Biomedical subjects

Yael Altuvia

Publications and source records attributed to Yael Altuvia.

6 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

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↗

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↗

PubMed2004

Alternative splicing generates an isoform of the human Sef gene with altered subcellular localization and specificity.

Receptor tyrosine kinases (RTKs) control a multitude of biological processes and are therefore subjected to multiple levels of regulation. Negative feedback is one of the mechanisms that provide an effective means to control RTK-mediated signaling. Sef has recently been identified as a specific antagonist of fibroblast growth factor (FGF) signaling in zebrafish and subsequently in mouse and human. Sef encodes a putative type I transmembrane protein that antagonizes the Ras/mitogen-activated protein kinase pathway in all three species. Mouse Sef was also shown to inhibit the phosphatidylinositol 3-kinase pathway. We show here that an alternative splicing mechanism generates an isoform of human Sef, hSef-b, which unlike the previously reported Sef (hSef-a) is a cytosolic protein. Contrary to hSef-a, which is ubiquitously expressed, hSef-b transcripts display a restricted pattern of expression in human tissues. hSef-b inhibits FGF-induced cell proliferation and prevents the activation of mitogen-activated protein kinase without affecting the upstream component MAPK kinase. Furthermore, hSef-b does not antagonize FGF induction of the phosphatidylinositol 3-kinase pathway. In addition to the effects on FGF signaling, hSef-b inhibited cellular response to platelet-derived growth factor but not other RTK ligands. Therefore, alternative splicing of the hSef gene expands the Sef feedback inhibition repertoire of RTK signaling.

Alternative Splicing↗

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

Insights from MHC-bound peptides.

Cytotoxic T cells recognize short antigenic peptides, the processing products of protein antigens, when they are bound to major histocompatibility complex (MHC) class I molecules. Peptide binding to MHC molecules has been studied extensively in numerous laboratories, providing vast amounts of sequence and structure data that have been used as a rich source for bioinformatic research. MHC-bound peptides and their flanking sequences provide information about the sequence requirements of the different processing stages, in particular, the cleavage by the proteasome and the binding to MHC molecules. Elucidation of these sequence requirements sheds light on the evolutionary forces that have shaped and designed these peptides, and should lead to the development of an integrative predictive algorithm. Remarkably, the peptide sequence and structure data are also valuable for the study of biological questions that are apparently unrelated to cellular immunity, namely, sequence-structure relationship and genome annotation. Here we describe our computational analyses of MHC-bound peptides, applied to all these biological topics.

Allergy and Immunology↗