An optimized energy potential can predict SH2 domain-peptide interactions

Peptide recognition modules (PRMs) are used throughout biology to mediate protein-protein interactions, and many PRMs are members of large protein domain families. Members of these families are often quite similar to each other, but each domain recognizes a distinct set of peptides, raising the question of how peptide recognition specificity is achieved using similar protein domains. The analysis of individual protein complex structures often gives answers that are not easily applicable to other members of the same PRM family. Bioinformatics-based approaches, one the other hand, may be difficult to interpret physically. Here we integrate structural information with a large, quantitative data set of SH2-peptide interactions to study the physical origin of domain-peptide specificity. We develop an energy model, inspired by protein folding, based on interactions between the amino acid positions in the domain and peptide. We use this model to successfully predict which SH2 domains and peptides interact and uncover the positions in each that are important for specificity. The energy model is general enough that it can be applied to other members of the SH2 family or to new peptides, and the cross-validation results suggest that these energy calculations will be useful for predicting binding interactions. It can also be adapted to study other PRM families, predict optimal peptides for a given SH2 domain, or study other biological interactions, e.g. protein-DNA interactions

Fundamentally different strategies for transcriptional regulation are revealed by analysis of binding motifs

To regulate a particular gene, a transcription factor (TF) needs to bind a specific genome location. How is this genome address specified amid the presence of ~10^6^-10^9^ decoy sites? Our analysis of 319 known TF binding motifs clearly demonstrates that prokaryotes and eukaryotes use strikingly different strategies to target TFs to specific genome locations; eukaryotic TFs exhibit widespread nonfunctional binding and require clustering of sites in regulatory regions for specificity

Using Topology of the Metabolic Network to Predict Viability of Mutant Strains

Background: Understanding the relationships between the structure (topology) and function of biological networks is a central question of systems biology. The idea that topology is a major determinant of systems function has become an attractive and highly-disputed hypothesis. While the structural analysis of interaction networks demonstrates a correlation between the topological properties of a node (protein, gene) in the network and its functional essentiality, the analysis of metabolic networks fails to find such correlations. In contrast, approaches utilizing both the topology and biochemical parameters of metabolic networks, e.g. flux balance analysis (FBA), are more successful in predicting phenotypes of knock-out strains. Results: We reconcile these seemingly conflicting results by showing that the topology of E. coli's metabolic network is, in fact, sufficient to predict the viability of knock-out strains with accuracy comparable to FBA on a large, unbiased dataset of mutants. This surprising result is obtained by introducing a novel topology-based measure of network transport: synthetic accessibility. We also show that other popular topology-based characteristics like node degree, graph diameter, and node usage (betweenness) fail to predict the viability of mutant strains. The success of synthetic accessibility demonstrates its ability to capture the essential properties of the metabolic network, such as the branching of chemical reactions and the directed transport of material from inputs to outputs. Conclusions: Our results (1) strongly support a link between the topology and function of biological networks; (2) in agreement with recent genetic studies, emphasize the minimal role of flux re-routing in providing robustness of mutant strains

Modeling transcriptional networks in Drosophila development at multiple scales

Quantitative models of developmental processes can provide insights at multiple scales. Ultimately, models may be particularly informative for key questions about network level behavior during development such as how does the system respond to environmental perturbation, or operate reliably in different genetic backgrounds? The transcriptional networks that pattern the Drosophila embryo have been the subject of numerous quantitative experimental studies coupled to modeling frameworks in recent years. In this review, we describe three studies that consider these networks at different levels of molecular detail and therefore result in different types of insights. We also discuss other developmental transcriptional networks operating in Drosophila, with the goal of highlighting what additional insights they may provide

Dissecting the sharp response of a canonical developmental enhancer reveals multiple sources of cooperativity.

Developmental enhancers integrate graded concentrations of transcription factors (TFs) to create sharp gene expression boundaries. Here we examine the hunchback P2 (HbP2) enhancer which drives a sharp expression pattern in the Drosophila blastoderm embryo in response to the transcriptional activator Bicoid (Bcd). We systematically interrogate cis and trans factors that influence the shape and position of expression driven by HbP2, and find that the prevailing model, based on pairwise cooperative binding of Bcd to HbP2 is not adequate. We demonstrate that other proteins, such as pioneer factors, Mediator and histone modifiers influence the shape and position of the HbP2 expression pattern. Comparing our results to theory reveals how higher-order cooperativity and energy expenditure impact boundary location and sharpness. Our results emphasize that the bacterial view of transcription regulation, where pairwise interactions between regulatory proteins dominate, must be reexamined in animals, where multiple molecular mechanisms collaborate to shape the gene regulatory function

Analysis of Genetic Variation Indicates DNA Shape Involvement in Purifying Selection.

