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A major mode of signal transduction in bacteria is the two-component system, which involves phosphorylation of an output-generating receiver protein by a signal-sensing histidine kinase. This differs from the more common one-component system--where both signal sensing and output generation are performed by the same protein--in the spatial separation of the two activities and the obligate need for post-translational modification (phosphorylation). Many described two-component systems involve a linear structure where a single kinase phosphorylates a cognate receiver. However, inherently branched network structures are being increasingly discovered, though their prevalence is unknown. Though the simpler one-component systems are more common than two-component systems, some organisms encode a disproportionately high number of the latter; though these organisms are generally described as having 'complex' lifestyles, no systematic description of their signaling networks has been proposed. Finally, the relative contributions of the two modes of signal transduction towards achieving an optimal regulatory cost for growth and survival in an environment remain poorly understood. Here we present a comparative genomics survey of ~165,000 regulatory proteins from ~850 prokaryotic genomes and suggest that organisms with elevated occurrence of two-component systems--which generally belong to phylogenetic classes with relatively poor representation in genomic databases--also code for more complex and branched two-component networks. Such branched signaling might compensate for the apparent paucity in the total number of regulatory proteins these organisms encode. Finally, such interconnected signaling networks might be more common than anticipated, indicating the pressing need for genome-scale experimental studies of signaling networks in many understudied phylogenetic groups of organisms., (This journal is © The Royal Society of Chemistry 2011)

Transcriptomic studies routinely measure expression levels across numerous conditions. These datasets allow identification of genes that are specifically expressed in a small number of conditions. However, there are currently no statistically robust methods for identifying such genes. Here we present SpeCond, a method to detect condition-specific genes that outperforms alternative approaches. We apply the method to a dataset of 32 human tissues to determine 2,673 specifically expressed genes. An implementation of SpeCond is freely available as a Bioconductor package at http://www.bioconductor.org/packages/release/bioc/html/SpeCond.html.

The unique composition and spatial arrangement of RNA-binding proteins (RBPs) on a transcript guide the diverse aspects of post-transcriptional regulation. Therefore, an essential step towards understanding transcript regulation at the molecular level is to gain positional information on the binding sites of RBPs. Protein-RNA interactions can be studied using biochemical methods, but these approaches do not address RNA binding in its native cellular context. Initial attempts to study protein-RNA complexes in their cellular environment employed affinity purification or immunoprecipitation combined with differential display or microarray analysis (RIP-CHIP). These approaches were prone to identifying indirect or non-physiological interactions. In order to increase the specificity and positional resolution, a strategy referred to as CLIP (UV cross-linking and immunoprecipitation) was introduced. CLIP combines UV cross-linking of proteins and RNA molecules with rigorous purification schemes including denaturing polyacrylamide gel electrophoresis. In combination with high-throughput sequencing technologies, CLIP has proven as a powerful tool to study protein-RNA interactions on a genome-wide scale (referred to as HITS-CLIP or CLIP-seq). Recently, PAR-CLIP was introduced that uses photoreactive ribonucleoside analogs for cross-linking. Despite the high specificity of the obtained data, CLIP experiments often generate cDNA libraries of limited sequence complexity. This is partly due to the restricted amount of co-purified RNA and the two inefficient RNA ligation reactions required for library preparation. In addition, primer extension assays indicated that many cDNAs truncate prematurely at the crosslinked nucleotide. Such truncated cDNAs are lost during the standard CLIP library preparation protocol. We recently developed iCLIP (individual-nucleotide resolution CLIP), which captures the truncated cDNAs by replacing one of the inefficient intermolecular RNA ligation steps with a more efficient intramolecular cDNA circularization (Figure 1). Importantly, sequencing the truncated cDNAs provides insights into the position of the cross-link site at nucleotide resolution. We successfully applied iCLIP to study hnRNP C particle organization on a genome-wide scale and assess its role in splicing regulation.

Nucleoid-associated proteins (NAPs) are global regulators of gene expression in Escherichia coli, which affect DNA conformation by bending, wrapping and bridging the DNA. Two of these--H-NS and Fis--bind to specific DNA sequences and structures. Because of their importance to global gene expression, the binding of these NAPs to the DNA was previously investigated on a genome-wide scale using ChIP-chip. However, variation in their binding profiles across the growth phase and the genome-scale nature of their impact on gene expression remain poorly understood. Here, we present a genome-scale investigation of H-NS and Fis binding to the E. coli chromosome using chromatin immunoprecipitation combined with high-throughput sequencing (ChIP-seq). By performing our experiments under multiple time-points during growth in rich media, we show that the binding regions of the two proteins are mutually exclusive under our experimental conditions. H-NS binds to significantly longer tracts of DNA than Fis, consistent with the linear spread of H-NS binding from high- to surrounding lower-affinity sites; the length of binding regions is associated with the degree of transcriptional repression imposed by H-NS. For Fis, a majority of binding events do not lead to differential expression of the proximal gene; however, it has a significant indirect effect on gene expression partly through its effects on the expression of other transcription factors. We propose that direct transcriptional regulation by Fis is associated with the interaction of tandem arrays of Fis molecules to the DNA and possible DNA bending, particularly at operon-upstream regions. Our study serves as a proof-of-principle for the use of ChIP-seq for global DNA-binding proteins in bacteria, which should become significantly more economical and feasible with the development of multiplexing techniques.

Transcriptional regulation is one the most basic mechanisms for controlling gene expression. Over the past few years, much research has been devoted to understanding the interplay between transcription factors, histone modifications and associated enzymes required to achieve this control. However, it is becoming increasingly apparent that the three-dimensional conformation of chromatin in the interphase nucleus also plays a critical role in regulating transcription. Chromatin localisation in the nucleus is highly organised, and early studies described strong interactions between chromatin and sub-nuclear components. Single-gene studies have shed light on how chromosomal architecture affects gene expression. Lately, this has been complemented by whole-genome studies that have determined the global chromatin conformation of living cells in interphase. These studies have greatly expanded our understanding of nuclear architecture and its interplay with different physiological processes. Despite these advances, however, most of the mechanisms used to impose the three-dimensional chromatin structure remain unknown. Here, we summarise the different levels of chromatin organisation in the nucleus and discuss current efforts into characterising the mechanisms that govern it.

