High-throughput SELEX SAGE method for quantitative modeling of transcription-factor binding sites.

The ability to determine the location and relative strength of all transcription-factor binding sites in a genome is important both for a comprehensive understanding of gene regulation and for effective promoter engineering in biotechnological applications. Here we present a bioinformatically driven experimental method to accurately define the DNA-binding sequence specificity of transcription factors. A generalized profile was used as a predictive quantitative model for binding sites, and its parameters were estimated from in vitro-selected ligands using standard hidden Markov model training algorithms. Computer simulations showed that several thousand low- to medium-affinity sequences are required to generate a profile of desired accuracy. To produce data on this scale, we applied high-throughput genomics methods to the biochemical problem addressed here. A method combining systematic evolution of ligands by exponential enrichment (SELEX) and serial analysis of gene expression (SAGE) protocols was coupled to an automated quality-controlled sequence extraction procedure based on Phred quality scores. This allowed the sequencing of a database of more than 10,000 potential DNA ligands for the CTF/NFI transcription factor. The resulting binding-site model defines the sequence specificity of this protein with a high degree of accuracy not achieved earlier and thereby makes it possible to identify previously unknown regulatory sequences in genomic DNA. A covariance analysis of the selected sites revealed non-independent base preferences at different nucleotide positions, providing insight into the binding mechanism.

Experimental analysis and computer prediction of CTF/NFI transcription factor DNA binding sites.

Accurate prediction of transcription factor binding sites is needed to unravel the function and regulation of genes discovered in genome sequencing projects. To evaluate current computer prediction tools, we have begun a systematic study of the sequence-specific DNA-binding of a transcription factor belonging to the CTF/NFI family. Using a systematic collection of rationally designed oligonucleotides combined with an in vitro DNA binding assay, we found that the sequence specificity of this protein cannot be represented by a simple consensus sequence or weight matrix. For instance, CTF/NFI uses a flexible DNA binding mode that allows for variations of the binding site length. From the experimental data, we derived a novel prediction method using a generalised profile as a binding site predictor. Experimental evaluation of the generalised profile indicated that it accurately predicts the binding affinity of the transcription factor to natural or synthetic DNA sequences. Furthermore, the in vitro measured binding affinities of a subset of oligonucleotides were found to correlate with their transcriptional activities in transfected cells. The combined computational-experimental approach exemplified in this work thus resulted in an accurate prediction method for CTF/NFI binding sites potentially functioning as regulatory regions in vivo.

ABS: a database of Annotated regulatory Binding Sites from orthologous promoters

Information about the genomic coordinates and the sequence of experimentally identified transcription factor binding sites is found scattered under a variety of diverse formats. The availability of standard collections of such high-quality data is important to design, evaluate and improve novel computational approaches to identify binding motifs on promoter sequences from related genes. ABS (http://genome.imim.es/datasets/abs2005/index.html) is a public database of known binding sites identified in promoters of orthologous vertebrate genes that have been manually curated from bibliography. We have annotated 650 experimental binding sites from 68 transcription factors and 100 orthologous target genes in human, mouse, rat or chicken genome sequences. Computational predictions and promoter alignment information are also provided for each entry. A simple and easy-to-use web interface facilitates data retrieval allowing different views of the information. In addition, the release 1.0 of ABS includes a customizable generator of artificial datasets based on the known sites contained in the collection and an evaluation tool to aid during the training and the assessment of motif-finding programs.

Adsorption of glyphosate in chilean soils and its relationship with unoccupied phosphate binding sites