Noncoding DNA sequences, which play various roles in gene expression and regulation, are under evolutionary pressure. Gene regulation requires specific protein-DNA binding events, and our previous studies showed that both DNA sequence and shape readout are employed by transcription factors (TFs) to achieve DNA binding specificity. By investigating the shape-disrupting properties of single nucleotide polymorphisms (SNPs) in human regulatory regions, we established a link between disruptive local DNA shape changes and loss of specific TF binding. Furthermore, we described cases where disease-associated SNPs may alter TF binding through DNA shape changes. This link led us to hypothesize that local DNA shape within and around TF binding sites is under selection pressure. To verify this hypothesis, we analyzed SNP data derived from 216 natural strains of Drosophila melanogaster. Comparing SNPs located in functional and nonfunctional regions within experimentally validated cis-regulatory modules (CRMs) from D. melanogaster that are active in the blastoderm stage of development, we found that SNPs within functional regions tended to cause smaller DNA shape variations. Furthermore, SNPs with higher minor allele frequency were more likely to result in smaller DNA shape variations. The same analysis based on a large number of SNPs in putative CRMs of the D. melanogaster genome derived from DNase I accessibility data confirmed these observations. Taken together, our results indicate that common SNPs in functional regions tend to maintain DNA shape, whereas shape-disrupting SNPs are more likely to be eliminated through purifying selection

Dissecting Sources of Quantitative Gene Expression Pattern Divergence Between Drosophila Species

The function of a transcriptional circuit is compared in three closely related species of Drosophila. Using quantitative imaging of gene expression, targeted transgenic reporter fly lines, and a computational framework, the sources of their differing expression outputs are identified

Combining guilt-by-association and guilt-by-profiling to predict Saccharomyces cerevisiae gene function

BackgroundLearning the function of genes is a major goal of computational genomics. Methods for inferring gene function have typically fallen into two categories: 'guilt-by-profiling', which exploits correlation between function and other gene characteristics; and 'guilt-by-association', which transfers function from one gene to another via biological relationships.ResultsWe have developed a strategy ('Funckenstein') that performs guilt-by-profiling and guilt-by-association and combines the results. Using a benchmark set of functional categories and input data for protein-coding genes in Saccharomyces cerevisiae, Funckenstein was compared with a previous combined strategy. Subsequently, we applied Funckenstein to 2,455 Gene Ontology terms. In the process, we developed 2,455 guilt-by-profiling classifiers based on 8,848 gene characteristics and 12 functional linkage graphs based on 23 biological relationships.ConclusionFunckenstein outperforms a previous combined strategy using a common benchmark dataset. The combination of 'guilt-by-profiling' and 'guilt-by-association' gave significant improvement over the component classifiers, showing the greatest synergy for the most specific functions. Performance was evaluated by cross-validation and by literature examination of the top-scoring novel predictions. These quantitative predictions should help prioritize experimental study of yeast gene functions

Spatial effects on the speed and reliability of protein-DNA search

Strong experimental and theoretical evidence shows that transcription factors and other specific DNA-binding proteins find their sites using a two-mode search: alternating between 3D diffusion through the cell and 1D sliding along the DNA. We consider the role spatial effects in the mechanism on two different scales. First, we reconcile recent experimental findings by showing that the 3D diffusion of the transcription factor is often local, i.e. the transcription factor lands quite near its dissociation site. Second, we discriminate between two types of searches: global searches and local searches. We show that these searches differ significantly in average search time and the variability of search time. Using experimentally measured parameter values, we also show that 1D and 3D search is not optimally balanced, leading to much larger estimates of search time. Together, these results lead to a number of biological implications including suggestions of how prokaryotes and eukaryotes achieve rapid gene regulation and the relationship between the search mechanism and noise in gene expression.Comment: 16 pages, 4 figure

Using genome-wide measurements for computational prediction of SH2–peptide interactions

Peptide-recognition modules (PRMs) are used throughout biology to mediate protein–protein interactions, and many PRMs are members of large protein domain families. Recent genome-wide measurements describe networks of peptide–PRM interactions. In these networks, very similar PRMs recognize distinct sets of peptides, raising the question of how peptide-recognition specificity is achieved using similar protein domains. The analysis of individual protein complex structures often gives answers that are not easily applicable to other members of the same PRM family. Bioinformatics-based approaches, one the other hand, may be difficult to interpret physically. Here we integrate structural information with a large, quantitative data set of SH2 domain–peptide interactions to study the physical origin of domain–peptide specificity. We develop an energy model, inspired by protein folding, based on interactions between the amino-acid positions in the domain and peptide. We use this model to successfully predict which SH2 domains and peptides interact and uncover the positions in each that are important for specificity. The energy model is general enough that it can be applied to other members of the SH2 family or to new peptides, and the cross-validation results suggest that these energy calculations will be useful for predicting binding interactions. It can also be adapted to study other PRM families, predict optimal peptides for a given SH2 domain, or study other biological interactions, e.g. protein–DNA interactions.National Institutes of Health. National Centers for Biomedical Computing (Informatics for Integrating Biology and the Bedside)National Institutes of Health (U.S.) (grant U54LM008748