Biomedical subjects

Hanah Margalit

Publications and source records attributed to Hanah Margalit.

At least 19 recordsLinked to original sources

PubMed2006

Characterization and prediction of protein-protein interactions within and between complexes.

Databases of experimentally determined protein interactions provide information on binary interactions and on involvement in multiprotein complexes. These data are valuable for understanding the general properties of the interaction between proteins as well as for the development of prediction schemes for unknown interactions. Here we analyze experimentally determined protein interactions by measuring various sequence, genomic, transcriptomic, and proteomic attributes of each interacting pair in the yeast Saccharomyces cerevisiae. We find that dividing the data into two groups, one that includes binary interactions within protein complexes (stable) and another that includes binary interactions that are not within complexes (transient), enables better characterization of the interactions by the different attributes and improves the prediction of new interactions. This analysis revealed that most attributes were more indicative in the set of intracomplex interactions. Using this data set for training, we integrated the different attributes by logistic regression and developed a predictive scheme that distinguishes between interacting and noninteracting protein pairs. Analysis of the logistic-regression model showed that one of the strongest contributors to the discrimination between interacting and noninteracting pairs is the presence of distinct pairs of domain signatures that were suggested previously to characterize interacting proteins. The predictive algorithm succeeds in identifying both intracomplex and other interactions (possibly the more stable ones), and its correct identification rate is 2-fold higher than that of large-scale yeast two-hybrid experiments.

Algorithms↗

PubMed2006

Proteins of the same fold and unrelated sequences have similar amino acid composition.

It is well established that there is a relationship between the amino acid composition of a protein and its structural class (i.e., alpha, beta, alpha + beta, or alpha/beta). Several studies have even shown the power of amino acid composition in predicting the secondary structure class of a protein. Herein, we show that significant similarity in amino acid composition exists not only between proteins of the same class, but even between proteins of the same fold. To test conjectural explanations for this phenomenon, we analyzed a set of structurally similar proteins that are dissimilar in sequence. Based on this analysis, we suggest that specific residues that are involved in intramolecular interactions may account for this surprising relationship between composition and structure.

Amino Acid Sequence↗

PubMed2006

Towards an integrated protein-protein interaction network: a relational Markov network approach.

Protein-protein interactions play a major role in most cellular processes. Thus, the challenge of identifying the full repertoire of interacting proteins in the cell is of great importance and has been addressed both experimentally and computationally. Today, large scale experimental studies of protein interactions, while partial and noisy, allow us to characterize properties of interacting proteins and develop predictive algorithms. Most existing algorithms, however, ignore possible dependencies between interacting pairs and predict them independently of one another. In this study, we present a computational approach that overcomes this drawback by predicting protein-protein interactions simultaneously. In addition, our approach allows us to integrate various protein attributes and explicitly account for uncertainty of assay measurements. Using the language of relational Markov networks, we build a unified probabilistic model that includes all of these elements. We show how we can learn our model properties and then use it to predict all unobserved interactions simultaneously. Our results show that by modeling dependencies between interactions, as well as by taking into account protein attributes and measurement noise, we achieve a more accurate description of the protein interaction network. Furthermore, our approach allows us to gain new insights into the properties of interacting proteins.

Algorithms↗

PubMed2006

Evolutionary conservation of domain-domain interactions.

BACKGROUND: Recently, there has been much interest in relating domain-domain interactions (DDIs) to protein-protein interactions (PPIs) and vice versa, in an attempt to understand the molecular basis of PPIs. RESULTS: Here we map structurally derived DDIs onto the cellular PPI networks of different organisms and demonstrate that there is a catalog of domain pairs that is used to mediate various interactions in the cell. We show that these DDIs occur frequently in protein complexes and that homotypic interactions (of a domain with itself) are abundant. A comparison of the repertoires of DDIs in the networks of Escherichia coli, Saccharomyces cerevisiae, Caenorhabditis elegans, Drosophila melanogaster, and Homo sapiens shows that many DDIs are evolutionarily conserved. CONCLUSION: Our results indicate that different organisms use the same 'building blocks' for PPIs, suggesting that the functionality of many domain pairs in mediating protein interactions is maintained in evolution.

Animals↗

PubMed2006

Co-evolution of transcription factors and their targets depends on mode of regulation.

