Protein structure comparison by probability-based matching of secondary structure elements

MOTIVATION: Protein structure comparison (PSC) has been used widely in studies of structural and functional genomics. However, PSC is computationally expensive and as a result almost all of the PSC methods currently in use look only for the optimal alignment and ignore many alternative alignments that are statistically significant and that may provide insight into protein evolution or folding. RESULTS: We have developed a new PSC method with efficiency to detect potentially viable alternative alignments in all-against-all database comparisons. The efficiency of the new PSC method derives from the ability to directly home in on a limited number of viable and ranked alignment solutions based on intuitively derived SSE (secondary structure element)-matching probabilities.     

Protein structure comparison using bipartite graph matching and its application to protein structure classification

A measure of protein structure similarity is calculated from the matching of pairs of secondary structure elements between two proteins. The interaction of each pair was estimated from their axial line segments and combined with other geometric features to produce an optimal discrimination between intrafamily and interfamily relationships. The matching used a fast bipartite graph-matching algorithm that avoids the computational complexity of searching for the full subgraph isomorphism between the two sets of interactions. The main algorithm used was the "stable marriage" algorithm, which works on the ranked "preferences" of one interaction for another. The method takes 1/10 of a second for a typical comparison making it suitable as a fast pre-filter for slower, more exhaustive approaches. An application to protein structure classification is described.     

Protein fold comparison by the alignment of topological strings

Using the definitions of protein folds encoded in a text string, a dynamic programming algorithm was devised to compare these and identify their largest common substructure and calculate the distance (in terms of the number of edit operations) that this lay from each structure. This provided a metric on which the folds were clustered into a 'phylogenetic' tree. This construction differs from previous automatic structure clustering algorithms as it has explicit representation of the structures at 'ancestral' branching nodes, even when these have no corresponding known structure. The resulting tree was compared with that compiled by an 'expert' in the field and while there was broad agreement, differences were found that resulted from differing degrees of emphasis being placed on the types of operations that can be used to transform structures. Some concluding speculations on the relationship of such trees to the evolutionary history and folding of the proteins are advanced.     

Angle-distance image matching techniques for protein structure comparison

Proteins that contain similar structural elements often have analogous functions regardless of the degree of sequence similarity or structure connectivity in space. In general, protein structure comparison (PSC) provides a straightforward methodology for biologists to determine critical aspects of structure and function. Here, we developed a novel PSC technique based on angle-distance image (A-D image) transformation and matching, which is independent of sequence similarity and connectivity of secondary structure elements (SSEs). An A-D image is constructed by utilizing protein secondary structure information. According to various types of SSEs, the mutual SSE pairs of the query protein are classified into three different types of sub-images. Subsequently, corresponding sub-images between query and target protein structures are compared using modified cross-correlation approaches to identify the similarity of various patterns. Structural relationships among proteins are displayed by hierarchical clustering trees, which facilitate the establishment of the evolutionary relationships between structure and function of various proteins.Four standard testing datasets and one newly created dataset were used to evaluate the proposed method. The results demonstrate that proteins from these five datasets can be categorized in conformity with their spatial distribution of SSEs. Moreover, for proteins with low sequence identity that share high structure similarity, the proposed algorithms are an efficient and effective method for structural comparison.     

Protein structure comparison: implications for the nature of 'fold space', and structure and function prediction

The identification of geometric relationships between protein structures offers a powerful approach to predicting the structure and function of proteins. Methods to detect such relationships range from human pattern recognition to a variety of mathematical algorithms. A number of schemes for the classification of protein structure have found widespread use and these implicitly assume the organization of protein structure space into discrete categories. Recently, an alternative view has emerged in which protein fold space is seen as continuous and multidimensional. Significant relationships have been observed between proteins that belong to what have been termed different 'folds'. There has been progress in the use of these relationships in the prediction of protein structure and function.     

RNA structure comparison,motif search and discovery using a reduced representation of RNA conformational space

Given the wealth of new RNA structures and the growing list of RNA functions in biology, it is of great interest to understand the repertoire of RNA folding motifs. The ability to identify new and known motifs within novel RNA structures, to compare tertiary structures with one another and to quantify the characteristics of a given RNA motif are major goals in the field of RNA research; however, there are few systematic ways to address these issues. Using a novel approach for visualizing and mathematically describing macromolecular structures, we have developed a means to quantitatively describe RNA molecules in order to rapidly analyze, compare and explore their features. This approach builds on the alternative eta,theta convention for describing RNA torsion angles and is executed using a new program called PRIMOS. Applying this methodology, we have successfully identified major regions of conformational change in the 50S and 30S ribosomal subunits, we have developed a means to search the database of RNA structures for the prevalence of known motifs and we have classified and identified new motifs. These applications illustrate the powerful capabilities of our new RNA structural convention, and they suggest future adaptations with important implications for bioinformatics and structural genomics.     

