The protein target list of the Northeast Structural Genomics Consortium.
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Data Display↗
Biomedical subjects
Mark Gerstein
Publications and source records attributed to Mark Gerstein.
Selection and characterization of small random transmembrane proteins that bind and activate the platelet-derived growth factor beta receptor.
Growth factor receptors are typically activated by the binding of soluble ligands to the extracellular domain of the receptor, but certain viral transmembrane proteins can induce growth factor receptor activation by binding to the receptor transmembrane domain. For example, homodimers of the transmembrane 44-amino acid bovine papillomavirus E5 protein bind the transmembrane region of the PDGF beta receptor tyrosine kinase, causing receptor dimerization, phosphorylation, and cell transformation. To determine whether it is possible to select novel biologically active transmembrane proteins that can activate growth factor receptors, we constructed and identified small proteins with random hydrophobic transmembrane domains that can bind and activate the PDGF beta receptor. Remarkably, cell transformation was induced by approximately 10% of the clones in a library in which 15 transmembrane amino acid residues of the E5 protein were replaced with random hydrophobic sequences. The transformation-competent transmembrane proteins formed dimers and stably bound and activated the PDGF beta receptor. Genetic studies demonstrated that the biological activity of the transformation-competent proteins depended on specific interactions with the transmembrane domain of the PDGF beta receptor. A consensus sequence distinct from the wild-type E5 sequence was identified that restored transforming activity to a non-transforming poly-leucine transmembrane sequence, indicating that divergent transmembrane sequence motifs can activate the PDGF beta receptor. Molecular modeling suggested that diverse transforming sequences shared similar protein structure, including the same homodimer interface as the wild-type E5 protein. These experiments have identified novel proteins with transmembrane sequences distinct from the E5 protein that can activate the PDGF beta receptor and transform cells. More generally, this approach may allow the creation and identification of small proteins that modulate the activity of a variety of cellular transmembrane proteins.
Animals↗
A method using active-site sequence conservation to find functional shifts in protein families: application to the enzymes of central metabolism, leading to the identification of an anomalous isocitrate dehydrogenase in pathogens.
We have introduced a method to identify functional shifts in protein families. Our method is based on the calculation of an active-site conservation ratio, which we call the "ASC ratio." For a structurally based alignment of a protein family, this ratio is the average sequence similarity of the active-site region compared to the full-length protein. The active-site region is defined as all the residues within a certain radius of the known functionally important groups. Using our method, we have analyzed enzymes of central metabolism from a large number of genomes (35). We found that for most of the enzymes, the active-site region is more highly conserved than the full-length sequence. However, for three tricarboxylic acid (TCA)-cycle enzymes, active-site sequences are considerably more diverged (than full-length ones). In particular, we were able to identify in six pathogens a novel isocitrate dehydrogenase that has very low sequence similarity around the active site. Detailed sequence-structure analysis indicates that while the active-site structure of isocitrate dehydrogenase is most likely similar between pathogens and nonpathogens, the unusual sequence divergence could result from an extra domain added at the N-terminus. This domain has a leucine-rich motif similar one in the Yersinia pestis cytotoxin and may therefore confer additional pathogenic functions.
Amino Acid Motifs↗
Transmembrane protein domains rarely use covalent domain recombination as an evolutionary mechanism.
Recombination of evolutionarily unrelated domains is a mechanism often used by evolution to produce variety in soluble proteins. By using a classification of polytopic transmembrane domains into families, we examined integral membrane proteins for evidence of this mechanism. Surprisingly, we found that domain recombination is not common for the transmembrane regions of membrane proteins, a majority of integral membrane proteins containing only a single transmembrane domain. We suggest that noncovalent oligomeric associations, which are common in membrane proteins, may provide an alternative source of evolutionary diversity.
Animals↗
Mining the structural genomics pipeline: identification of protein properties that affect high-throughput experimental analysis.
Structural genomics projects represent major undertakings that will change our understanding of proteins. They generate unique datasets that, for the first time, present a standardized view of proteins in terms of their physical and chemical properties. By analyzing these datasets here, we are able to discover correlations between a protein's characteristics and its progress through each stage of the structural genomics pipeline, from cloning, expression, purification, and ultimately to structural determination. First, we use tree-based analyses (decision trees and random forest algorithms) to discover the most significant protein features that influence a protein's amenability to high-throughput experimentation. Based on this, we identify potential bottlenecks in various stages of the structural genomics process through specialized "pipeline schematics". We find that the properties of a protein that are most significant are: (i.) whether it is conserved across many organisms; (ii). the percentage composition of charged residues; (iii). the occurrence of hydrophobic patches; (iv). the number of binding partners it has; and (v). its length. Conversely, a number of other properties that might have been thought to be important, such as nuclear localization signals, are not significant. Thus, using our tree-based analyses, we are able to identify combinations of features that best differentiate the small group of proteins for which a structure has been determined from all the currently selected targets. This information may prove useful in optimizing high-throughput experimentation. Further information is available from http://mining.nesg.org/.
Algorithms↗
TopNet: a tool for comparing biological sub-networks, correlating protein properties with topological statistics.