BACKGROUND: Differences in the transcription regulation network are at the root of much of the phenotypic variation observed among organisms. These differences may be achieved either by changing the repertoire of regulators and/or their targets, or by rewiring the network. Following these changes and studying their logic is crucial for understanding the evolution of regulatory networks. RESULTS: We use the well characterized transcription regulatory network of Escherichia coli K12 and follow the evolutionary changes in the repertoire of regulators and their targets across a large number of fully sequenced gamma-proteobacteria. By focusing on close relatives of E. coli K12, we study the dynamics of the evolution of transcription regulation across a relatively short evolutionary timescale. We show significant differences in the evolution of repressors and activators. Repressors are only lost from a genome once their targets have themselves been lost, or once the network has significantly rewired. In contrast, activators are often lost even when their targets remain in the genome. As a result, E. coli K12 repressors that regulate many targets are rarely absent from organisms that are closely related to E. coli K12, while activators with a similar number of targets are often absent in these organisms. CONCLUSION: We demonstrate that the mode of regulation exerted by transcription factors has a strong effect on their evolution. Repressors co-evolve tightly with their target genes. In contrast, activators can be lost independently of their targets. In fact, loss of an activator can lead to efficient shutdown of an unnecessary pathway.

Escherichia coli↗

PubMed2005

Assigning functions to genes: identification of S-phase expressed genes in Leishmania major based on post-transcriptional control elements.

Assigning functions to genes is one of the major challenges of the post-genomic era. Usually, functions are assigned based on similarity of the coding sequences to sequences of known genes, or by identification of transcriptional cis-regulatory elements that are known to be associated with specific pathways or conditions. In trypanosomatids, where regulation of gene expression takes place mainly at the post-transcriptional level, new approaches for function assignment are needed. Here we demonstrate the identification of novel S-phase expressed genes in Leishmania major, based on a post-transcriptional control element that was recognized in Crithidia fasciculata as involved in the cell cycle-dependent expression of several nuclear and mitochondrial S-phase expressed genes. Hypothesizing that a similar regulatory mechanism is manifested in L.major, we have applied a computational search for similar control elements in the genome of L.major. Our computational scan yielded 132 genes, of which 33% are homologues of known DNA metabolism genes and 63% lack any annotation. Experimental testing of seven of these genes revealed that their mRNAs cycle throughout the cell cycle, reaching a maximum level during S-phase or just prior to it. It is suggested that screening for post-transcriptional control elements associated with a specific function provides an efficient method for assigning functions to trypanosomatid genes.

Animals↗

PubMed2005

Ab initio prediction of transcription factor targets using structural knowledge.

Current approaches for identification and detection of transcription factor binding sites rely on an extensive set of known target genes. Here we describe a novel structure-based approach applicable to transcription factors with no prior binding data. Our approach combines sequence data and structural information to infer context-specific amino acid-nucleotide recognition preferences. These are used to predict binding sites for novel transcription factors from the same structural family. We demonstrate our approach on the Cys(2)His(2) Zinc Finger protein family, and show that the learned DNA-recognition preferences are compatible with experimental results. We use these preferences to perform a genome-wide scan for direct targets of Drosophila melanogaster Cys(2)His(2) transcription factors. By analyzing the predicted targets along with gene annotation and expression data we infer the function and activity of these proteins.

Journal Article↗

PubMed2005

Clustering and conservation patterns of human microRNAs.

MicroRNAs (miRNAs) are approximately 22 nt-long non-coding RNA molecules, believed to play important roles in gene regulation. We present a comprehensive analysis of the conservation and clustering patterns of known miRNAs in human. We show that human miRNA gene clustering is significantly higher than expected at random. A total of 37% of the known human miRNA genes analyzed in this study appear in clusters of two or more with pairwise chromosomal distances of at most 3000 nt. Comparison of the miRNA sequences with their homologs in four other organisms reveals a typical conservation pattern, persistent throughout the clusters. Furthermore, we show enrichment in the typical conservation patterns and other miRNA-like properties in the vicinity of known miRNA genes, compared with random genomic regions. This may imply that additional, yet unknown, miRNAs reside in these regions, consistent with the current recognition that there are overlooked miRNAs. Indeed, by comparing our predictions with cloning results and with identified miRNA genes in other mammals, we corroborate the predictions of 18 additional human miRNA genes in the vicinity of the previously known ones. Our study raises the proportion of clustered human miRNAs that are <3000 nt apart to 42%. This suggests that the clustering of miRNA genes is higher than currently acknowledged, alluding to its evolutionary and functional implications.

