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Mark Gerstein

Publications and source records attributed to Mark Gerstein.

At least 37 records · Page 2Linked to original sources
PubMed2006

Predicting interactions in protein networks by completing defective cliques.

UNLABELLED: Datasets obtained by large-scale, high-throughput methods for detecting protein-protein interactions typically suffer from a relatively high level of noise. We describe a novel method for improving the quality of these datasets by predicting missed protein-protein interactions, using only the topology of the protein interaction network observed by the large-scale experiment. The central idea of the method is to search the protein interaction network for defective cliques (nearly complete complexes of pairwise interacting proteins), and predict the interactions that complete them. We formulate an algorithm for applying this method to large-scale networks, and show that in practice it is efficient and has good predictive performance. More information can be found on our website http://topnet.gersteinlab.org/clique/ CONTACT: Mark.Gerstein@yale.edu SUPPLEMENTARY INFORMATION: Supplementary Materials are available at Bioinformatics online.

Algorithms↗
PubMed2006

Target hub proteins serve as master regulators of development in yeast.

To understand the organization of the transcriptional networks that govern cell differentiation, we have investigated the transcriptional circuitry controlling pseudohyphal development in Saccharomyces cerevisiae. The binding targets of Ste12, Tec1, Sok2, Phd1, Mga1, and Flo8 were globally mapped across the yeast genome. The factors and their targets form a complex binding network, containing patterns characteristic of autoregulation, feedback and feed-forward loops, and cross-talk. Combinatorial binding to intergenic regions was commonly observed, which allowed for the identification of a novel binding association between Mga1 and Flo8, in which Mga1 requires Flo8 for binding to promoter regions. Further analysis of the network showed that the promoters of MGA1 and PHD1 were bound by all of the factors used in this study, identifying them as key target hubs. Overexpression of either of these two proteins specifically induced pseudohyphal growth under noninducing conditions, highlighting them as master regulators of the system. Our results indicate that target hubs can serve as master regulators whose activity is sufficient for the induction of complex developmental responses and therefore represent important regulatory nodes in biological networks.

Blotting, Western↗
PubMed2006

Integrated prediction of the helical membrane protein interactome in yeast.

At least a quarter of all genes in most genomes contain putative transmembrane (TM) helices, and helical membrane protein interactions are a major component of the overall cellular interactome. However, current experimental techniques for large-scale detection of protein-protein interactions are biased against membrane proteins. Here, we define protein-protein interaction broadly as co-complexation, and develop a weighted-voting procedure to predict interactions among yeast helical membrane proteins by optimally combining evidence based on diverse genome-wide information such as sequence, function, localization, abundance, regulation, and phenotype. We use logistic regression to simultaneously optimize the weights of all evidence sources for best discrimination based on a set of known helical membrane protein interactions. The resulting integrated classifier not only significantly outperforms classifiers based on any single genomic feature, but also does better than a benchmark Naïve Bayes classifier (using a simplifying assumption of conditional independence among features). Finally, we apply the optimized classifier genome-wide, and construct a comprehensive map of predicted helical membrane protein interactome in yeast. This can serve as a guide for prioritizing further experimental validation efforts.

Membrane Proteins↗
PubMed2006

The Database of Macromolecular Motions: new features added at the decade mark.

The database of molecular motions, MolMovDB (http://molmovdb.org), has been in existence for the past decade. It classifies macromolecular motions and provides tools to interpolate between two conformations (the Morph Server) and predict possible motions in a single structure. In 2005, we expanded the services offered on MolMovDB. In particular, we further developed the Morph Server to produce improved interpolations between two submitted structures. We added support for multiple chains to the original adiabatic mapping interpolation, allowing the analysis of subunit motions. We also added the option of using FRODA interpolation, which allows for more complex pathways, potentially overcoming steric barriers. We added an interface to a hinge prediction service, which acts on single structures and predicts likely residue points for flexibility. We developed tools to relate such points of flexibility in a structure to particular key residue positions, i.e. active sites or highly conserved positions. Lastly, we began relating our motion classification scheme to function using descriptions from the Gene Ontology Consortium.

