Using electrostatic potentials to predict DNA-binding sites on DNA-binding proteins

A method to detect DNA-binding sites on the surface of a protein structure is important for functional annotation. This work describes the analysis of residue patches on the surface of DNA-binding proteins and the development of a method of predicting DNA-binding sites using a single feature of these surface patches. Surface patches and the DNA-binding sites were initially analysed for accessibility, electrostatic potential, residue propensity, hydrophobicity and residue conservation. From this, it was observed that the DNA-binding sites were, in general, amongst the top 10% of patches with the largest positive electrostatic scores. This knowledge led to the development of a prediction method in which patches of surface residues were selected such that they excluded residues with negative electrostatic scores. This method was used to make predictions for a data set of 56 non-homologous DNA-binding proteins. Correct predictions made for 68% of the data set.     

Linking proteome and genome: how to identify parasite proteins

Parasite genome projects are generating an avalanche of sequence data. If this resource is to be exploited effectively for drug and vaccine design, there is an urgent need to make the link between these DNA sequences and the functional proteins of the parasite, which they encode. Here, we seek to demystify the revolutionary advances in protein identification based on mass spectrometry.     

Predicting DNA-binding sites of proteins from amino acid sequence

Background  

Understanding the molecular details of protein-DNA interactions is critical for deciphering the mechanisms of gene regulation. We present a machine learning approach for the identification of amino acid residues involved in protein-DNA interactions.     

Unstable proteins: how to subject them to chromatographic separations for purification procedures

The chromatographic separation of an unstable protein is often a challenge to the scientist working in the field of life sciences. Especially for the purification of sensitive enzymes, making use of conventional chromatographic techniques is difficult and frequently results in a complete loss of biological activity of the target protein. This report summarizes some general strategies that may help to keep unstable proteins in their native conformation during the rather harsh conditions of a purification procedure. In this context, a recently developed hollow fiber membrane module, suitable for performing on-line dialysis, is introduced and examples of its application to liquid column chromatography are given. Many innovative separation techniques, characterized by dramatic improvements in both performance and separation time, have recently been developed. Since the chromatographic separation of unstable proteins requires the use of modern state-of-the-art equipment and technology, emphasis is given to newly developed separation techniques such as expanded bed adsorption, perfusion chromatography, protein free flow electrophoresis and the use of tentacle gels. In addition, examples of recently published purifications of unstable proteins are discussed with respect to strategies ensuring the preservation of the native protein structure during chromatographic separation.     

Running into problems: how cells cope with replicating damaged DNA

The ability of cells to fully and faithfully replicate DNA is essential for preventing genomic instability and cancer. DNA is susceptible to damage both in resting and in actively replicating cells. Thus, genome duplication necessarily involves replication of damaged DNA. The many mechanism cells use to avoid or overcome the problems of replicating an imperfect DNA template are discussed.     

High-affinity binding sites for oxygenated sterols in rat liver microsomes: possible identity with antiestrogen binding sites

Oxygenated derivatives of cholesterol are known to exhibit a number of biological activities including the inhibition of cholesterol biosynthesis and of cell proliferation, but their mechanism of action remains unclear. Previous studies have identified a cytosolic protein which binds 25-hydroxycholesterol, as well as several other oxysterols, with high affinity, possibly mediating some of their effects. We now report the existence of a high-affinity oxysterol binding site in rat liver microsomes which is distinct from the cytosolic binding protein. Among the oxygenated sterols examined, 5 alpha-cholestan-3 beta-ol-7-one (7-ketocholestanol) had the highest affinity for this microsomal binding site (Kd = 2.7 nM). Using 7-keto[3H]cholestanol as the radioactive ligand, we found that binding of this oxysterol to the microsomal binding site was saturable and reversible and was displaceable by the following oxysterols in descending order of potency: 7-ketocholestanol greater than 6-ketocholestanol greater than 7 beta-hydroxycholesterol = 7-ketocholesterol greater than cholesten-3 beta,5 alpha, 6 beta-triol = 7 alpha-hydroxycholesterol greater than 4-cholesten-3-one. All other sterols studied, including, notably, 25-hydroxycholesterol, had little or no inhibitory effect on 7-keto[3H]cholestanol binding. Additional studies revealed that the microsomal oxysterol binding site was probably identical to the antiestrogen binding site described by other workers. First, saturation analysis and kinetic studies demonstrated that the antiestrogen tamoxifen competed directly with 7-keto[3H]cholestanol for the same binding site. Second, the ability of different oxysterols and antiestrogens to inhibit 7-keto[3H]cholestanol binding to the microsomal binding site paralleled their ability to inhibit [3H]tamoxifen binding to the antiestrogen binding site. Third, the tissue distribution of binding sites for 7-keto[3H]cholestanol was similar to that of the antiestrogen binding site. We conclude that: (1) in rat liver microsomes there are high-affinity oxysterol binding sites whose ligand specificity is different from that of the cytosolic oxysterol binding protein; and (2) the microsomal oxysterol binding site is probably identical to the antiestrogen binding site. The biological significance of these observations remains to be explored.     