Base Sequence↗

PubMed2005

Relative predicted protein levels of functionally associated proteins are conserved across organisms.

We show that the predicted protein levels of functionally related proteins change in a coordinated fashion over many unicellular organisms. For each protein, we created a profile containing a protein abundance measure in each of a set of organisms. We show that for functionally related proteins these profiles tend to be correlated. Using the Codon Adaptation Index as a predictor of protein abundance in 48 unicellular organisms, we demonstrated this phenomenon for two types of functional relations: for proteins that physically interact and for proteins involved in consecutive steps within a metabolic pathway. Our results suggest that the protein abundance levels of functionally related proteins co-evolve.

Archaeal Proteins↗

PubMed2005

Chromosomal organization is shaped by the transcription regulatory network.

Transcription regulation, a key step in the control of gene expression, has been the focus of several large-scale studies; however, little attention has been given to its relationship with the chromosomal arrangement of transcription units. We studied this relationship systematically in Escherichia coli and Saccharomyces cerevisiae using network analysis methods. Our analysis reveals links between transcription regulation and chromosomal organization, suggesting that in both organisms transcription regulation has shaped the organization of transcription units on the chromosome. Differences found between the organisms reflect the inherent differences in transcription regulation between prokaryotes and eukaryotes.

Chromosomes, Bacterial↗

PubMed2004

Network motifs in integrated cellular networks of transcription-regulation and protein-protein interaction.

Genes and proteins generate molecular circuitry that enables the cell to process information and respond to stimuli. A major challenge is to identify characteristic patterns in this network of interactions that may shed light on basic cellular mechanisms. Previous studies have analyzed aspects of this network, concentrating on either transcription-regulation or protein-protein interactions. Here we search for composite network motifs: characteristic network patterns consisting of both transcription-regulation and protein-protein interactions that recur significantly more often than in random networks. To this end we developed algorithms for detecting motifs in networks with two or more types of interactions and applied them to an integrated data set of protein-protein interactions and transcription regulation in Saccharomyces cerevisiae. We found a two-protein mixed-feedback loop motif, five types of three-protein motifs exhibiting coregulation and complex formation, and many motifs involving four proteins. Virtually all four-protein motifs consisted of combinations of smaller motifs. This study presents a basic framework for detecting the building blocks of networks with multiple types of interactions.

Amino Acid Motifs↗

PubMed2004

IntAct: an open source molecular interaction database.

IntAct provides an open source database and toolkit for the storage, presentation and analysis of protein interactions. The web interface provides both textual and graphical representations of protein interactions, and allows exploring interaction networks in the context of the GO annotations of the interacting proteins. A web service allows direct computational access to retrieve interaction networks in XML format. IntAct currently contains approximately 2200 binary and complex interactions imported from the literature and curated in collaboration with the Swiss-Prot team, making intensive use of controlled vocabularies to ensure data consistency. All IntAct software, data and controlled vocabularies are available at http://www.ebi.ac.uk/intact.

Animals↗

PubMed2004

A structure-based approach for prediction of MHC-binding peptides.

Identification of immunodominant peptides is the first step in the rational design of peptide vaccines aimed at T-cell immunity. The advances in sequencing techniques and the accumulation of many protein sequences without the purified protein challenge the development of computer algorithms to identify dominant T-cell epitopes based on sequence data alone. Here, we focus on antigenic peptides recognized by cytotoxic T cells. The selection of T-cell epitopes along a protein sequence is influenced by the specificity of each of the processing stages that precede antigen presentation. The most selective of these processing stages is the binding of the peptides to the major histocompatibility complex molecules, and therefore many of the predictive algorithms focus on this stage. Most of these algorithms are based on known binding peptides whose sequences have been used for the characterization of binding motifs or profiles. Here, we describe a structure-based algorithm that does not rely on previous binding data. It is based on observations from crystal structures that many of the bound peptides adopt similar conformations and placements within the MHC groove. The algorithm uses a structural template of the peptide in the MHC groove upon which peptide candidates are threaded and their fit to the MHC groove is evaluated by statistical pairwise potentials. It can rank all possible peptides along a protein sequence or within a suspected group of peptides, directing the experimental efforts towards the most promising peptides. This approach is especially useful when no previous peptide binding data are available.

Algorithms↗

PubMed2003

Detection of regulatory circuits by integrating the cellular networks of protein-protein interactions and transcription regulation.