Databases, Protein↗
PubMed2006

BoCaTFBS: a boosted cascade learner to refine the binding sites suggested by ChIP-chip experiments.

Comprehensive mapping of transcription factor binding sites is essential in postgenomic biology. For this, we propose a mining approach combining noisy data from ChIP (chromatin immunoprecipitation)-chip experiments with known binding site patterns. Our method (BoCaTFBS) uses boosted cascades of classifiers for optimum efficiency, in which components are alternating decision trees; it exploits interpositional correlations; and it explicitly integrates massive negative information from ChIP-chip experiments. We applied BoCaTFBS within the ENCODE project and showed that it outperforms many traditional binding site identification methods (for instance, profiles).

Algorithms↗
PubMed2006

ProCAT: a data analysis approach for protein microarrays.

Protein microarrays provide a versatile method for the analysis of many protein biochemical activities. Existing DNA microarray analytical methods do not translate to protein microarrays due to differences between the technologies. Here we report a new approach, ProCAT, which corrects for background bias and spatial artifacts, identifies significant signals, filters nonspecific spots, and normalizes the resulting signal to protein abundance. ProCAT provides a powerful and flexible new approach for analyzing many types of protein microarrays.

Cyclic AMP-Dependent Protein Kinases↗
PubMed2006

Design principles of molecular networks revealed by global comparisons and composite motifs.

BACKGROUND: Molecular networks are of current interest, particularly with the publication of many large-scale datasets. Previous analyses have focused on topologic structures of individual networks. RESULTS: Here, we present a global comparison of four basic molecular networks: regulatory, co-expression, interaction, and metabolic. In terms of overall topologic correlation--whether nearby proteins in one network are close in another--we find that the four are quite similar. However, focusing on the occurrence of local features, we introduce the concept of composite hubs, namely hubs shared by more than one network. We find that the three 'action' networks (metabolic, co-expression, and interaction) share the same scaffolding of hubs, whereas the regulatory network uses distinctly different regulator hubs. Finally, we examine the inter-relationship between the regulatory network and the three action networks, focusing on three composite motifs--triangles, trusses, and bridges--involving different degrees of regulation of gene pairs. Our analysis shows that interaction and co-expression networks have short-range relationships, with directly interacting and co-expressed proteins sharing regulators. However, the metabolic network contains many long-distance relationships: far-away enzymes in a pathway often have time-delayed expression relationships, which are well coordinated by bridges connecting their regulators. CONCLUSION: We demonstrate how basic molecular networks are distinct yet connected and well coordinated. Many of our conclusions can be mapped onto structured social networks, providing intuitive comparisons. In particular, the long-distance regulation in metabolic networks agrees with its counterpart in social networks (namely, assembly lines). Conversely, the segregation of regulator hubs from other hubs diverges from social intuitions (as managers often are centers of interactions).

Amino Acid Motifs↗
PubMed2005

Design optimization methods for genomic DNA tiling arrays.

A recent development in microarray research entails the unbiased coverage, or tiling, of genomic DNA for the large-scale identification of transcribed sequences and regulatory elements. A central issue in designing tiling arrays is that of arriving at a single-copy tile path, as significant sequence cross-hybridization can result from the presence of non-unique probes on the array. Due to the fragmentation of genomic DNA caused by the widespread distribution of repetitive elements, the problem of obtaining adequate sequence coverage increases with the sizes of subsequence tiles that are to be included in the design. This becomes increasingly problematic when considering complex eukaryotic genomes that contain many thousands of interspersed repeats. The general problem of sequence tiling can be framed as finding an optimal partitioning of non-repetitive subsequences over a prescribed range of tile sizes, on a DNA sequence comprising repetitive and non-repetitive regions. Exact solutions to the tiling problem become computationally infeasible when applied to large genomes, but successive optimizations are developed that allow their practical implementation. These include an efficient method for determining the degree of similarity of many oligonucleotide sequences over large genomes, and two algorithms for finding an optimal tile path composed of longer sequence tiles. The first algorithm, a dynamic programming approach, finds an optimal tiling in linear time and space; the second applies a heuristic search to reduce the space complexity to a constant requirement. A Web resource has also been developed, accessible at http://tiling.gersteinlab.org, to generate optimal tile paths from user-provided DNA sequences.