Prediction of mono- and di-nucleotide-specific DNA-binding sites in proteins using neural networks

Background  

DNA recognition by proteins is one of the most important processes in living systems. Therefore, understanding the recognition process in general, and identifying mutual recognition sites in proteins and DNA in particular, carries great significance. The sequence and structural dependence of DNA-binding sites in proteins has led to the development of successful machine learning methods for their prediction. However, all existing machine learning methods predict DNA-binding sites, irrespective of their target sequence and hence, none of them is helpful in identifying specific protein-DNA contacts. In this work, we formulate the problem of predicting specific DNA-binding sites in terms of contacts between the residue environments of proteins and the identity of a mononucleotide or a dinucleotide step in DNA. The aim of this work is to take a protein sequence or structural features as inputs and predict for each amino acid residue if it binds to DNA at locations identified by one of the four possible mononucleotides or one of the 10 unique dinucleotide steps. Contact predictions are made at various levels of resolution viz. in terms of side chain, backbone and major or minor groove atoms of DNA.     

High-affinity calcium-binding proteins in Escherichia coli

Crude extracts of Escherichia coli contain at least three heat stable proteins of Mr, 33,000, 47,000, and 60,000, which bind 45Ca2+ in buffers containing micromolar calcium and physiological salt concentrations. Fractions containing these proteins neither activated the calmodulin-dependent enzyme, NAD kinase, nor inhibited the activity of this enzyme in the presence of brain calmodulin. Radioimmunoassay of crude extracts for calmodulin indicated the presence of a calmodulin-like antigen. Crude extracts also contain proteins that interact with 2-trifluoromethyl-10H-(3'-aminopropyl)phenothiazine-Sepharose in a calcium-dependent manner, but proteins eluted from this resin did not bind calcium with high affinity.     

High-affinity insulin binding: insulin interacts with two receptor ligand binding sites

The interaction of insulin with its receptor is complex. Kinetic and equilibrium binding studies suggest coexistence of high- and low-affinity binding sites or negative cooperativity. These phenomena and high-affinity interactions are dependent on the dimeric structure of the receptor. Structure-function studies of insulin analogs suggest insulin has two receptor binding sites, implying a bivalent interaction with the receptor. Alanine scanning studies of the secreted recombinant receptor implicate the L1 domain and a C-terminal peptide of the receptor alpha subunit as components of one ligand binding site. Functional studies suggest that the first and second type III fibronectin repeats of the receptor contain a second ligand binding site. We have used structure-directed alanine scanning mutagenesis to identify determinants in these domains involved in ligand interactions. cDNAs encoding alanine mutants of the holo-receptor were transiently expressed in 293 cells, and the binding properties of the expressed receptor were determined. Alanine mutations of Lys(484), Leu(552), Asp(591), Ile(602), Lys(616), Asp(620), and Pro(621) compromised affinities for insulin 2-5-fold. With the exception of Asp(620), none of these mutations compromised the affinity of the recombinant secreted receptor for insulin, indicating that the perturbation of the interaction is at the site of mutation and not an indirect effect on the interaction with the binding site of the secreted receptor. These residues thus form part of a novel ligand binding site of the insulin receptor. Complementation experiments demonstrate that insulin interacts in trans with both receptor binding sites to generate high-affinity interactions.     

