Gene Function Classification Using Bayesian Models with Hierarchy-Based Priors

We investigate the application of hierarchical classification schemes to the annotation of gene function based on several characteristics of protein sequences including phylogenic descriptors, sequence based attributes, and predicted secondary structure. We discuss three Bayesian models and compare their performance in terms of predictive accuracy. These models are the ordinary multinomial logit (MNL) model, a hierarchical model based on a set of nested MNL models, and a MNL model with a prior that introduces correlations between the parameters for classes that are nearby in the hierarchy. We also provide a new scheme for combining different sources of information. We use these models to predict the functional class of Open Reading Frames (ORFs) from the E. coli genome. The results from all three models show substantial improvement over previous methods, which were based on the C5 algorithm. The MNL model using a prior based on the hierarchy outperforms both the non-hierarchical MNL model and the nested MNL model. In contrast to previous attempts at combining these sources of information, our approach results in a higher accuracy rate when compared to models that use each data source alone. Together, these results show that gene function can be predicted with higher accuracy than previously achieved, using Bayesian models that incorporate suitable prior information

PhyloPattern: regular expressions to identify complex patterns in phylogenetic trees

<p>Abstract</p> <p>Background</p> <p>To effectively apply evolutionary concepts in genome-scale studies, large numbers of phylogenetic trees have to be automatically analysed, at a level approaching human expertise. Complex architectures must be recognized within the trees, so that associated information can be extracted.</p> <p>Results</p> <p>Here, we present a new software library, PhyloPattern, for automating tree manipulations and analysis. PhyloPattern includes three main modules, which address essential tasks in high-throughput phylogenetic tree analysis: node annotation, pattern matching, and tree comparison. PhyloPattern thus allows the programmer to focus on: i) the use of predefined or user defined annotation functions to perform immediate or deferred evaluation of node properties, ii) the search for user-defined patterns in large phylogenetic trees, iii) the pairwise comparison of trees by dynamically generating patterns from one tree and applying them to the other.</p> <p>Conclusion</p> <p>PhyloPattern greatly simplifies and accelerates the work of the computer scientist in the evolutionary biology field. The library has been used to automatically identify phylogenetic evidence for domain shuffling or gene loss events in the evolutionary histories of protein sequences. However any workflow that relies on phylogenetic tree analysis, could be automated with PhyloPattern.</p

Effects of autologous bone marrow stem cell transplantation on beta-adrenoceptor density and electrical activation pattern in a rabbit model of non-ischemic heart failure

BACKGROUND: Since only little is known on stem cell therapy in non-ischemic heart failure we wanted to know whether a long-term improvement of cardiac function in non-ischemic heart failure can be achieved by stem cell transplantation. METHODS: White male New Zealand rabbits were treated with doxorubicine (3 mg/kg/week; 6 weeks) to induce dilative non-ischemic cardiomyopathy. Thereafter, we obtained autologous bone marrow stem cells (BMSC) and injected 1.5–2.0 Mio cells in 1 ml medium by infiltrating the myocardium via a left anterolateral thoracotomy in comparison to sham-operated rabbits. 4 weeks later intracardiac contractility was determined in-vivo using a Millar catheter. Thereafter, the heart was excised and processed for radioligand binding assays to detect β(1)- and β(2)-adrenoceptor density. In addition, catecholamine plasma levels were determined via HPLC. In a subgroup we investigated cardiac electrophysiology by use of 256 channel mapping. RESULTS: In doxorubicine-treated animals β-adrenoceptor density was significantly down-regulated in left ventricle and septum, but not in right ventricle, thereby indicating a typical left ventricular heart failure. Sham-operated rabbits exhibited the same down-regulation. In contrast, BMSC transplantation led to significantly less β-adrenoceptor down-regulation in septum and left ventricle. Cardiac contractility was significantly decreased in heart failure and sham-operated rabbits, but was significantly higher in BMSC-transplanted hearts. Norepinephrine and epinephrine plasma levels were enhanced in heart failure and sham-operated animals, while these were not different from normal in BMSC-transplanted animals. Electrophysiological mapping revealed unaltered electrophysiology and did not show signs of arrhythmogeneity. CONCLUSION: BMSC transplantation improves sympathoadrenal dysregualtion in non-ischemic heart failure

BSSF: a fingerprint based ultrafast binding site similarity search and function analysis server