The objective of this work was to investigate glyphosate adsorption by soils and its relationship with unoccupied binding sites for phosphate adsorption. Soil samples of three Chilean soils series - Valdivia (Andisol), Clarillo (Inceptisol) and Chicureo (Vertisol) - were incubated with different herbicide concentrations. Glyphosate remaining in solution was determined by adjusting a HPLC method with a UV detector. Experimental maximum adsorption capacity were 15,000, 14,300 and 4,700 mg g¹ for Valdivia, Clarillo, and Chicureo soils, respectively. Linear, Freundlich, and Langmuir models were used to describe glyphosate adsorption. Isotherms describing glyphosate adsorption differed among soils. Maximum adjusted adsorption capacity with the Langmuir model was 231,884, 17,874 and 5,670 mg g-1 for Valdivia, Clarillo, and Chicureo soils, respectively. Glyphosate adsorption on the Valdivia soil showed a linear behavior at the range of concentrations used and none of the adjusted models became asymptotic. The high glyphosate adsorption capacity of the Valdivia soil was probably a result of its high exchangeable Al, extractable Fe, and alophan and imogolite clay type. Adsorption was very much related to phosphate dynamics in the Valdivia soil, which showed the larger unoccupied phosphate binding sites. However relationship between unoccupied phosphate binding sites and glyphosate adsorption in the other two soils (Clarillo and Chicureo) was not clear.

Quantitative modelling of transcription factor binding sites

Abstract One of the most important issues in molecular biology is to understand regulatory mechanisms that control gene expression. Gene expression is often regulated by proteins, called transcription factors which bind to short (5 to 20 base pairs),degenerate segments of DNA. Experimental efforts towards understanding the sequence specificity of transcription factors is laborious and expensive, but can be substantially accelerated with the use of computational predictions. This thesis describes the use of algorithms and resources for transcriptionfactor binding site analysis in addressing quantitative modelling, where probabilitic models are built to represent binding properties of a transcription factor and can be used to find new functional binding sites in genomes. Initially, an open-access database(HTPSELEX) was created, holding high quality binding sequences for two eukaryotic families of transcription factors namely CTF/NF1 and LEFT/TCF. The binding sequences were elucidated using a recently described experimental procedure called HTP-SELEX, that allows generation of large number (> 1000) of binding sites using mass sequencing technology. For each HTP-SELEX experiments we also provide accurate primary experimental information about the protein material used, details of the wet lab protocol, an archive of sequencing trace files, and assembled clone sequences of binding sequences. The database also offers reasonably large SELEX libraries obtained with conventional low-throughput protocols.The database is available at http://wwwisrec.isb-sib.ch/htpselex/ and and ftp://ftp.isrec.isb-sib.ch/pub/databases/htpselex. The Expectation-Maximisation(EM) algorithm is one the frequently used methods to estimate probabilistic models to represent the sequence specificity of transcription factors. We present computer simulations in order to estimate the precision of EM estimated models as a function of data set parameters(like length of initial sequences, number of initial sequences, percentage of nonbinding sequences). We observed a remarkable robustness of the EM algorithm with regard to length of training sequences and the degree of contamination. The HTPSELEX database and the benchmarked results of the EM algorithm formed part of the foundation for the subsequent project, where a statistical framework called hidden Markov model has been developed to represent sequence specificity of the transcription factors CTF/NF1 and LEF1/TCF using the HTP-SELEX experiment data. The hidden Markov model framework is capable of both predicting and classifying CTF/NF1 and LEF1/TCF binding sites. A covariance analysis of the binding sites revealed non-independent base preferences at different nucleotide positions, providing insight into the binding mechanism. We next tested the LEF1/TCF model by computing binding scores for a set of LEF1/TCF binding sequences for which relative affinities were determined experimentally using non-linear regression. The predicted and experimentally determined binding affinities were in good correlation.

Distinct OGT-Binding Sites Promote HCF-1 Cleavage.

Human HCF-1 (also referred to as HCFC-1) is a transcriptional co-regulator that undergoes a complex maturation process involving extensive O-GlcNAcylation and site-specific proteolysis. HCF-1 proteolysis results in two active, noncovalently associated HCF-1N and HCF-1C subunits that regulate distinct phases of the cell-division cycle. HCF-1 O-GlcNAcylation and site-specific proteolysis are both catalyzed by O-GlcNAc transferase (OGT), which thus displays an unusual dual enzymatic activity. OGT cleaves HCF-1 at six highly conserved 26 amino acid repeat sequences called HCF-1PRO repeats. Here we characterize the substrate requirements for OGT cleavage of HCF-1. We show that the HCF-1PRO-repeat cleavage signal possesses particular OGT-binding properties. The glutamate residue at the cleavage site that is intimately involved in the cleavage reaction specifically inhibits association with OGT and its bound cofactor UDP-GlcNAc. Further, we identify a novel OGT-binding sequence nearby the first HCF-1PRO-repeat cleavage signal that enhances cleavage. These results demonstrate that distinct OGT-binding sites in HCF-1 promote proteolysis, and provide novel insights into the mechanism of this unusual protease activity.