Transcriptional initiation is arguably the most important control point for gene expression. It is regulated by a combination of factors, including DNA sequence and its three-dimensional topology, proteins and small molecules. In this chapter, we focus on the trans-acting factors of bacterial regulation. Initiation begins with the recruitment of the RNA polymerase holoenzyme to a specific locus upstream of the gene known as its promoter. The sigma factor, which is a component of the holoenzyme, provides the most fundamental mechanisms for orchestrating broad changes in gene expression state. It is responsible for promoter recognition as well as recruiting the holoenzyme to the promoter. Distinct sigma factors compete with for binding to a common pool of RNA polymerases, thus achieving condition-dependent differential expression. Another important class of bacterial regulators is transcription factors, which activate or repress transcription of target genes typically in response to an environmental or cellular trigger. These factors may be global or local depending on the number of genes and range of cellular functions that they target. The activities of both global and local transcription factors may be regulated either at a post-transcriptional level via signal-sensing protein domains or at the level of their own expression. In addition to modulating polymerase recruitment to promoters, several global factors are considered as "nucleoid-associated proteins" that impose structural constraints on the chromosome by altering the conformation of the bound DNA, thus influencing other processes involving DNA such as replication and recombination. This chapter concludes with a discussion of how regulatory interactions between transcription factors and their target genes can be represented as a network.

The identification of susceptibility genes for human disease is a major goal of current biomedical research. Both sequence and structural variation have emerged as major genetic sources of phenotypic variability and growing evidence points to copy number variation as a particularly important source of susceptibility for disease. Here we propose and validate a strategy to identify genes in which changes in dosage alter susceptibility to disease-relevant phenotypes in the mouse. Our approach relies on sensitized phenotypic screening of megabase-sized chromosomal deletion and deficiency lines carrying altered copy numbers of ∼30 linked genes. This approach offers several advantages as a method to systematically identify genes involved in disease susceptibility. To examine the feasibility of such a screen, we performed sensitized phenotyping in five therapeutic areas (metabolic syndrome, immune dysfunction, atherosclerosis, cancer and behaviour) of a 0.8 Mb reciprocal chromosomal duplication and deficiency on chromosome 11 containing 27 genes. Gene dosage in the region significantly affected risk for high-fat diet-induced metabolic syndrome, antigen-induced immune hypersensitivity, ApoE-induced atherosclerosis, and home cage activity. Follow up studies on individual gene knockouts for two candidates in the region showed that copy number variation in Stat5 was responsible for the phenotypic variation in antigen-induced immune hypersensitivity and metabolic syndrome. These data demonstrate the power of sensitized phenotypic screening of segmental aneuploidy lines to identify disease susceptibility genes., (Copyright © 2011 EMBO Molecular Medicine.)

The regulation of alternative splicing involves interactions between RNA-binding proteins and pre-mRNA positions close to the splice sites. T-cell intracellular antigen 1 (TIA1) and TIA1-like 1 (TIAL1) locally enhance exon inclusion by recruiting U1 snRNP to 5' splice sites. However, effects of TIA proteins on splicing of distal exons have not yet been explored. We used UV-crosslinking and immunoprecipitation (iCLIP) to find that TIA1 and TIAL1 bind at the same positions on human RNAs. Binding downstream of 5' splice sites was used to predict the effects of TIA proteins in enhancing inclusion of proximal exons and silencing inclusion of distal exons. The predictions were validated in an unbiased manner using splice-junction microarrays, RT-PCR, and minigene constructs, which showed that TIA proteins maintain splicing fidelity and regulate alternative splicing by binding exclusively downstream of 5' splice sites. Surprisingly, TIA binding at 5' splice sites silenced distal cassette and variable-length exons without binding in proximity to the regulated alternative 3' splice sites. Using transcriptome-wide high-resolution mapping of TIA-RNA interactions we evaluated the distal splicing effects of TIA proteins. These data are consistent with a model where TIA proteins shorten the time available for definition of an alternative exon by enhancing recognition of the preceding 5' splice site. Thus, our findings indicate that changes in splicing kinetics could mediate the distal regulation of alternative splicing., Competing Interests: The authors have declared that no competing interests exist.

Cyclic-di-GMP is a bacterial second messenger that controls the switch between motile and sessile states. It is synthesized by proteins containing the enzymatic GGDEF domain and degraded by the EAL domain. Many bacterial genomes encode several copies of proteins containing these domains, raising questions on how the activities of parallel c-di-GMP signalling systems are segregated to avoid potentially deleterious cross-talk. Moreover, many 'hybrid' proteins contain both GGDEF and EAL domains; the relationship between the two apparently opposing enzymatic activities has been termed a 'biochemical conundrum'. Here, we present a computational analysis of 11 248 GGDEF- and EAL-containing proteins in 867 prokaryotic genomes to address these two outstanding questions. Over half of these proteins contain a signal for cell-surface localization, and a majority accommodate a signal-sensing partner domain; these indicate widespread prevalence of post-translational regulation that may segregate the activities of proteins that are co-expressed. By examining the conservation of amino acid residues in the GGDEF and EAL catalytic sites, we show that there are predominantly two types of hybrid proteins. In the first, both sites are intact; an additional regulatory partner domain, present in most of these proteins, might determine the balance between the two enzymatic activities. In the second type, only the EAL catalytic site is intact; these--unlike EAL-only proteins--generally contain a signal-sensing partner domain, suggesting distinct modes of regulation for EAL activity under different sequence contexts. Finally, we discuss the role of proteins that have lost GGDEF and EAL catalytic sites as potential c-di-GMP-binding effectors. Our findings will serve as a genomic framework for interpreting ongoing molecular investigations of these proteins.

Transcription factor (TF) perturbation experiments give valuable insights into gene regulation. Genome-scale evidence from microarray measurements may be used to identify regulatory interactions between TFs and targets. Recently, Hu and colleagues published a comprehensive study covering 269 TF knockout mutants for the yeast Saccharomyces cerevisiae. However, the information that can be extracted from this valuable dataset is limited by the method employed to process the microarray data. Here, we present a reanalysis of the original data using improved statistical techniques freely available from the BioConductor project. We identify over 100,000 differentially expressed genes-nine times the total reported by Hu et al. We validate the biological significance of these genes by assessing their functions, the occurrence of upstream TF-binding sites, and the prevalence of protein-protein interactions. The reanalysed dataset outperforms the original across all measures, indicating that we have uncovered a vastly expanded list of relevant targets. In summary, this work presents a high-quality reanalysis that maximizes the information contained in the Hu et al. compendium. The dataset is available from ArrayExpress (accession: E-MTAB-109) and it will be invaluable to any scientist interested in the yeast transcriptional regulatory system.