<p>Abstract</p> <p>Background</p> <p>Genome sequencing and post-genomics projects such as structural genomics are extending the frontier of the study of sequence-structure-function relationship of genes and their products. Although many sequence/structure-based methods have been devised with the aim of deciphering this delicate relationship, there still remain large gaps in this fundamental problem, which continuously drives researchers to develop novel methods to extract relevant information from sequences and structures and to infer the functions of newly identified genes by genomics technology.</p> <p>Results</p> <p>Here we present an ultrafast method, named BSSF(Binding Site Similarity & Function), which enables researchers to conduct similarity searches in a comprehensive three-dimensional binding site database extracted from PDB structures. This method utilizes a fingerprint representation of the binding site and a validated statistical Z-score function scheme to judge the similarity between the query and database items, even if their similarities are only constrained in a sub-pocket. This fingerprint based similarity measurement was also validated on a known binding site dataset by comparing with geometric hashing, which is a standard 3D similarity method. The comparison clearly demonstrated the utility of this ultrafast method. After conducting the database searching, the hit list is further analyzed to provide basic statistical information about the occurrences of Gene Ontology terms and Enzyme Commission numbers, which may benefit researchers by helping them to design further experiments to study the query proteins.</p> <p>Conclusions</p> <p>This ultrafast web-based system will not only help researchers interested in drug design and structural genomics to identify similar binding sites, but also assist them by providing further analysis of hit list from database searching.</p

BSSF: a fingerprint based ultrafast binding site similarity search and function analysis server

<p>Abstract</p> <p>Background</p> <p>Genome sequencing and post-genomics projects such as structural genomics are extending the frontier of the study of sequence-structure-function relationship of genes and their products. Although many sequence/structure-based methods have been devised with the aim of deciphering this delicate relationship, there still remain large gaps in this fundamental problem, which continuously drives researchers to develop novel methods to extract relevant information from sequences and structures and to infer the functions of newly identified genes by genomics technology.</p> <p>Results</p> <p>Here we present an ultrafast method, named BSSF(Binding Site Similarity & Function), which enables researchers to conduct similarity searches in a comprehensive three-dimensional binding site database extracted from PDB structures. This method utilizes a fingerprint representation of the binding site and a validated statistical Z-score function scheme to judge the similarity between the query and database items, even if their similarities are only constrained in a sub-pocket. This fingerprint based similarity measurement was also validated on a known binding site dataset by comparing with geometric hashing, which is a standard 3D similarity method. The comparison clearly demonstrated the utility of this ultrafast method. After conducting the database searching, the hit list is further analyzed to provide basic statistical information about the occurrences of Gene Ontology terms and Enzyme Commission numbers, which may benefit researchers by helping them to design further experiments to study the query proteins.</p> <p>Conclusions</p> <p>This ultrafast web-based system will not only help researchers interested in drug design and structural genomics to identify similar binding sites, but also assist them by providing further analysis of hit list from database searching.</p

CSMET: Comparative Genomic Motif Detection via Multi-Resolution Phylogenetic Shadowing

Functional turnover of transcription factor binding sites (TFBSs), such as whole-motif loss or gain, are common events during genome evolution. Conventional probabilistic phylogenetic shadowing methods model the evolution of genomes only at nucleotide level, and lack the ability to capture the evolutionary dynamics of functional turnover of aligned sequence entities. As a result, comparative genomic search of non-conserved motifs across evolutionarily related taxa remains a difficult challenge, especially in higher eukaryotes, where the cis-regulatory regions containing motifs can be long and divergent; existing methods rely heavily on specialized pattern-driven heuristic search or sampling algorithms, which can be difficult to generalize and hard to interpret based on phylogenetic principles. We propose a new method: Conditional Shadowing via Multi-resolution Evolutionary Trees, or CSMET, which uses a context-dependent probabilistic graphical model that allows aligned sites from different taxa in a multiple alignment to be modeled by either a background or an appropriate motif phylogeny conditioning on the functional specifications of each taxon. The functional specifications themselves are the output of a phylogeny which models the evolution not of individual nucleotides, but of the overall functionality (e.g., functional retention or loss) of the aligned sequence segments over lineages. Combining this method with a hidden Markov model that autocorrelates evolutionary rates on successive sites in the genome, CSMET offers a principled way to take into consideration lineage-specific evolution of TFBSs during motif detection, and a readily computable analytical form of the posterior distribution of motifs under TFBS turnover. On both simulated and real Drosophila cis-regulatory modules, CSMET outperforms other state-of-the-art comparative genomic motif finders