Optimal foraging and beyond: how starlings cope with changes in food availability

Foraging adaptations include behavioral and physiological responses, but most optimal foraging models deal exclusively with behavioral decision variables, taking other dimensions as constraints. To overcome this limitation, we measured behavioral and physiological responses of European starlings Sturnus vulgaris to changes in food availability in a laboratory environment. The birds lived in a closed economy with a choice of two foraging modes (flying and walking) and were observed under two treatments (hard and easy) that differed in the work required to obtain food. Comparing the hard with the easy treatment, we found the following differences. In the hard treatment, daily amount of work was higher, but daily intake was lower. Even though work was greater, total daily expenditure was smaller, partly because overnight metabolism was lower. Body mass was lower, but daily oscillation in body mass did not differ. Feces' caloric density was lower, indicating greater food utilization. Energy expenditure rate expressed as multiples of basal metabolic rate (BMR) increased during the working period from 3.5 x BMR (easy) to 5.2 x BMR (hard), but over the 24-h period, it was close to 2.4 x BMR in both treatments. We also found that rate of expenditure during flight was very high in both treatments (52.3 W in easy and 45.5 W in hard), as expected for short (as opposed to cruising) flights. The relative preferences between walking and flying were incompatible with maximizing the ratio of energy gains per unit of expenditure (efficiency) but compatible with maximizing net gain per unit of time during the foraging cycle (net rate). Neither currency explained the results when nonforaging time was included. Time was not a direct constraint: the birds rested more than 90% of the time in both treatments. Understanding this complex picture requires reasoning with ecological, physiological, and cognitive arguments. We defend the role of optimality as an appropriate tool to guide this integrative perspective.     

Re-programming DNA-binding specificity in zinc finger proteins for targeting unique address in a genome

Recent studies provide a glimpse of future potential therapeutic applications of custom-designed zinc finger proteins in achieving highly specific genomic manipulation. Custom-design of zinc finger proteins with tailor-made specificity is currently limited by the availability of information on recognition helices for all possible DNA targets. However, recent advances suggest that a combination of design and selection method is best suited to identify custom zinc finger DNA-binding proteins for known genome target sites. Design of functionally self-contained zinc finger proteins can be achieved by (a) modular protein engineering and (b) computational prediction. Here, we explore the novel functionality obtained by engineered zinc finger proteins and the computational approaches for prediction of recognition helices of zinc finger proteins that can raise our ability to re-program zinc finger proteins with desired novel DNA-binding specificities.     

Detection of two tissue-specific DNA-binding proteins with affinity for sites in the mouse beta-globin intervening sequence 2.

To identify proteins from uninduced murine erythroleukemia nuclear extracts which specifically bind to sequences from the DNase I-hypersensitive region within the mouse beta-globin intervening sequence 2 (IVS2), a gel electrophoretic mobility shift assay was used. Two distinct sequence-specific binding proteins were detected. The specific binding sites for these factors were delineated by both DNase I protection footprinting and methylation interference. Factor B1 bound specifically to two homologous sites, B1-A and B1-B, approximately 100 base pairs apart within the IVS2 and on opposite strands. These two regions could interact with factor B1 independently. Factor B1 was limited to cells of hematopoietic lineages. Factor B2 bound to a site approximately 5 base pairs away from the B1-A site and was limited to cells of the erythroid lineage. The limited tissue distribution of these factors and the locations of their binding sites suggest that one or both of these factors may be involved in the formation of the tissue-specific DNase I-hypersensitive site in the IVS2 of the mouse beta-globin gene.     