Biological networks are a topic of great current interest, particularly with the publication of a number of large genome-wide interaction datasets. They are globally characterized by a variety of graph-theoretic statistics, such as the degree distribution, clustering coefficient, characteristic path length and diameter. Moreover, real protein networks are quite complex and can often be divided into many sub-networks through systematic selection of different nodes and edges. For instance, proteins can be sub-divided by expression level, length, amino-acid composition, solubility, secondary structure and function. A challenging research question is to compare the topologies of sub- networks, looking for global differences associated with different types of proteins. TopNet is an automated web tool designed to address this question, calculating and comparing topological characteristics for different sub-networks derived from any given protein network. It provides reasonable solutions to the calculation of network statistics for sub-networks embedded within a larger network and gives simplified views of a sub-network of interest, allowing one to navigate through it. After constructing TopNet, we applied it to the interaction networks and protein classes currently available for yeast. We were able to find a number of potential biological correlations. In particular, we found that soluble proteins had more interactions than membrane proteins. Moreover, amongst soluble proteins, those that were highly expressed, had many polar amino acids, and had many alpha helices, tended to have the most interaction partners. Interestingly, TopNet also turned up some systematic biases in the current yeast interaction network: on average, proteins with a known functional classification had many more interaction partners than those without. This phenomenon may reflect the incompleteness of the experimentally determined yeast interaction network.
Algorithms↗
Using 3D Hidden Markov Models that explicitly represent spatial coordinates to model and compare protein structures.
BACKGROUND: Hidden Markov Models (HMMs) have proven very useful in computational biology for such applications as sequence pattern matching, gene-finding, and structure prediction. Thus far, however, they have been confined to representing 1D sequence (or the aspects of structure that could be represented by character strings). RESULTS: We develop an HMM formalism that explicitly uses 3D coordinates in its match states. The match states are modeled by 3D Gaussian distributions centered on the mean coordinate position of each alpha carbon in a large structural alignment. The transition probabilities depend on the spread of the neighboring match states and on the number of gaps found in the structural alignment. We also develop methods for aligning query structures against 3D HMMs and scoring the result probabilistically. For 1D HMMs these tasks are accomplished by the Viterbi and forward algorithms. However, these will not work in unmodified form for the 3D problem, due to non-local quality of structural alignment, so we develop extensions of these algorithms for the 3D case. Several applications of 3D HMMs for protein structure classification are reported. A good separation of scores for different fold families suggests that the described construct is quite useful for protein structure analysis. CONCLUSION: We have created a rigorous 3D HMM representation for protein structures and implemented a complete set of routines for building 3D HMMs in C and Perl. The code is freely available from http://www.molmovdb.org/geometry/3dHMM, and at this site we also have a simple prototype server to demonstrate the features of the described approach.
Algorithms↗
A map of the interactome network of the metazoan C. elegans.
To initiate studies on how protein-protein interaction (or "interactome") networks relate to multicellular functions, we have mapped a large fraction of the Caenorhabditis elegans interactome network. Starting with a subset of metazoan-specific proteins, more than 4000 interactions were identified from high-throughput, yeast two-hybrid (HT=Y2H) screens. Independent coaffinity purification assays experimentally validated the overall quality of this Y2H data set. Together with already described Y2H interactions and interologs predicted in silico, the current version of the Worm Interactome (WI5) map contains approximately 5500 interactions. Topological and biological features of this interactome network, as well as its integration with phenome and transcriptome data sets, lead to numerous biological hypotheses.
Animals↗
Exploring the range of protein flexibility, from a structural proteomics perspective.
Changes in protein conformation play a vital role in biochemical processes, from biopolymer synthesis to membrane transport. Initial systematizations of protein flexibility, in a database framework, concentrated on the movement of domains and linkers. Movements were described in terms of simple sliding and hinging mechanisms of individual secondary structural elements. Recently, the accelerated pace and sophistication of methods for structural characterization of proteins has allowed high-resolution studies of increasingly complex assemblies and conformational changes. New data emphasize a breadth of possible structural mechanisms, particularly the ability to drastically alter protein architecture and the native flexibility of many structures.
Pliability↗
Major molecular differences between mammalian sexes are involved in drug metabolism and renal function.
Many anatomical differences exist between males and females; these are manifested on a molecular level by different hormonal environments. Although several molecular differences in adult tissues have been identified, a comprehensive investigation of the gene expression differences between males and females has not been performed. We surveyed the expression patterns of 13,977 mouse genes in male and female hypothalamus, kidney, liver, and reproductive tissues. Extensive differential gene expression was observed not only in the reproductive tissues, but also in the kidney and liver. The differentially expressed genes are involved in drug and steroid metabolism, osmotic regulation, or as yet unresolved cellular roles. In contrast, very few molecular differences were observed between the male and female hypothalamus in both mice and humans. We conclude that there are persistent differences in gene expression between adult males and females. These molecular differences have important implications for the physiological differences between males and females.
Animals↗
Large-scale analysis of pseudogenes in the human genome.
