing approach is of limited usefulness for structures that only share common substructures. Second, the goal to find a globally optimal alignment leads to an optimization problem that is computationally intractable. To overcome these disadvantages, we propose a semiglobal approach to graph alignment in analogy to semiglobal sequence alignment that combines the advantages of local and global graph matching.     

Efficient mapping of ligand migration channel networks in dynamic proteins

For many proteins such as myoglobin, the binding site lies in the interior, and there is no obvious route from the exterior to the binding site in the average structure. Although computer simulations for a limited number of proteins have found some transiently open channels, it is not clear if there exist more channels elsewhere or how the channels are regulated. A systematic approach that can map out the whole ligand migration channel network is lacking. Ligand migration in a dynamic protein resembles closely a well-studied problem in robotics, namely, the navigation of a mobile robot in a dynamic environment. In this work, we present a novel robotic motion planning inspired approach that can map the ligand migration channel network in a dynamic protein. The method combines an efficient spatial mapping of protein inner space with a temporal exploration of protein structural heterogeneity, which is represented by a structure ensemble. The spatial mapping of each conformation in the ensemble produces a partial map of protein inner cavities and their inter-connectivity. These maps are then merged to form a super map that contains all the channels that open dynamically. Results on the pathways in myoglobin for gaseous ligands demonstrate the efficiency of our approach in mapping the ligand migration channel networks. The results, obtained in a significantly less amount of time than trajectory-based approaches, are in agreement with previous simulation results. Additionally, the method clearly illustrates how and what conformational changes open or close a channel.     

Structure-based identification of catalytic residues

The identification of catalytic residues is an essential step in functional characterization of enzymes. We present a purely structural approach to this problem, which is motivated by the difficulty of evolution-based methods to annotate structural genomics targets that have few or no homologs in the databases. Our approach combines a state-of-the-art support vector machine (SVM) classifier with novel structural features that augment structural clues by spatial averaging and Z scoring. Special attention is paid to the class imbalance problem that stems from the overwhelming number of non-catalytic residues in enzymes compared to catalytic residues. This problem is tackled by: (1) optimizing the classifier to maximize a performance criterion that considers both Type I and Type II errors in the classification of catalytic and non-catalytic residues; (2) under-sampling non-catalytic residues before SVM training; and (3) during SVM training, penalizing errors in learning catalytic residues more than errors in learning non-catalytic residues. Tested on four enzyme datasets, one specifically designed by us to mimic the structural genomics scenario and three previously evaluated datasets, our structure-based classifier is never inferior to similar structure-based classifiers and comparable to classifiers that use both structural and evolutionary features. In addition to the evaluation of the performance of catalytic residue identification, we also present detailed case studies on three proteins. This analysis suggests that many false positive predictions may correspond to binding sites and other functional residues. A web server that implements the method, our own-designed database, and the source code of the programs are publicly available at http://www.cs.bgu.ac.il/～meshi/functionPrediction.     

Amino Acid Principal Component Analysis (AAPCA) and its applications in protein structural class prediction

The extremely complicated nature of many biological problems makes them bear the features of fuzzy sets, such as with vague, imprecise, noisy, ambiguous, or input-missing information For instance, the current data in classifying protein structural classes are typically a fuzzy set To deal with this kind of problem, the AAPCA (Amino Acid Principal Component Analysis) approach was introduced. In the AAPCA approach the 20-dimensional amino acid composition space is reduced to an orthogonal space with fewer dimensions, and the original base functions are converted into a set of orthogonal and normalized base functions The advantage of such an approach is that it can minimize the random errors and redundant information in protein dataset through a principal component selection, remarkably improving the success rates in predicting protein structural classes It is anticipated that the AAPCA approach can be used to deal with many other classification problems in proteins as well.     

Mapping landscape corridors

Corridors are important geographic features for biological conservation and biodiversity assessment. The identification and mapping of corridors is usually based on visual interpretations of movement patterns (functional corridors) or habitat maps (structural corridors). We present a method for automated corridor mapping with morphological image processing, and demonstrate the approach with a forest map derived from satellite imagery of northern Slovakia. We show how the approach can be used to differentiate between relatively narrow (‘line’) and wide (‘strip’) structural corridors by mapping corridors at multiple scales of observation, and indicate how to map functional corridors with maps of observed or simulated organism movement. An application to environmental reporting is demonstrated by assessing structural forest corridors in relation to forest types in northern Slovakia.     

On the identifiability of linear compartmental systems: a revisited transfer function approach based on topological properties

This work deals with the problem of the a priori identifiability of compartmental systems from input-output experiments. A new approach is presented, in which, having associated a directed graph with the matrix to be identified, a set of “forms” is defined which are functions of the elements of matrix itself. It is shown how, by exploiting the topological properties of the graph and its subgraphs, the problem can be simplified into one of smaller dimensions. Examples are provided to illustrate this new approach.     