Here, we report the biochemical characterization of the nonspecific lethal (NSL) complex (NSL1, NSL2, NSL3, MCRS2, MBD-R2, and WDS) that associates with the histone acetyltransferase MOF in both Drosophila and mammals. Chromatin immunoprecipitation-Seq analysis revealed association of NSL1 and MCRS2 with the promoter regions of more than 4000 target genes, 70% of these being actively transcribed. This binding is functional, as depletion of MCRS2, MBD-R2, and NSL3 severely affects gene expression genome wide. The NSL complex members bind to their target promoters independently of MOF. However, depletion of MCRS2 affects MOF recruitment to promoters. NSL complex stability is interdependent and relies mainly on the presence of NSL1 and MCRS2. Tethering of NSL3 to a heterologous promoter leads to robust transcription activation and is sensitive to the levels of NSL1, MCRS2, and MOF. Taken together, we conclude that the NSL complex acts as a major transcriptional regulator in Drosophila., (Copyright (c) 2010 Elsevier Inc. All rights reserved.)

The genetic code-the binding specificity of all transfer-RNAs--defines how protein primary structure is determined by DNA sequence. DNA also dictates when and where proteins are expressed, and this information is encoded in a pattern of specific sequence motifs that are recognized by transcription factors. However, the DNA-binding specificity is only known for a small fraction of the approximately 1400 human transcription factors (TFs). We describe here a high-throughput method for analyzing transcription factor binding specificity that is based on systematic evolution of ligands by exponential enrichment (SELEX) and massively parallel sequencing. The method is optimized for analysis of large numbers of TFs in parallel through the use of affinity-tagged proteins, barcoded selection oligonucleotides, and multiplexed sequencing. Data are analyzed by a new bioinformatic platform that uses the hundreds of thousands of sequencing reads obtained to control the quality of the experiments and to generate binding motifs for the TFs. The described technology allows higher throughput and identification of much longer binding profiles than current microarray-based methods. In addition, as our method is based on proteins expressed in mammalian cells, it can also be used to characterize DNA-binding preferences of full-length proteins or proteins requiring post-translational modifications. We validate the method by determining binding specificities of 14 different classes of TFs and by confirming the specificities for NFATC1 and RFX3 using ChIP-seq. Our results reveal unexpected dimeric modes of binding for several factors that were thought to preferentially bind DNA as monomers.

Transcriptional regulation is one of the most important processes for modulating gene expression. Though much of this control is attributed to transcription factors, histones, and associated enzymes, it is increasingly apparent that the spatial organization of chromosomes within the nucleus has a profound effect on transcriptional activity. Studies in yeast indicate that the nuclear pore complex might promote transcription by recruiting chromatin to the nuclear periphery. In higher eukaryotes, however, it is not known whether such regulation has global significance. Here we establish nucleoporins as a major class of global regulators for gene expression in Drosophila melanogaster. Using chromatin-immunoprecipitation combined with microarray hybridisation, we show that Nup153 and Megator (Mtor) bind to 25% of the genome in continuous domains extending 10 kb to 500 kb. These Nucleoporin-Associated Regions (NARs) are dominated by markers for active transcription, including high RNA polymerase II occupancy and histone H4K16 acetylation. RNAi-mediated knock-down of Nup153 alters the expression of approximately 5,700 genes, with a pronounced down-regulatory effect within NARs. We find that nucleoporins play a central role in coordinating dosage compensation-an organism-wide process involving the doubling of expression of the male X chromosome. NARs are enriched on the male X chromosome and occupy 75% of this chromosome. Furthermore, Nup153-depletion abolishes the normal function of the male-specific dosage compensation complex. Finally, by extensive 3D imaging, we demonstrate that NARs contribute to gene expression control irrespective of their sub-nuclear localization. Therefore, we suggest that NAR-binding is used for chromosomal organization that enables gene expression control., Competing Interests: The authors have declared that no competing interests exist.

Much of modern biological research can be organised under unifying concepts such as 'Network Biology' or 'Systems Biology'. These provide frameworks for discussion and evaluation, which is particularly necessary given the large number of interconnected components being measured in the genomic era. Conversely, they embody simplifications and assumptions that place limits on what can be deduced from experimental data. Understanding these constraints is essential not only for scientific interpretation, but also in evaluating new experimental methods and conceptual advances.

Organisms must adapt to make optimal use of the metabolic system in response to environmental changes. In the long-term, this involves evolution of the genomic repertoire of enzymes; in the short-term, transcriptional control ensures that appropriate enzymes are expressed in response to transitory extracellular conditions. Unicellular organisms are particularly susceptible to environmental changes; however, genome-scale impact of these modulatory effects has not been explored so far in bacteria. Here, we integrate genome-scale data to investigate the evolutionary trends and transcriptional control of metabolism in Escherichia coli K12. Globally, the regulatory system is organized in a clear hierarchy of general and specific transcription factors (TFs) that control differing ranges of metabolic functions. Further, catabolic, anabolic, and central metabolic pathways are targeted by distinct combinations of these TFs. Locally, enzymes catalyzing sequential reactions in a metabolic pathway are co-regulated by the same TFs. Regulation is more complex at junctions: General TFs control the overall activity of all connecting reactions, whereas specific TFs control individual enzymes. Divergent junctions play a special role in delineating metabolic pathways and decouple the regulation of incoming and outgoing reactions. We find little evidence for differential usage of isozymes, which are generally co-expressed in similar conditions, and thus are likely to reinforce the metabolic system through redundancy. Finally, we show that enzymes controlled by the same TFs have a strong tendency to co-evolve, suggesting a significant constraint to maintain similar regulatory regimes during evolution. Catabolic, anabolic, and central energy pathways evolve differently, emphasizing the role of the environment in shaping the metabolic system. Many of the observations also occur in yeast, and our findings may apply across large evolutionary distances.

The gram-negative enteric bacterium Proteus mirabilis is a frequent cause of urinary tract infections in individuals with long-term indwelling catheters or with complicated urinary tracts (e.g., due to spinal cord injury or anatomic abnormality). P. mirabilis bacteriuria may lead to acute pyelonephritis, fever, and bacteremia. Most notoriously, this pathogen uses urease to catalyze the formation of kidney and bladder stones or to encrust or obstruct indwelling urinary catheters. Here we report the complete genome sequence of P. mirabilis HI4320, a representative strain cultured in our laboratory from the urine of a nursing home patient with a long-term (> or =30 days) indwelling urinary catheter. The genome is 4.063 Mb long and has a G+C content of 38.88%. There is a single plasmid consisting of 36,289 nucleotides. Annotation of the genome identified 3,685 coding sequences and seven rRNA loci. Analysis of the sequence confirmed the presence of previously identified virulence determinants, as well as a contiguous 54-kb flagellar regulon and 17 types of fimbriae. Genes encoding a potential type III secretion system were identified on a low-G+C-content genomic island containing 24 intact genes that appear to encode all components necessary to assemble a type III secretion system needle complex. In addition, the P. mirabilis HI4320 genome possesses four tandem copies of the zapE metalloprotease gene, genes encoding six putative autotransporters, an extension of the atf fimbrial operon to six genes, including an mrpJ homolog, and genes encoding at least five iron uptake mechanisms, two potential type IV secretion systems, and 16 two-component regulators.