Local Function Conservation in Sequence and Structure Space

We assess the variability of protein function in protein sequence and structure space. Various regions in this space exhibit considerable difference in the local conservation of molecular function. We analyze and capture local function conservation by means of logistic curves. Based on this analysis, we propose a method for predicting molecular function of a query protein with known structure but unknown function. The prediction method is rigorously assessed and compared with a previously published function predictor. Furthermore, we apply the method to 500 functionally unannotated PDB structures and discuss selected examples. The proposed approach provides a simple yet consistent statistical model for the complex relations between protein sequence, structure, and function. The GOdot method is available online (http://godot.bioinf.mpi-inf.mpg.de)

Exploring the Evolution of Novel Enzyme Functions within Structurally Defined Protein Superfamilies

In order to understand the evolution of enzyme reactions and to gain an overview of biological catalysis we have combined sequence and structural data to generate phylogenetic trees in an analysis of 276 structurally defined enzyme superfamilies, and used these to study how enzyme functions have evolved. We describe in detail the analysis of two superfamilies to illustrate different paradigms of enzyme evolution. Gathering together data from all the superfamilies supports and develops the observation that they have all evolved to act on a diverse set of substrates, whilst the evolution of new chemistry is much less common. Despite that, by bringing together so much data, we can provide a comprehensive overview of the most common and rare types of changes in function. Our analysis demonstrates on a larger scale than previously studied, that modifications in overall chemistry still occur, with all possible changes at the primary level of the Enzyme Commission (E.C.) classification observed to a greater or lesser extent. The phylogenetic trees map out the evolutionary route taken within a superfamily, as well as all the possible changes within a superfamily. This has been used to generate a matrix of observed exchanges from one enzyme function to another, revealing the scale and nature of enzyme evolution and that some types of exchanges between and within E.C. classes are more prevalent than others. Surprisingly a large proportion (71%) of all known enzyme functions are performed by this relatively small set of 276 superfamilies. This reinforces the hypothesis that relatively few ancient enzymatic domain superfamilies were progenitors for most of the chemistry required for life

GOPred: GO Molecular Function Prediction by Combined Classifiers

Functional protein annotation is an important matter for in vivo and in silico biology. Several computational methods have been proposed that make use of a wide range of features such as motifs, domains, homology, structure and physicochemical properties. There is no single method that performs best in all functional classification problems because information obtained using any of these features depends on the function to be assigned to the protein. In this study, we portray a novel approach that combines different methods to better represent protein function. First, we formulated the function annotation problem as a classification problem defined on 300 different Gene Ontology (GO) terms from molecular function aspect. We presented a method to form positive and negative training examples while taking into account the directed acyclic graph (DAG) structure and evidence codes of GO. We applied three different methods and their combinations. Results show that combining different methods improves prediction accuracy in most cases. The proposed method, GOPred, is available as an online computational annotation tool (http://kinaz.fen.bilkent.edu.tr/gopred)

The FGGY carbohydrate kinase family : insights into the evolution of functional specificities

© The Author(s), 2011. This article is distributed under the terms of the Creative Commons Attribution License. The definitive version was published in PLoS Computational Biology 7 (2011): e1002318, doi:10.1371/journal.pcbi.1002318.Function diversification in large protein families is a major mechanism driving expansion of cellular networks, providing organisms with new metabolic capabilities and thus adding to their evolutionary success. However, our understanding of the evolutionary mechanisms of functional diversity in such families is very limited, which, among many other reasons, is due to the lack of functionally well-characterized sets of proteins. Here, using the FGGY carbohydrate kinase family as an example, we built a confidently annotated reference set (CARS) of proteins by propagating experimentally verified functional assignments to a limited number of homologous proteins that are supported by their genomic and functional contexts. Then, we analyzed, on both the phylogenetic and the molecular levels, the evolution of different functional specificities in this family. The results show that the different functions (substrate specificities) encoded by FGGY kinases have emerged only once in the evolutionary history following an apparently simple divergent evolutionary model. At the same time, on the molecular level, one isofunctional group (L-ribulokinase, AraB) evolved at least two independent solutions that employed distinct specificity-determining residues for the recognition of a same substrate (L-ribulose). Our analysis provides a detailed model of the evolution of the FGGY kinase family. It also shows that only combined molecular and phylogenetic approaches can help reconstruct a full picture of functional diversifications in such diverse families.This study was funded by NIH and DOE grants