PconsFam: An Interactive Database of Structure Predictions of Pfam Families

At present, about half of the protein domain families lack a structural representative. However, in the last decade, predicting contact maps and using these to model the tertiary structure for these protein families have become an alternative approach to gain structural insight. At present, reliable models for several hundreds of protein families have been created using this approach. To increase the use of this approach, we present PconsFam, which is an intuitive and interactive database for predicted contact maps and tertiary structure models of the entire Pfam database. By modeling all possible families, both with and without a representative structure, using the PconsFold2 pipeline, and running quality assessment estimator on the models, we predict an estimation for how confident the contact maps and structures are for each family.     

Structural forecasting of species persistence under changing environments

The persistence of a species in a given place not only depends on its intrinsic capacity to consume and transform resources into offspring, but also on how changing environmental conditions affect its growth rate. However, the complexity of factors has typically taken us to choose between understanding and predicting the persistence of species. To tackle this limitation, we propose a probabilistic approach rooted on the statistical concepts of ensemble theory applied to statistical mechanics and on the mathematical concepts of structural stability applied to population dynamics models – what we call structural forecasting. We show how this new approach allows us to estimate a probability of persistence for single species in local communities; to understand and interpret this probability conditional on the information we have concerning a system; and to provide out‐of‐sample predictions of species persistence as good as the best experimental approaches without the need of extensive amounts of data.     

Structural insights into the target specificity of plant invertase and pectin methylesterase inhibitory proteins

Pectin methylesterase (PME) and invertase are key enzymes in plant carbohydrate metabolism. Inhibitors of both enzymes constitute a sequence family of extracellular proteins. Members of this family are selectively targeted toward either PME or invertase. In a comparative structural approach we have studied how this target specificity is implemented on homologous sequences. By extending crystallographic work on the invertase inhibitor Nt-CIF to a pectin methylesterase inhibitor (PMEI) from Arabidopsis thaliana, we show an alpha-helical hairpin motif to be an independent and mobile structural entity in PMEI. Removal of this hairpin fully inactivates the inhibitor. A chimera composed of the alpha-hairpin of PMEI and the four-helix bundle of Nt-CIF is still active against PME. By contrast, combining the corresponding segment of Nt-CIF with the four-helix bundle of PMEI renders the protein inactive toward either PME or invertase. Our experiments provide insight in how these homologous inhibitors can make differential use of similar structural modules to achieve distinct functions. Integrating our results with previous findings, we present a model for the PME-PMEI complex with important implications.     

The formation of sense organs in Drosophila: a logical approach

The genetic analysis of development has revealed the importance of small sets of interacting genes in most morphogenetic processes. The results of gene interactions have so far been examined intuitively. This approach is largely sufficient when one deals with simple interactions, a feedback circuit for example. As more components become involved, however, it is difficult to make sure that the intuitive approach gives a comprehensive view of the behaviour of the system. In this paper, we illustrate the use of a logical approach to describe the genetic circuit that underlies the singling out of sense organ precursor cells in Drosophila. We show how to apply logical modelling to a realistic problem, and how this approach allows an easy assessment of the dynamic properties of the system, i.e., of its possible evolutions and of its reactions to fluctuations and perturbations.     

Hallmarks of Caenorhabditis elegans N-glycosylation: complexity and controversy

Caenorhabditis elegans has become one of the most widely used model organisms for a range of molecular cell biological applications and is being increasingly used by glycobiologists. However, a major problem has been the lack of knowledge of the structure of the protein-linked glycans from this organism. In recent years several groups have published structural data, particularly N-glycan structural data. However, some of these data are contradictory. In this review we critically assess all the N-glycan structural data and consider how close we are in our goal of defining the glycome of C. elegans.     

Application of String Kernels in Protein Sequence Classification

INTRODUCTION: The production of biological information has become much greater than its consumption. The key issue now is how to organise and manage the huge amount of novel information to facilitate access to this useful and important biological information. One core problem in classifying biological information is the annotation of new protein sequences with structural and functional features. METHOD: This article introduces the application of string kernels in classifying protein sequences into homogeneous families. A string kernel approach used in conjunction with support vector machines has been shown to achieve good performance in text categorisation tasks. We evaluated and analysed the performance of this approach, and we present experimental results on three selected families from the SCOP (Structural Classification of Proteins) database. We then compared the overall performance of this method with the existing protein classification methods on benchmark SCOP datasets. RESULTS: According to the F1 performance measure and the rate of false positive (RFP) measure, the string kernel method performs well in classifying protein sequences. The method outperformed all the generative-based methods and is comparable with the SVM-Fisher method. DISCUSSION: Although the string kernel approach makes no use of prior biological knowledge, it still captures sufficient biological information to enable it to outperform some of the state-of-the-art methods.