Dosage compensation, mediated by the MSL complex, regulates X-chromosomal gene expression in Drosophila. Here we report that the histone H4 lysine 16 (H4K16) specific histone acetyltransferase MOF displays differential binding behavior depending on whether the target gene is located on the X chromosome versus the autosomes. More specifically, on the male X chromosome, where MSL1 and MSL3 are preferentially associated with the 3' end of dosage compensated genes, MOF displays a bimodal distribution binding to promoters and the 3' ends of genes. In contrast, on MSL1/MSL3 independent X-linked genes and autosomal genes in males and females, MOF binds primarily to promoters. Binding of MOF to autosomes is functional, as H4K16 acetylation and the transcription levels of a number of genes are affected upon MOF depletion. Therefore, MOF is not only involved in the onset of dosage compensation, but also acts as a regulator of gene expression in the Drosophila genome.

Escherichia coli strains are the major cause of urinary tract infections in humans. Such strains can be divided into virulent, UPEC strains causing symptomatic infections, and asymptomatic, commensal-like strains causing asymptomatic bacteriuria, ABU. The best-characterized ABU strain is strain 83972. Global gene expression profiling of strain 83972 has been carried out under seven different sets of environmental conditions ranging from laboratory minimal medium to human bladders. The data reveal highly specific gene expression responses to different conditions. A number of potential fitness factors for the human urinary tract could be identified. Also, presence/ absence data of the gene expression was used as an adaptive genomics tool to model the gene pool of 83972 using primarily UPEC strain CFT073 as a scaffold. In our analysis, 96% of the transcripts filtered present in strain 83972 can be found in CFT073, and genes on six of the seven pathogenicity islands were expressed in 83972. Despite the very different patient symptom profiles, the two strains seem to be very similar. Genes expressed in CFT073 but not in 83972 were identified and can be considered as virulence factor candidates. Strain 83972 is a deconstructed pathogen rather than a commensal strain that has acquired fitness properties.

Transcriptional regulatory systems play a central role in coordinating bacterial responses to diverse stimuli. These systems can be studied in progressive stages: from input signals to the final output. At the input stage, transcription factors (TFs) can be classified by their activation from endogenous or exogenous stimuli; in Escherichia coli, up to three-quarters of regulators are estimated to respond directly to extracellular signals through phosphorylation and small-molecule binding. At the processing stage, the signals feed into a densely connected network. The endogenous regulators form most of the connections between TFs and, by dynamically rewiring interactions, they coordinate and distribute the appropriate responses for distinct cellular conditions. At the output stage, network motifs (which are specific patterns of interconnections within a small group of TFs and target genes) determine the precise temporal programme of gene expression changes. Eventually, these components of the regulatory system could be assembled to describe complex bacterial behaviour at the level of whole organisms.

Studies on various model systems have shown that a relatively small number of transcription factors can set up strikingly complex spatial and temporal patterns of gene expression. This is achieved mainly by means of combinatorial or differential gene regulation, i.e. regulation of a gene by two or more transcription factors simultaneously or under different conditions. While a number of specific molecular details of the mechanisms of combinatorial regulation have emerged, our understanding of the general principles of combinatorial regulation on a genomic scale is still limited. In this work, we approach this problem by using the largest assembled transcriptional regulatory network for yeast. A specific network transformation procedure was used to obtain the co-regulatory network describing the set of all significant associations among transcription factors in regulating common target genes. Analysis of the global properties of the co-regulatory network suggested the presence of two classes of regulatory hubs: (i) those that make many co-regulatory associations, thus serving as integrators of disparate cellular processes; and (ii) those that make few co-regulatory associations, and thereby specifically regulate one or a few major cellular processes. Investigation of the local structure of the co-regulatory network revealed a significantly higher than expected modular organization, which might have emerged as a result of selection by functional constraints. These constraints probably emerge from the need for extensive modular backup and the requirement to integrate transcriptional inputs of multiple distinct functional systems. We then explored the transcriptional control of three major regulatory systems (ubiquitin signaling, protein kinase and transcriptional regulation systems) to understand specific aspects of their upstream control. As a result, we observed that ubiquitin E3 ligases are regulated primarily by unique transcription factors, whereas E1 and E2 enzymes share common transcription factors to a much greater extent. This suggested that the deployment of E3s unique to specific functional contexts may be mediated significantly at the transcriptional level. Likewise, we were able to uncover evidence for much higher upstream transcription control of transcription factors themselves, in comparison to components of other regulatory systems. We believe that the results presented here might provide a framework for testing the role of co-regulatory associations in eukaryotic transcriptional control.

A credit to microarray technology is its broad application. Two experiments--the tiling microarray experiment and the protein microarray experiment--are exemplars of the versatility of the microarrays. With the technology's expanding list of uses, the corresponding bioinformatics must evolve in step. There currently exists a rich literature developing statistical techniques for analyzing traditional gene-centric DNA microarrays, so the first challenge in analyzing the advanced technologies is to identify which of the existing statistical protocols are relevant and where and when revised methods are needed. A second challenge is making these often very technical ideas accessible to the broader microarray community. The aim of this chapter is to present some of the most widely used statistical techniques for normalizing and scoring traditional microarray data and indicate their potential utility for analyzing the newer protein and tiling microarray experiments. In so doing, we will assume little or no prior training in statistics of the reader. Areas covered include background correction, intensity normalization, spatial normalization, and the testing of statistical significance.

Microarrays have become indispensable tools for studying the gene expression of particular organisms on a genomic scale. However, despite its widespread use, there are several draw-backs to the current technology. First, it requires prior knowledge of the DNA sequence encoded in the organism of interest, and second, chips must be designed specifically for each genome, greatly increasing the initial cost incurred in manufacturing the arrays.