Algorithms↗
PubMed2005

Global analysis of protein phosphorylation in yeast.

Protein phosphorylation is estimated to affect 30% of the proteome and is a major regulatory mechanism that controls many basic cellular processes. Until recently, our biochemical understanding of protein phosphorylation on a global scale has been extremely limited; only one half of the yeast kinases have known in vivo substrates and the phosphorylating kinase is known for less than 160 phosphoproteins. Here we describe, with the use of proteome chip technology, the in vitro substrates recognized by most yeast protein kinases: we identified over 4,000 phosphorylation events involving 1,325 different proteins. These substrates represent a broad spectrum of different biochemical functions and cellular roles. Distinct sets of substrates were recognized by each protein kinase, including closely related kinases of the protein kinase A family and four cyclin-dependent kinases that vary only in their cyclin subunits. Although many substrates reside in the same cellular compartment or belong to the same functional category as their phosphorylating kinase, many others do not, indicating possible new roles for several kinases. Furthermore, integration of the phosphorylation results with protein-protein interaction and transcription factor binding data revealed novel regulatory modules. Our phosphorylation results have been assembled into a first-generation phosphorylation map for yeast. Because many yeast proteins and pathways are conserved, these results will provide insights into the mechanisms and roles of protein phosphorylation in many eukaryotes.

Eukaryotic Cells↗
PubMed2005

Biochemical and genetic analysis of the yeast proteome with a movable ORF collection.

Functional analysis of the proteome is an essential part of genomic research. To facilitate different proteomic approaches, a MORF (moveable ORF) library of 5854 yeast expression plasmids was constructed, each expressing a sequence-verified ORF as a C-terminal ORF fusion protein, under regulated control. Analysis of 5573 MORFs demonstrates that nearly all verified ORFs are expressed, suggests the authenticity of 48 ORFs characterized as dubious, and implicates specific processes including cytoskeletal organization and transcriptional control in growth inhibition caused by overexpression. Global analysis of glycosylated proteins identifies 109 new confirmed N-linked and 345 candidate glycoproteins, nearly doubling the known yeast glycome.

Cell Growth Processes↗
PubMed2005

Global changes in STAT target selection and transcription regulation upon interferon treatments.

The STAT (signal transducer and activator of transcription) proteins play a crucial role in the regulation of gene expression, but their targets and the manner in which they select them remain largely unknown. Using chromatin immunoprecipitation and DNA microarray analysis (ChIP-chip), we have identified the regions of human chromosome 22 bound by STAT1 and STAT2 in interferon-treated cells. Analysis of the genomic loci proximal to these binding sites introduced new candidate STAT1 and STAT2 target genes, several of which are affiliated with proliferation and apoptosis. The genes on chromosome 22 that exhibited interferon-induced up- or down-regulated expression were determined and correlated with the STAT-binding site information, revealing the potential regulatory effects of STAT1 and STAT2 on their target genes. Importantly, the comparison of STAT1-binding sites upon interferon (IFN)-gamma and IFN-alpha treatments revealed dramatic changes in binding locations between the two treatments. The IFN-alpha induction revealed nonconserved STAT1 occupancy at IFN-gamma-induced sites, as well as novel sites of STAT1 binding not evident in IFN-gamma-treated cells. Many of these correlated with binding by STAT2, but others were STAT2 independent, suggesting that multiple mechanisms direct STAT1 binding to its targets under different activation conditions. Overall, our results reveal a wealth of new information regarding IFN/STAT-binding targets and also fundamental insights into mechanisms of regulation of gene expression in different cell states.