Design of accurate predictors for DNA-binding sites in proteins using hybrid SVM-PSSM method

In this paper, we investigate the design of accurate predictors for DNA-binding sites in proteins from amino acid sequences. As a result, we propose a hybrid method using support vector machine (SVM) in conjunction with evolutionary information of amino acid sequences in terms of their position-specific scoring matrices (PSSMs) for prediction of DNA-binding sites. Considering the numbers of binding and non-binding residues in proteins are significantly unequal, two additional weights as well as SVM parameters are analyzed and adopted to maximize net prediction (NP, an average of sensitivity and specificity) accuracy. To evaluate the generalization ability of the proposed method SVM-PSSM, a DNA-binding dataset PDC-59 consisting of 59 protein chains with low sequence identity on each other is additionally established. The SVM-based method using the same six-fold cross-validation procedure and PSSM features has NP=80.15% for the training dataset PDNA-62 and NP=69.54% for the test dataset PDC-59, which are much better than the existing neural network-based method by increasing the NP values for training and test accuracies up to 13.45% and 16.53%, respectively. Simulation results reveal that SVM-PSSM performs well in predicting DNA-binding sites of novel proteins from amino acid sequences.     

A structure-based method for identifying DNA-binding proteins and their sites of DNA-interaction

A classification model of a DNA-binding protein chain was created based on identification of alpha helices within the chain likely to bind to DNA. Using the model, all chains in the Protein Data Bank were classified. For many of the chains classified with high confidence, previous documentation for DNA-binding was found, yet no sequence homology to the structures used to train the model was detected. The result indicates that the chain model can be used to supplement sequence based methods for annotating the function of DNA-binding. Four new candidates for DNA-binding were found, including two structures solved through structural genomics efforts. For each of the candidate structures, possible sites of DNA-binding are indicated by listing the residue ranges of alpha helices likely to interact with DNA.     

A structure-based method for identifying DNA-binding proteins and their sites of DNA-interaction

A classification model of a DNA-binding protein chain was created based on identification of alpha helices within the chain likely to bind to DNA. Using the model, all chains in the Protein Data Bank were classified. For many of the chains classified with high confidence, previous documentation for DNA-binding was found, yet no sequence homology to the structures used to train the model was detected. The result indicates that the chain model can be used to supplement sequence based methods for annotating the function of DNA-binding. Four new candidates for DNA-binding were found, including two structures solved through structural genomics efforts. For each of the candidate structures, possible sites of DNA-binding are indicated by listing the residue ranges of alpha helices likely to interact with DNA.     

Conditioned genome reconstruction: how to avoid choosing the conditioning genome

Genome phylogenies can be inferred from data on the presence and absence of genes across taxa. Logdet distances may be a good method, because they allow expected genome size to vary across the tree. Recently, Lake and Rivera proposed conditioned genome reconstruction (calculation of logdet distances using only those genes present in a conditioning genome) to deal with unobservable genes that are absent from every taxon of interest. We prove that their method can consistently estimate the topology for almost any choice of conditioning genome. Nevertheless, the choice of conditioning genome is important for small samples. For real bacterial genome data, different choices of conditioning genome can result in strong bootstrap support for different tree topologies. To overcome this problem, we developed supertree methods that combine information from all choices of conditioning genome. One of these methods, based on the BIONJ algorithm, performs well on simulated data and may have applications to other supertree problems. However, an analysis of 40 bacterial genomes using this method supports an incorrect clade of parasites. This is a common feature of model-based gene content methods and is due to parallel gene loss.     

Multiple DNA-binding sites in Tetrahymena telomerase

Telomerase is a ribonucleoprotein enzyme that maintains chromosome ends through de novo addition of telomeric DNA. The ability of telomerase to interact with its DNA substrate at sites outside its catalytic centre (‘anchor sites’) is important for its unique ability to undergo repeat addition processivity. We have developed a direct and quantitative equilibrium primer-binding assay to measure DNA-binding affinities of regions of the catalytic protein subunit of recombinant Tetrahymena telomerase (TERT). There are specific telomeric DNA-binding sites in at least four regions of TERT (the TEN, RBD, RT and C-terminal domains). Together, these sites contribute to specific and high-affinity DNA binding, with a K_d of ~8 nM. Both the K_m and K_d increased in a stepwise manner as the primer length was reduced; thus recombinant Tetrahymena telomerase, like the endogenous enzyme, contains multiple anchor sites. The N-terminal TEN domain, which has previously been implicated in DNA binding, shows only low affinity binding. However, there appears to be cooperativity between the TEN and RNA-binding domains. Our data suggest that different DNA-binding sites are used by the enzyme during different stages of the addition cycle.