Network analysis has been applied widely, providing a unifying language to describe disparate systems ranging from social interactions to power grids. It has recently been used in molecular biology, but so far the resulting networks have only been analysed statically. Here we present the dynamics of a biological network on a genomic scale, by integrating transcriptional regulatory information and gene-expression data for multiple conditions in Saccharomyces cerevisiae. We develop an approach for the statistical analysis of network dynamics, called SANDY, combining well-known global topological measures, local motifs and newly derived statistics. We uncover large changes in underlying network architecture that are unexpected given current viewpoints and random simulations. In response to diverse stimuli, transcription factors alter their interactions to varying degrees, thereby rewiring the network. A few transcription factors serve as permanent hubs, but most act transiently only during certain conditions. By studying sub-network structures, we show that environmental responses facilitate fast signal propagation (for example, with short regulatory cascades), whereas the cell cycle and sporulation direct temporal progression through multiple stages (for example, with highly inter-connected transcription factors). Indeed, to drive the latter processes forward, phase-specific transcription factors inter-regulate serially, and ubiquitously active transcription factors layer above them in a two-tiered hierarchy. We anticipate that many of the concepts presented here--particularly the large-scale topological changes and hub transience--will apply to other biological networks, including complex sub-systems in higher eukaryotes.

Proteins function mainly through interactions, especially with DNA and other proteins. While some large-scale interaction networks are now available for a number of model organisms, their experimental generation remains difficult. Consequently, interolog mapping--the transfer of interaction annotation from one organism to another using comparative genomics--is of significant value. Here we quantitatively assess the degree to which interologs can be reliably transferred between species as a function of the sequence similarity of the corresponding interacting proteins. Using interaction information from Saccharomyces cerevisiae, Caenorhabditis elegans, Drosophila melanogaster, and Helicobacter pylori, we find that protein-protein interactions can be transferred when a pair of proteins has a joint sequence identity >80% or a joint E-value <10(-70). (These "joint" quantities are the geometric means of the identities or E-values for the two pairs of interacting proteins.) We generalize our interolog analysis to protein-DNA binding, finding such interactions are conserved at specific thresholds between 30% and 60% sequence identity depending on the protein family. Furthermore, we introduce the concept of a "regulog"--a conserved regulatory relationship between proteins across different species. We map interologs and regulogs from yeast to a number of genomes with limited experimental annotation (e.g., Arabidopsis thaliana) and make these available through an online database at http://interolog.gersteinlab.org. Specifically, we are able to transfer approximately 90,000 potential protein-protein interactions to the worm. We test a number of these in two-hybrid experiments and are able to verify 45 overlaps, which we show to be statistically significant., (Copyright 2004 Cold Spring Harbor Laboratory Press)

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.

The cyclic AMP-responsive element-binding protein (CREB) is an important transcription factor that can be activated by hormonal stimulation and regulates neuronal function and development. An unbiased, global analysis of where CREB binds has not been performed. We have mapped for the first time the binding distribution of CREB along an entire human chromosome. Chromatin immunoprecipitation of CREB-associated DNA and subsequent hybridization of the associated DNA to a genomic DNA microarray containing all of the nonrepetitive DNA of human chromosome 22 revealed 215 binding sites corresponding to 192 different loci and 100 annotated potential gene targets. We found binding near or within many genes involved in signal transduction and neuronal function. We also found that only a small fraction of CREB binding sites lay near well-defined 5' ends of genes; the majority of sites were found elsewhere, including introns and unannotated regions. Several of the latter lay near novel unannotated transcriptionally active regions. Few CREB targets were found near full-length cyclic AMP response element sites; the majority contained shorter versions or close matches to this sequence. Several of the CREB targets were altered in their expression by treatment with forskolin; interestingly, both induced and repressed genes were found. Our results provide novel molecular insights into how CREB mediates its functions in humans.

We have mapped the chromosomal binding site distribution of a transcription factor in human cells. The NF-kappaB family of transcription factors plays an essential role in regulating the induction of genes involved in several physiological processes, including apoptosis, immunity, and inflammation. The binding sites of the NF-kappaB family member p65 were determined by using chromatin immunoprecipitation and a genomic microarray of human chromosome 22 DNA. Sites of binding were observed along the entire chromosome in both coding and noncoding regions, with an enrichment at the 5' end of genes. Strikingly, a significant proportion of binding was seen in intronic regions, demonstrating that transcription factor binding is not restricted to promoter regions. NF-kappaB binding was also found at genes whose expression was regulated by tumor necrosis factor alpha, a known inducer of NF-kappaB-dependent gene expression, as well as adjacent to genes whose expression is not affected by tumor necrosis factor alpha. Many of these latter genes are either known to be activated by NF-kappaB under other conditions or are consistent with NF-kappaB's role in the immune and apoptotic responses. Our results suggest that binding is not restricted to promoter regions and that NF-kappaB binding occurs at a significant number of genes whose expression is not altered, thereby suggesting that binding alone is not sufficient for gene activation.

Motivation: Defining regulatory networks, linking transcription factors (TFs) to their targets, is a central problem in post-genomic biology. One might imagine one could readily determine these networks through inspection of gene expression data. However, the relationship between the expression timecourse of a transcription factor and its target is not obvious (e.g. simple correlation over the timecourse), and current analysis methods, such as hierarchical clustering, have not been very successful in deciphering them., Results: Here we introduce an approach based on support vector machines (SVMs) to predict the targets of a transcription factor by identifying subtle relationships between their expression profiles. In particular, we used SVMs to predict the regulatory targets for 36 transcription factors in the Saccharomyces cerevisiae genome based on the microarray expression data from many different physiological conditions. We trained and tested our SVM on a data set constructed to include a significant number of both positive and negative examples, directly addressing data imbalance issues. This was non-trivial given that most of the known experimental information is only for positives. Overall, we found that 63% of our TF-target relationships were confirmed through cross-validation. We further assessed the performance of our regulatory network identifications by comparing them with the results from two recent genome-wide ChIP-chip experiments. Overall, we find the agreement between our results and these experiments is comparable to the agreement (albeit low) between the two experiments. We find that this network has a delocalized structure with respect to chromosomal positioning, with a given transcription factor having targets spread fairly uniformly across the genome., Availability: The overall network of the relationships is available on the web at http://bioinfo.mbb.yale.edu/expression/echipchip

From merging several data sources, we created an extensive map of the transcriptional regulatory network in Saccharomyces cerevisiae, comprising 7419 interactions connecting 180 transcription factors (TFs) with their target genes. We integrated this network with gene-expression data, relating the expression profiles of TFs and target genes. We found that genes targeted by the same TF tend to be co-expressed, with the degree of co-expression increasing if genes share more than one TF. Moreover, shared targets of a TF tend to have similar cellular functions. By contrast, the expression relationships between the TFs and their targets are much more complicated, often exhibiting time-shifted or inverted behavior. Further information is available at http://bioinfo.mbb.yale.edu/regulation/TIG/