Antineoplastic Agents↗
PubMed2005

Network security and data integrity in academia: an assessment and a proposal for large-scale archiving.

A direct impediment to the optimal use of online databases is the increasing prevalence, severity, and toll of computer and network security incidents. Funding agencies should set up working groups that can provide essential services such as universal backup, archival storage, and mirroring of community resources, consistent with the key goal of security in academia: to preserve data and results for posterity.

Archives↗
PubMed2005

PubNet: a flexible system for visualizing literature derived networks.

We have developed PubNet, a web-based tool that extracts several types of relationships returned by PubMed queries and maps them into networks, allowing for graphical visualization, textual navigation, and topological analysis. PubNet supports the creation of complex networks derived from the contents of individual citations, such as genes, proteins, Protein Data Bank (PDB) IDs, Medical Subject Headings (MeSH) terms, and authors. This feature allows one to, for example, examine a literature derived network of genes based on functional similarity.

Databases, Bibliographic↗
PubMed2005

Proton sensitivity of ASIC1 appeared with the rise of fishes by changes of residues in the region that follows TM1 in the ectodomain of the channel.

The acid-sensitive ion channel 1 (ASIC1) is a neuronal Na+ channel insensitive to changes in membrane potential but is gated by external protons. Proton sensitivity is believed to be essential for the role of ASIC1 in modulating synaptic transmission and nociception in the mammalian nervous system. To examine the structural determinants that confer proton sensitivity, we cloned and functionally characterized ASIC1 from different species of the chordate lineage: lamprey, shark, toadfish and chicken. We observed that ASIC1s from early vertebrates (lamprey and shark) were proton insensitive in spite of a high degree of amino acid conservation (66-67%) with their mammalian counterparts. Sequence analysis showed that proton-sensitive ASIC1s could not be distinguished from proton-insensitive channels by any signature in the protein sequence. Chimeras made with rat ASIC1 (rASIC1) and lamprey or shark indicated that most of the ectodomain of rASIC1 was required to confer proton sensitivity and the distinct kinetics of activation and desensitization of the rat channel. Proton-sensitive chimeras contained the segment D78-E136, together with residues D351, Q358 and E359 of the rat sequence. However, none of the functional chimeras containing only part of the rat extracellular domain retained the kinetics of activation and desensitization of rASIC1, suggesting that residues distributed in several regions of the ectodomain contribute to allosteric changes underlying activation and desensitization. The results also demonstrate that gating by protons is not a feature common to all ASIC1 channels. Proton sensitivity arose recently in evolution, implying that agonists different from protons activate ASIC1 in lower vertebrates.

Acid Sensing Ion Channels↗
PubMed2005

Transcribed processed pseudogenes in the human genome: an intermediate form of expressed retrosequence lacking protein-coding ability.

Pseudogenes, in the case of protein-coding genes, are gene copies that have lost the ability to code for a protein; they are typically identified through annotation of disabled, decayed or incomplete protein-coding sequences. Processed pseudogenes (PPsigs) are made through mRNA retrotransposition. There is overwhelming genomic evidence for thousands of human PPsigs and also dozens of human processed genes that comprise complete retrotransposed copies of other genes. Here, we survey for an intermediate entity, the transcribed processed pseudogene (TPPsig), which is disabled but nonetheless transcribed. TPPsigs may affect expression of paralogous genes, as observed in the case of the mouse makorin1-p1 TPPsig. To elucidate their role, we identified human TPPsigs by mapping expressed sequences onto PPsigs and, reciprocally, extracting TPPsigs from known mRNAs. We consider only those PPsigs that are homologous to either non-mammalian eukaryotic proteins or protein domains of known structure, and require detection of identical coding-sequence disablements in both the expressed and genomic sequences. Oligonucleotide microarray data provide further expression verification. Overall, we find 166-233 TPPsigs ( approximately 4-6% of PPsigs). Proteins/transcripts with the highest numbers of homologous TPPsigs generally have many homologous PPsigs and are abundantly expressed. TPPsigs are significantly over-represented near both the 5' and 3' ends of genes; this suggests that TPPsigs can be formed through gene-promoter co-option, or intrusion into untranslated regions. However, roughly half of the TPPsigs are located away from genes in the intergenic DNA and thus may be co-opting cryptic promoters of undesignated origin. Furthermore, TPPsigs are unlike other PPsigs and processed genes in the following ways: (i) they do not show a significant tendency to either deposit on or originate from the X chromosome; (ii) only 5% of human TPPsigs have potential orthologs in mouse. This latter finding indicates that the vast majority of TPPsigs is lineage specific. This is likely linked to well-documented extensive lineage-specific SINE/LINE activity. The list of TPPsigs is available at: http://www.biology.mcgill.ca/faculty/harrison/tppg/bppg.tov (or) http:pseudogene.org.