The post-genomic era is marked by huge amounts of data generated by large-scale functional genomic and proteomic experiments. A major challenge is to integrate the various types of genome-scale information in order to reveal the intra- and inter- relationships between genes and proteins that constitute a living cell. Here we present a novel application of classical graph algorithms to integrate the cellular networks of protein-protein interactions and transcription regulation. We demonstrate how integration of these two networks enables the discovery of simple as well as complex regulatory circuits that involve both protein-protein and protein-DNA interactions. These circuits may serve for positive or negative feedback mechanisms. By applying our approach to data from the yeast Saccharomyces cerevisiae, we were able to identify known simple and complex regulatory circuits and to discover many putative circuits, whose biological relevance has been assessed using various types of experimental data. The newly identified relations provide new insight into the processes that take place in the cell, insight that could not be gained by analyzing each type of data independently. The computational scheme that we propose may be used to integrate additional functional genomic and proteomic data and to reveal other types of relations, in yeast as well as in higher organisms.

DNA, Fungal↗

PubMed2003

How reliable are experimental protein-protein interaction data?

Data of protein-protein interactions provide valuable insight into the molecular networks underlying a living cell. However, their accuracy is often questioned, calling for a rigorous assessment of their reliability. The computation offered here provides an intelligible mean to assess directly the rate of true positives in a data set of experimentally determined interacting protein pairs. We show that the reliability of high-throughput yeast two-hybrid assays is about 50%, and that the size of the yeast interactome is estimated to be 10,000-16,600 interactions.

Protein Binding↗

PubMed2003

Conserved sequence elements associated with exon skipping.

One of the major forms of alternative splicing, which generates multiple mRNA isoforms differing in the precise combinations of their exon sequences, is exon skipping. While in constitutive splicing all exons are included, in the skipped pattern(s) one or more exons are skipped. The regulation of this process is still not well understood; so far, cis- regulatory elements (such as exonic splicing enhancers) were identified in individual cases. We therefore set to investigate the possibility that exon skipping is controlled by sequences in the adjacent introns. We employed a computer analysis on 54 sequences documented as undergoing exon skipping, and identified two motifs both in the upstream and downstream introns of the skipped exons. One motif is highly enriched in pyrimidines (mostly C residues), and the other motif is highly enriched in purines (mostly G residues). The two motifs differ from the known cis-elements present at the 5' and 3' splice site. Interestingly, the two motifs are complementary, and their relative positional order is conserved in the flanking introns. These suggest that base pairing interactions can underlie a mechanism that involves secondary structure to regulate exon skipping. Remarkably, the two motifs are conserved in mouse orthologous genes that undergo exon skipping.

Alternative Splicing↗

PubMed2003

A survey of small RNA-encoding genes in Escherichia coli.

Small RNA (sRNA) molecules have gained much interest lately, as recent genome-wide studies have shown that they are widespread in a variety of organisms. The relatively small family of 10 known sRNA-encoding genes in Escherichia coli has been significantly expanded during the past two years with the discovery of 45 novel genes. Most of these genes are still uncharacterized and their cellular roles are unknown. In this survey we examined the sequence and genomic features of the 55 currently known sRNA-encoding genes in E.coli, attempting to identify their common characteristics. Such characterization is important for both expanding our understanding of this unique gene family and for improving the methods to predict and identify sRNA-encoding genes based on genomic information.

Base Composition↗

PubMed2003

Hierarchy of sequence-dependent features associated with prokaryotic translation.

Protein expression in the cell is affected by various sequence-dependent features. Several such sequence-dependent features have been individually studied,yet they have not been compared quantitatively in terms of their relative influence on protein expression,and a hierarchy of these elements has not been determined. Here we present a quantitative analysis examining sequence-dependent features involved in prokaryotic translation,namely,the base-pairing potential between the mRNA Shine-Dalgarno sequence and the ribosomal RNA,codon bias,and the identity of the stop codon. We analyzed these features both at intra- and intergenomic levels using the Escherichia coli and Haemophilus influenzae genomes. Within each genome,we examined the relationship between each feature and protein expression levels determined by 2D-gel analyses. At the intergenomic level,comparative genomic principles were applied to study the relative preservation of the different sequence-dependent properties between orthologs. From these analyses,we determined that biased codon usage is the property that is most highly associated with protein expression and that is most conserved. The identity of the stop codon and the base-pairing potential of the mRNA Shine-Dalgarno sequence and the rRNA seem to have less of an effect on protein expression.

3' Untranslated Regions↗