Regulatable and modulable background expression control in prokaryotic synthetic circuits by auxiliary repressor binding sites.

Expression control in synthetic genetic circuitry, for example, for construction of sensitive biosensors, is hampered by the lack of DNA parts that maintain ultralow background yet achieve high output upon signal integration by the cells. Here, we demonstrate how placement of auxiliary transcription factor binding sites within a regulatable promoter context can yield an important gain in signal-to-noise output ratios from prokaryotic biosensor circuits. As a proof of principle, we use the arsenite-responsive ArsR repressor protein from Escherichia coli and its cognate operator. Additional ArsR operators placed downstream of its target promoter can act as a transcription roadblock in a distance-dependent manner and reduce background expression of downstream-placed reporter genes. We show that the transcription roadblock functions both in cognate and heterologous promoter contexts. Secondary ArsR operators placed upstream of their promoter can also improve signal-to-noise output while maintaining effector dependency. Importantly, background control can be released through the addition of micromolar concentrations of arsenite. The ArsR-operator system thus provides a flexible system for additional gene expression control, which, given the extreme sensitivity to micrograms per liter effector concentrations, could be applicable in more general contexts.

Binding sites and actions of Tx1, a neurotoxin from the venom of the spider Phoneutria nigriventer, in guinea pig ileum

Tx1, a neurotoxin isolated from the venom of the South American spider Phoneutria nigriventer, produces tail elevation, behavioral excitation and spastic paralysis of the hind limbs after intracerebroventricular injection in mice. Since Tx1 contracts isolated guinea pig ileum, we have investigated the effect of this toxin on acetylcholine release, as well as its binding to myenteric plexus-longitudinal muscle membranes from the guinea pig ileum. [125I]-Tx1 binds specifically and with high affinity (Kd = 0.36 ± 0.02 nM) to a single, non-interacting (nH = 1.1), low capacity (Bmax 1.1 pmol/mg protein) binding site. In competition experiments using several compounds (including ion channel ligands), only PhTx2 and PhTx3 competed with [125I]-Tx1 for specific binding sites (K0.5 apparent = 7.50 x 10-4 g/l and 1.85 x 10-5 g/l, respectively). PhTx2 and PhTx3, fractions from P. nigriventer venom, contain toxins acting on sodium and calcium channels, respectively. However, the neurotoxin PhTx2-6, one of the isoforms found in the PhTx2 pool, did not affect [125I]-Tx1 binding. Tx1 reduced the [3H]-ACh release evoked by the PhTx2 pool by 33%, but did not affect basal or KCl-induced [3H]-ACh release. Based on these results, as well as on the homology of Tx1 with toxins acting on calcium channels (w-Aga IA and IB) and its competition with [125I]-w-Cono GVIA in the central nervous system, we suggest that the target site for Tx1 may be calcium channels.

Studies on ATP-diphosphohydrolase nucleotide-binding sites by intrinsic fluorescence

Potato apyrase, a soluble ATP-diphosphohydrolase, was purified to homogeneity from several clonal varieties of Solanum tuberosum. Depending on the source of the enzyme, differences in kinetic and physicochemical properties have been described, which cannot be explained by the amino acid residues present in the active site. In order to understand the different kinetic behavior of the Pimpernel (ATPase/ADPase = 10) and Desirée (ATPase/ADPase = 1) isoenzymes, the nucleotide-binding site of these apyrases was explored using the intrinsic fluorescence of tryptophan. The intrinsic fluorescence of the two apyrases was slightly different. The maximum emission wavelengths of the Desirée and Pimpernel enzymes were 336 and 340 nm, respectively, suggesting small differences in the microenvironment of Trp residues. The Pimpernel enzyme emitted more fluorescence than the Desirée apyrase at the same concentration although both enzymes have the same number of Trp residues. The binding of the nonhydrolyzable substrate analogs decreased the fluorescence emission of both apyrases, indicating the presence of conformational changes in the neighborhood of Trp residues. Experiments with quenchers of different polarities, such as acrylamide, Cs+ and I- indicated the existence of differences in the nucleotide-binding site, as further shown by quenching experiments in the presence of nonhydrolyzable substrate analogs. Differences in the nucleotide-binding site may explain, at least in part, the kinetic differences of the Pimpernel and Desirée isoapyrases.