Protein structure comparison using iterated double dynamic programming

A protein structure comparison method is described that allows the generation of large populations of high-scoring alternate alignments. This was achieved by incorporating a random element into an iterative double dynamic programming algorithm. The maximum scores from repeated comparisons of a pair of structures converged on a value that was taken as the global maximum. This lay 15% over the score obtained from the single fixed (unrandomized) calculation. The effect of the gap penalty was observed through the shift of the alignment populations, characterized by their alignment length and root-mean-square deviation (RMSD). The best (lowest RMSD) values found in these populations provided a base-line against which other methods were compared.     

Protein kinase biochemistry and drug discovery

Protein kinases are fascinating biological catalysts with a rapidly expanding knowledge base, a growing appreciation in cell regulatory control, and an ascendant role in successful therapeutic intervention. To better understand protein kinases, the molecular underpinnings of phosphoryl group transfer, protein phosphorylation, and inhibitor interactions are examined. This analysis begins with a survey of phosphate group and phosphoprotein properties which provide context to the evolutionary selection of phosphorylation as a central mechanism for biological regulation of most cellular processes. Next, the kinetic and catalytic mechanisms of protein kinases are examined with respect to model aqueous systems to define the elements of catalysis. A brief structural biology overview further delves into the molecular basis of catalysis and regulation of catalytic activity. Concomitant with a prominent role in normal physiology, protein kinases have important roles in the disease state. To facilitate effective kinase drug discovery, classic and emerging approaches for characterizing kinase inhibitors are evaluated including biochemical assay design, inhibitor mechanism of action analysis, and proper kinetic treatment of irreversible inhibitors. As the resulting protein kinase inhibitors can modulate intended and unintended targets, profiling methods are discussed which can illuminate a more complete range of an inhibitor's biological activities to enable more meaningful cellular studies and more effective clinical studies. Taken as a whole, a wealth of protein kinase biochemistry knowledge is available, yet it is clear that a substantial extent of our understanding in this field remains to be discovered which should yield many new opportunities for therapeutic intervention.     

Protein structure comparison using the markov transition model of evolution

A number of automatic protein structure comparison methods have been proposed; however, their similarity score functions are often decided by the researchers' intuition and trial-and-error, and not by theoretical background. We propose a novel theory to evaluate protein structure similarity, which is based on the Markov transition model of evolution. Our similarity score between structures i and j is defined as log P(j --> i)/P(i), where P(j --> i) is the probability that structure j changes to structure i during the evolutionary process, and P(i) is the probability that structure i appears by chance. This is a reasonable definition of structure similarity, especially for finding evolutionarily related (homologous) similarity. The probability P(j --> i) is estimated by the Markov transition model, which is similar to the Dayhoff's substitution model between amino acids. To estimate the parameters of the model, homologous protein structure pairs are collected using sequence similarity, and the numbers of structure transitions within the pairs are counted. Next these numbers are transformed to a transition probability matrix of the Markov transition. Transition probabilities for longer time are obtained by multiplying the probability matrix by itself several times. In this study, we generated three types of structure similarity scores: an environment score, a residue-residue distance score, and a secondary structure elements (SSE) score. Using these scores, we developed the structure comparison program, Matras (MArkovian TRAnsition of protein Structure). It employs a hierarchical alignment algorithm, in which a rough alignment is first obtained by SSEs, and then is improved with more detailed functions. We attempted an all-versus-all comparison of the SCOP database, and evaluated its ability to recognize a superfamily relationship, which was manually assigned to be homologous in the SCOP database. A comparison with the FSSP database shows that our program can recognize more homologous similarity than FSSP. We also discuss the reliability of our method, by studying the disagreement between structural classifications by Matras and SCOP.     

Protein structure discovery: A software package to computer proteomics tasks (Review)

A software information system called Protein Structure Discovery was developed. The system can be used to solve a wide range of tasks in the field of computer proteomics, including prediction of function, structure, and immune properties of proteins. A special section of the system allows the evaluation of quantitative and qualitative effects of mutations on the structural and functional properties of proteins. There are 19 different programs integrated into the system, including: PDBSite, a database of protein functional sites; PDBSiteScan, a program to predict functional sites in three-dimensional structures of proteins; and a Web-ProAnalyst program to quantitatively analysis the structure-activity relationships of proteins. The Protein Structure Discovery has a Web interface and is available for users via the Internet (http://www-bionet.sscc.ru/psd/). For example, the binding sites of zinc ion and ADP showed a high stability of the method to errors in the reconstruction of spatial structures of proteins in the recognition of functional sites in model structures.     