DNA microarrays are widely used in biological research; by analyzing differential hybridization on a single microarray slide, one can detect changes in mRNA expression levels, increases in DNA copy numbers and the location of transcription factor binding sites on a genomic scale. Having performed the experiments, the major challenge is to process large, noisy datasets in order to identify the specific array elements that are significantly differentially hybridized. This normally requires aggregating different, often incompatible programs into a multi-step pipeline. Here we present ExpressYourself, a fully integrated platform for processing microarray data. In completely automated fashion, it will correct the background array signal, normalize the Cy5 and Cy3 signals, score levels of differential hybridization, combine the results of replicate experiments, filter problematic regions of the array and assess the quality of individual and replicate experiments. ExpressYourself is designed with a highly modular architecture so various types of microarray analysis algorithms can readily be incorporated as they are developed; for example, the system currently implements several normalization methods, including those that simultaneously consider signal intensity and slide location. The processed data are presented using a web-based graphical interface to facilitate comparison with the original images of the array slides. In particular, Express Yourself is able to regenerate images of the original microarray after applying various steps of processing, which greatly facilities identification of position-specific artifacts. The program is freely available for use at http://bioinfo.mbb.yale.edu/expressyourself.

A DNA microarray representing nearly all of the unique sequences of human Chromosome 22 was constructed and used to measure global-transcriptional activity in placental poly(A)(+) RNA. We found that many of the known, related and predicted genes are expressed. More importantly, our study reveals twice as many transcribed bases as have been reported previously. Many of the newly discovered expressed fragments were verified by RNA blot analysis and a novel technique called differential hybridization mapping (DHM). Interestingly, a significant fraction of these novel fragments are expressed antisense to previously annotated introns. The coding potential of these novel expressed regions is supported by their sequence conservation in the mouse genome. This study has greatly increased our understanding of the biological information encoded on a human chromosome. To facilitate the dissemination of these results to the scientific community, we have developed a comprehensive Web resource to present the findings of this study and other features of human Chromosome 22 at http://array.mbb.yale.edu/chr22.

In the yeast Saccharomyces cerevisiae, SBF (Swi4-Swi6 cell cycle box binding factor) and MBF (MluI binding factor) are the major transcription factors regulating the START of the cell cycle, a time just before DNA replication, bud growth initiation, and spindle pole body (SPB) duplication. These two factors bind to the promoters of 235 genes, but bind less than a quarter of the promoters upstream of genes with peak transcript levels at the G1 phase of the cell cycle. Several functional categories, which are known to be crucial for G1/S events, such as SPB duplication/migration and DNA synthesis, are under-represented in the list of SBF and MBF gene targets. SBF binds the promoters of several other transcription factors, including HCM1, PLM2, POG1, TOS4, TOS8, TYE7, YAP5, YHP1, and YOX1. Here, we demonstrate that these factors are targets of SBF using an independent assay. To further elucidate the transcriptional circuitry that regulates the G1-to-S-phase progression, these factors were epitope-tagged and their binding targets were identified by chIp-chip analysis. These factors bind the promoters of genes with roles in G1/S events including DNA replication, bud growth, and spindle pole complex formation, as well as the general activities of mitochondrial function, transcription, and protein synthesis. Although functional overlap exists between these factors and MBF and SBF, each of these factors has distinct functional roles. Most of these factors bind the promoters of other transcription factors known to be cell cycle regulated or known to be important for cell cycle progression and differentiation processes indicating that a complex network of transcription factors coordinates the diverse activities that initiate a new cell cycle.

We investigate the conservation of amino acid residue sequences in 21 DNA-binding protein families and study the effects that mutations have on DNA-sequence recognition. The observations are best understood by assigning each protein family to one of three classes: (i) non-specific, where binding is independent of DNA sequence; (ii) highly specific, where binding is specific and all members of the family target the same DNA sequence; and (iii) multi-specific, where binding is also specific, but individual family members target different DNA sequences. Overall, protein residues in contact with the DNA are better conserved than the rest of the protein surface, but there is a complex underlying trend of conservation for individual residue positions. Amino acid residues that interact with the DNA backbone are well conserved across all protein families and provide a core of stabilising contacts for homologous protein-DNA complexes. In contrast, amino acid residues that interact with DNA bases have variable levels of conservation depending on the family classification. In non-specific families, base-contacting residues are well conserved and interactions are always found in the minor groove where there is little discrimination between base types. In highly specific families, base-contacting residues are highly conserved and allow member proteins to recognise the same target sequence. In multi-specific families, base-contacting residues undergo frequent mutations and enable different proteins to recognise distinct target sequences. Finally, we report that interactions with bases in the target sequence often follow (though not always) a universal code of amino acid-base recognition and the effects of amino acid mutations can be most easily understood for these interactions.

Background: The sequencing of genomes provides us with an inventory of the 'molecular parts' in nature, such as protein families and folds, and their functions in living organisms. Through the analysis of such inventories, it has been shown that different genomes have very different usage of parts; for example, the common folds in the worm are very different from those in Escherichia coli., Results: Despite these differences, we find that the genomic occurrence of generalized parts follows a well-known mathematical framework called the power law, with a few parts occurring many times and most occurring only a few times. This observation is true in a wide variety of genomic contexts. Earlier studies found power laws in a few specific cases, such as the occurrence of protein families. Here, we find many further cases of power-law behavior, for example in the occurrence of pseudogenes and in levels of gene expression. We show comprehensively that this behavior applies across many different genomes, for many different types of parts (DNA words, InterPro families, protein superfamilies and folds, pseudogene families and pseudomotifs), and for the many disparate attributes associated with these parts (their functions, interactions and expression levels)., Conclusions: Power-law behavior provides a concise mathematical description of an important biological feature: the sheer dominance of a few members over the overall population. We present this behavior in a unified framework and propose that all these observations are connected to an underlying DNA duplication process as genomes evolved to their current state.