The binding site distance test score: a robust method for the assessment of predicted protein binding sites

We propose a novel method for scoring the accuracy of protein binding site predictions – the Binding-site Distance Test (BDT) score. Recently, the Matthews Correlation Coefficient (MCC) has been used to evaluate binding site predictions, both by developers of new methods and by the assessors for the community wide prediction experiment – CASP8. Whilst being a rigorous scoring method, the MCC does not take into account the actual 3D location of the predicted residues from the observed binding site. Thus, an incorrectly predicted site that is nevertheless close to the observed binding site will obtain an identical score to the same number of nonbinding residues predicted at random. The MCC is somewhat affected by the subjectivity of determining observed binding residues and the ambiguity of choosing distance cutoffs. By contrast the BDT method produces continuous scores ranging between 0 and 1, relating to the distance between the predicted and observed residues. Residues predicted close to the binding site will score higher than those more distant, providing a better reflection of the true accuracy of predictions. The CASP8 function predictions were evaluated using both the MCC and BDT methods and the scores were compared. The BDT was found to strongly correlate with the MCC scores whilst also being less susceptible to the subjectivity of defining binding residues. We therefore suggest that this new simple score is a potentially more robust method for future evaluations of protein-ligand binding site predictions.

Interactions of decay-accelerating factor (DAF) with haemagglutinating human enteroviruses: utilizing variation in primate DAF to map virus binding sites

A cellular receptor for the haemagglutinating enteroviruses (HEV), and the protein that mediates haemagglutination, is the membrane complement regulatory protein decay accelerating factor (DAF; CD55). Although primate DAF is highly conserved, significant differences exist to enable cell lines derived from primates to be utilized for the characterization of the DAF binding phenotype of human enteroviruses. Thus, several distinct DAF-binding phenotypes of a selection of HEVs (viz. coxsackievirus A21 and echoviruses 6, 7, 11-13, 29) were identified from binding and infection assays using a panel of primate cells derived from human, orang-utan, African Green monkey and baboon tissues. These studies complement our recent determination of the crystal structure of SCR(34) of human DAF [Williams, P., Chaudhry, Y., Goodfellow, I. G., Billington, J., Powell, R., Spiller, O. B., Evans, D. J. & Lea, S. (2003). J Biol Chem 278, 10691-10696] and have enabled us to better map the regions of DAF with which enteroviruses interact and, in certain cases, predict specific virus-receptor contacts.

In silico identification and characterization of protein-ligand binding sites

Protein–ligand binding site prediction methods aim to predict, from amino acid sequence, protein–ligand interactions, putative ligands, and ligand binding site residues using either sequence information, structural information, or a combination of both. In silico characterization of protein–ligand interactions has become extremely important to help determine a protein’s functionality, as in vivo-based functional elucidation is unable to keep pace with the current growth of sequence databases. Additionally, in vitro biochemical functional elucidation is time-consuming, costly, and may not be feasible for large-scale analysis, such as drug discovery. Thus, in silico prediction of protein–ligand interactions must be utilized to aid in functional elucidation. Here, we briefly discuss protein function prediction, prediction of protein–ligand interactions, the Critical Assessment of Techniques for Protein Structure Prediction (CASP) and the Continuous Automated EvaluatiOn (CAMEO) competitions, along with their role in shaping the field. We also discuss, in detail, our cutting-edge web-server method, FunFOLD for the structurally informed prediction of protein–ligand interactions. Furthermore, we provide a step-by-step guide on using the FunFOLD web server and FunFOLD3 downloadable application, along with some real world examples, where the FunFOLD methods have been used to aid functional elucidation.