Ghrelin 发现和结构的研究现状

Ghrelin是生长激素促分泌素受体(growth hormone secretagogue receptor,GHSR)的内源性配体,是1999年由Kojima等[]人从大鼠胃组织中发现的含28个氨基酸的多肽,主要由胃黏膜泌酸腺X/A样细胞分泌并通过与其特异性受体结合而产生多种生物学效应。本文总结了近几年的文章,旨在通过对Ghrelin的发现和结构的介绍,为以后的相关研究奠定基础。     

Ghrelin发现和结构的研究现状

Ghrelin是生长激素促分泌素受体（growth hormone secretagogue receptor,GHSR）的内源性配体,是1999年由Kojima等人从大鼠胃组织中发现的含28个氨基酸的多肽,主要由胃黏膜泌酸腺X／A样细胞分泌并通过与其特异性受体结合而产生多种生物学效应。本文总结了近几年的文章,旨在通过对Ghrelin的发现和结构的介绍,为以后的相关研究奠定基础。     

Protein folding: matching theory and experiment.

The impact of folding funnels and folding simulations on the way experimentalists interpret results is examined. The image of the transition state has changed from a unique species that has a strained configuration, with a correspondingly high free energy, to a more ordinary folding intermediate, whose balance between limited conformational entropy and stabilizing contacts places it at the top of the free energy barrier. Evidence for a broad transition barrier comes from studies showing that mutations can change the position of the barrier. The main controversial issue now is whether populated folding intermediates are productive on-pathway intermediates or dead-end traps. Direct experimental evidence is needed. Theories suggesting that populated intermediates are trapped in a glasslike state are usually based on mechanisms which imply that trapping would only be extremely short-lived (e.g., nanoseconds) in water at 25 degrees C. There seems to be little experimental evidence for long-lived trapping in monomers, if folding aggregates are excluded. On the other hand, there is good evidence for kinetic trapping in dimers. alpha-Helix formation is currently the fastest known process in protein folding, and incipient helices are present at the start of folding. Fast helix formation has the effect of narrowing drastically the choice of folding routes. Thus helix formation can direct folding. It changes the folding metaphor from pouring liquid down a folding funnel to a train leaving a switchyard with only a few choices of exit tracks.     

Protein biomarker discovery for head and neck cancer

Squamous cell carcinoma of the head and neck (HNSCC) is the sixth most common cancer worldwide. Despite improvements in diagnosis and treatment, the five-year-survival rate of advanced HNSCC has only moderately increased, which is largely due to the high proportion of patients that present with advanced disease stage and the frequent development of relapse and second primary tumors. Protein biomarkers allowing early detection of primary HNSCC or relapse may aid to improve clinical outcome. Screening for precursor changes in the mucosal linings preceding the development of invasive tumors and for accurate prediction of risk of malignant transformation, may be propitious opportunities, which are as yet difficult. This review summarizes recent results in HNSCC proteomics for biomarker research. Despite the wide diversity of experimental designs, a few common markers have been detected. Although some of these potential biomarkers are very promising, they still have to be further clinically validated. Finally, treatment of advanced cancers of several sites within the head and neck has shifted significantly during the last decade, and also, targeted drugs have entered the clinic. This has major consequences for the research questions in HNSCC research and accordingly for the future direction of proteome research in HNSCC biomarker discovery.     

Protein docking using surface matching and supervised machine learning

Computational prediction of protein complex structures through docking offers a means to gain a mechanistic understanding of protein interactions that mediate biological processes. This is particularly important as the number of experimentally determined structures of isolated proteins exceeds the number of structures of complexes. A comprehensive docking procedure is described in which efficient sampling of conformations is achieved by matching surface normal vectors, fast filtering for shape complementarity, clustering by RMSD, and scoring the docked conformations using a supervised machine learning approach. Contacting residue pair frequencies, residue propensities, evolutionary conservation, and shape complementarity score for each docking conformation are used as input data to a Random Forest classifier. The performance of the Random Forest approach for selecting correctly docked conformations was assessed by cross-validation using a nonredundant benchmark set of X-ray structures for 93 heterodimer and 733 homodimer complexes. The single highest rank docking solution was the correct (near-native) structure for slightly more than one third of the complexes. Furthermore, the fraction of highly ranked correct structures was significantly higher than the overall fraction of correct structures, for almost all complexes. A detailed analysis of the difficult to predict complexes revealed that the majority of the homodimer cases were explained by incorrect oligomeric state annotation. Evolutionary conservation and shape complementarity score as well as both underrepresented and overrepresented residue types and residue pairs were found to make the largest contributions to the overall prediction accuracy. Finally, the method was also applied to docking unbound subunit structures from a previously published benchmark set.     

A comparison of set-theoretical and graph-theoretical approaches in topological biology

Two somewhat different approaches to topological biology have been developed in recent years. The latest, set-theoretical approach leads rather immediately to a number of conclusions which are verified experimentally. It also predicts a number of new biological relations. The older, graph-theoretical approach does not lead directly to those conclusions but suggests a number of combinatorial relations between number of organs and number of cell types. Such suggestions are absent from the set-theoretical approach. It is shown that the above-mentioned combinatorial relations are independent of the graph-theoretical method proper and can be introduced into the set-theoretical approach through the addition of an independent postulate. A possible addition to the principle of biotopological mapping is suggested, which brings into focus the relations between the organism and its individual organs and which has a predictive value.