Based on searches for disabled homologs to known proteins, we have identified a large population of pseudogenes in four sequenced eukaryotic genomes-the worm, yeast, fly and human (chromosomes 21 and 22 only). Each of our nearly 2500 pseudogenes is characterized by one or more disablements mid-domain, such as premature stops and frameshifts. Here, we perform a comprehensive survey of the amino acid and nucleotide composition of these pseudogenes in comparison to that of functional genes and intergenic DNA. We show that pseudogenes invariably have an amino acid composition intermediate between genes and translated intergenic DNA. Although the degree of intermediacy varies among the four organisms, in all cases, it is most evident for amino acid types that differ most in occurrence between genes and intergenic regions. The same intermediacy also applies to codon frequencies, especially in the worm and human. Moreover, the intermediate composition of pseudogenes applies even though the composition of the genes in the four organisms is markedly different, showing a strong correlation with the overall A/T content of the genomic sequence. Pseudogenes can be divided into 'ancient' and 'modern' subsets, based on the level of sequence identity with their closest matching homolog (within the same genome). Modern pseudogenes usually have a much closer sequence composition to genes than ancient pseudogenes. Collectively, our results indicate that the composition of pseudogenes that are under no selective constraints progressively drifts from that of coding DNA towards non-coding DNA. Therefore, we propose that the degree to which pseudogenes approach a random sequence composition may be useful in dating different sets of pseudogenes, as well as to assess the rate at which intergenic DNA accumulates mutations. Our compositional analyses with the interactive viewer are available over the web at http://genecensus.org/pseudogene.

The expression of the beta-like globin genes is intricately regulated by a series of both general and tissue-restricted transcription factors. The hemapoietic lineage-specific transcription factor GATA-1 is important for erythroid differentiation and has been implicated in regulating the expression of the erythroid-specific genes including the genes of the beta-globin locus. In the human erythroleukemic K562 cell line, only one DNA region has been identified previously as a putative site of GATA-1 interaction by in vivo footprinting studies. We mapped GATA-1 binding throughout the beta-globin locus by using chIp-chip analysis of K562 cells. We found that GATA-1 binds in a region encompassing the HS2 core element, as was previously identified, and an additional region of GATA-1 binding upstream of the gammaG gene. This approach will be of general utility for mapping transcription factor binding sites within the beta-globin locus and throughout the genome.

We have developed an initial approach for annotating and surveying pseudogenes in the human genome. We search human genomic DNA for regions that are similar to known protein sequences and contain obvious disablements (i.e., mid-sequence stop codons or frameshifts), while ensuring minimal overlap with annotations of known genes. Pseudogenes can be divided into "processed" and "nonprocessed"; the former are reverse transcribed from mRNA (and therefore have no intron structure), whereas the latter presumably arise from genomic duplications. We annotate putative processed pseudogenes based on whether there is a continuous span of homology that is >70% of the length of the closest matching human protein (i.e., with introns removed), or whether there is evidence of polyadenylation. We have applied our approach to chromosomes 21 and 22, the first parts of the human genome completely sequenced, finding 190 new pseudogene annotations beyond the 264 reported by the sequencing centers. In total, on chromosomes 21 and 22, there are 189 processed pseudogenes, 195 nonprocessed pseudogenes, and, additionally, 70 pseudogenic immunoglobulin gene segments. (Detailed assignments are available at http://bioinfo.mbb.yale.edu/genome/pseudogene or http://genecensus.org/pseudogene.) By extrapolation, we predict that there could be up to approximately 20,000 pseudogenes in the whole human genome, with a little more than half of them processed. We have determined the main populations and clusters of pseudogenes on chromosomes 21 and 22. There are notable excesses of pseudogenes relative to genes near the centromeres of both chromosomes, indicating the existence of pseudogenic "hot-spots" in the genome. We have looked at the distribution of InterPro families and Gene Ontology (GO) functional categories in our pseudogenes. Overall, the families in both processed and nonprocessed pseudogene populations occur according to a similar power-law distribution as that found for the occurrence of gene families, with a few big families and many small ones. The processed population is, in particular, enriched in highly expressed ribosomal-protein sequences (approximately 20%), which appear fairly evenly distributed across the chromosomes. We compared processed pseudogenes of different evolutionary ages, observing a high degree of similarity between "ancient" and "modern" subpopulations. This may be attributable to the consistently high expression of ribosomal proteins over evolutionary time. Finally, we find that chromosome 22 pseudogene population is dominated by immunoglobulin segments, which have a greater rate of disablement per amino acid than the other pseudogene populations and are also substantially more diverged.

Global surveys of genomes measure the usage of essential molecular parts, defined here as protein families, superfamilies or folds, in different organisms. Based on surveys of the first 20 completely sequenced genomes, we observe that the occurrence of these parts follows a power-law distribution. That is, the number of distinct parts (F) with a given genomic occurrence (V) decays as F=aV(-b), with a few parts occurring many times and most occurring infrequently. For a given organism, the distributions of families, superfamilies and folds are nearly identical, and this is reflected in the size of the decay exponent b. Moreover, the exponent varies between different organisms, with those of smaller genomes displaying a steeper decay (i.e. larger b). Clearly, the power law indicates a preference to duplicate genes that encode for molecular parts which are already common. Here, we present a minimal, but biologically meaningful model that accurately describes the observed power law. Although the model performs equally well for all three protein classes, we focus on the occurrence of folds in preference to families and superfamilies. This is because folds are comparatively insensitive to the effects of point mutations that can cause a family member to diverge beyond detectable similarity. In the model, genomes evolve through two basic operations: (i) duplication of existing genes; (ii) net flow of new genes. The flow term is closely related to the exponent b and can accommodate considerable gene loss; however, we demonstrate that the observed data is reproduced best with a net inflow, i.e. with more gene gain than loss. Moreover, we show that prokaryotes have much higher rates of gene acquisition than eukaryotes, probably reflecting lateral transfer. A further natural outcome from our model is an estimation of the fold composition of the initial genome, which potentially relates to the common ancestor for modern organisms. Supplementary material pertaining to this work is available from www.partslist.org/powerlaw., (Copyright 2001 Academic Press.)

With the completion of genome sequences, the current challenge for biology is to determine the functions of all gene products and to understand how they contribute in making an organism viable. For the first time, biological systems can be viewed as being finite, with a limited set of molecular parts. However, the full range of biological processes controlled by these parts is extremely complex. Thus, a key approach in genomic research is to divide the cellular contents into distinct sub-populations, which are often given an "-omic" term. For example, the proteome is the full complement of proteins encoded by the genome, and the secretome is the part of it secreted from the cell. Carrying this further, we suggest the term "translatome" to describe the members of the proteome weighted by their abundance, and the "functome" to describe all the functions carried out by these. Once the individual sub-populations are defined and analyzed, we can then try to reconstruct the full organism by interrelating them, eventually allowing for a full and dynamic view of the cell. All this is, of course, made possible because of the increasing amount of large-scale data resulting from functional genomics experiments. However, there are still many difficulties resulting from the noisiness and complexity of the information. To some degree, these can be overcome through averaging with broad proteomic categories such as those implicit in functional and structural classifications. For illustration, we discuss one example in detail, interrelating transcript and cellular protein populations (transcriptome and translatome). Further information is available at http://bioinfo.mbb.yale.edu/what-is-it.