Pseudogenes are considered as genomic fossils: disabled copies of functional genes that were once active in the ancient genome. Recently, whole-genome computational approaches have revealed thousands of pseudogenes in the genomes of the human and other eukaryotes. Identification of these pseudogenes can improve the accuracy of gene annotation. It also offers new insight on the evolutionary history and the stability of the genome as a whole.
Amino Acid Sequence↗
Analyzing protein function on a genomic scale: the importance of gold-standard positives and negatives for network prediction.
The concept of 'protein function' is rather 'fuzzy' because it is often based on whimsical terms or contradictory nomenclature. This currently presents a challenge for functional genomics because precise definitions are essential for most computational approaches. Addressing this challenge, the notion of networks between biological entities (including molecular and genetic interaction networks as well as transcriptional regulatory relationships) potentially provides a unifying language suitable for the systematic description of protein function. Predicting the edges in protein networks requires reference sets of examples with known outcome (that is, 'gold standards'). Such reference sets should ideally include positive examples - as is now widely appreciated - but also, equally importantly, negative ones. Moreover, it is necessary to consider the expected relative occurrence of positives and negatives because this affects the misclassification rates of experiments and computational predictions. For instance, a reason why genome-wide, experimental protein-protein interaction networks have high inaccuracies is that the prior probability of finding interactions (positives) rather than non-interacting protein pairs (negatives) in unbiased screens is very small. These problems can be addressed by constructing well-defined sets of non-interacting proteins from subcellular localization data, which allows computing the probability of interactions based on evidence from multiple datasets.
Algorithms↗
Conformational changes associated with protein-protein interactions.
Motions related to protein-protein binding events can be surveyed from the perspective of the Database of Macromolecular Movements. There are a number of alternative conceptual models that describe these events, particularly induced fit and pre-existing equilibrium. There is evidence for both alternatives from recent studies of conformational change. However, there is increasing support for the pre-existing equilibrium model, whereby proteins are found to simultaneously exist in populations of diverse conformations.
Allosteric Regulation↗
Structure and evolution of transcriptional regulatory networks.
The regulatory interactions between transcription factors and their target genes can be conceptualised as a directed graph. At a global level, these regulatory networks display a scale-free topology, indicating the presence of regulatory hubs. At a local level, substructures such as motifs and modules can be discerned in these networks. Despite the general organisational similarity of networks across the phylogenetic spectrum, there are interesting qualitative differences among the network components, such as the transcription factors. Although the DNA-binding domains of the transcription factors encoded by a given organism are drawn from a small set of ancient conserved superfamilies, their relative abundance often shows dramatic variation among different phylogenetic groups. Large portions of these networks appear to have evolved through extensive duplication of transcription factors and targets, often with inheritance of regulatory interactions from the ancestral gene. Interactions are conserved to varying degrees among genomes. Insights from the structure and evolution of these networks can be translated into predictions and used for engineering of the regulatory networks of different organisms.
Amino Acid Motifs↗
Comparative analysis of processed pseudogenes in the mouse and human genomes.
Pseudogenes are important resources in evolutionary and comparative genomics because they provide molecular records of the ancient genes that existed in the genome millions of years ago. We have systematically identified approximately 5000 processed pseudogenes in the mouse genome, and estimated that approximately 60% are lineage specific, created after the mouse and human diverged. In both mouse and human genomes, similar types of genes give rise to many processed pseudogenes. These tend to be housekeeping genes, which are highly expressed in the germ line. Ribosomal-protein genes, in particular, form the largest sub-group. The processed pseudogenes in the mouse occur with a distinctly different chromosomal distribution than LINEs or SINEs - preferentially in GC-poor regions. Finally, the age distribution of mouse-processed pseudogenes closely resembles that of LINEs, in contrast to human, where the age distribution closely follows Alus (SINEs).
Alu Elements↗
Genomic analysis of essentiality within protein networks.
In this article, we introduce the notion of 'marginal essentiality' through combining quantitatively the results from large-scale phenotypic experiments (e.g. growth rate inhibition from knockouts). We find that this quantity relates to many of the topological characteristics of protein-protein interaction networks. In particular, proteins with a greater degree of marginal essentiality tend to be network hubs (i.e. with many interactions) and tend to have a shorter characteristic path length to their neighbors. We extend our network analysis to encompass transcriptional regulatory networks. Although transcription factors with many targets tend to be essential, surprisingly, we find that genes that are regulated by many transcription factors are usually not essential.
Animals↗
Computational analysis of membrane proteins: genomic occurrence, structure prediction and helix interactions.
We review recent computational advances in the study of membrane proteins, focusing on those that have at least one transmembrane helix. Transmembrane protein regions are, in many respects, easier to investigate computationally than experimentally, due to the uniformity of their structure and interactions (e.g. consisting predominately of nearly parallel helices packed together) on one hand and presenting the challenges of solubility on the other. We present the progress made on identifying and classifying membrane proteins into families, predicting their structure from amino-acid sequence patterns (using many different methods), and analyzing their interactions and packing The total result of this work allows us for the first time to begin to think about the membrane protein interactome, the set of all interactions between distinct transmembrane helices in the lipid bilayer.
Computational Biology↗
Computer security in academia-a potential roadblock to distributed annotation of the human genome.
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Academies and Institutes↗