Amino Acid Sequence↗
PubMed2005

Integrated pseudogene annotation for human chromosome 22: evidence for transcription.

Pseudogenes are inheritable genetic elements formally defined by two properties: their similarity to functioning genes and their presumed lack of activity. However, their precise characterization, particularly with respect to the latter quality, has proven elusive. An opportunity to explore this issue arises from the recent emergence of tiling-microarray data showing that intergenic regions (containing pseudogenes) are transcribed to a great degree. Here we focus on the transcriptional activity of pseudogenes on human chromosome 22. First, we integrated several sets of annotation to define a unified list of 525 pseudogenes on the chromosome. To characterize these further, we developed a comprehensive list of genomic features based on conservation in related organisms, expression evidence, and the presence of upstream regulatory sites. Of the 525 unified pseudogenes we could confidently classify 154 as processed and 49 as duplicated. Using data from tiling microarrays, especially from recent high-resolution oligonucleotide arrays, we found some evidence that up to a fifth of the 525 pseudogenes are potentially transcribed. Expressed sequence tags (EST) comparison further validated a number of these, and overall we found 17 pseudogenes with strong support for transcription. In particular, one of the pseudogenes with both EST and microarray evidence for transcription turned out to be a duplicated pseudogene in the cat eye syndrome critical region. Although we could not identify a meaningful number of transcription factor-binding sites (based on chromatin immunoprecipitation-chip data) near pseudogenes, we did find that approximately 12% of the pseudogenes had upstream CpG islands. Finally, analysis of corresponding syntenic regions in the mouse, rat and chimp genomes indicates, as previously suggested, that pseudogenes are less conserved than genes, but more preserved than the intergenic background (all notation is available from http://www.pseudogene.org).

Animals↗
PubMed2005

Sequence variation in G-protein-coupled receptors: analysis of single nucleotide polymorphisms.

We assessed the disease-causing potential of single nucleotide polymorphisms (SNPs) based on a simple set of sequence-based features. We focused on SNPs from the dbSNP database in G-protein-coupled receptors (GPCRs), a large class of important transmembrane (TM) proteins. Apart from the location of the SNP in the protein, we evaluated the predictive power of three major classes of features to differentiate between disease-causing mutations and neutral changes: (i) properties derived from amino-acid scales, such as volume and hydrophobicity; (ii) position-specific phylogenetic features reflecting evolutionary conservation, such as normalized site entropy, residue frequency and SIFT score; and (iii) substitution-matrix scores, such as those derived from the BLOSUM62, GRANTHAM and PHAT matrices. We validated our approach using a control dataset consisting of known disease-causing mutations and neutral variations. Logistic regression analyses indicated that position-specific phylogenetic features that describe the conservation of an amino acid at a specific site are the best discriminators of disease mutations versus neutral variations, and integration of all our features improves discrimination power. Overall, we identify 115 SNPs in GPCRs from dbSNP that are likely to be associated with disease and thus are good candidates for genotyping in association studies.

Amino Acid Substitution↗