To assess whether there are universal rules that govern amino acid-base recognition, we investigate hydrogen bonds, van der Waals contacts and water-mediated bonds in 129 protein-DNA complex structures. DNA-backbone interactions are the most numerous, providing stability rather than specificity. For base interactions, there are significant base-amino acid type correlations, which can be rationalised by considering the stereochemistry of protein side chains and the base edges exposed in the DNA structure. Nearly two-thirds of the direct read-out of DNA sequences involves complex networks of hydrogen bonds, which enhance specificity. Two-thirds of all protein-DNA interactions comprise van der Waals contacts, compared to about one-sixth each of hydrogen and water-mediated bonds. This highlights the central importance of these contacts for complex formation, which have previously been relegated to a secondary role. Although common, water-mediated bonds are usually non-specific, acting as space-fillers at the protein-DNA interface. In conclusion, the majority of amino acid-base interactions observed follow general principles that apply across all protein-DNA complexes, although there are individual exceptions. Therefore, we distinguish between interactions whose specificities are 'universal' and 'context-dependent'. An interactive Web-based atlas of side chain-base contacts provides access to the collected data, including analyses and visualisation of the three-dimensional geometry of the interactions.

A detailed computational analysis of 32 protein-RNA complexes is presented. A number of physical and chemical properties of the intermolecular interfaces are calculated and compared with those observed in protein-double-stranded DNA and protein-single-stranded DNA complexes. The interface properties of the protein-RNA complexes reveal the diverse nature of the binding sites. van der Waals contacts played a more prevalent role than hydrogen bond contacts, and preferential binding to guanine and uracil was observed. The positively charged residue, arginine, and the single aromatic residues, phenylalanine and tyrosine, all played key roles in the RNA binding sites. A comparison between protein-RNA and protein-DNA complexes showed that whilst base and backbone contacts (both hydrogen bonding and van der Waals) were observed with equal frequency in the protein-RNA complexes, backbone contacts were more dominant in the protein-DNA complexes. Although similar modes of secondary structure interactions have been observed in RNA and DNA binding proteins, the current analysis emphasises the differences that exist between the two types of nucleic acid binding protein at the atomic contact level.

Background: The recent flood of data from genome sequences and functional genomics has given rise to new field, bioinformatics, which combines elements of biology and computer science., Objectives: Here we propose a definition for this new field and review some of the research that is being pursued, particularly in relation to transcriptional regulatory systems., Methods: Our definition is as follows: Bioinformatics is conceptualizing biology in terms of macromolecules (in the sense of physical-chemistry) and then applying "informatics" techniques (derived from disciplines such as applied maths, computer science, and statistics) to understand and organize the information associated with these molecules, on a large-scale., Results and Conclusions: Analyses in bioinformatics predominantly focus on three types of large datasets available in molecular biology: macromolecular structures, genome sequences, and the results of functional genomics experiments (e.g. expression data). Additional information includes the text of scientific papers and "relationship data" from metabolic pathways, taxonomy trees, and protein-protein interaction networks. Bioinformatics employs a wide range of computational techniques including sequence and structural alignment, database design and data mining, macromolecular geometry, phylogenetic tree construction, prediction of protein structure and function, gene finding, and expression data clustering. The emphasis is on approaches integrating a variety of computational methods and heterogeneous data sources. Finally, bioinformatics is a practical discipline. We survey some representative applications, such as finding homologues, designing drugs, and performing large-scale censuses. Additional information pertinent to the review is available over the web at http://bioinfo.mbb.yale.edu/what-is-it.

On the basis of a structural analysis of 240 protein-DNA complexes contained in the Protein Data Bank (PDB), we have classified the DNA-binding proteins involved into eight different structural/functional groups, which are further classified into 54 structural families. Here we present this classification and review the functions, structures and binding interactions of these protein-DNA complexes.

The determination of protein structures has furthered our understanding of how various proteins perform their functions. With the large number of structures currently available in the PDB, it is necessary to be able to easily study these proteins in detail. Here new software tools are presented which aim to facilitate this analysis; these include the PDBsum WWW site which provides a summary description of all PDB entries, the programs TOPS and NUCPLOT to plot schematic diagrams representing protein topology and DNA-binding interactions, SAS a WWW-based sequence-analysis tool incorporating structural data, and WWW servers for the analysis of protein-protein interfaces and analyses of over 300 haem-binding proteins.

Proteins that bind to DNA are found in all areas of genetic activity within the cell. To help understand how these proteins perform their various functions, it is useful to analyse which residues are involved in binding to the DNA and how they interact with the bases and sugar-phosphate backbone of nucleic acids. Here we describe a program called NUCPLOT which can automatically identify these interactions from the 3D atomic coordinates of the complex from a PDB file and generate a plot that shows all the interactions in a schematic manner. The program produces a PostScript output file representing hydrogen, van der Waals and covalent bonds between the protein and the DNA. The resulting diagram is both clear and simple and allows immediate identification of important interactions within the structure. It also facilitates comparison of binding found in different structures. NUCPLOT is a completely automatic program, which can be used for any protein-DNA complex and will also work for certain protein-RNA structures.

One of the primary factors determining how proteins interact with other molecules is the size of clefts in the protein's surface. In enzymes, for example, the active site is often characterized by a particularly large and deep cleft, while interactions between the molecules of a protein dimer tend to involve approximately planar surfaces. Here we present an analysis of how cleft volumes in proteins relate to their molecular interactions and functions. Three separate datasets are used, representing enzyme-ligand binding, protein-protein dimerization and antibody-antigen complexes. We find that, in single-chain enzymes, the ligand is bound in the largest cleft in over 83% of the proteins. Usually the largest cleft is considerably larger than the others, suggesting that size is a functional requirement. Thus, in many cases, the likely active sites of an enzyme can be identified using purely geometrical criteria alone. In other cases, where there is no predominantly large cleft, chemical interactions are required for pinpointing the correct location. In antibody-antigen interactions the antibody usually presents a large cleft for antigen binding. In contrast, protein-protein interactions in homodimers are characterized by approximately planar interfaces with several clefts involved. However, the largest cleft in each subunit still tends to be involved.